Personality change in college: Latent growth and stability models
Linh Nguyen
4/26/2021
Meta-data
require(lavaan)
require(tidyverse)
require(semPlot)
set.seed(202102)
sessionInfo()## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] semPlot_1.1.2 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4
## [5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.6
## [9] ggplot2_3.3.3 tidyverse_1.3.0 lavaan_0.6-7
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 ellipsis_0.3.1
## [4] htmlTable_2.1.0 corpcor_1.6.9 base64enc_0.1-3
## [7] fs_1.5.0 rstudioapi_0.13 lubridate_1.7.9.2
## [10] xml2_1.3.2 splines_4.0.4 mnormt_2.0.2
## [13] knitr_1.31 glasso_1.11 Formula_1.2-4
## [16] jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.5
## [19] cluster_2.1.0 dbplyr_2.1.0 png_0.1-7
## [22] regsem_1.6.2 compiler_4.0.4 httr_1.4.2
## [25] backports_1.2.1 assertthat_0.2.1 Matrix_1.3-2
## [28] cli_2.3.0 htmltools_0.5.1.1 tools_4.0.4
## [31] OpenMx_2.18.1 igraph_1.2.6 coda_0.19-4
## [34] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
## [37] Rcpp_1.0.6 carData_3.0-4 cellranger_1.1.0
## [40] jquerylib_0.1.3 vctrs_0.3.6 nlme_3.1-152
## [43] lisrelToR_0.1.4 psych_2.0.12 xfun_0.21
## [46] openxlsx_4.2.3 lme4_1.1-26 rvest_0.3.6
## [49] lifecycle_1.0.0 gtools_3.8.2 XML_3.99-0.5
## [52] statmod_1.4.35 MASS_7.3-53 scales_1.1.1
## [55] hms_1.0.0 kutils_1.70 parallel_4.0.4
## [58] RColorBrewer_1.1-2 yaml_2.2.1 pbapply_1.4-3
## [61] gridExtra_2.3 sass_0.3.1 rpart_4.1-15
## [64] latticeExtra_0.6-29 stringi_1.5.3 sem_3.1-11
## [67] checkmate_2.0.0 boot_1.3-27 zip_2.1.1
## [70] truncnorm_1.0-8 rlang_0.4.10 pkgconfig_2.0.3
## [73] Rsolnp_1.16 arm_1.11-2 evaluate_0.14
## [76] lattice_0.20-41 htmlwidgets_1.5.3 tidyselect_1.1.0
## [79] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [82] generics_0.1.0 Hmisc_4.4-2 DBI_1.1.1
## [85] pillar_1.4.7 haven_2.3.1 foreign_0.8-81
## [88] withr_2.4.1 rockchalk_1.8.144 survival_3.2-7
## [91] abind_1.4-5 nnet_7.3-15 modelr_0.1.8
## [94] crayon_1.4.1 fdrtool_1.2.16 tmvnsim_1.0-2
## [97] rmarkdown_2.7 jpeg_0.1-8.1 qgraph_1.6.9
## [100] grid_4.0.4 readxl_1.3.1 data.table_1.14.0
## [103] pbivnorm_0.6.0 matrixcalc_1.0-3 reprex_1.0.0
## [106] digest_0.6.27 xtable_1.8-4 mi_1.0
## [109] stats4_4.0.4 munsell_0.5.0 bslib_0.2.4Preprocessing
Data
data <- read.csv(file = "lavaanData.csv", na.strings = "-999", header = F)
colnames(data) <- c(#self reports:
paste0("w1bf_", 1:100),
paste0("w1epsi_", 1:12),
paste0("w2bf_", 1:100),
paste0("w2epsi_", 1:12),
paste0("w3bf_", 1:100),
paste0("w3epsi_", 1:12),
paste0("w4bf_", 1:100),
paste0("w4epsi_", 1:12),
#peer reports:
paste0("pw1bf_", 1:100),
paste0("pw1epsi_", 1:12),
paste0("pw2bf_", 1:100),
paste0("pw2epsi_", 1:12),
paste0("pw3bf_", 1:100),
paste0("pw3epsi_", 1:12),
paste0("pw4bf_", 1:100),
paste0("pw4epsi_", 1:12))Parcels
# self aspect randomization - 0, 4, 6, 7, 8 vs. 1, 2, 3, 5, 9
sample(c(0,1,2,3,4,5,6,7,8,9), 5)## [1] 4 6 0 7 8# peer aspect randomization - 1, 2, 4, 5, 6 vs. 0, 3, 7, 8, 9
sample(c(0,1,2,3,4,5,6,7,8,9), 5)## [1] 2 5 1 4 6# self domain randomization - 1,3,4,6,7,9,10,30,40,90 vs. 0,2,5,8,00,20,50,60,70,80
sample(c(0,1,2,3,4,5,6,7,8,9,
00,10,20,30,40,50,60,70,80,90), 10)## [1] 9 4 3 30 1 10 40 7 90 6# peer domain randomization - 0,2,3,4,8,10,30,40,50,80 vs. 1,5,6,7,9,00,20,60,70,90
sample(c(0,1,2,3,4,5,6,7,8,9,
00,10,20,30,40,50,60,70,80,90), 10)## [1] 30 50 2 0 3 80 10 8 40 4# self identity randomization
sample(c(1,3,7,10,11,12), 3) #confusion - 7,10,11 vs. 1,3,12## [1] 7 11 10sample(c(2,4,5,6,8,9), 3) #coherence - 2,5,6 vs. 4,8,9## [1] 5 2 6# peer identity randomization
sample(c(1,3,7,10,11,12), 3) #confusion - 3,7,12 vs. 1,10,11## [1] 3 7 12sample(c(2,4,5,6,8,9), 3) #coherence - 4,5,9 vs. 2,6,8## [1] 4 9 5# >>> Aspects ----
# assertiveness
data <- data %>%
mutate(# self
assertW1S = rowMeans(select(data, w1bf_9, w1bf_19, w1bf_29, w1bf_39, w1bf_49, w1bf_59,
w1bf_69, w1bf_79, w1bf_89, w1bf_99), na.rm = T),
assertW2S = rowMeans(select(data, w2bf_9, w2bf_19, w2bf_29, w2bf_39, w2bf_49, w2bf_59,
w2bf_69, w2bf_79, w2bf_89, w2bf_99), na.rm = T),
assertW3S = rowMeans(select(data, w3bf_9, w3bf_19, w3bf_29, w3bf_39, w3bf_49, w3bf_59,
w3bf_69, w3bf_79, w3bf_89, w3bf_99), na.rm = T),
assertW4S = rowMeans(select(data, w4bf_9, w4bf_19, w4bf_29, w4bf_39, w4bf_49, w4bf_59,
w4bf_69, w4bf_79, w4bf_89, w4bf_99), na.rm = T),
# peer
assertW1P = rowMeans(select(data, pw1bf_9, pw1bf_19, pw1bf_29, pw1bf_39, pw1bf_49, pw1bf_59,
pw1bf_69, pw1bf_79, pw1bf_89, pw1bf_99), na.rm = T),
assertW2P = rowMeans(select(data, pw2bf_9, pw2bf_19, pw2bf_29, pw2bf_39, pw2bf_49, pw2bf_59,
pw2bf_69, pw2bf_79, pw2bf_89, pw2bf_99), na.rm = T),
assertW3P = rowMeans(select(data, pw3bf_9, pw3bf_19, pw3bf_29, pw3bf_39, pw3bf_49, pw3bf_59,
pw3bf_69, pw3bf_79, pw3bf_89, pw3bf_99), na.rm = T),
assertW4P = rowMeans(select(data, pw4bf_9, pw4bf_19, pw4bf_29, pw4bf_39, pw4bf_49, pw4bf_59,
pw4bf_69, pw4bf_79, pw4bf_89, pw4bf_99), na.rm = T))
data <- data %>%
mutate(# first self parcel
assertW1S1 = rowMeans(select(data, w1bf_9, w1bf_49, w1bf_69, w1bf_79, w1bf_89),na.rm = T),
assertW2S1 = rowMeans(select(data, w2bf_9, w2bf_49, w2bf_69, w2bf_79, w2bf_89),na.rm = T),
assertW3S1 = rowMeans(select(data, w3bf_9, w3bf_49, w3bf_69, w3bf_79, w3bf_89),na.rm = T),
assertW4S1 = rowMeans(select(data, w4bf_9, w4bf_49, w4bf_69, w4bf_79, w4bf_89),na.rm = T),
# second self parcel
assertW1S2 = rowMeans(select(data, w1bf_19, w1bf_29, w1bf_39, w1bf_59, w1bf_99),na.rm = T),
assertW2S2 = rowMeans(select(data, w2bf_19, w2bf_29, w2bf_39, w2bf_59, w2bf_99),na.rm = T),
assertW3S2 = rowMeans(select(data, w3bf_19, w3bf_29, w3bf_39, w3bf_59, w3bf_99),na.rm = T),
assertW4S2 = rowMeans(select(data, w4bf_19, w4bf_29, w4bf_39, w4bf_59, w4bf_99),na.rm = T),
# first peer parcel
assertW1P1 = rowMeans(select(data, pw1bf_19, pw1bf_29, pw1bf_49, pw1bf_59, pw1bf_69), na.rm = T),
assertW2P1 = rowMeans(select(data, pw2bf_19, pw2bf_29, pw2bf_49, pw2bf_59, pw2bf_69), na.rm = T),
assertW3P1 = rowMeans(select(data, pw3bf_19, pw3bf_29, pw3bf_49, pw3bf_59, pw3bf_69), na.rm = T),
assertW4P1 = rowMeans(select(data, pw4bf_19, pw4bf_29, pw4bf_49, pw4bf_59, pw4bf_69), na.rm = T),
# second peer parcel
assertW1P2 = rowMeans(select(data, pw1bf_9, pw1bf_39, pw1bf_79, pw1bf_89, pw1bf_99), na.rm = T),
assertW2P2 = rowMeans(select(data, pw2bf_9, pw2bf_39, pw2bf_79, pw2bf_89, pw2bf_99), na.rm = T),
assertW3P2 = rowMeans(select(data, pw3bf_9, pw3bf_39, pw3bf_79, pw3bf_89, pw3bf_99), na.rm = T),
assertW4P2 = rowMeans(select(data, pw4bf_9, pw4bf_39, pw4bf_79, pw4bf_89, pw4bf_99), na.rm = T))
# compassion
data <- data %>%
mutate(# self
compaW1S = rowMeans(select(data, w1bf_2, w1bf_12, w1bf_22, w1bf_32, w1bf_42, w1bf_52,
w1bf_62, w1bf_72, w1bf_82, w1bf_92), na.rm = T),
compaW2S = rowMeans(select(data, w2bf_2, w2bf_12, w2bf_22, w2bf_32, w2bf_42, w2bf_52,
w2bf_62, w2bf_72, w2bf_82, w2bf_92), na.rm = T),
compaW3S = rowMeans(select(data, w3bf_2, w3bf_12, w3bf_22, w3bf_32, w3bf_42, w3bf_52,
w3bf_62, w3bf_72, w3bf_82, w3bf_92), na.rm = T),
compaW4S = rowMeans(select(data, w4bf_2, w4bf_12, w4bf_22, w4bf_32, w4bf_42, w4bf_52,
w4bf_62, w4bf_72, w4bf_82, w4bf_92), na.rm = T),
# peer
compaW1P = rowMeans(select(data, pw1bf_2, pw1bf_12, pw1bf_22, pw1bf_32, pw1bf_42, pw1bf_52,
pw1bf_62, pw1bf_72, pw1bf_82, pw1bf_92), na.rm = T),
compaW2P = rowMeans(select(data, pw2bf_2, pw2bf_12, pw2bf_22, pw2bf_32, pw2bf_42, pw2bf_52,
pw2bf_62, pw2bf_72, pw2bf_82, pw2bf_92), na.rm = T),
compaW3P = rowMeans(select(data, pw3bf_2, pw3bf_12, pw3bf_22, pw3bf_32, pw3bf_42, pw3bf_52,
pw3bf_62, pw3bf_72, pw3bf_82, pw3bf_92), na.rm = T),
compaW4P = rowMeans(select(data, pw4bf_2, pw4bf_12, pw4bf_22, pw4bf_32, pw4bf_42, pw4bf_52,
pw4bf_62, pw4bf_72, pw4bf_82, pw4bf_92), na.rm = T))
data <- data %>%
mutate(# first self parcel
compaW1S1 = rowMeans(select(data, w1bf_2, w1bf_42, w1bf_62, w1bf_72, w1bf_82),na.rm = T),
compaW2S1 = rowMeans(select(data, w2bf_2, w2bf_42, w2bf_62, w2bf_72, w2bf_82),na.rm = T),
compaW3S1 = rowMeans(select(data, w3bf_2, w3bf_42, w3bf_62, w3bf_72, w3bf_82),na.rm = T),
compaW4S1 = rowMeans(select(data, w4bf_2, w4bf_42, w4bf_62, w4bf_72, w4bf_82),na.rm = T),
# second self parcel
compaW1S2 = rowMeans(select(data, w1bf_12, w1bf_22, w1bf_32, w1bf_52, w1bf_92),na.rm = T),
compaW2S2 = rowMeans(select(data, w2bf_12, w2bf_22, w2bf_32, w2bf_52, w2bf_92),na.rm = T),
compaW3S2 = rowMeans(select(data, w3bf_12, w3bf_22, w3bf_32, w3bf_52, w3bf_92),na.rm = T),
compaW4S2 = rowMeans(select(data, w4bf_12, w4bf_22, w4bf_32, w4bf_52, w4bf_92),na.rm = T),
# first peer parcel
compaW1P1 = rowMeans(select(data, pw1bf_12, pw1bf_22, pw1bf_42, pw1bf_52, pw1bf_62), na.rm = T),
compaW2P1 = rowMeans(select(data, pw2bf_12, pw2bf_22, pw2bf_42, pw2bf_52, pw2bf_62), na.rm = T),
compaW3P1 = rowMeans(select(data, pw3bf_12, pw3bf_22, pw3bf_42, pw3bf_52, pw3bf_62), na.rm = T),
compaW4P1 = rowMeans(select(data, pw4bf_12, pw4bf_22, pw4bf_42, pw4bf_52, pw4bf_62), na.rm = T),
# second peer parcel
compaW1P2 = rowMeans(select(data, pw1bf_2, pw1bf_32, pw1bf_72, pw1bf_82, pw1bf_92), na.rm = T),
compaW2P2 = rowMeans(select(data, pw2bf_2, pw2bf_32, pw2bf_72, pw2bf_82, pw2bf_92), na.rm = T),
compaW3P2 = rowMeans(select(data, pw3bf_2, pw3bf_32, pw3bf_72, pw3bf_82, pw3bf_92), na.rm = T),
compaW4P2 = rowMeans(select(data, pw4bf_2, pw4bf_32, pw4bf_72, pw4bf_82, pw4bf_92), na.rm = T))
# enthusiasm
data <- data %>%
mutate(# self
enthuW1S = rowMeans(select(data, w1bf_4, w1bf_14, w1bf_24, w1bf_34, w1bf_44, w1bf_54,
w1bf_64, w1bf_74, w1bf_84, w1bf_94), na.rm = T),
enthuW2S = rowMeans(select(data, w2bf_4, w2bf_14, w2bf_24, w2bf_34, w2bf_44, w2bf_54,
w2bf_64, w2bf_74, w2bf_84, w2bf_94), na.rm = T),
enthuW3S = rowMeans(select(data, w3bf_4, w3bf_14, w3bf_24, w3bf_34, w3bf_44, w3bf_54,
w3bf_64, w3bf_74, w3bf_84, w3bf_94), na.rm = T),
enthuW4S = rowMeans(select(data, w4bf_4, w4bf_14, w4bf_24, w4bf_34, w4bf_44, w4bf_54,
w4bf_64, w4bf_74, w4bf_84, w4bf_94), na.rm = T),
# peer
enthuW1P = rowMeans(select(data, pw1bf_4, pw1bf_14, pw1bf_24, pw1bf_34, pw1bf_44, pw1bf_54,
pw1bf_64, pw1bf_74, pw1bf_84, pw1bf_94), na.rm = T),
enthuW2P = rowMeans(select(data, pw2bf_4, pw2bf_14, pw2bf_24, pw2bf_34, pw2bf_44, pw2bf_54,
pw2bf_64, pw2bf_74, pw2bf_84, pw2bf_94), na.rm = T),
enthuW3P = rowMeans(select(data, pw3bf_4, pw3bf_14, pw3bf_24, pw3bf_34, pw3bf_44, pw3bf_54,
pw3bf_64, pw3bf_74, pw3bf_84, pw3bf_94), na.rm = T),
enthuW4P = rowMeans(select(data, pw4bf_4, pw4bf_14, pw4bf_24, pw4bf_34, pw4bf_44, pw4bf_54,
pw4bf_64, pw4bf_74, pw4bf_84, pw4bf_94), na.rm = T))
data <- data %>%
mutate(# first self parcel
enthuW1S1 = rowMeans(select(data, w1bf_4, w1bf_44, w1bf_64, w1bf_74, w1bf_84),na.rm = T),
enthuW2S1 = rowMeans(select(data, w2bf_4, w2bf_44, w2bf_64, w2bf_74, w2bf_84),na.rm = T),
enthuW3S1 = rowMeans(select(data, w3bf_4, w3bf_44, w3bf_64, w3bf_74, w3bf_84),na.rm = T),
enthuW4S1 = rowMeans(select(data, w4bf_4, w4bf_44, w4bf_64, w4bf_74, w4bf_84),na.rm = T),
# second self parcel
enthuW1S2 = rowMeans(select(data, w1bf_14, w1bf_24, w1bf_34, w1bf_54, w1bf_94),na.rm = T),
enthuW2S2 = rowMeans(select(data, w2bf_14, w2bf_24, w2bf_34, w2bf_54, w2bf_94),na.rm = T),
enthuW3S2 = rowMeans(select(data, w3bf_14, w3bf_24, w3bf_34, w3bf_54, w3bf_94),na.rm = T),
enthuW4S2 = rowMeans(select(data, w4bf_14, w4bf_24, w4bf_34, w4bf_54, w4bf_94),na.rm = T),
# first peer parcel
enthuW1P1 = rowMeans(select(data, pw1bf_14, pw1bf_24, pw1bf_44, pw1bf_54, pw1bf_64), na.rm = T),
enthuW2P1 = rowMeans(select(data, pw2bf_14, pw2bf_24, pw2bf_44, pw2bf_54, pw2bf_64), na.rm = T),
enthuW3P1 = rowMeans(select(data, pw3bf_14, pw3bf_24, pw3bf_44, pw3bf_54, pw3bf_64), na.rm = T),
enthuW4P1 = rowMeans(select(data, pw4bf_14, pw4bf_24, pw4bf_44, pw4bf_54, pw4bf_64), na.rm = T),
# second peer parcel
enthuW1P2 = rowMeans(select(data, pw1bf_4, pw1bf_34, pw1bf_74, pw1bf_84, pw1bf_94), na.rm = T),
enthuW2P2 = rowMeans(select(data, pw2bf_4, pw2bf_34, pw2bf_74, pw2bf_84, pw2bf_94), na.rm = T),
enthuW3P2 = rowMeans(select(data, pw3bf_4, pw3bf_34, pw3bf_74, pw3bf_84, pw3bf_94), na.rm = T),
enthuW4P2 = rowMeans(select(data, pw4bf_4, pw4bf_34, pw4bf_74, pw4bf_84, pw4bf_94), na.rm = T))
# industriousness
data <- data %>%
mutate(# self
indusW1S = rowMeans(select(data, w1bf_3, w1bf_13, w1bf_23, w1bf_33, w1bf_43, w1bf_53,
w1bf_63, w1bf_73, w1bf_83, w1bf_93), na.rm = T),
indusW2S = rowMeans(select(data, w2bf_3, w2bf_13, w2bf_23, w2bf_33, w2bf_43, w2bf_53,
w2bf_63, w2bf_73, w2bf_83, w2bf_93), na.rm = T),
indusW3S = rowMeans(select(data, w3bf_3, w3bf_13, w3bf_23, w3bf_33, w3bf_43, w3bf_53,
w3bf_63, w3bf_73, w3bf_83, w3bf_93), na.rm = T),
indusW4S = rowMeans(select(data, w4bf_3, w4bf_13, w4bf_23, w4bf_33, w4bf_43, w4bf_53,
w4bf_63, w4bf_73, w4bf_83, w4bf_93), na.rm = T),
# peer
indusW1P = rowMeans(select(data, pw1bf_3, pw1bf_13, pw1bf_23, pw1bf_33, pw1bf_43, pw1bf_53,
pw1bf_63, pw1bf_73, pw1bf_83, pw1bf_93), na.rm = T),
indusW2P = rowMeans(select(data, pw2bf_3, pw2bf_13, pw2bf_23, pw2bf_33, pw2bf_43, pw2bf_53,
pw2bf_63, pw2bf_73, pw2bf_83, pw2bf_93), na.rm = T),
indusW3P = rowMeans(select(data, pw3bf_3, pw3bf_13, pw3bf_23, pw3bf_33, pw3bf_43, pw3bf_53,
pw3bf_63, pw3bf_73, pw3bf_83, pw3bf_93), na.rm = T),
indusW4P = rowMeans(select(data, pw4bf_3, pw4bf_13, pw4bf_23, pw4bf_33, pw4bf_43, pw4bf_53,
pw4bf_63, pw4bf_73, pw4bf_83, pw4bf_93), na.rm = T))
data <- data %>%
mutate(# first self parcel
indusW1S1 = rowMeans(select(data, w1bf_3, w1bf_43, w1bf_63, w1bf_73, w1bf_83),na.rm = T),
indusW2S1 = rowMeans(select(data, w2bf_3, w2bf_43, w2bf_63, w2bf_73, w2bf_83),na.rm = T),
indusW3S1 = rowMeans(select(data, w3bf_3, w3bf_43, w3bf_63, w3bf_73, w3bf_83),na.rm = T),
indusW4S1 = rowMeans(select(data, w4bf_3, w4bf_43, w4bf_63, w4bf_73, w4bf_83),na.rm = T),
# second self parcel
indusW1S2 = rowMeans(select(data, w1bf_13, w1bf_23, w1bf_33, w1bf_53, w1bf_93),na.rm = T),
indusW2S2 = rowMeans(select(data, w2bf_13, w2bf_23, w2bf_33, w2bf_53, w2bf_93),na.rm = T),
indusW3S2 = rowMeans(select(data, w3bf_13, w3bf_23, w3bf_33, w3bf_53, w3bf_93),na.rm = T),
indusW4S2 = rowMeans(select(data, w4bf_13, w4bf_23, w4bf_33, w4bf_53, w4bf_93),na.rm = T),
# first peer parcel
indusW1P1 = rowMeans(select(data, pw1bf_13, pw1bf_23, pw1bf_43, pw1bf_53, pw1bf_63), na.rm = T),
indusW2P1 = rowMeans(select(data, pw2bf_13, pw2bf_23, pw2bf_43, pw2bf_53, pw2bf_63), na.rm = T),
indusW3P1 = rowMeans(select(data, pw3bf_13, pw3bf_23, pw3bf_43, pw3bf_53, pw3bf_63), na.rm = T),
indusW4P1 = rowMeans(select(data, pw4bf_13, pw4bf_23, pw4bf_43, pw4bf_53, pw4bf_63), na.rm = T),
# second peer parcel
indusW1P2 = rowMeans(select(data, pw1bf_3, pw1bf_33, pw1bf_73, pw1bf_83, pw1bf_93), na.rm = T),
indusW2P2 = rowMeans(select(data, pw2bf_3, pw2bf_33, pw2bf_73, pw2bf_83, pw2bf_93), na.rm = T),
indusW3P2 = rowMeans(select(data, pw3bf_3, pw3bf_33, pw3bf_73, pw3bf_83, pw3bf_93), na.rm = T),
indusW4P2 = rowMeans(select(data, pw4bf_3, pw4bf_33, pw4bf_73, pw4bf_83, pw4bf_93), na.rm = T))
# intellect
data <- data %>%
mutate(# self
intelW1S = rowMeans(select(data, w1bf_5, w1bf_15, w1bf_25, w1bf_35, w1bf_45, w1bf_55,
w1bf_65, w1bf_75, w1bf_85, w1bf_95), na.rm = T),
intelW2S = rowMeans(select(data, w2bf_5, w2bf_15, w2bf_25, w2bf_35, w2bf_45, w2bf_55,
w2bf_65, w2bf_75, w2bf_85, w2bf_95), na.rm = T),
intelW3S = rowMeans(select(data, w3bf_5, w3bf_15, w3bf_25, w3bf_35, w3bf_45, w3bf_55,
w3bf_65, w3bf_75, w3bf_85, w3bf_95), na.rm = T),
intelW4S = rowMeans(select(data, w4bf_5, w4bf_15, w4bf_25, w4bf_35, w4bf_45, w4bf_55,
w4bf_65, w4bf_75, w4bf_85, w4bf_95), na.rm = T),
# peer
intelW1P = rowMeans(select(data, pw1bf_5, pw1bf_15, pw1bf_25, pw1bf_35, pw1bf_45, pw1bf_55,
pw1bf_65, pw1bf_75, pw1bf_85, pw1bf_95), na.rm = T),
intelW2P = rowMeans(select(data, pw2bf_5, pw2bf_15, pw2bf_25, pw2bf_35, pw2bf_45, pw2bf_55,
pw2bf_65, pw2bf_75, pw2bf_85, pw2bf_95), na.rm = T),
intelW3P = rowMeans(select(data, pw3bf_5, pw3bf_15, pw3bf_25, pw3bf_35, pw3bf_45, pw3bf_55,
pw3bf_65, pw3bf_75, pw3bf_85, pw3bf_95), na.rm = T),
intelW4P = rowMeans(select(data, pw4bf_5, pw4bf_15, pw4bf_25, pw4bf_35, pw4bf_45, pw4bf_55,
pw4bf_65, pw4bf_75, pw4bf_85, pw4bf_95), na.rm = T))
data <- data %>%
mutate(# first self parcel
intelW1S1 = rowMeans(select(data, w1bf_5, w1bf_45, w1bf_65, w1bf_75, w1bf_85),na.rm = T),
intelW2S1 = rowMeans(select(data, w2bf_5, w2bf_45, w2bf_65, w2bf_75, w2bf_85),na.rm = T),
intelW3S1 = rowMeans(select(data, w3bf_5, w3bf_45, w3bf_65, w3bf_75, w3bf_85),na.rm = T),
intelW4S1 = rowMeans(select(data, w4bf_5, w4bf_45, w4bf_65, w4bf_75, w4bf_85),na.rm = T),
# second self parcel
intelW1S2 = rowMeans(select(data, w1bf_15, w1bf_25, w1bf_35, w1bf_55, w1bf_95),na.rm = T),
intelW2S2 = rowMeans(select(data, w2bf_15, w2bf_25, w2bf_35, w2bf_55, w2bf_95),na.rm = T),
intelW3S2 = rowMeans(select(data, w3bf_15, w3bf_25, w3bf_35, w3bf_55, w3bf_95),na.rm = T),
intelW4S2 = rowMeans(select(data, w4bf_15, w4bf_25, w4bf_35, w4bf_55, w4bf_95),na.rm = T),
# first peer parcel
intelW1P1 = rowMeans(select(data, pw1bf_15, pw1bf_25, pw1bf_45, pw1bf_55, pw1bf_65), na.rm = T),
intelW2P1 = rowMeans(select(data, pw2bf_15, pw2bf_25, pw2bf_45, pw2bf_55, pw2bf_65), na.rm = T),
intelW3P1 = rowMeans(select(data, pw3bf_15, pw3bf_25, pw3bf_45, pw3bf_55, pw3bf_65), na.rm = T),
intelW4P1 = rowMeans(select(data, pw4bf_15, pw4bf_25, pw4bf_45, pw4bf_55, pw4bf_65), na.rm = T),
# second peer parcel
intelW1P2 = rowMeans(select(data, pw1bf_5, pw1bf_35, pw1bf_75, pw1bf_85, pw1bf_95), na.rm = T),
intelW2P2 = rowMeans(select(data, pw2bf_5, pw2bf_35, pw2bf_75, pw2bf_85, pw2bf_95), na.rm = T),
intelW3P2 = rowMeans(select(data, pw3bf_5, pw3bf_35, pw3bf_75, pw3bf_85, pw3bf_95), na.rm = T),
intelW4P2 = rowMeans(select(data, pw4bf_5, pw4bf_35, pw4bf_75, pw4bf_85, pw4bf_95), na.rm = T))
# openness aspect
data <- data %>%
mutate(# self
openaW1S = rowMeans(select(data, w1bf_100, w1bf_10, w1bf_20, w1bf_30, w1bf_40, w1bf_50,
w1bf_60, w1bf_70, w1bf_80, w1bf_90), na.rm = T),
openaW2S = rowMeans(select(data, w2bf_100, w2bf_10, w2bf_20, w2bf_30, w2bf_40, w2bf_50,
w2bf_60, w2bf_70, w2bf_80, w2bf_90), na.rm = T),
openaW3S = rowMeans(select(data, w3bf_100, w3bf_10, w3bf_20, w3bf_30, w3bf_40, w3bf_50,
w3bf_60, w3bf_70, w3bf_80, w3bf_90), na.rm = T),
openaW4S = rowMeans(select(data, w4bf_100, w4bf_10, w4bf_20, w4bf_30, w4bf_40, w4bf_50,
w4bf_60, w4bf_70, w4bf_80, w4bf_90), na.rm = T),
# peer
openaW1P = rowMeans(select(data, pw1bf_100, pw1bf_10, pw1bf_20, pw1bf_30, pw1bf_40, pw1bf_50,
pw1bf_60, pw1bf_70, pw1bf_80, pw1bf_90), na.rm = T),
openaW2P = rowMeans(select(data, pw2bf_100, pw2bf_10, pw2bf_20, pw2bf_30, pw2bf_40, pw2bf_50,
pw2bf_60, pw2bf_70, pw2bf_80, pw2bf_90), na.rm = T),
openaW3P = rowMeans(select(data, pw3bf_100, pw3bf_10, pw3bf_20, pw3bf_30, pw3bf_40, pw3bf_50,
pw3bf_60, pw3bf_70, pw3bf_80, pw3bf_90), na.rm = T),
openaW4P = rowMeans(select(data, pw4bf_100, pw4bf_10, pw4bf_20, pw4bf_30, pw4bf_40, pw4bf_50,
pw4bf_60, pw4bf_70, pw4bf_80, pw4bf_90), na.rm = T))
data <- data %>%
mutate(# first self parcel
openaW1S1 = rowMeans(select(data, w1bf_100, w1bf_40, w1bf_60, w1bf_70, w1bf_84),na.rm = T),
openaW2S1 = rowMeans(select(data, w2bf_100, w2bf_40, w2bf_60, w2bf_70, w2bf_84),na.rm = T),
openaW3S1 = rowMeans(select(data, w3bf_100, w3bf_40, w3bf_60, w3bf_70, w3bf_84),na.rm = T),
openaW4S1 = rowMeans(select(data, w4bf_100, w4bf_40, w4bf_60, w4bf_70, w4bf_84),na.rm = T),
# second self parcel
openaW1S2 = rowMeans(select(data, w1bf_10, w1bf_20, w1bf_30, w1bf_50, w1bf_94),na.rm = T),
openaW2S2 = rowMeans(select(data, w2bf_10, w2bf_20, w2bf_30, w2bf_50, w2bf_94),na.rm = T),
openaW3S2 = rowMeans(select(data, w3bf_10, w3bf_20, w3bf_30, w3bf_50, w3bf_94),na.rm = T),
openaW4S2 = rowMeans(select(data, w4bf_10, w4bf_20, w4bf_30, w4bf_50, w4bf_94),na.rm = T),
# first peer parcel
openaW1P1 = rowMeans(select(data, pw1bf_10, pw1bf_20, pw1bf_40, pw1bf_50, pw1bf_64), na.rm = T),
openaW2P1 = rowMeans(select(data, pw2bf_10, pw2bf_20, pw2bf_40, pw2bf_50, pw2bf_64), na.rm = T),
openaW3P1 = rowMeans(select(data, pw3bf_10, pw3bf_20, pw3bf_40, pw3bf_50, pw3bf_64), na.rm = T),
openaW4P1 = rowMeans(select(data, pw4bf_10, pw4bf_20, pw4bf_40, pw4bf_50, pw4bf_64), na.rm = T),
# second peer parcel
openaW1P2 = rowMeans(select(data, pw1bf_100, pw1bf_30, pw1bf_70, pw1bf_80, pw1bf_94), na.rm = T),
openaW2P2 = rowMeans(select(data, pw2bf_100, pw2bf_30, pw2bf_70, pw2bf_80, pw2bf_94), na.rm = T),
openaW3P2 = rowMeans(select(data, pw3bf_100, pw3bf_30, pw3bf_70, pw3bf_80, pw3bf_94), na.rm = T),
openaW4P2 = rowMeans(select(data, pw4bf_100, pw4bf_30, pw4bf_70, pw4bf_80, pw4bf_94), na.rm = T))
# orderliness
data <- data %>%
mutate(# self
orderW1S = rowMeans(select(data, w1bf_8, w1bf_18, w1bf_28, w1bf_38, w1bf_48, w1bf_58,
w1bf_68, w1bf_78, w1bf_88, w1bf_98), na.rm = T),
orderW2S = rowMeans(select(data, w2bf_8, w2bf_18, w2bf_28, w2bf_38, w2bf_48, w2bf_58,
w2bf_68, w2bf_78, w2bf_88, w2bf_98), na.rm = T),
orderW3S = rowMeans(select(data, w3bf_8, w3bf_18, w3bf_28, w3bf_38, w3bf_48, w3bf_58,
w3bf_68, w3bf_78, w3bf_88, w3bf_98), na.rm = T),
orderW4S = rowMeans(select(data, w4bf_8, w4bf_18, w4bf_28, w4bf_38, w4bf_48, w4bf_58,
w4bf_68, w4bf_78, w4bf_88, w4bf_98), na.rm = T),
# peer
orderW1P = rowMeans(select(data, pw1bf_8, pw1bf_18, pw1bf_28, pw1bf_38, pw1bf_48, pw1bf_58,
pw1bf_68, pw1bf_78, pw1bf_88, pw1bf_98), na.rm = T),
orderW2P = rowMeans(select(data, pw2bf_8, pw2bf_18, pw2bf_28, pw2bf_38, pw2bf_48, pw2bf_58,
pw2bf_68, pw2bf_78, pw2bf_88, pw2bf_98), na.rm = T),
orderW3P = rowMeans(select(data, pw3bf_8, pw3bf_18, pw3bf_28, pw3bf_38, pw3bf_48, pw3bf_58,
pw3bf_68, pw3bf_78, pw3bf_88, pw3bf_98), na.rm = T),
orderW4P = rowMeans(select(data, pw4bf_8, pw4bf_18, pw4bf_28, pw4bf_38, pw4bf_48, pw4bf_58,
pw4bf_68, pw4bf_78, pw4bf_88, pw4bf_98), na.rm = T))
data <- data %>%
mutate(# first self parcel
orderW1S1 = rowMeans(select(data, w1bf_8, w1bf_48, w1bf_68, w1bf_78, w1bf_88),na.rm = T),
orderW2S1 = rowMeans(select(data, w2bf_8, w2bf_48, w2bf_68, w2bf_78, w2bf_88),na.rm = T),
orderW3S1 = rowMeans(select(data, w3bf_8, w3bf_48, w3bf_68, w3bf_78, w3bf_88),na.rm = T),
orderW4S1 = rowMeans(select(data, w4bf_8, w4bf_48, w4bf_68, w4bf_78, w4bf_88),na.rm = T),
# second self parcel
orderW1S2 = rowMeans(select(data, w1bf_18, w1bf_28, w1bf_38, w1bf_58, w1bf_98),na.rm = T),
orderW2S2 = rowMeans(select(data, w2bf_18, w2bf_28, w2bf_38, w2bf_58, w2bf_98),na.rm = T),
orderW3S2 = rowMeans(select(data, w3bf_18, w3bf_28, w3bf_38, w3bf_58, w3bf_98),na.rm = T),
orderW4S2 = rowMeans(select(data, w4bf_18, w4bf_28, w4bf_38, w4bf_58, w4bf_98),na.rm = T),
# first peer parcel
orderW1P1 = rowMeans(select(data, pw1bf_18, pw1bf_28, pw1bf_48, pw1bf_58, pw1bf_68), na.rm = T),
orderW2P1 = rowMeans(select(data, pw2bf_18, pw2bf_28, pw2bf_48, pw2bf_58, pw2bf_68), na.rm = T),
orderW3P1 = rowMeans(select(data, pw3bf_18, pw3bf_28, pw3bf_48, pw3bf_58, pw3bf_68), na.rm = T),
orderW4P1 = rowMeans(select(data, pw4bf_18, pw4bf_28, pw4bf_48, pw4bf_58, pw4bf_68), na.rm = T),
# second peer parcel
orderW1P2 = rowMeans(select(data, pw1bf_8, pw1bf_38, pw1bf_78, pw1bf_88, pw1bf_98), na.rm = T),
orderW2P2 = rowMeans(select(data, pw2bf_8, pw2bf_38, pw2bf_78, pw2bf_88, pw2bf_98), na.rm = T),
orderW3P2 = rowMeans(select(data, pw3bf_8, pw3bf_38, pw3bf_78, pw3bf_88, pw3bf_98), na.rm = T),
orderW4P2 = rowMeans(select(data, pw4bf_8, pw4bf_38, pw4bf_78, pw4bf_88, pw4bf_98), na.rm = T))
# politeness
data <- data %>%
mutate(# self
politW1S = rowMeans(select(data, w1bf_7, w1bf_17, w1bf_27, w1bf_37, w1bf_47, w1bf_57,
w1bf_67, w1bf_77, w1bf_87, w1bf_97), na.rm = T),
politW2S = rowMeans(select(data, w2bf_7, w2bf_17, w2bf_27, w2bf_37, w2bf_47, w2bf_57,
w2bf_67, w2bf_77, w2bf_87, w2bf_97), na.rm = T),
politW3S = rowMeans(select(data, w3bf_7, w3bf_17, w3bf_27, w3bf_37, w3bf_47, w3bf_57,
w3bf_67, w3bf_77, w3bf_87, w3bf_97), na.rm = T),
politW4S = rowMeans(select(data, w4bf_7, w4bf_17, w4bf_27, w4bf_37, w4bf_47, w4bf_57,
w4bf_67, w4bf_77, w4bf_87, w4bf_97), na.rm = T),
# peer
politW1P = rowMeans(select(data, pw1bf_7, pw1bf_17, pw1bf_27, pw1bf_37, pw1bf_47, pw1bf_57,
pw1bf_67, pw1bf_77, pw1bf_87, pw1bf_97), na.rm = T),
politW2P = rowMeans(select(data, pw2bf_7, pw2bf_17, pw2bf_27, pw2bf_37, pw2bf_47, pw2bf_57,
pw2bf_67, pw2bf_77, pw2bf_87, pw2bf_97), na.rm = T),
politW3P = rowMeans(select(data, pw3bf_7, pw3bf_17, pw3bf_27, pw3bf_37, pw3bf_47, pw3bf_57,
pw3bf_67, pw3bf_77, pw3bf_87, pw3bf_97), na.rm = T),
politW4P = rowMeans(select(data, pw4bf_7, pw4bf_17, pw4bf_27, pw4bf_37, pw4bf_47, pw4bf_57,
pw4bf_67, pw4bf_77, pw4bf_87, pw4bf_97), na.rm = T))
data <- data %>%
mutate(# first self parcel
politW1S1 = rowMeans(select(data, w1bf_7, w1bf_47, w1bf_67, w1bf_77, w1bf_87),na.rm = T),
politW2S1 = rowMeans(select(data, w2bf_7, w2bf_47, w2bf_67, w2bf_77, w2bf_87),na.rm = T),
politW3S1 = rowMeans(select(data, w3bf_7, w3bf_47, w3bf_67, w3bf_77, w3bf_87),na.rm = T),
politW4S1 = rowMeans(select(data, w4bf_7, w4bf_47, w4bf_67, w4bf_77, w4bf_87),na.rm = T),
# second self parcel
politW1S2 = rowMeans(select(data, w1bf_17, w1bf_27, w1bf_37, w1bf_57, w1bf_97),na.rm = T),
politW2S2 = rowMeans(select(data, w2bf_17, w2bf_27, w2bf_37, w2bf_57, w2bf_97),na.rm = T),
politW3S2 = rowMeans(select(data, w3bf_17, w3bf_27, w3bf_37, w3bf_57, w3bf_97),na.rm = T),
politW4S2 = rowMeans(select(data, w4bf_17, w4bf_27, w4bf_37, w4bf_57, w4bf_97),na.rm = T),
# first peer parcel
politW1P1 = rowMeans(select(data, pw1bf_17, pw1bf_27, pw1bf_47, pw1bf_57, pw1bf_67), na.rm = T),
politW2P1 = rowMeans(select(data, pw2bf_17, pw2bf_27, pw2bf_47, pw2bf_57, pw2bf_67), na.rm = T),
politW3P1 = rowMeans(select(data, pw3bf_17, pw3bf_27, pw3bf_47, pw3bf_57, pw3bf_67), na.rm = T),
politW4P1 = rowMeans(select(data, pw4bf_17, pw4bf_27, pw4bf_47, pw4bf_57, pw4bf_67), na.rm = T),
# second peer parcel
politW1P2 = rowMeans(select(data, pw1bf_7, pw1bf_37, pw1bf_77, pw1bf_87, pw1bf_97), na.rm = T),
politW2P2 = rowMeans(select(data, pw2bf_7, pw2bf_37, pw2bf_77, pw2bf_87, pw2bf_97), na.rm = T),
politW3P2 = rowMeans(select(data, pw3bf_7, pw3bf_37, pw3bf_77, pw3bf_87, pw3bf_97), na.rm = T),
politW4P2 = rowMeans(select(data, pw4bf_7, pw4bf_37, pw4bf_77, pw4bf_87, pw4bf_97), na.rm = T))
# volatility
data <- data %>%
mutate(# self
volatW1S = rowMeans(select(data, w1bf_6, w1bf_16, w1bf_26, w1bf_36, w1bf_46, w1bf_56,
w1bf_66, w1bf_76, w1bf_86, w1bf_96), na.rm = T),
volatW2S = rowMeans(select(data, w2bf_6, w2bf_16, w2bf_26, w2bf_36, w2bf_46, w2bf_56,
w2bf_66, w2bf_76, w2bf_86, w2bf_96), na.rm = T),
volatW3S = rowMeans(select(data, w3bf_6, w3bf_16, w3bf_26, w3bf_36, w3bf_46, w3bf_56,
w3bf_66, w3bf_76, w3bf_86, w3bf_96), na.rm = T),
volatW4S = rowMeans(select(data, w4bf_6, w4bf_16, w4bf_26, w4bf_36, w4bf_46, w4bf_56,
w4bf_66, w4bf_76, w4bf_86, w4bf_96), na.rm = T),
# peer
volatW1P = rowMeans(select(data, pw1bf_6, pw1bf_16, pw1bf_26, pw1bf_36, pw1bf_46, pw1bf_56,
pw1bf_66, pw1bf_76, pw1bf_86, pw1bf_96), na.rm = T),
volatW2P = rowMeans(select(data, pw2bf_6, pw2bf_16, pw2bf_26, pw2bf_36, pw2bf_46, pw2bf_56,
pw2bf_66, pw2bf_76, pw2bf_86, pw2bf_96), na.rm = T),
volatW3P = rowMeans(select(data, pw3bf_6, pw3bf_16, pw3bf_26, pw3bf_36, pw3bf_46, pw3bf_56,
pw3bf_66, pw3bf_76, pw3bf_86, pw3bf_96), na.rm = T),
volatW4P = rowMeans(select(data, pw4bf_6, pw4bf_16, pw4bf_26, pw4bf_36, pw4bf_46, pw4bf_56,
pw4bf_66, pw4bf_76, pw4bf_86, pw4bf_96), na.rm = T))
data <- data %>%
mutate(# first self parcel
volatW1S1 = rowMeans(select(data, w1bf_6, w1bf_46, w1bf_66, w1bf_76, w1bf_86),na.rm = T),
volatW2S1 = rowMeans(select(data, w2bf_6, w2bf_46, w2bf_66, w2bf_76, w2bf_86),na.rm = T),
volatW3S1 = rowMeans(select(data, w3bf_6, w3bf_46, w3bf_66, w3bf_76, w3bf_86),na.rm = T),
volatW4S1 = rowMeans(select(data, w4bf_6, w4bf_46, w4bf_66, w4bf_76, w4bf_86),na.rm = T),
# second self parcel
volatW1S2 = rowMeans(select(data, w1bf_16, w1bf_26, w1bf_36, w1bf_56, w1bf_96),na.rm = T),
volatW2S2 = rowMeans(select(data, w2bf_16, w2bf_26, w2bf_36, w2bf_56, w2bf_96),na.rm = T),
volatW3S2 = rowMeans(select(data, w3bf_16, w3bf_26, w3bf_36, w3bf_56, w3bf_96),na.rm = T),
volatW4S2 = rowMeans(select(data, w4bf_16, w4bf_26, w4bf_36, w4bf_56, w4bf_96),na.rm = T),
# first peer parcel
volatW1P1 = rowMeans(select(data, pw1bf_16, pw1bf_26, pw1bf_46, pw1bf_56, pw1bf_66), na.rm = T),
volatW2P1 = rowMeans(select(data, pw2bf_16, pw2bf_26, pw2bf_46, pw2bf_56, pw2bf_66), na.rm = T),
volatW3P1 = rowMeans(select(data, pw3bf_16, pw3bf_26, pw3bf_46, pw3bf_56, pw3bf_66), na.rm = T),
volatW4P1 = rowMeans(select(data, pw4bf_16, pw4bf_26, pw4bf_46, pw4bf_56, pw4bf_66), na.rm = T),
# second peer parcel
volatW1P2 = rowMeans(select(data, pw1bf_6, pw1bf_36, pw1bf_76, pw1bf_86, pw1bf_96), na.rm = T),
volatW2P2 = rowMeans(select(data, pw2bf_6, pw2bf_36, pw2bf_76, pw2bf_86, pw2bf_96), na.rm = T),
volatW3P2 = rowMeans(select(data, pw3bf_6, pw3bf_36, pw3bf_76, pw3bf_86, pw3bf_96), na.rm = T),
volatW4P2 = rowMeans(select(data, pw4bf_6, pw4bf_36, pw4bf_76, pw4bf_86, pw4bf_96), na.rm = T))
# withdrawal
data <- data %>%
mutate(# self
withdW1S = rowMeans(select(data, w1bf_1, w1bf_11, w1bf_21, w1bf_31, w1bf_41, w1bf_51,
w1bf_61, w1bf_71, w1bf_81, w1bf_91), na.rm = T),
withdW2S = rowMeans(select(data, w2bf_1, w2bf_11, w2bf_21, w2bf_31, w2bf_41, w2bf_51,
w2bf_61, w2bf_71, w2bf_81, w2bf_91), na.rm = T),
withdW3S = rowMeans(select(data, w3bf_1, w3bf_11, w3bf_21, w3bf_31, w3bf_41, w3bf_51,
w3bf_61, w3bf_71, w3bf_81, w3bf_91), na.rm = T),
withdW4S = rowMeans(select(data, w4bf_1, w4bf_11, w4bf_21, w4bf_31, w4bf_41, w4bf_51,
w4bf_61, w4bf_71, w4bf_81, w4bf_91), na.rm = T),
# peer
withdW1P = rowMeans(select(data, pw1bf_1, pw1bf_11, pw1bf_21, pw1bf_31, pw1bf_41, pw1bf_51,
pw1bf_61, pw1bf_71, pw1bf_81, pw1bf_91), na.rm = T),
withdW2P = rowMeans(select(data, pw2bf_1, pw2bf_11, pw2bf_21, pw2bf_31, pw2bf_41, pw2bf_51,
pw2bf_61, pw2bf_71, pw2bf_81, pw2bf_91), na.rm = T),
withdW3P = rowMeans(select(data, pw3bf_1, pw3bf_11, pw3bf_21, pw3bf_31, pw3bf_41, pw3bf_51,
pw3bf_61, pw3bf_71, pw3bf_81, pw3bf_91), na.rm = T),
withdW4P = rowMeans(select(data, pw4bf_1, pw4bf_11, pw4bf_21, pw4bf_31, pw4bf_41, pw4bf_51,
pw4bf_61, pw4bf_71, pw4bf_81, pw4bf_91), na.rm = T))
data <- data %>%
mutate(# first self parcel
withdW1S1 = rowMeans(select(data, w1bf_1, w1bf_41, w1bf_61, w1bf_71, w1bf_81),na.rm = T),
withdW2S1 = rowMeans(select(data, w2bf_1, w2bf_41, w2bf_61, w2bf_71, w2bf_81),na.rm = T),
withdW3S1 = rowMeans(select(data, w3bf_1, w3bf_41, w3bf_61, w3bf_71, w3bf_81),na.rm = T),
withdW4S1 = rowMeans(select(data, w4bf_1, w4bf_41, w4bf_61, w4bf_71, w4bf_81),na.rm = T),
# second self parcel
withdW1S2 = rowMeans(select(data, w1bf_11, w1bf_21, w1bf_31, w1bf_51, w1bf_91),na.rm = T),
withdW2S2 = rowMeans(select(data, w2bf_11, w2bf_21, w2bf_31, w2bf_51, w2bf_91),na.rm = T),
withdW3S2 = rowMeans(select(data, w3bf_11, w3bf_21, w3bf_31, w3bf_51, w3bf_91),na.rm = T),
withdW4S2 = rowMeans(select(data, w4bf_11, w4bf_21, w4bf_31, w4bf_51, w4bf_91),na.rm = T),
# first peer parcel
withdW1P1 = rowMeans(select(data, pw1bf_11, pw1bf_21, pw1bf_41, pw1bf_51, pw1bf_61), na.rm = T),
withdW2P1 = rowMeans(select(data, pw2bf_11, pw2bf_21, pw2bf_41, pw2bf_51, pw2bf_61), na.rm = T),
withdW3P1 = rowMeans(select(data, pw3bf_11, pw3bf_21, pw3bf_41, pw3bf_51, pw3bf_61), na.rm = T),
withdW4P1 = rowMeans(select(data, pw4bf_11, pw4bf_21, pw4bf_41, pw4bf_51, pw4bf_61), na.rm = T),
# second peer parcel
withdW1P2 = rowMeans(select(data, pw1bf_1, pw1bf_31, pw1bf_71, pw1bf_81, pw1bf_91), na.rm = T),
withdW2P2 = rowMeans(select(data, pw2bf_1, pw2bf_31, pw2bf_71, pw2bf_81, pw2bf_91), na.rm = T),
withdW3P2 = rowMeans(select(data, pw3bf_1, pw3bf_31, pw3bf_71, pw3bf_81, pw3bf_91), na.rm = T),
withdW4P2 = rowMeans(select(data, pw4bf_1, pw4bf_31, pw4bf_71, pw4bf_81, pw4bf_91), na.rm = T))
# >>> Domains ----
### agreeableness
data <- data %>%
mutate(# first self parcel
agreeW1S1 = rowMeans(select(data, w1bf_12, w1bf_32, w1bf_42, w1bf_62, w1bf_72,
w1bf_92, w1bf_17, w1bf_37, w1bf_47, w1bf_97),na.rm = T),
agreeW2S1 = rowMeans(select(data, w2bf_12, w2bf_32, w2bf_42, w2bf_62, w2bf_72,
w2bf_92, w2bf_17, w2bf_37, w2bf_47, w2bf_97),na.rm = T),
agreeW3S1 = rowMeans(select(data, w3bf_12, w3bf_32, w3bf_42, w3bf_62, w3bf_72,
w3bf_92, w3bf_17, w3bf_37, w3bf_47, w3bf_97),na.rm = T),
agreeW4S1 = rowMeans(select(data, w4bf_12, w4bf_32, w4bf_42, w4bf_62, w4bf_72,
w4bf_92, w4bf_17, w4bf_37, w4bf_47, w4bf_97),na.rm = T),
# second self parcel
agreeW1S2 = rowMeans(select(data, w1bf_2, w1bf_22, w1bf_52, w1bf_82, w1bf_7,
w1bf_27, w1bf_57, w1bf_67, w1bf_77, w1bf_87),na.rm = T),
agreeW2S2 = rowMeans(select(data, w2bf_2, w2bf_22, w2bf_52, w2bf_82, w2bf_7,
w2bf_27, w2bf_57, w2bf_67, w2bf_77, w2bf_87),na.rm = T),
agreeW3S2 = rowMeans(select(data, w3bf_2, w3bf_22, w3bf_52, w3bf_82, w3bf_7,
w3bf_27, w3bf_57, w3bf_67, w3bf_77, w3bf_87),na.rm = T),
agreeW4S2 = rowMeans(select(data, w4bf_2, w4bf_22, w4bf_52, w4bf_82, w4bf_7,
w4bf_27, w4bf_57, w4bf_67, w4bf_77, w4bf_87),na.rm = T),
# first peer parcel
agreeW1P1 = rowMeans(select(data, pw1bf_2, pw1bf_22, pw1bf_32, pw1bf_42, pw1bf_82,
pw1bf_17, pw1bf_37, pw1bf_47, pw1bf_57, pw1bf_87), na.rm = T),
agreeW2P1 = rowMeans(select(data, pw2bf_2, pw2bf_22, pw2bf_32, pw2bf_42, pw2bf_82,
pw2bf_17, pw2bf_37, pw2bf_47, pw2bf_57, pw2bf_87), na.rm = T),
agreeW3P1 = rowMeans(select(data, pw3bf_2, pw3bf_22, pw3bf_32, pw3bf_42, pw3bf_82,
pw3bf_17, pw3bf_37, pw3bf_47, pw3bf_57, pw3bf_87), na.rm = T),
agreeW4P1 = rowMeans(select(data, pw4bf_2, pw4bf_22, pw4bf_32, pw4bf_42, pw4bf_82,
pw4bf_17, pw4bf_37, pw4bf_47, pw4bf_57, pw4bf_87), na.rm = T),
# second peer parcel
agreeW1P2 = rowMeans(select(data, pw1bf_12, pw1bf_52, pw1bf_62, pw1bf_72, pw1bf_92,
pw1bf_7, pw1bf_27, pw1bf_67, pw1bf_77, pw1bf_97), na.rm = T),
agreeW2P2 = rowMeans(select(data, pw1bf_12, pw1bf_52, pw1bf_62, pw1bf_72, pw1bf_92,
pw1bf_7, pw1bf_27, pw1bf_67, pw1bf_77, pw1bf_97), na.rm = T),
agreeW3P2 = rowMeans(select(data, pw1bf_12, pw1bf_52, pw1bf_62, pw1bf_72, pw1bf_92,
pw1bf_7, pw1bf_27, pw1bf_67, pw1bf_77, pw1bf_97), na.rm = T),
agreeW4P2 = rowMeans(select(data, pw1bf_12, pw1bf_52, pw1bf_62, pw1bf_72, pw1bf_92,
pw1bf_7, pw1bf_27, pw1bf_67, pw1bf_77, pw1bf_97), na.rm = T))
### conscientiousness
data <- data %>%
mutate(# first self parcel
consciW1S1 = rowMeans(select(data, w1bf_13, w1bf_33, w1bf_43, w1bf_63, w1bf_73,
w1bf_93, w1bf_18, w1bf_38, w1bf_48, w1bf_98),na.rm = T),
consciW2S1 = rowMeans(select(data, w2bf_13, w2bf_33, w2bf_43, w2bf_63, w2bf_73,
w2bf_93, w2bf_18, w2bf_38, w2bf_48, w2bf_98),na.rm = T),
consciW3S1 = rowMeans(select(data, w3bf_13, w3bf_33, w3bf_43, w3bf_63, w3bf_73,
w3bf_93, w3bf_18, w3bf_38, w3bf_48, w3bf_98),na.rm = T),
consciW4S1 = rowMeans(select(data, w4bf_13, w4bf_33, w4bf_43, w4bf_63, w4bf_73,
w4bf_93, w4bf_18, w4bf_38, w4bf_48, w4bf_98),na.rm = T),
# second self parcel
consciW1S2 = rowMeans(select(data, w1bf_3, w1bf_23, w1bf_53, w1bf_83, w1bf_8,
w1bf_28, w1bf_58, w1bf_68, w1bf_78, w1bf_88),na.rm = T),
consciW2S2 = rowMeans(select(data, w2bf_3, w2bf_23, w2bf_53, w2bf_83, w2bf_8,
w2bf_28, w2bf_58, w2bf_68, w2bf_78, w2bf_88),na.rm = T),
consciW3S2 = rowMeans(select(data, w3bf_3, w3bf_23, w3bf_53, w3bf_83, w3bf_8,
w3bf_28, w3bf_58, w3bf_68, w3bf_78, w3bf_88),na.rm = T),
consciW4S2 = rowMeans(select(data, w4bf_3, w4bf_23, w4bf_53, w4bf_83, w4bf_8,
w4bf_28, w4bf_58, w4bf_68, w4bf_78, w4bf_88),na.rm = T),
# first peer parcel
consciW1P1 = rowMeans(select(data, pw1bf_3, pw1bf_23, pw1bf_33, pw1bf_43, pw1bf_83,
pw1bf_18, pw1bf_38, pw1bf_48, pw1bf_58, pw1bf_88), na.rm = T),
consciW2P1 = rowMeans(select(data, pw2bf_3, pw2bf_23, pw2bf_33, pw2bf_43, pw2bf_83,
pw2bf_18, pw2bf_38, pw2bf_48, pw2bf_58, pw2bf_88), na.rm = T),
consciW3P1 = rowMeans(select(data, pw3bf_3, pw3bf_23, pw3bf_33, pw3bf_43, pw3bf_83,
pw3bf_18, pw3bf_38, pw3bf_48, pw3bf_58, pw3bf_88), na.rm = T),
consciW4P1 = rowMeans(select(data, pw4bf_3, pw4bf_23, pw4bf_33, pw4bf_43, pw4bf_83,
pw4bf_18, pw4bf_38, pw4bf_48, pw4bf_58, pw4bf_88), na.rm = T),
# second peer parcel
consciW1P2 = rowMeans(select(data, pw1bf_13, pw1bf_53, pw1bf_63, pw1bf_73, pw1bf_93,
pw1bf_8, pw1bf_28, pw1bf_68, pw1bf_78, pw1bf_98), na.rm = T),
consciW2P2 = rowMeans(select(data, pw1bf_13, pw1bf_53, pw1bf_63, pw1bf_73, pw1bf_93,
pw1bf_8, pw1bf_28, pw1bf_68, pw1bf_78, pw1bf_98), na.rm = T),
consciW3P2 = rowMeans(select(data, pw1bf_13, pw1bf_53, pw1bf_63, pw1bf_73, pw1bf_93,
pw1bf_8, pw1bf_28, pw1bf_68, pw1bf_78, pw1bf_98), na.rm = T),
consciW4P2 = rowMeans(select(data, pw1bf_13, pw1bf_53, pw1bf_63, pw1bf_73, pw1bf_93,
pw1bf_8, pw1bf_28, pw1bf_68, pw1bf_78, pw1bf_98), na.rm = T))
### extraversion
data <- data %>%
mutate(# first self parcel
extraW1S1 = rowMeans(select(data, w1bf_14, w1bf_34, w1bf_44, w1bf_64, w1bf_74,
w1bf_94, w1bf_19, w1bf_39, w1bf_49, w1bf_99),na.rm = T),
extraW2S1 = rowMeans(select(data, w2bf_14, w2bf_34, w2bf_44, w2bf_64, w2bf_74,
w2bf_94, w2bf_19, w2bf_39, w2bf_49, w2bf_99),na.rm = T),
extraW3S1 = rowMeans(select(data, w3bf_14, w3bf_34, w3bf_44, w3bf_64, w3bf_74,
w3bf_94, w3bf_19, w3bf_39, w3bf_49, w3bf_99),na.rm = T),
extraW4S1 = rowMeans(select(data, w4bf_14, w4bf_34, w4bf_44, w4bf_64, w4bf_74,
w4bf_94, w4bf_19, w4bf_39, w4bf_49, w4bf_99),na.rm = T),
# second self parcel
extraW1S2 = rowMeans(select(data, w1bf_4, w1bf_24, w1bf_54, w1bf_84, w1bf_9,
w1bf_29, w1bf_59, w1bf_69, w1bf_79, w1bf_89),na.rm = T),
extraW2S2 = rowMeans(select(data, w2bf_4, w2bf_24, w2bf_54, w2bf_84, w2bf_9,
w2bf_29, w2bf_59, w2bf_69, w2bf_79, w2bf_89),na.rm = T),
extraW3S2 = rowMeans(select(data, w3bf_4, w3bf_24, w3bf_54, w3bf_84, w3bf_9,
w3bf_29, w3bf_59, w3bf_69, w3bf_79, w3bf_89),na.rm = T),
extraW4S2 = rowMeans(select(data, w4bf_4, w4bf_24, w4bf_54, w4bf_84, w4bf_9,
w4bf_29, w4bf_59, w4bf_69, w4bf_79, w4bf_89),na.rm = T),
# first peer parcel
extraW1P1 = rowMeans(select(data, pw1bf_4, pw1bf_24, pw1bf_34, pw1bf_44, pw1bf_84,
pw1bf_19, pw1bf_39, pw1bf_49, pw1bf_59, pw1bf_89), na.rm = T),
extraW2P1 = rowMeans(select(data, pw2bf_4, pw2bf_24, pw2bf_34, pw2bf_44, pw2bf_84,
pw2bf_19, pw2bf_39, pw2bf_49, pw2bf_59, pw2bf_89), na.rm = T),
extraW3P1 = rowMeans(select(data, pw3bf_4, pw3bf_24, pw3bf_34, pw3bf_44, pw3bf_84,
pw3bf_19, pw3bf_39, pw3bf_49, pw3bf_59, pw3bf_89), na.rm = T),
extraW4P1 = rowMeans(select(data, pw4bf_4, pw4bf_24, pw4bf_34, pw4bf_44, pw4bf_84,
pw4bf_19, pw4bf_39, pw4bf_49, pw4bf_59, pw4bf_89), na.rm = T),
# second peer parcel
extraW1P2 = rowMeans(select(data, pw1bf_14, pw1bf_54, pw1bf_64, pw1bf_74, pw1bf_94,
pw1bf_9, pw1bf_29, pw1bf_69, pw1bf_79, pw1bf_99), na.rm = T),
extraW2P2 = rowMeans(select(data, pw1bf_14, pw1bf_54, pw1bf_64, pw1bf_74, pw1bf_94,
pw1bf_9, pw1bf_29, pw1bf_69, pw1bf_79, pw1bf_99), na.rm = T),
extraW3P2 = rowMeans(select(data, pw1bf_14, pw1bf_54, pw1bf_64, pw1bf_74, pw1bf_94,
pw1bf_9, pw1bf_29, pw1bf_69, pw1bf_79, pw1bf_99), na.rm = T),
extraW4P2 = rowMeans(select(data, pw1bf_14, pw1bf_54, pw1bf_64, pw1bf_74, pw1bf_94,
pw1bf_9, pw1bf_29, pw1bf_69, pw1bf_79, pw1bf_99), na.rm = T))
### neuroticism
data <- data %>%
mutate(# first self parcel
neuroW1S1 = rowMeans(select(data, w1bf_11, w1bf_31, w1bf_41, w1bf_61, w1bf_71,
w1bf_91, w1bf_16, w1bf_36, w1bf_46, w1bf_96),na.rm = T),
neuroW2S1 = rowMeans(select(data, w2bf_11, w2bf_31, w2bf_41, w2bf_61, w2bf_71,
w2bf_91, w2bf_16, w2bf_36, w2bf_46, w2bf_96),na.rm = T),
neuroW3S1 = rowMeans(select(data, w3bf_11, w3bf_31, w3bf_41, w3bf_61, w3bf_71,
w3bf_91, w3bf_16, w3bf_36, w3bf_46, w3bf_96),na.rm = T),
neuroW4S1 = rowMeans(select(data, w4bf_11, w4bf_31, w4bf_41, w4bf_61, w4bf_71,
w4bf_91, w4bf_16, w4bf_36, w4bf_46, w4bf_96),na.rm = T),
# second self parcel
neuroW1S2 = rowMeans(select(data, w1bf_1, w1bf_21, w1bf_51, w1bf_81, w1bf_6,
w1bf_26, w1bf_56, w1bf_66, w1bf_76, w1bf_86),na.rm = T),
neuroW2S2 = rowMeans(select(data, w2bf_1, w2bf_21, w2bf_51, w2bf_81, w2bf_6,
w2bf_26, w2bf_56, w2bf_66, w2bf_76, w2bf_86),na.rm = T),
neuroW3S2 = rowMeans(select(data, w3bf_1, w3bf_21, w3bf_51, w3bf_81, w3bf_6,
w3bf_26, w3bf_56, w3bf_66, w3bf_76, w3bf_86),na.rm = T),
neuroW4S2 = rowMeans(select(data, w4bf_1, w4bf_21, w4bf_51, w4bf_81, w4bf_6,
w4bf_26, w4bf_56, w4bf_66, w4bf_76, w4bf_86),na.rm = T),
# first peer parcel
neuroW1P1 = rowMeans(select(data, pw1bf_1, pw1bf_21, pw1bf_31, pw1bf_41, pw1bf_81,
pw1bf_16, pw1bf_36, pw1bf_46, pw1bf_56, pw1bf_86), na.rm = T),
neuroW2P1 = rowMeans(select(data, pw2bf_1, pw2bf_21, pw2bf_31, pw2bf_41, pw2bf_81,
pw2bf_16, pw2bf_36, pw2bf_46, pw2bf_56, pw2bf_86), na.rm = T),
neuroW3P1 = rowMeans(select(data, pw3bf_1, pw3bf_21, pw3bf_31, pw3bf_41, pw3bf_81,
pw3bf_16, pw3bf_36, pw3bf_46, pw3bf_56, pw3bf_86), na.rm = T),
neuroW4P1 = rowMeans(select(data, pw4bf_1, pw4bf_21, pw4bf_31, pw4bf_41, pw4bf_81,
pw4bf_16, pw4bf_36, pw4bf_46, pw4bf_56, pw4bf_86), na.rm = T),
# second peer parcel
neuroW1P2 = rowMeans(select(data, pw1bf_11, pw1bf_51, pw1bf_61, pw1bf_71, pw1bf_91,
pw1bf_6, pw1bf_26, pw1bf_66, pw1bf_76, pw1bf_96), na.rm = T),
neuroW2P2 = rowMeans(select(data, pw1bf_11, pw1bf_51, pw1bf_61, pw1bf_71, pw1bf_91,
pw1bf_6, pw1bf_26, pw1bf_66, pw1bf_76, pw1bf_96), na.rm = T),
neuroW3P2 = rowMeans(select(data, pw1bf_11, pw1bf_51, pw1bf_61, pw1bf_71, pw1bf_91,
pw1bf_6, pw1bf_26, pw1bf_66, pw1bf_76, pw1bf_96), na.rm = T),
neuroW4P2 = rowMeans(select(data, pw1bf_11, pw1bf_51, pw1bf_61, pw1bf_71, pw1bf_91,
pw1bf_6, pw1bf_26, pw1bf_66, pw1bf_76, pw1bf_96), na.rm = T))
### openness domain
data <- data %>%
mutate(# first self parcel
opendW1S1 = rowMeans(select(data, w1bf_10, w1bf_30, w1bf_40, w1bf_60, w1bf_70,
w1bf_90, w1bf_15, w1bf_35, w1bf_45, w1bf_95),na.rm = T),
opendW2S1 = rowMeans(select(data, w2bf_10, w2bf_30, w2bf_40, w2bf_60, w2bf_70,
w2bf_90, w2bf_15, w2bf_35, w2bf_45, w2bf_95),na.rm = T),
opendW3S1 = rowMeans(select(data, w3bf_10, w3bf_30, w3bf_40, w3bf_60, w3bf_70,
w3bf_90, w3bf_15, w3bf_35, w3bf_45, w3bf_95),na.rm = T),
opendW4S1 = rowMeans(select(data, w4bf_10, w4bf_30, w4bf_40, w4bf_60, w4bf_70,
w4bf_90, w4bf_15, w4bf_35, w4bf_45, w4bf_95),na.rm = T),
# second self parcel
opendW1S2 = rowMeans(select(data, w1bf_100, w1bf_20, w1bf_50, w1bf_80, w1bf_5,
w1bf_25, w1bf_55, w1bf_65, w1bf_75, w1bf_85),na.rm = T),
opendW2S2 = rowMeans(select(data, w2bf_100, w2bf_20, w2bf_50, w2bf_80, w2bf_5,
w2bf_25, w2bf_55, w2bf_65, w2bf_75, w2bf_85),na.rm = T),
opendW3S2 = rowMeans(select(data, w3bf_100, w3bf_20, w3bf_50, w3bf_80, w3bf_5,
w3bf_25, w3bf_55, w3bf_65, w3bf_75, w3bf_85),na.rm = T),
opendW4S2 = rowMeans(select(data, w4bf_100, w4bf_20, w4bf_50, w4bf_80, w4bf_5,
w4bf_25, w4bf_55, w4bf_65, w4bf_75, w4bf_85),na.rm = T),
# first peer parcel
opendW1P1 = rowMeans(select(data, pw1bf_100, pw1bf_20, pw1bf_30, pw1bf_40, pw1bf_80,
pw1bf_15, pw1bf_35, pw1bf_45, pw1bf_55, pw1bf_85), na.rm = T),
opendW2P1 = rowMeans(select(data, pw2bf_100, pw2bf_20, pw2bf_30, pw2bf_40, pw2bf_80,
pw2bf_15, pw2bf_35, pw2bf_45, pw2bf_55, pw2bf_85), na.rm = T),
opendW3P1 = rowMeans(select(data, pw3bf_100, pw3bf_20, pw3bf_30, pw3bf_40, pw3bf_80,
pw3bf_15, pw3bf_35, pw3bf_45, pw3bf_55, pw3bf_85), na.rm = T),
opendW4P1 = rowMeans(select(data, pw4bf_100, pw4bf_20, pw4bf_30, pw4bf_40, pw4bf_80,
pw4bf_15, pw4bf_35, pw4bf_45, pw4bf_55, pw4bf_85), na.rm = T),
# second peer parcel
opendW1P2 = rowMeans(select(data, pw1bf_10, pw1bf_50, pw1bf_60, pw1bf_70, pw1bf_90,
pw1bf_5, pw1bf_25, pw1bf_65, pw1bf_75, pw1bf_95), na.rm = T),
opendW2P2 = rowMeans(select(data, pw1bf_10, pw1bf_50, pw1bf_60, pw1bf_70, pw1bf_90,
pw1bf_5, pw1bf_25, pw1bf_65, pw1bf_75, pw1bf_95), na.rm = T),
opendW3P2 = rowMeans(select(data, pw1bf_10, pw1bf_50, pw1bf_60, pw1bf_70, pw1bf_90,
pw1bf_5, pw1bf_25, pw1bf_65, pw1bf_75, pw1bf_95), na.rm = T),
opendW4P2 = rowMeans(select(data, pw1bf_10, pw1bf_50, pw1bf_60, pw1bf_70, pw1bf_90,
pw1bf_5, pw1bf_25, pw1bf_65, pw1bf_75, pw1bf_95), na.rm = T))
# >>> Identity ----
### Confusion
data <- data %>%
mutate(# first self parcel
confuW1S1 = rowMeans(select(data, w1epsi_7, w1epsi_10, w1epsi_11),na.rm = T),
confuW2S1 = rowMeans(select(data, w2epsi_7, w2epsi_10, w2epsi_11),na.rm = T),
confuW3S1 = rowMeans(select(data, w3epsi_7, w3epsi_10, w3epsi_11),na.rm = T),
confuW4S1 = rowMeans(select(data, w4epsi_7, w4epsi_10, w4epsi_11),na.rm = T),
# second self parcel
confuW1S2 = rowMeans(select(data, w1epsi_1, w1epsi_3, w1epsi_12),na.rm = T),
confuW2S2 = rowMeans(select(data, w2epsi_1, w2epsi_3, w2epsi_12),na.rm = T),
confuW3S2 = rowMeans(select(data, w3epsi_1, w3epsi_3, w3epsi_12),na.rm = T),
confuW4S2 = rowMeans(select(data, w4epsi_1, w4epsi_3, w4epsi_12),na.rm = T),
# first peer parcel
confuW1P1 = rowMeans(select(data, pw1epsi_3, pw1epsi_7, pw1epsi_12), na.rm = T),
confuW2P1 = rowMeans(select(data, pw2epsi_3, pw2epsi_7, pw2epsi_12), na.rm = T),
confuW3P1 = rowMeans(select(data, pw3epsi_3, pw3epsi_7, pw3epsi_12), na.rm = T),
confuW4P1 = rowMeans(select(data, pw4epsi_3, pw4epsi_7, pw4epsi_12), na.rm = T),
# second peer parcel
confuW1P2 = rowMeans(select(data, pw1epsi_1, pw1epsi_10, pw1epsi_11), na.rm = T),
confuW2P2 = rowMeans(select(data, pw2epsi_1, pw2epsi_10, pw2epsi_11), na.rm = T),
confuW3P2 = rowMeans(select(data, pw3epsi_1, pw3epsi_10, pw3epsi_11), na.rm = T),
confuW4P2 = rowMeans(select(data, pw4epsi_1, pw4epsi_10, pw4epsi_11), na.rm = T))
### Coherence
data <- data %>%
mutate(# first self parcel
coherW1S1 = rowMeans(select(data, w1epsi_2, w1epsi_5, w1epsi_6),na.rm = T),
coherW2S1 = rowMeans(select(data, w2epsi_2, w2epsi_5, w2epsi_6),na.rm = T),
coherW3S1 = rowMeans(select(data, w3epsi_2, w3epsi_5, w3epsi_6),na.rm = T),
coherW4S1 = rowMeans(select(data, w4epsi_2, w4epsi_5, w4epsi_6),na.rm = T),
# second self parcel
coherW1S2 = rowMeans(select(data, w1epsi_4, w1epsi_8, w1epsi_9),na.rm = T),
coherW2S2 = rowMeans(select(data, w2epsi_4, w2epsi_8, w2epsi_9),na.rm = T),
coherW3S2 = rowMeans(select(data, w3epsi_4, w3epsi_8, w3epsi_9),na.rm = T),
coherW4S2 = rowMeans(select(data, w4epsi_4, w4epsi_8, w4epsi_9),na.rm = T),
# first peer parcel
coherW1P1 = rowMeans(select(data, pw1epsi_4, pw1epsi_5, pw1epsi_9), na.rm = T),
coherW2P1 = rowMeans(select(data, pw2epsi_4, pw2epsi_5, pw2epsi_9), na.rm = T),
coherW3P1 = rowMeans(select(data, pw3epsi_4, pw3epsi_5, pw3epsi_9), na.rm = T),
coherW4P1 = rowMeans(select(data, pw4epsi_4, pw4epsi_5, pw4epsi_9), na.rm = T),
# second peer parcel
coherW1P2 = rowMeans(select(data, pw1epsi_2, pw1epsi_6, pw1epsi_8), na.rm = T),
coherW2P2 = rowMeans(select(data, pw2epsi_2, pw2epsi_6, pw2epsi_8), na.rm = T),
coherW3P2 = rowMeans(select(data, pw3epsi_2, pw3epsi_6, pw3epsi_8), na.rm = T),
coherW4P2 = rowMeans(select(data, pw4epsi_2, pw4epsi_6, pw4epsi_8), na.rm = T))
data[data == "NaN"] <- NALatent growth model
LGM Agreeableness
with aspects as parcels
lgmAgree <- '
# factor at each time point with same loading
agree1 =~ compaW1S + a * politW1S +
peer * compaW1P + aa * politW1P
agree2 =~ compaW2S + a * politW2S +
peer * compaW2P + aa * politW2P
agree3 =~ compaW3S + a * politW3S +
peer * compaW3P + aa * politW3P
agree4 =~ compaW4S + a * politW4S +
peer * compaW4P + aa * politW4P
# second order factor for intercept and slope
interc =~ 1*agree1 + 1*agree2 + 1*agree3 + 1*agree4
slope =~ 0*agree1 + 6*agree2 + 13*agree3 + 19*agree4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
compaW1S ~ 0*1
compaW2S ~ 0*1
compaW3S ~ 0*1
compaW4S ~ 0*1
# fix equal intercepts
politW1S ~ b*1
politW2S ~ b*1
politW3S ~ b*1
politW4S ~ b*1
compaW1P ~ c*1
compaW2P ~ c*1
compaW3P ~ c*1
compaW4P ~ c*1
politW1P ~ d*1
politW2P ~ d*1
politW3P ~ d*1
politW4P ~ d*1
# error covariance - similar aspects across waves and informants
compaW1S ~~ compaW2S + compaW3S + compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW2S ~~ compaW3S + compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW3S ~~ compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW4S ~~ compaW1P + compaW2P + compaW3P + compaW4P
politW1S ~~ politW2S + politW3S + politW4S +
politW1P + politW2P + politW3P + politW4P
politW2S ~~ politW3S + politW4S +
politW1P + politW2P + politW3P + politW4P
politW3S ~~ politW4S +
politW1P + politW2P + politW3P + politW4P
politW4S ~~ politW1P + politW2P + politW3P + politW4P
compaW1P ~~ compaW2P + compaW3P + compaW4P
compaW2P ~~ compaW3P + compaW4P
compaW3P ~~ compaW4P
politW1P ~~ politW2P + politW3P + politW4P
politW2P ~~ politW3P + politW4P
politW3P ~~ politW4P
'
lgmAgree <- sem(lgmAgree, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.summary(lgmAgree, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 1672 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 105
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree1 =~
## compW1S 1.000 8.042 17.183
## poltW1S (a) 0.000 NA 0.004 0.007
## compW1P (peer) 3.473 NA 27.933 47.495
## poltW1P (aa) 0.001 NA 0.008 0.013
## agree2 =~
## compW2S 1.000 7.818 16.238
## poltW2S (a) 0.000 NA 0.004 0.007
## compW2P (peer) 3.473 NA 27.153 47.424
## poltW2P (aa) 0.001 NA 0.008 0.013
## agree3 =~
## compW3S 1.000 8.068 16.853
## poltW3S (a) 0.000 NA 0.004 0.007
## compW3P (peer) 3.473 NA 28.023 50.664
## poltW3P (aa) 0.001 NA 0.008 0.012
## agree4 =~
## compW4S 1.000 8.725 17.943
## poltW4S (a) 0.000 NA 0.004 0.007
## compW4P (peer) 3.473 NA 30.306 51.837
## poltW4P (aa) 0.001 NA 0.008 0.013
## interc =~
## agree1 1.000 0.862 0.862
## agree2 1.000 0.887 0.887
## agree3 1.000 0.859 0.859
## agree4 1.000 0.794 0.794
## slope =~
## agree1 0.000 0.000 0.000
## agree2 6.000 0.117 0.117
## agree3 13.000 0.246 0.246
## agree4 19.000 0.332 0.332
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.347 NA 0.328 0.328
## .compaW1S ~~
## .compaW2S -49.981 NA -49.981 -0.798
## .compaW3S -52.417 NA -52.417 -0.811
## .compaW4S -54.493 NA -54.493 -0.779
## .compaW1P -224.559 NA -224.559 -1.002
## .compaW2P -173.992 NA -173.992 -0.798
## .compaW3P -182.425 NA -182.425 -0.811
## .compaW4P -189.676 NA -189.676 -0.780
## .compaW2S ~~
## .compaW3S -56.265 NA -56.265 -0.895
## .compaW4S -59.181 NA -59.181 -0.871
## .compaW1P ~~
## .compaW2S -174.003 NA -174.003 -0.799
## .compaW2S ~~
## .compaW2P -212.197 NA -212.197 -1.002
## .compaW3P -195.953 NA -195.953 -0.896
## .compaW4P -206.088 NA -206.088 -0.872
## .compaW3S ~~
## .compaW4S -64.681 NA -64.681 -0.922
## .compaW1P ~~
## .compaW3S -182.448 NA -182.448 -0.811
## .compaW2P ~~
## .compaW3S -195.975 NA -195.975 -0.896
## .compaW3S ~~
## .compaW3P -226.012 NA -226.012 -1.002
## .compaW4P -225.281 NA -225.281 -0.923
## .compaW1P ~~
## .compaW4S -189.683 NA -189.683 -0.780
## .compaW2P ~~
## .compaW4S -206.106 NA -206.106 -0.872
## .compaW3P ~~
## .compaW4S -225.256 NA -225.256 -0.923
## .compaW4S ~~
## .compaW4P -264.350 NA -264.350 -1.001
## .politW1S ~~
## .politW2S 0.214 NA 0.214 0.749
## .politW3S 0.199 NA 0.199 0.678
## .politW4S 0.199 NA 0.199 0.688
## .politW1P 0.163 NA 0.163 0.508
## .politW2P 0.124 NA 0.124 0.416
## .politW3P 0.126 NA 0.126 0.373
## .politW4P 0.136 NA 0.136 0.403
## .politW2S ~~
## .politW3S 0.237 NA 0.237 0.803
## .politW4S 0.224 NA 0.224 0.770
## .politW1P ~~
## .politW2S 0.149 NA 0.149 0.463
## .politW2S ~~
## .politW2P 0.134 NA 0.134 0.448
## .politW3P 0.103 NA 0.103 0.302
## .politW4P 0.126 NA 0.126 0.373
## .politW3S ~~
## .politW4S 0.255 NA 0.255 0.856
## .politW1P ~~
## .politW3S 0.156 NA 0.156 0.473
## .politW2P ~~
## .politW3S 0.136 NA 0.136 0.443
## .politW3S ~~
## .politW3P 0.122 NA 0.122 0.348
## .politW4P 0.131 NA 0.131 0.378
## .politW1P ~~
## .politW4S 0.159 NA 0.159 0.488
## .politW2P ~~
## .politW4S 0.150 NA 0.150 0.496
## .politW3P ~~
## .politW4S 0.136 NA 0.136 0.395
## .politW4S ~~
## .politW4P 0.159 NA 0.159 0.466
## .compaW1P ~~
## .compaW2P -604.494 NA -604.494 -0.797
## .compaW3P -633.747 NA -633.747 -0.810
## .compaW4P -658.848 NA -658.848 -0.779
## .compaW2P ~~
## .compaW3P -680.701 NA -680.701 -0.895
## .compaW4P -715.888 NA -715.888 -0.870
## .compaW3P ~~
## .compaW4P -782.428 NA -782.428 -0.922
## .politW1P ~~
## .politW2P 0.246 NA 0.246 0.734
## .politW3P 0.246 NA 0.246 0.645
## .politW4P 0.266 NA 0.266 0.704
## .politW2P ~~
## .politW3P 0.270 NA 0.270 0.761
## .politW4P 0.274 NA 0.274 0.777
## .politW3P ~~
## .politW4P 0.334 NA 0.334 0.835
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 4.139 NA 0.597 0.597
## slope 0.000 NA 0.002 0.002
## .compaW1S 0.000 0.000 0.000
## .compaW2S 0.000 0.000 0.000
## .compaW3S 0.000 0.000 0.000
## .compaW4S 0.000 0.000 0.000
## .politW1S (b) 3.717 NA 3.717 6.969
## .politW2S (b) 3.717 NA 3.717 6.935
## .politW3S (b) 3.717 NA 3.717 6.752
## .politW4S (b) 3.717 NA 3.717 6.863
## .compaW1P (c) -10.427 NA -10.427 -17.729
## .compaW2P (c) -10.427 NA -10.427 -18.211
## .compaW3P (c) -10.427 NA -10.427 -18.852
## .compaW4P (c) -10.427 NA -10.427 -17.836
## .politW1P (d) 3.793 NA 3.793 6.320
## .politW2P (d) 3.793 NA 3.793 6.788
## .politW3P (d) 3.793 NA 3.793 5.977
## .politW4P (d) 3.793 NA 3.793 6.019
## .agree1 0.000 0.000 0.000
## .agree2 0.000 0.000 0.000
## .agree3 0.000 0.000 0.000
## .agree4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S -64.457 NA -64.457 -294.266
## .politW1S 0.285 NA 0.285 1.000
## .compaW1P -779.931 NA -779.931 -2254.742
## .politW1P 0.360 NA 0.360 1.000
## .compaW2S -60.882 NA -60.882 -262.683
## .politW2S 0.287 NA 0.287 1.000
## .compaW2P -736.980 NA -736.980 -2248.046
## .politW2P 0.312 NA 0.312 1.000
## .compaW3S -64.862 NA -64.862 -283.012
## .politW3S 0.303 NA 0.303 1.000
## .compaW3P -784.980 NA -784.980 -2565.888
## .politW3P 0.403 NA 0.403 1.000
## .compaW4S -75.890 NA -75.890 -320.960
## .politW4S 0.293 NA 0.293 1.000
## .compaW4P -918.087 NA -918.087 -2686.068
## .politW4P 0.397 NA 0.397 1.000
## .agree1 16.634 NA 0.257 0.257
## .agree2 8.075 NA 0.132 0.132
## .agree3 4.107 NA 0.063 0.063
## .agree4 6.521 NA 0.086 0.086
## interc 48.042 NA 1.000 1.000
## slope 0.023 NA 1.000 1.000semPaths(lgmAgree, what = "col", whatLabels = "est", intercepts = T)
with random parcels
lgmAgree <- '
# factor at each time point with same loading
agree1 =~ agreeW1S1 + a * agreeW1S2 +
peer * agreeW1P1 + aa * agreeW1P2
agree2 =~ agreeW2S1 + a * agreeW2S2 +
peer * agreeW2P1 + aa * agreeW2P2
agree3 =~ agreeW3S1 + a * agreeW3S2 +
peer * agreeW3P1 + aa * agreeW3P2
agree4 =~ agreeW4S1 + a * agreeW4S2 +
peer * agreeW4P1 + aa * agreeW4P2
# second order factor for intercept and slope
interc =~ 1*agree1 + 1*agree2 + 1*agree3 + 1*agree4
slope =~ 0*agree1 + 6*agree2 + 13*agree3 + 19*agree4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
agreeW1S1 ~ 0*1
agreeW2S1 ~ 0*1
agreeW3S1 ~ 0*1
agreeW4S1 ~ 0*1
# fix equal intercepts
agreeW1S2 ~ b*1
agreeW2S2 ~ b*1
agreeW3S2 ~ b*1
agreeW4S2 ~ b*1
agreeW1P1 ~ c*1
agreeW2P1 ~ c*1
agreeW3P1 ~ c*1
agreeW4P1 ~ c*1
agreeW1P2 ~ d*1
agreeW2P2 ~ d*1
agreeW3P2 ~ d*1
agreeW4P2 ~ d*1
# error covariance - similar parcels across waves
agreeW1S1 ~~ agreeW2S1 + agreeW3S1 + agreeW4S1
agreeW2S1 ~~ agreeW3S1 + agreeW4S1
agreeW3S1 ~~ agreeW4S1
agreeW1S2 ~~ agreeW2S2 + agreeW3S2 + agreeW4S2
agreeW2S2 ~~ agreeW3S2 + agreeW4S2
agreeW3S2 ~~ agreeW4S2
agreeW1P1 ~~ agreeW2P1 + agreeW3P1 + agreeW4P1
agreeW2P1 ~~ agreeW3P1 + agreeW4P1
agreeW3P1 ~~ agreeW4P1
agreeW1P2 ~~ agreeW2P2 + agreeW3P2 + agreeW4P2
agreeW2P2 ~~ agreeW3P2 + agreeW4P2
agreeW3P2 ~~ agreeW4P2
# error covariance - same method at one wave
agreeW1S1 ~~ agreeW1S2
agreeW1P1 ~~ agreeW1P2
agreeW2S1 ~~ agreeW2S2
agreeW2P1 ~~ agreeW2P2
agreeW3S1 ~~ agreeW3S2
agreeW3P1 ~~ agreeW3P2
agreeW4S1 ~~ agreeW4S2
agreeW4P1 ~~ agreeW4P2
'
lgmAgree <- sem(lgmAgree, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmAgree, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 314 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree1 =~
## agrW1S1 1.000 1.348 0.949
## agrW1S2 (a) 0.948 NA 1.278 0.946
## agrW1P1 (peer) 0.935 NA 1.261 0.967
## agrW1P2 (aa) 0.930 NA 1.253 0.972
## agree2 =~
## agrW2S1 1.000 1.259 0.954
## agrW2S2 (a) 0.948 NA 1.193 0.953
## agrW2P1 (peer) 0.935 NA 1.177 0.920
## agrW2P2 (aa) 0.930 NA 1.170 0.940
## agree3 =~
## agrW3S1 1.000 1.262 0.945
## agrW3S2 (a) 0.948 NA 1.196 0.943
## agrW3P1 (peer) 0.935 NA 1.180 0.900
## agrW3P2 (aa) 0.930 NA 1.173 0.947
## agree4 =~
## agrW4S1 1.000 1.324 0.949
## agrW4S2 (a) 0.948 NA 1.255 0.945
## agrW4P1 (peer) 0.935 NA 1.238 0.925
## agrW4P2 (aa) 0.930 NA 1.231 0.961
## interc =~
## agree1 1.000 0.863 0.863
## agree2 1.000 0.925 0.925
## agree3 1.000 0.922 0.922
## agree4 1.000 0.879 0.879
## slope =~
## agree1 0.000 NA NA
## agree2 6.000 NA NA
## agree3 13.000 NA NA
## agree4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.014 NA 0.509 0.509
## .agreeW1S1 ~~
## .agreeW2S1 0.086 NA 0.086 0.489
## .agreeW3S1 0.065 NA 0.065 0.332
## .agreeW4S1 0.019 NA 0.019 0.099
## .agreeW2S1 ~~
## .agreeW3S1 0.087 NA 0.087 0.502
## .agreeW4S1 0.094 NA 0.094 0.541
## .agreeW3S1 ~~
## .agreeW4S1 0.065 NA 0.065 0.336
## .agreeW1S2 ~~
## .agreeW2S2 0.083 NA 0.083 0.500
## .agreeW3S2 0.063 NA 0.063 0.339
## .agreeW4S2 0.040 NA 0.040 0.212
## .agreeW2S2 ~~
## .agreeW3S2 0.090 NA 0.090 0.554
## .agreeW4S2 0.065 NA 0.065 0.391
## .agreeW3S2 ~~
## .agreeW4S2 0.036 NA 0.036 0.197
## .agreeW1P1 ~~
## .agreeW2P1 0.050 NA 0.050 0.300
## .agreeW3P1 0.056 NA 0.056 0.298
## .agreeW4P1 0.047 NA 0.047 0.281
## .agreeW2P1 ~~
## .agreeW3P1 0.219 NA 0.219 0.763
## .agreeW4P1 0.208 NA 0.208 0.818
## .agreeW3P1 ~~
## .agreeW4P1 0.245 NA 0.245 0.843
## .agreeW1P2 ~~
## .agreeW2P2 0.144 NA 0.144 1.119
## .agreeW3P2 0.132 NA 0.132 1.093
## .agreeW4P2 0.124 NA 0.124 1.155
## .agreeW2P2 ~~
## .agreeW3P2 0.169 NA 0.169 1.004
## .agreeW4P2 0.156 NA 0.156 1.043
## .agreeW3P2 ~~
## .agreeW4P2 0.144 NA 0.144 1.027
## .agreeW1S1 ~~
## .agreeW1S2 0.097 NA 0.097 0.498
## .agreeW1P1 ~~
## .agreeW1P2 -0.000 NA -0.000 -0.003
## .agreeW2S1 ~~
## .agreeW2S2 -0.022 NA -0.022 -0.143
## .agreeW2P1 ~~
## .agreeW2P2 -0.000 NA -0.000 -0.000
## .agreeW3S1 ~~
## .agreeW3S2 0.094 NA 0.094 0.509
## .agreeW3P1 ~~
## .agreeW3P2 -0.000 NA -0.000 -0.000
## .agreeW4S1 ~~
## .agreeW4S2 0.121 NA 0.121 0.629
## .agreeW4P1 ~~
## .agreeW4P2 -0.000 NA -0.000 -0.001
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.712 NA 3.190 3.190
## slope -0.000 NA NA NA
## .agreeW1S1 0.000 0.000 0.000
## .agreeW2S1 0.000 0.000 0.000
## .agreeW3S1 0.000 0.000 0.000
## .agreeW4S1 0.000 0.000 0.000
## .agreeW1S2 (b) 0.278 NA 0.278 0.206
## .agreeW2S2 (b) 0.278 NA 0.278 0.222
## .agreeW3S2 (b) 0.278 NA 0.278 0.219
## .agreeW4S2 (b) 0.278 NA 0.278 0.210
## .agreeW1P1 (c) 0.137 NA 0.137 0.105
## .agreeW2P1 (c) 0.137 NA 0.137 0.107
## .agreeW3P1 (c) 0.137 NA 0.137 0.105
## .agreeW4P1 (c) 0.137 NA 0.137 0.103
## .agreeW1P2 (d) 0.327 NA 0.327 0.254
## .agreeW2P2 (d) 0.327 NA 0.327 0.263
## .agreeW3P2 (d) 0.327 NA 0.327 0.264
## .agreeW4P2 (d) 0.327 NA 0.327 0.256
## .agree1 0.000 0.000 0.000
## .agree2 0.000 0.000 0.000
## .agree3 0.000 0.000 0.000
## .agree4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .agreeW1S1 0.199 NA 0.199 0.099
## .agreeW1S2 0.191 NA 0.191 0.105
## .agreeW1P1 0.109 NA 0.109 0.064
## .agreeW1P2 0.092 NA 0.092 0.056
## .agreeW2S1 0.156 NA 0.156 0.090
## .agreeW2S2 0.145 NA 0.145 0.093
## .agreeW2P1 0.251 NA 0.251 0.153
## .agreeW2P2 0.180 NA 0.180 0.116
## .agreeW3S1 0.192 NA 0.192 0.107
## .agreeW3S2 0.180 NA 0.180 0.112
## .agreeW3P1 0.327 NA 0.327 0.190
## .agreeW3P2 0.158 NA 0.158 0.103
## .agreeW4S1 0.194 NA 0.194 0.100
## .agreeW4S2 0.190 NA 0.190 0.107
## .agreeW4P1 0.258 NA 0.258 0.144
## .agreeW4P2 0.124 NA 0.124 0.076
## .agree1 0.462 NA 0.254 0.254
## .agree2 0.082 NA 0.052 0.052
## .agree3 -0.032 NA -0.020 -0.020
## .agree4 0.067 NA 0.038 0.038
## interc 1.355 NA 1.000 1.000
## slope -0.001 NA NA NAsemPaths(lgmAgree, what = "col", whatLabels = "est", intercepts = T)
parcelAllocation
item.syntax <- c(paste0("f1 =~ w1bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f1 =~ pw1bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f2 =~ w2bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f2 =~ pw2bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f3 =~ w3bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f3 =~ pw3bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f4 =~ w4bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)),
paste0("f4 =~ pw4bf_", c(2,12,22,32,42,52,62,72,82,92,
7,17,27,37,47,57,67,77,87,97)))
mod.parcels <- '
f1 =~ par1 + par2 + par3 + par4
f2 =~ par5 + par6 + par7 + par8
f3 =~ par9 + par10 + par11 + par12
f4 =~ par13 + par14 + par15 + par16
'
parcel.names <- paste0("par", 1:16)
parcelAllocation(mod.parcels, data = data, nAlloc = 100,
parcel.names = parcel.names, item.syntax = item.syntax,
std.lv = TRUE)## Error in parcelAllocation(mod.parcels, data = data, nAlloc = 100, parcel.names = parcel.names, : could not find function "parcelAllocation"dataList <- parcelAllocation(mod.parcels, data = data, nAlloc = 100,
parcel.names = parcel.names,
item.syntax = item.syntax)## Error in parcelAllocation(mod.parcels, data = data, nAlloc = 100, parcel.names = parcel.names, : could not find function "parcelAllocation"with latent method factors
lgmAgree <- '
# factor at each time point with same loading
agree1 =~ agreeW1S1 + a * agreeW1S2 +
peer * agreeW1P1 + aa * agreeW1P2
agree2 =~ agreeW2S1 + a * agreeW2S2 +
peer * agreeW2P1 + aa * agreeW2P2
agree3 =~ agreeW3S1 + a * agreeW3S2 +
peer * agreeW3P1 + aa * agreeW3P2
agree4 =~ agreeW4S1 + a * agreeW4S2 +
peer * agreeW4P1 + aa * agreeW4P2
# second order factor for intercept and slope
interc =~ 1*agree1 + 1*agree2 + 1*agree3 + 1*agree4
slope =~ 0*agree1 + 6*agree2 + 13*agree3 + 19*agree4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
agreeW1S1 ~ 0*1
agreeW2S1 ~ 0*1
agreeW3S1 ~ 0*1
agreeW4S1 ~ 0*1
# fix equal intercepts
agreeW1S2 ~ b*1
agreeW2S2 ~ b*1
agreeW3S2 ~ b*1
agreeW4S2 ~ b*1
agreeW1P1 ~ c*1
agreeW2P1 ~ c*1
agreeW3P1 ~ c*1
agreeW4P1 ~ c*1
agreeW1P2 ~ d*1
agreeW2P2 ~ d*1
agreeW3P2 ~ d*1
agreeW4P2 ~ d*1
# latent method variances
self =~ agreeW1S1 + agreeW1S2 +
agreeW2S1 + agreeW2S2 +
agreeW3S1 + agreeW3S2 +
agreeW4S1 + agreeW4S2
peer =~ agreeW1P1 + agreeW1P2 +
agreeW2P1 + agreeW2P2 +
agreeW3P1 + agreeW3P2 +
agreeW4P1 + agreeW4P2
'
lgmAgree <- sem(lgmAgree, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmAgree, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 392 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 70
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree1 =~
## agrW1S1 1.000 1.069 0.840
## agrW1S2 (a) 0.960 NA 1.026 0.840
## agrW1P1 (peer) 0.837 NA 0.895 0.538
## agrW1P2 (aa) 0.803 NA 0.859 0.476
## agree2 =~
## agrW2S1 1.000 1.080 0.886
## agrW2S2 (a) 0.960 NA 1.037 0.859
## agrW2P1 (peer) 0.837 NA 0.904 0.608
## agrW2P2 (aa) 0.803 NA 0.868 0.482
## agree3 =~
## agrW3S1 1.000 1.096 0.877
## agrW3S2 (a) 0.960 NA 1.051 0.868
## agrW3P1 (peer) 0.837 NA 0.917 0.656
## agrW3P2 (aa) 0.803 NA 0.880 0.491
## agree4 =~
## agrW4S1 1.000 1.111 0.939
## agrW4S2 (a) 0.960 NA 1.066 0.935
## agrW4P1 (peer) 0.837 NA 0.930 0.678
## agrW4P2 (aa) 0.803 NA 0.892 0.500
## interc =~
## agree1 1.000 1.000 1.000
## agree2 1.000 0.990 0.990
## agree3 1.000 0.976 0.976
## agree4 1.000 0.962 0.962
## slope =~
## agree1 0.000 0.000 0.000
## agree2 6.000 0.044 0.044
## agree3 13.000 0.093 0.093
## agree4 19.000 0.134 0.134
## self =~
## agrW1S1 1.000 0.790 0.621
## agrW1S2 0.920 NA 0.727 0.595
## agrW2S1 0.762 NA 0.602 0.494
## agrW2S2 0.806 NA 0.637 0.528
## agrW3S1 0.643 NA 0.508 0.406
## agrW3S2 0.645 NA 0.510 0.421
## agrW4S1 0.398 NA 0.315 0.266
## agrW4S2 0.396 NA 0.313 0.274
## peer =~
## agrW1P1 1.000 1.133 0.681
## agrW1P2 1.180 NA 1.337 0.741
## agrW2P1 0.730 NA 0.827 0.556
## agrW2P2 1.147 NA 1.299 0.721
## agrW3P1 0.601 NA 0.681 0.487
## agrW3P2 1.100 NA 1.247 0.696
## agrW4P1 0.577 NA 0.654 0.477
## agrW4P2 1.063 NA 1.204 0.675
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.002 NA 0.216 0.216
## self -0.089 NA -0.106 -0.106
## peer 0.383 NA 0.316 0.316
## slope ~~
## self 0.005 NA 0.856 0.856
## peer 0.009 NA 0.989 0.989
## self ~~
## peer 0.725 NA 0.810 0.810
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.321 NA 3.107 3.107
## slope 0.001 NA 0.076 0.076
## .agreeW1S1 0.000 0.000 0.000
## .agreeW2S1 0.000 0.000 0.000
## .agreeW3S1 0.000 0.000 0.000
## .agreeW4S1 0.000 0.000 0.000
## .agreeW1S2 (b) 0.278 NA 0.278 0.228
## .agreeW2S2 (b) 0.278 NA 0.278 0.231
## .agreeW3S2 (b) 0.278 NA 0.278 0.230
## .agreeW4S2 (b) 0.278 NA 0.278 0.244
## .agreeW1P1 (c) 0.265 NA 0.265 0.159
## .agreeW2P1 (c) 0.265 NA 0.265 0.178
## .agreeW3P1 (c) 0.265 NA 0.265 0.190
## .agreeW4P1 (c) 0.265 NA 0.265 0.194
## .agreeW1P2 (d) 0.409 NA 0.409 0.227
## .agreeW2P2 (d) 0.409 NA 0.409 0.227
## .agreeW3P2 (d) 0.409 NA 0.409 0.228
## .agreeW4P2 (d) 0.409 NA 0.409 0.229
## .agree1 0.000 0.000 0.000
## .agree2 0.000 0.000 0.000
## .agree3 0.000 0.000 0.000
## .agree4 0.000 0.000 0.000
## self 0.000 0.000 0.000
## peer 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .agreeW1S1 0.029 NA 0.029 0.018
## .agreeW1S2 0.070 NA 0.070 0.047
## .agreeW1P1 0.045 NA 0.045 0.016
## .agreeW1P2 -0.000 NA -0.000 -0.000
## .agreeW2S1 0.044 NA 0.044 0.029
## .agreeW2S2 0.064 NA 0.064 0.044
## .agreeW2P1 0.180 NA 0.180 0.081
## .agreeW2P2 -0.000 NA -0.000 -0.000
## .agreeW3S1 0.129 NA 0.129 0.083
## .agreeW3S2 0.127 NA 0.127 0.087
## .agreeW3P1 0.150 NA 0.150 0.077
## .agreeW3P2 0.000 NA 0.000 0.000
## .agreeW4S1 0.056 NA 0.056 0.040
## .agreeW4S2 0.056 NA 0.056 0.043
## .agreeW4P1 0.055 NA 0.055 0.029
## .agreeW4P2 -0.000 NA -0.000 -0.000
## .agree1 0.000 NA 0.000 0.000
## .agree2 0.000 NA 0.000 0.000
## .agree3 -0.000 NA -0.000 -0.000
## .agree4 0.000 NA 0.000 0.000
## interc 1.143 NA 1.000 1.000
## slope 0.000 NA 1.000 1.000
## self 0.624 NA 1.000 1.000
## peer 1.283 NA 1.000 1.000semPaths(lgmAgree, what = "col", whatLabels = "est", intercepts = T)
with random parcels + equality and positive constraints in residual covar
lgmAgree <- '
# factor at each time point with same loading
agree1 =~ agreeW1S1 + a * agreeW1S2 +
peer * agreeW1P1 + aa * agreeW1P2
agree2 =~ agreeW2S1 + a * agreeW2S2 +
peer * agreeW2P1 + aa * agreeW2P2
agree3 =~ agreeW3S1 + a * agreeW3S2 +
peer * agreeW3P1 + aa * agreeW3P2
agree4 =~ agreeW4S1 + a * agreeW4S2 +
peer * agreeW4P1 + aa * agreeW4P2
# second order factor for intercept and slope
interc =~ 1*agree1 + 1*agree2 + 1*agree3 + 1*agree4
slope =~ 0*agree1 + 6*agree2 + 13*agree3 + 19*agree4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
agreeW1S1 ~ 0*1
agreeW2S1 ~ 0*1
agreeW3S1 ~ 0*1
agreeW4S1 ~ 0*1
# fix equal intercepts
agreeW1S2 ~ b*1
agreeW2S2 ~ b*1
agreeW3S2 ~ b*1
agreeW4S2 ~ b*1
agreeW1P1 ~ c*1
agreeW2P1 ~ c*1
agreeW3P1 ~ c*1
agreeW4P1 ~ c*1
agreeW1P2 ~ d*1
agreeW2P2 ~ d*1
agreeW3P2 ~ d*1
agreeW4P2 ~ d*1
# error covariance - similar parcels across waves
agreeW1S1 ~~ covs1*agreeW2S1 + covs2*agreeW3S1 + covs3*agreeW4S1
agreeW2S1 ~~ covs1*agreeW3S1 + covs2*agreeW4S1
agreeW3S1 ~~ covs1*agreeW4S1
agreeW1S2 ~~ covs1*agreeW2S2 + covs2*agreeW3S2 + covs3*agreeW4S2
agreeW2S2 ~~ covs1*agreeW3S2 + covs2*agreeW4S2
agreeW3S2 ~~ covs1*agreeW4S2
agreeW1P1 ~~ covp1*agreeW2P1 + covp2*agreeW3P1 + covp3*agreeW4P1
agreeW2P1 ~~ covp1*agreeW3P1 + covp2*agreeW4P1
agreeW3P1 ~~ covp1*agreeW4P1
agreeW1P2 ~~ covp1*agreeW2P2 + covp2*agreeW3P2 + covp3*agreeW4P2
agreeW2P2 ~~ covp1*agreeW3P2 + covp2*agreeW4P2
agreeW3P2 ~~ covp1*agreeW4P2
# positive constraints for variances
agree1 ~~ var1*agree1
agree2 ~~ var2*agree2
agree3 ~~ var3*agree3
agree4 ~~ var4*agree4
interc ~~ var5*interc
slope ~~ var6*slope
var1 > 0
var2 > 0
var3 > 0
var4 > 0
var5 > 0
var6 > 0
agreeW1S1 ~~ var7*agreeW1S1
agreeW2S1 ~~ var8*agreeW2S1
agreeW3S1 ~~ var9*agreeW3S1
agreeW4S1 ~~ var10*agreeW4S1
agreeW1S2 ~~ var11*agreeW1S2
agreeW2S2 ~~ var12*agreeW2S2
agreeW3S2 ~~ var13*agreeW3S2
agreeW4S2 ~~ var14*agreeW4S2
agreeW1P1 ~~ var15*agreeW1P1
agreeW2P1 ~~ var16*agreeW2P1
agreeW3P1 ~~ var17*agreeW3P1
agreeW4P1 ~~ var18*agreeW4P1
agreeW1P2 ~~ var19*agreeW1P2
agreeW2P2 ~~ var20*agreeW2P2
agreeW3P2 ~~ var21*agreeW3P2
agreeW4P2 ~~ var22*agreeW4P2
var7 > 0
var8 > 0
var9 > 0
var10 > 0
var11 > 0
var12 > 0
var13 > 0
var14 > 0
var15 > 0
var16 > 0
var17 > 0
var18 > 0
var19 > 0
var20 > 0
var21 > 0
var22 > 0
'
lgmAgree <- sem(lgmAgree, data = data, missing = "FIML")## Warning in computeOmega(Sigma.hat = Sigma.hat, Mu.hat = Mu.hat, lavsamplestats = lavsamplestats, : lav_model_gradient: Sigma.hat is not positive definite
## Warning in computeOmega(Sigma.hat = Sigma.hat, Mu.hat = Mu.hat, lavsamplestats = lavsamplestats, : lav_model_gradient: Sigma.hat is not positive definite## Error in chol.default(S): the leading minor of order 3 is not positive definitesummary(lgmAgree, fit.measures = T, standardized = T)## Length Class Mode
## 1 character charactersemPaths(lgmAgree, what = "col", whatLabels = "est", intercepts = T)## Error in semPlotModel.default("\n\n# factor at each time point with same loading\nagree1 =~ agreeW1S1 + a * agreeW1S2 + \n peer * agreeW1P1 + aa * agreeW1P2\n\nagree2 =~ agreeW2S1 + a * agreeW2S2 + \n peer * agreeW2P1 + aa * agreeW2P2\n\nagree3 =~ agreeW3S1 + a * agreeW3S2 + \n peer * agreeW3P1 + aa * agreeW3P2\n \nagree4 =~ agreeW4S1 + a * agreeW4S2 + \n peer * agreeW4P1 + aa * agreeW4P2\n\n# second order factor for intercept and slope\ninterc =~ 1*agree1 + 1*agree2 + 1*agree3 + 1*agree4\nslope =~ 0*agree1 + 6*agree2 + 13*agree3 + 19*agree4\ninterc ~~ slope\ninterc ~ 1\nslope ~ 1\n\n# fix zero intercepts\nagreeW1S1 ~ 0*1\nagreeW2S1 ~ 0*1\nagreeW3S1 ~ 0*1\nagreeW4S1 ~ 0*1\n\n# fix equal intercepts\nagreeW1S2 ~ b*1\nagreeW2S2 ~ b*1\nagreeW3S2 ~ b*1\nagreeW4S2 ~ b*1\n\nagreeW1P1 ~ c*1\nagreeW2P1 ~ c*1\nagreeW3P1 ~ c*1\nagreeW4P1 ~ c*1\n\nagreeW1P2 ~ d*1\nagreeW2P2 ~ d*1\nagreeW3P2 ~ d*1\nagreeW4P2 ~ d*1\n\n# error covariance - similar parcels across waves\nagreeW1S1 ~~ covs1*agreeW2S1 + covs2*agreeW3S1 + covs3*agreeW4S1 \nagreeW2S1 ~~ covs1*agreeW3S1 + covs2*agreeW4S1\nagreeW3S1 ~~ covs1*agreeW4S1\n\nagreeW1S2 ~~ covs1*agreeW2S2 + covs2*agreeW3S2 + covs3*agreeW4S2\nagreeW2S2 ~~ covs1*agreeW3S2 + covs2*agreeW4S2\nagreeW3S2 ~~ covs1*agreeW4S2\n\nagreeW1P1 ~~ covp1*agreeW2P1 + covp2*agreeW3P1 + covp3*agreeW4P1\nagreeW2P1 ~~ covp1*agreeW3P1 + covp2*agreeW4P1\nagreeW3P1 ~~ covp1*agreeW4P1\n\nagreeW1P2 ~~ covp1*agreeW2P2 + covp2*agreeW3P2 + covp3*agreeW4P2\nagreeW2P2 ~~ covp1*agreeW3P2 + covp2*agreeW4P2\nagreeW3P2 ~~ covp1*agreeW4P2\n\n# positive constraints for variances\nagree1 ~~ var1*agree1\nagree2 ~~ var2*agree2\nagree3 ~~ var3*agree3\nagree4 ~~ var4*agree4\ninterc ~~ var5*interc\nslope ~~ var6*slope\nvar1 > 0\nvar2 > 0\nvar3 > 0\nvar4 > 0\nvar5 > 0\nvar6 > 0\nagreeW1S1 ~~ var7*agreeW1S1\nagreeW2S1 ~~ var8*agreeW2S1\nagreeW3S1 ~~ var9*agreeW3S1\nagreeW4S1 ~~ var10*agreeW4S1\nagreeW1S2 ~~ var11*agreeW1S2\nagreeW2S2 ~~ var12*agreeW2S2\nagreeW3S2 ~~ var13*agreeW3S2\nagreeW4S2 ~~ var14*agreeW4S2\nagreeW1P1 ~~ var15*agreeW1P1\nagreeW2P1 ~~ var16*agreeW2P1\nagreeW3P1 ~~ var17*agreeW3P1\nagreeW4P1 ~~ var18*agreeW4P1\nagreeW1P2 ~~ var19*agreeW1P2\nagreeW2P2 ~~ var20*agreeW2P2\nagreeW3P2 ~~ var21*agreeW3P2\nagreeW4P2 ~~ var22*agreeW4P2\nvar7 > 0\nvar8 > 0\nvar9 > 0\nvar10 > 0\nvar11 > 0\nvar12 > 0\nvar13 > 0\nvar14 > 0\nvar15 > 0\nvar16 > 0\nvar17 > 0\nvar18 > 0\nvar19 > 0\nvar20 > 0\nvar21 > 0\nvar22 > 0\n", : Input string neither an existing file or Lavaan model.LGM Conscientiousness
with aspects as parcels
lgmConsci <- '
# factor at each time point with same loading
consci1 =~ indusW1S + a * orderW1S +
peer * indusW1P + aa * orderW1P
consci2 =~ indusW2S + a * orderW2S +
peer * indusW2P + aa * orderW2P
consci3 =~ indusW3S + a * orderW3S +
peer * indusW3P + aa * orderW3P
consci4 =~ indusW4S + a * orderW4S +
peer * indusW4P + aa * orderW4P
# second order factor for intercept and slope
interc =~ 1*consci1 + 1*consci2 + 1*consci3 + 1*consci4
slope =~ 0*consci1 + 6*consci2 + 13*consci3 + 19*consci4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
indusW1S ~ 0*1
indusW2S ~ 0*1
indusW3S ~ 0*1
indusW4S ~ 0*1
# fix equal intercepts
orderW1S ~ b*1
orderW2S ~ b*1
orderW3S ~ b*1
orderW4S ~ b*1
indusW1P ~ c*1
indusW2P ~ c*1
indusW3P ~ c*1
indusW4P ~ c*1
orderW1P ~ d*1
orderW2P ~ d*1
orderW3P ~ d*1
orderW4P ~ d*1
# error covariance - similar aspects across waves and informants
indusW1S ~~ indusW2S + indusW3S + indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW2S ~~ indusW3S + indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW3S ~~ indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW4S ~~ indusW1P + indusW2P + indusW3P + indusW4P
orderW1S ~~ orderW2S + orderW3S + orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW2S ~~ orderW3S + orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW3S ~~ orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW4S ~~ orderW1P + orderW2P + orderW3P + orderW4P
indusW1P ~~ indusW2P + indusW3P + indusW4P
indusW2P ~~ indusW3P + indusW4P
indusW3P ~~ indusW4P
orderW1P ~~ orderW2P + orderW3P + orderW4P
orderW2P ~~ orderW3P + orderW4P
orderW3P ~~ orderW4P
'
lgmConsci <- sem(lgmConsci, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.summary(lgmConsci, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 388 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 105
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci1 =~
## indsW1S 1.000 0.097 0.163
## ordrW1S (a) 1.374 NA 0.134 0.215
## indsW1P (peer) 4.988 NA 0.485 0.778
## ordrW1P (aa) 4.525 NA 0.440 0.685
## consci2 =~
## indsW2S 1.000 0.087 0.140
## ordrW2S (a) 1.374 NA 0.119 0.214
## indsW2P (peer) 4.988 NA 0.432 0.707
## ordrW2P (aa) 4.525 NA 0.392 0.679
## consci3 =~
## indsW3S 1.000 0.077 0.131
## ordrW3S (a) 1.374 NA 0.105 0.180
## indsW3P (peer) 4.988 NA 0.382 0.620
## ordrW3P (aa) 4.525 NA 0.347 0.549
## consci4 =~
## indsW4S 1.000 0.082 0.137
## ordrW4S (a) 1.374 NA 0.112 0.177
## indsW4P (peer) 4.988 NA 0.407 0.701
## ordrW4P (aa) 4.525 NA 0.369 0.601
## interc =~
## consci1 1.000 0.811 0.811
## consci2 1.000 0.912 0.912
## consci3 1.000 1.030 1.030
## consci4 1.000 0.968 0.968
## slope =~
## consci1 0.000 NA NA
## consci2 6.000 NA NA
## consci3 13.000 NA NA
## consci4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.000 NA -0.061 -0.061
## .indusW1S ~~
## .indusW2S 0.275 NA 0.275 0.765
## .indusW3S 0.235 NA 0.235 0.688
## .indusW4S 0.239 NA 0.239 0.689
## .indusW1P 0.062 NA 0.062 0.270
## .indusW2P 0.086 NA 0.086 0.340
## .indusW3P 0.083 NA 0.083 0.291
## .indusW4P 0.060 NA 0.060 0.247
## .indusW2S ~~
## .indusW3S 0.285 NA 0.285 0.803
## .indusW4S 0.294 NA 0.294 0.816
## .indusW1P ~~
## .indusW2S 0.048 NA 0.048 0.200
## .indusW2S ~~
## .indusW2P 0.101 NA 0.101 0.384
## .indusW3P 0.102 NA 0.102 0.344
## .indusW4P 0.080 NA 0.080 0.318
## .indusW3S ~~
## .indusW4S 0.291 NA 0.291 0.851
## .indusW1P ~~
## .indusW3S 0.037 NA 0.037 0.164
## .indusW2P ~~
## .indusW3S 0.091 NA 0.091 0.362
## .indusW3S ~~
## .indusW3P 0.064 NA 0.064 0.227
## .indusW4P 0.046 NA 0.046 0.190
## .indusW1P ~~
## .indusW4S 0.066 NA 0.066 0.286
## .indusW2P ~~
## .indusW4S 0.106 NA 0.106 0.419
## .indusW3P ~~
## .indusW4S 0.074 NA 0.074 0.260
## .indusW4S ~~
## .indusW4P 0.063 NA 0.063 0.258
## .orderW1S ~~
## .orderW2S 0.246 NA 0.246 0.743
## .orderW3S 0.263 NA 0.263 0.752
## .orderW4S 0.247 NA 0.247 0.652
## .orderW1P 0.083 NA 0.083 0.290
## .orderW2P 0.122 NA 0.122 0.474
## .orderW3P 0.093 NA 0.093 0.290
## .orderW4P 0.102 NA 0.102 0.342
## .orderW2S ~~
## .orderW3S 0.259 NA 0.259 0.826
## .orderW4S 0.255 NA 0.255 0.754
## .orderW1P ~~
## .orderW2S 0.101 NA 0.101 0.397
## .orderW2S ~~
## .orderW2P 0.117 NA 0.117 0.507
## .orderW3P 0.098 NA 0.098 0.339
## .orderW4P 0.097 NA 0.097 0.361
## .orderW3S ~~
## .orderW4S 0.291 NA 0.291 0.811
## .orderW1P ~~
## .orderW3S 0.096 NA 0.096 0.354
## .orderW2P ~~
## .orderW3S 0.142 NA 0.142 0.581
## .orderW3S ~~
## .orderW3P 0.091 NA 0.091 0.300
## .orderW4P 0.106 NA 0.106 0.376
## .orderW1P ~~
## .orderW4S 0.109 NA 0.109 0.375
## .orderW2P ~~
## .orderW4S 0.155 NA 0.155 0.587
## .orderW3P ~~
## .orderW4S 0.126 NA 0.126 0.382
## .orderW4S ~~
## .orderW4P 0.127 NA 0.127 0.415
## .indusW1P ~~
## .indusW2P 0.094 NA 0.094 0.558
## .indusW3P 0.115 NA 0.115 0.607
## .indusW4P 0.106 NA 0.106 0.651
## .indusW2P ~~
## .indusW3P 0.152 NA 0.152 0.728
## .indusW4P 0.141 NA 0.141 0.788
## .indusW3P ~~
## .indusW4P 0.166 NA 0.166 0.826
## .orderW1P ~~
## .orderW2P 0.145 NA 0.145 0.730
## .orderW3P 0.193 NA 0.193 0.781
## .orderW4P 0.142 NA 0.142 0.619
## .orderW2P ~~
## .orderW3P 0.172 NA 0.172 0.769
## .orderW4P 0.147 NA 0.147 0.708
## .orderW3P ~~
## .orderW4P 0.204 NA 0.204 0.786
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.186 NA 40.371 40.371
## slope -0.000 NA NA NA
## .indusW1S 0.000 0.000 0.000
## .indusW2S 0.000 0.000 0.000
## .indusW3S 0.000 0.000 0.000
## .indusW4S 0.000 0.000 0.000
## .orderW1S (b) -0.777 NA -0.777 -1.249
## .orderW2S (b) -0.777 NA -0.777 -1.395
## .orderW3S (b) -0.777 NA -0.777 -1.327
## .orderW4S (b) -0.777 NA -0.777 -1.229
## .indusW1P (c) -12.202 NA -12.202 -19.548
## .indusW2P (c) -12.202 NA -12.202 -20.000
## .indusW3P (c) -12.202 NA -12.202 -19.790
## .indusW4P (c) -12.202 NA -12.202 -21.019
## .orderW1P (d) -11.001 NA -11.001 -17.103
## .orderW2P (d) -11.001 NA -11.001 -19.066
## .orderW3P (d) -11.001 NA -11.001 -17.409
## .orderW4P (d) -11.001 NA -11.001 -17.906
## .consci1 0.000 0.000 0.000
## .consci2 0.000 0.000 0.000
## .consci3 0.000 0.000 0.000
## .consci4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S 0.346 NA 0.346 0.973
## .orderW1S 0.369 NA 0.369 0.954
## .indusW1P 0.154 NA 0.154 0.395
## .orderW1P 0.220 NA 0.220 0.531
## .indusW2S 0.373 NA 0.373 0.980
## .orderW2S 0.296 NA 0.296 0.954
## .indusW2P 0.186 NA 0.186 0.500
## .orderW2P 0.180 NA 0.180 0.540
## .indusW3S 0.338 NA 0.338 0.983
## .orderW3S 0.332 NA 0.332 0.968
## .indusW3P 0.234 NA 0.234 0.616
## .orderW3P 0.279 NA 0.279 0.699
## .indusW4S 0.346 NA 0.346 0.981
## .orderW4S 0.388 NA 0.388 0.969
## .indusW4P 0.171 NA 0.171 0.509
## .orderW4P 0.241 NA 0.241 0.639
## .consci1 0.003 NA 0.342 0.342
## .consci2 0.001 NA 0.191 0.191
## .consci3 0.000 NA 0.038 0.038
## .consci4 0.002 NA 0.231 0.231
## interc 0.006 NA 1.000 1.000
## slope -0.000 NA NA NAsemPaths(lgmConsci, what = "col", whatLabels = "est", intercepts = T)
with random parcels
lgmConsci <- '
# factor at each time point with same loading
consci1 =~ consciW1S1 + a * consciW1S2 +
peer * consciW1P1 + aa * consciW1P2
consci2 =~ consciW2S1 + a * consciW2S2 +
peer * consciW2P1 + aa * consciW2P2
consci3 =~ consciW3S1 + a * consciW3S2 +
peer * consciW3P1 + aa * consciW3P2
consci4 =~ consciW4S1 + a * consciW4S2 +
peer * consciW4P1 + aa * consciW4P2
# second order factor for intercept and slope
interc =~ 1*consci1 + 1*consci2 + 1*consci3 + 1*consci4
slope =~ 0*consci1 + 6*consci2 + 13*consci3 + 19*consci4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
consciW1S1 ~ 0*1
consciW2S1 ~ 0*1
consciW3S1 ~ 0*1
consciW4S1 ~ 0*1
# fix equal intercepts
consciW1S2 ~ b*1
consciW2S2 ~ b*1
consciW3S2 ~ b*1
consciW4S2 ~ b*1
consciW1P1 ~ c*1
consciW2P1 ~ c*1
consciW3P1 ~ c*1
consciW4P1 ~ c*1
consciW1P2 ~ d*1
consciW2P2 ~ d*1
consciW3P2 ~ d*1
consciW4P2 ~ d*1
# error covariance - similar parcels across waves
consciW1S1 ~~ consciW2S1 + consciW3S1 + consciW4S1
consciW2S1 ~~ consciW3S1 + consciW4S1
consciW3S1 ~~ consciW4S1
consciW1S2 ~~ consciW2S2 + consciW3S2 + consciW4S2
consciW2S2 ~~ consciW3S2 + consciW4S2
consciW3S2 ~~ consciW4S2
consciW1P1 ~~ consciW2P1 + consciW3P1 + consciW4P1
consciW2P1 ~~ consciW3P1 + consciW4P1
consciW3P1 ~~ consciW4P1
consciW1P2 ~~ consciW2P2 + consciW3P2 + consciW4P2
consciW2P2 ~~ consciW3P2 + consciW4P2
consciW3P2 ~~ consciW4P2
# error covariance - same method at one wave
consciW1S1 ~~ consciW1S2
consciW1P1 ~~ consciW1P2
consciW2S1 ~~ consciW2S2
consciW2P1 ~~ consciW2P2
consciW3S1 ~~ consciW3S2
consciW3P1 ~~ consciW3P2
consciW4S1 ~~ consciW4S2
consciW4P1 ~~ consciW4P2
'
lgmConsci <- sem(lgmConsci, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmConsci, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 364 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci1 =~
## cnsW1S1 1.000 1.566 0.917
## cnsW1S2 (a) 0.912 NA 1.428 0.926
## cnsW1P1 (peer) 0.989 NA 1.548 0.963
## cnsW1P2 (aa) 0.885 NA 1.385 0.973
## consci2 =~
## cnsW2S1 1.000 1.380 0.966
## cnsW2S2 (a) 0.912 NA 1.259 0.977
## cnsW2P1 (peer) 0.989 NA 1.365 0.936
## cnsW2P2 (aa) 0.885 NA 1.221 0.911
## consci3 =~
## cnsW3S1 1.000 1.369 0.978
## cnsW3S2 (a) 0.912 NA 1.248 0.956
## cnsW3P1 (peer) 0.989 NA 1.353 0.917
## cnsW3P2 (aa) 0.885 NA 1.211 0.912
## consci4 =~
## cnsW4S1 1.000 1.386 0.986
## cnsW4S2 (a) 0.912 NA 1.264 0.958
## cnsW4P1 (peer) 0.989 NA 1.371 0.936
## cnsW4P2 (aa) 0.885 NA 1.226 0.918
## interc =~
## consci1 1.000 0.856 0.856
## consci2 1.000 0.971 0.971
## consci3 1.000 0.980 0.980
## consci4 1.000 0.967 0.967
## slope =~
## consci1 0.000 NA NA
## consci2 6.000 NA NA
## consci3 13.000 NA NA
## consci4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.006 NA 0.231 0.231
## .consciW1S1 ~~
## .consciW2S1 0.060 NA 0.060 0.239
## .consciW3S1 0.023 NA 0.023 0.115
## .consciW4S1 0.071 NA 0.071 0.446
## .consciW2S1 ~~
## .consciW3S1 0.021 NA 0.021 0.198
## .consciW4S1 0.025 NA 0.025 0.284
## .consciW3S1 ~~
## .consciW4S1 0.010 NA 0.010 0.148
## .consciW1S2 ~~
## .consciW2S2 -0.001 NA -0.001 -0.005
## .consciW3S2 0.032 NA 0.032 0.145
## .consciW4S2 -0.014 NA -0.014 -0.064
## .consciW2S2 ~~
## .consciW3S2 0.038 NA 0.038 0.358
## .consciW4S2 0.031 NA 0.031 0.299
## .consciW3S2 ~~
## .consciW4S2 0.041 NA 0.041 0.282
## .consciW1P1 ~~
## .consciW2P1 0.050 NA 0.050 0.224
## .consciW3P1 0.086 NA 0.086 0.336
## .consciW4P1 0.034 NA 0.034 0.150
## .consciW2P1 ~~
## .consciW3P1 0.243 NA 0.243 0.807
## .consciW4P1 0.187 NA 0.187 0.707
## .consciW3P1 ~~
## .consciW4P1 0.250 NA 0.250 0.822
## .consciW1P2 ~~
## .consciW2P2 0.222 NA 0.222 1.226
## .consciW3P2 0.213 NA 0.213 1.200
## .consciW4P2 0.206 NA 0.206 1.192
## .consciW2P2 ~~
## .consciW3P2 0.302 NA 0.302 1.002
## .consciW4P2 0.295 NA 0.295 1.002
## .consciW3P2 ~~
## .consciW4P2 0.289 NA 0.289 1.003
## .consciW1S1 ~~
## .consciW1S2 0.325 NA 0.325 0.820
## .consciW1P1 ~~
## .consciW1P2 0.000 NA 0.000 0.003
## .consciW2S1 ~~
## .consciW2S2 0.027 NA 0.027 0.263
## .consciW2P1 ~~
## .consciW2P2 -0.000 NA -0.000 -0.000
## .consciW3S1 ~~
## .consciW3S2 0.047 NA 0.047 0.418
## .consciW3P1 ~~
## .consciW3P2 0.000 NA 0.000 0.000
## .consciW4S1 ~~
## .consciW4S2 0.033 NA 0.033 0.376
## .consciW4P1 ~~
## .consciW4P2 0.000 NA 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.547 NA 2.646 2.646
## slope 0.000 NA NA NA
## .consciW1S1 0.000 0.000 0.000
## .consciW2S1 0.000 0.000 0.000
## .consciW3S1 0.000 0.000 0.000
## .consciW4S1 0.000 0.000 0.000
## .consciW1S2 (b) 0.389 NA 0.389 0.253
## .consciW2S2 (b) 0.389 NA 0.389 0.302
## .consciW3S2 (b) 0.389 NA 0.389 0.298
## .consciW4S2 (b) 0.389 NA 0.389 0.295
## .consciW1P1 (c) 0.305 NA 0.305 0.189
## .consciW2P1 (c) 0.305 NA 0.305 0.209
## .consciW3P1 (c) 0.305 NA 0.305 0.206
## .consciW4P1 (c) 0.305 NA 0.305 0.208
## .consciW1P2 (d) 0.403 NA 0.403 0.283
## .consciW2P2 (d) 0.403 NA 0.403 0.301
## .consciW3P2 (d) 0.403 NA 0.403 0.304
## .consciW4P2 (d) 0.403 NA 0.403 0.302
## .consci1 0.000 0.000 0.000
## .consci2 0.000 0.000 0.000
## .consci3 0.000 0.000 0.000
## .consci4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .consciW1S1 0.462 NA 0.462 0.159
## .consciW1S2 0.339 NA 0.339 0.143
## .consciW1P1 0.189 NA 0.189 0.073
## .consciW1P2 0.107 NA 0.107 0.053
## .consciW2S1 0.136 NA 0.136 0.067
## .consciW2S2 0.075 NA 0.075 0.045
## .consciW2P1 0.262 NA 0.262 0.123
## .consciW2P2 0.307 NA 0.307 0.171
## .consciW3S1 0.086 NA 0.086 0.044
## .consciW3S2 0.147 NA 0.147 0.086
## .consciW3P1 0.346 NA 0.346 0.159
## .consciW3P2 0.296 NA 0.296 0.168
## .consciW4S1 0.055 NA 0.055 0.028
## .consciW4S2 0.142 NA 0.142 0.081
## .consciW4P1 0.267 NA 0.267 0.124
## .consciW4P2 0.281 NA 0.281 0.158
## .consci1 0.655 NA 0.267 0.267
## .consci2 0.047 NA 0.024 0.024
## .consci3 -0.018 NA -0.010 -0.010
## .consci4 0.037 NA 0.019 0.019
## interc 1.797 NA 1.000 1.000
## slope -0.000 NA NA NAsemPaths(lgmConsci, what = "col", whatLabels = "est", intercepts = T)
LGM Extraversion
with aspects as parcels
lgmExtra <- '
# factor at each time point with same loading
extra1 =~ assertW1S + a * enthuW1S +
peer * assertW1P + aa * enthuW1P
extra2 =~ assertW2S + a * enthuW2S +
peer * assertW2P + aa * enthuW2P
extra3 =~ assertW3S + a * enthuW3S +
peer * assertW3P + aa * enthuW3P
extra4 =~ assertW4S + a * enthuW4S +
peer * assertW4P + aa * enthuW4P
# second order factor for intercept and slope
interc =~ 1*extra1 + 1*extra2 + 1*extra3 + 1*extra4
slope =~ 0*extra1 + 6*extra2 + 13*extra3 + 19*extra4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
assertW1S ~ 0*1
assertW2S ~ 0*1
assertW3S ~ 0*1
assertW4S ~ 0*1
# fix equal intercepts
enthuW1S ~ b*1
enthuW2S ~ b*1
enthuW3S ~ b*1
enthuW4S ~ b*1
assertW1P ~ c*1
assertW2P ~ c*1
assertW3P ~ c*1
assertW4P ~ c*1
enthuW1P ~ d*1
enthuW2P ~ d*1
enthuW3P ~ d*1
enthuW4P ~ d*1
# error covariance - similar aspects across waves and informants
assertW1S ~~ assertW2S + assertW3S + assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW2S ~~ assertW3S + assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW3S ~~ assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW4S ~~ assertW1P + assertW2P + assertW3P + assertW4P
enthuW1S ~~ enthuW2S + enthuW3S + enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW2S ~~ enthuW3S + enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW3S ~~ enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW4S ~~ enthuW1P + enthuW2P + enthuW3P + enthuW4P
assertW1P ~~ assertW2P + assertW3P + assertW4P
assertW2P ~~ assertW3P + assertW4P
assertW3P ~~ assertW4P
enthuW1P ~~ enthuW2P + enthuW3P + enthuW4P
enthuW2P ~~ enthuW3P + enthuW4P
enthuW3P ~~ enthuW4P
'
lgmExtra <- sem(lgmExtra, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmExtra, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 1083 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 105
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 150.466
## Degrees of freedom 65
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2412.811
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.963
## Tucker-Lewis Index (TLI) 0.931
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1277.743
## Loglikelihood unrestricted model (H1) -1202.510
##
## Akaike (AIC) 2729.486
## Bayesian (BIC) 3038.930
## Sample-size adjusted Bayesian (BIC) 2763.108
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.086
## P-value RMSEA <= 0.05 0.011
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.087
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## extra1 =~
## assrW1S 1.000 4.024 5.963
## enthW1S (a) 0.006 0.001 4.726 0.000 0.022 0.035
## assrW1P (peer) 1.236 NA 4.973 8.142
## enthW1P (aa) 0.008 0.001 5.988 0.000 0.033 0.053
## extra2 =~
## assrW2S 1.000 3.980 5.807
## enthW2S (a) 0.006 0.001 4.747 0.000 0.022 0.036
## assrW2P (peer) 1.236 NA 4.919 8.469
## enthW2P (aa) 0.008 0.001 5.923 0.000 0.032 0.054
## extra3 =~
## assrW3S 1.000 3.563 5.261
## enthW3S (a) 0.006 0.001 4.691 0.000 0.020 0.032
## assrW3P (peer) 1.236 NA 4.404 7.782
## enthW3P (aa) 0.008 0.001 5.874 0.000 0.029 0.056
## extra4 =~
## assrW4S 1.000 3.392 4.994
## enthW4S (a) 0.006 0.001 4.756 0.000 0.019 0.029
## assrW4P (peer) 1.236 NA 4.192 8.008
## enthW4P (aa) 0.008 0.001 6.106 0.000 0.028 0.053
## interc =~
## extra1 1.000 1.003 1.003
## extra2 1.000 1.014 1.014
## extra3 1.000 1.132 1.132
## extra4 1.000 1.190 1.190
## slope =~
## extra1 0.000 0.000 0.000
## extra2 6.000 0.167 0.167
## extra3 13.000 0.405 0.405
## extra4 19.000 0.621 0.621
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.237 0.093 -2.553 0.011 -0.530 -0.530
## .assertW1S ~~
## .assertW2S -14.475 0.688 -21.032 0.000 -14.475 -0.931
## .assertW3S -12.820 0.200 -64.142 0.000 -12.820 -0.924
## .assertW4S -11.429 0.581 -19.657 0.000 -11.429 -0.867
## .assertW1P -19.830 1.619 -12.249 0.000 -19.830 -1.013
## .assertW2P -18.205 1.017 -17.906 0.000 -18.205 -0.939
## .assertW3P -16.179 0.392 -41.296 0.000 -16.179 -0.934
## .assertW4P -14.437 0.674 -21.419 0.000 -14.437 -0.875
## .assertW2S ~~
## .assertW3S -12.333 0.187 -66.012 0.000 -12.333 -0.899
## .assertW4S -11.367 0.659 -17.245 0.000 -11.367 -0.872
## .assertW1P ~~
## .assertW2S -18.171 1.015 -17.895 0.000 -18.171 -0.939
## .assertW2S ~~
## .assertW2P -19.408 0.987 -19.665 0.000 -19.408 -1.013
## .assertW3P -15.600 0.208 -74.957 0.000 -15.600 -0.911
## .assertW4P -14.419 0.731 -19.716 0.000 -14.419 -0.884
## .assertW3S ~~
## .assertW4S -11.325 1.017 -11.139 0.000 -11.325 -0.974
## .assertW1P ~~
## .assertW3S -16.153 0.389 -41.565 0.000 -16.153 -0.935
## .assertW2P ~~
## .assertW3S -15.596 0.206 -75.784 0.000 -15.596 -0.913
## .assertW3S ~~
## .assertW3P -15.564 0.905 -17.193 0.000 -15.564 -1.019
## .assertW4P -14.403 1.170 -12.310 0.000 -14.403 -0.990
## .assertW1P ~~
## .assertW4S -14.391 0.674 -21.359 0.000 -14.391 -0.877
## .assertW2P ~~
## .assertW4S -14.374 0.731 -19.651 0.000 -14.374 -0.885
## .assertW3P ~~
## .assertW4S -14.377 1.172 -12.272 0.000 -14.377 -0.991
## .assertW4S ~~
## .assertW4P -14.103 1.757 -8.028 0.000 -14.103 -1.020
## .enthuW1S ~~
## .enthuW2S 0.309 0.032 9.684 0.000 0.309 0.780
## .enthuW3S 0.285 0.031 9.111 0.000 0.285 0.717
## .enthuW4S 0.299 0.033 8.968 0.000 0.299 0.729
## .enthuW1P 0.170 0.031 5.466 0.000 0.170 0.435
## .enthuW2P 0.157 0.030 5.163 0.000 0.157 0.411
## .enthuW3P 0.106 0.026 4.050 0.000 0.106 0.321
## .enthuW4P 0.079 0.030 2.625 0.009 0.079 0.235
## .enthuW2S ~~
## .enthuW3S 0.322 0.033 9.698 0.000 0.322 0.825
## .enthuW4S 0.325 0.035 9.376 0.000 0.325 0.807
## .enthuW1P ~~
## .enthuW2S 0.159 0.031 5.118 0.000 0.159 0.413
## .enthuW2S ~~
## .enthuW2P 0.152 0.031 4.947 0.000 0.152 0.405
## .enthuW3P 0.116 0.026 4.457 0.000 0.116 0.357
## .enthuW4P 0.067 0.031 2.195 0.028 0.067 0.205
## .enthuW3S ~~
## .enthuW4S 0.340 0.036 9.461 0.000 0.340 0.842
## .enthuW1P ~~
## .enthuW3S 0.163 0.031 5.255 0.000 0.163 0.425
## .enthuW2P ~~
## .enthuW3S 0.168 0.030 5.598 0.000 0.168 0.448
## .enthuW3S ~~
## .enthuW3P 0.113 0.026 4.329 0.000 0.113 0.347
## .enthuW4P 0.095 0.029 3.277 0.001 0.095 0.288
## .enthuW1P ~~
## .enthuW4S 0.174 0.034 5.196 0.000 0.174 0.438
## .enthuW2P ~~
## .enthuW4S 0.174 0.032 5.428 0.000 0.174 0.449
## .enthuW3P ~~
## .enthuW4S 0.122 0.028 4.351 0.000 0.122 0.362
## .enthuW4S ~~
## .enthuW4P 0.089 0.032 2.839 0.005 0.089 0.264
## .assertW1P ~~
## .assertW2P -22.420 1.426 -15.719 0.000 -22.420 -0.930
## .assertW3P -19.888 0.601 -33.089 0.000 -19.888 -0.923
## .assertW4P -17.756 0.770 -23.050 0.000 -17.756 -0.865
## .assertW2P ~~
## .assertW3P -19.178 0.202 -94.973 0.000 -19.178 -0.899
## .assertW4P -17.715 0.785 -22.570 0.000 -17.715 -0.872
## .assertW3P ~~
## .assertW4P -17.677 1.334 -13.255 0.000 -17.677 -0.973
## .enthuW1P ~~
## .enthuW2P 0.300 0.037 8.196 0.000 0.300 0.813
## .enthuW3P 0.248 0.032 7.853 0.000 0.248 0.776
## .enthuW4P 0.220 0.033 6.596 0.000 0.220 0.682
## .enthuW2P ~~
## .enthuW3P 0.246 0.030 8.071 0.000 0.246 0.788
## .enthuW4P 0.230 0.033 6.874 0.000 0.230 0.729
## .enthuW3P ~~
## .enthuW4P 0.226 0.030 7.440 0.000 0.226 0.828
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.452 0.042 82.559 0.000 0.856 0.856
## slope -0.001 0.001 -0.766 0.444 -0.008 -0.008
## .assertW1S 0.000 0.000 0.000
## .assertW2S 0.000 0.000 0.000
## .assertW3S 0.000 0.000 0.000
## .assertW4S 0.000 0.000 0.000
## .enthuW1S (b) 3.690 0.036 102.239 0.000 3.690 5.811
## .enthuW2S (b) 3.690 0.036 102.239 0.000 3.690 5.908
## .enthuW3S (b) 3.690 0.036 102.239 0.000 3.690 5.897
## .enthuW4S (b) 3.690 0.036 102.239 0.000 3.690 5.713
## .assertW1P (c) -0.641 0.048 -13.229 0.000 -0.641 -1.049
## .assertW2P (c) -0.641 0.048 -13.229 0.000 -0.641 -1.103
## .assertW3P (c) -0.641 0.048 -13.229 0.000 -0.641 -1.133
## .assertW4P (c) -0.641 0.048 -13.229 0.000 -0.641 -1.224
## .enthuW1P (d) 3.801 0.038 99.350 0.000 3.801 6.171
## .enthuW2P (d) 3.801 0.038 99.350 0.000 3.801 6.327
## .enthuW3P (d) 3.801 0.038 99.350 0.000 3.801 7.295
## .enthuW4P (d) 3.801 0.038 99.350 0.000 3.801 7.223
## .extra1 0.000 0.000 0.000
## .extra2 0.000 0.000 0.000
## .extra3 0.000 0.000 0.000
## .extra4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S -15.739 1.191 -13.214 0.000 -15.739 -34.563
## .enthuW1S 0.403 0.035 11.466 0.000 0.403 0.999
## .assertW1P -24.362 2.163 -11.262 0.000 -24.362 -65.288
## .enthuW1P 0.378 0.042 9.100 0.000 0.378 0.997
## .assertW2S -15.373 0.715 -21.488 0.000 -15.373 -32.720
## .enthuW2S 0.390 0.036 10.775 0.000 0.390 0.999
## .assertW2P -23.861 1.329 -17.960 0.000 -23.861 -70.719
## .enthuW2P 0.360 0.039 9.155 0.000 0.360 0.997
## .assertW3S -12.238 0.774 -15.819 0.000 -12.238 -26.677
## .enthuW3S 0.391 0.037 10.622 0.000 0.391 0.999
## .assertW3P -19.073 1.050 -18.166 0.000 -19.073 -59.562
## .enthuW3P 0.271 0.030 9.101 0.000 0.271 0.997
## .assertW4S -11.045 1.492 -7.403 0.000 -11.045 -23.935
## .enthuW4S 0.417 0.042 9.886 0.000 0.417 0.999
## .assertW4P -17.300 2.062 -8.390 0.000 -17.300 -63.127
## .enthuW4P 0.276 0.036 7.581 0.000 0.276 0.997
## .extra1 -0.090 1.014 -0.088 0.930 -0.006 -0.006
## .extra2 1.962 0.712 2.757 0.006 0.124 0.124
## .extra3 0.500 0.557 0.897 0.370 0.039 0.039
## .extra4 -0.207 0.843 -0.245 0.806 -0.018 -0.018
## interc 16.284 1.234 13.194 0.000 1.000 1.000
## slope 0.012 0.005 2.269 0.023 1.000 1.000semPaths(lgmExtra, what = "col", whatLabels = "est", intercepts = T)
with random parcels
lgmExtra <- '
# factor at each time point with same loading
extra1 =~ extraW1S1 + a * extraW1S2 +
peer * extraW1P1 + aa * extraW1P2
extra2 =~ extraW2S1 + a * extraW2S2 +
peer * extraW2P1 + aa * extraW2P2
extra3 =~ extraW3S1 + a * extraW3S2 +
peer * extraW3P1 + aa * extraW3P2
extra4 =~ extraW4S1 + a * extraW4S2 +
peer * extraW4P1 + aa * extraW4P2
# second order factor for intercept and slope
interc =~ 1*extra1 + 1*extra2 + 1*extra3 + 1*extra4
slope =~ 0*extra1 + 6*extra2 + 13*extra3 + 19*extra4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
extraW1S1 ~ 0*1
extraW2S1 ~ 0*1
extraW3S1 ~ 0*1
extraW4S1 ~ 0*1
# fix equal intercepts
extraW1S2 ~ b*1
extraW2S2 ~ b*1
extraW3S2 ~ b*1
extraW4S2 ~ b*1
extraW1P1 ~ c*1
extraW2P1 ~ c*1
extraW3P1 ~ c*1
extraW4P1 ~ c*1
extraW1P2 ~ d*1
extraW2P2 ~ d*1
extraW3P2 ~ d*1
extraW4P2 ~ d*1
# error covariance - similar parcels across waves
extraW1S1 ~~ extraW2S1 + extraW3S1 + extraW4S1
extraW2S1 ~~ extraW3S1 + extraW4S1
extraW3S1 ~~ extraW4S1
extraW1S2 ~~ extraW2S2 + extraW3S2 + extraW4S2
extraW2S2 ~~ extraW3S2 + extraW4S2
extraW3S2 ~~ extraW4S2
extraW1P1 ~~ extraW2P1 + extraW3P1 + extraW4P1
extraW2P1 ~~ extraW3P1 + extraW4P1
extraW3P1 ~~ extraW4P1
extraW1P2 ~~ extraW2P2 + extraW3P2 + extraW4P2
extraW2P2 ~~ extraW3P2 + extraW4P2
extraW3P2 ~~ extraW4P2
# error covariance - same method at one wave
extraW1S1 ~~ extraW1S2
extraW1P1 ~~ extraW1P2
extraW2S1 ~~ extraW2S2
extraW2P1 ~~ extraW2P2
extraW3S1 ~~ extraW3S2
extraW3P1 ~~ extraW3P2
extraW4S1 ~~ extraW4S2
extraW4P1 ~~ extraW4P2
'
lgmExtra <- sem(lgmExtra, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmExtra, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 390 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## extra1 =~
## extW1S1 1.000 1.522 0.942
## extW1S2 (a) 0.899 NA 1.369 0.949
## extW1P1 (peer) 0.944 NA 1.437 0.953
## extW1P2 (aa) 0.903 NA 1.375 0.982
## extra2 =~
## extW2S1 1.000 1.356 0.982
## extW2S2 (a) 0.899 NA 1.219 0.966
## extW2P1 (peer) 0.944 NA 1.280 0.922
## extW2P2 (aa) 0.903 NA 1.224 0.938
## extra3 =~
## extW3S1 1.000 1.332 0.970
## extW3S2 (a) 0.899 NA 1.198 0.945
## extW3P1 (peer) 0.944 NA 1.258 0.953
## extW3P2 (aa) 0.903 NA 1.203 0.942
## extra4 =~
## extW4S1 1.000 1.363 0.962
## extW4S2 (a) 0.899 NA 1.225 0.966
## extW4P1 (peer) 0.944 NA 1.287 0.961
## extW4P2 (aa) 0.903 NA 1.230 0.943
## interc =~
## extra1 1.000 0.845 0.845
## extra2 1.000 0.949 0.949
## extra3 1.000 0.966 0.966
## extra4 1.000 0.944 0.944
## slope =~
## extra1 0.000 NA NA
## extra2 6.000 NA NA
## extra3 13.000 NA NA
## extra4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.010 NA 0.304 0.304
## .extraW1S1 ~~
## .extraW2S1 0.018 NA 0.018 0.132
## .extraW3S1 0.025 NA 0.025 0.140
## .extraW4S1 0.033 NA 0.033 0.156
## .extraW2S1 ~~
## .extraW3S1 0.029 NA 0.029 0.343
## .extraW4S1 0.018 NA 0.018 0.180
## .extraW3S1 ~~
## .extraW4S1 0.040 NA 0.040 0.308
## .extraW1S2 ~~
## .extraW2S2 0.030 NA 0.030 0.198
## .extraW3S2 0.012 NA 0.012 0.061
## .extraW4S2 0.018 NA 0.018 0.122
## .extraW2S2 ~~
## .extraW3S2 -0.017 NA -0.017 -0.124
## .extraW4S2 0.020 NA 0.020 0.187
## .extraW3S2 ~~
## .extraW4S2 0.011 NA 0.011 0.080
## .extraW1P1 ~~
## .extraW2P1 0.074 NA 0.074 0.305
## .extraW3P1 0.067 NA 0.067 0.370
## .extraW4P1 0.075 NA 0.075 0.441
## .extraW2P1 ~~
## .extraW3P1 0.158 NA 0.158 0.739
## .extraW4P1 0.123 NA 0.123 0.616
## .extraW3P1 ~~
## .extraW4P1 0.080 NA 0.080 0.540
## .extraW1P2 ~~
## .extraW2P2 0.161 NA 0.161 1.365
## .extraW3P2 0.145 NA 0.145 1.294
## .extraW4P2 0.144 NA 0.144 1.270
## .extraW2P2 ~~
## .extraW3P2 0.194 NA 0.194 1.005
## .extraW4P2 0.197 NA 0.197 1.007
## .extraW3P2 ~~
## .extraW4P2 0.187 NA 0.187 1.005
## .extraW1S1 ~~
## .extraW1S2 0.187 NA 0.187 0.757
## .extraW1P1 ~~
## .extraW1P2 0.001 NA 0.001 0.005
## .extraW2S1 ~~
## .extraW2S2 0.026 NA 0.026 0.314
## .extraW2P1 ~~
## .extraW2P2 0.000 NA 0.000 0.000
## .extraW3S1 ~~
## .extraW3S2 0.092 NA 0.092 0.662
## .extraW3P1 ~~
## .extraW3P2 -0.000 NA -0.000 -0.000
## .extraW4S1 ~~
## .extraW4S2 0.058 NA 0.058 0.454
## .extraW4P1 ~~
## .extraW4P2 -0.000 NA -0.000 -0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.599 NA 2.796 2.796
## slope -0.000 NA NA NA
## .extraW1S1 0.000 0.000 0.000
## .extraW2S1 0.000 0.000 0.000
## .extraW3S1 0.000 0.000 0.000
## .extraW4S1 0.000 0.000 0.000
## .extraW1S2 (b) 0.356 NA 0.356 0.247
## .extraW2S2 (b) 0.356 NA 0.356 0.282
## .extraW3S2 (b) 0.356 NA 0.356 0.281
## .extraW4S2 (b) 0.356 NA 0.356 0.281
## .extraW1P1 (c) 0.264 NA 0.264 0.175
## .extraW2P1 (c) 0.264 NA 0.264 0.190
## .extraW3P1 (c) 0.264 NA 0.264 0.200
## .extraW4P1 (c) 0.264 NA 0.264 0.197
## .extraW1P2 (d) 0.569 NA 0.569 0.406
## .extraW2P2 (d) 0.569 NA 0.569 0.436
## .extraW3P2 (d) 0.569 NA 0.569 0.445
## .extraW4P2 (d) 0.569 NA 0.569 0.436
## .extra1 0.000 0.000 0.000
## .extra2 0.000 0.000 0.000
## .extra3 0.000 0.000 0.000
## .extra4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .extraW1S1 0.294 NA 0.294 0.113
## .extraW1S2 0.209 NA 0.209 0.100
## .extraW1P1 0.207 NA 0.207 0.091
## .extraW1P2 0.068 NA 0.068 0.035
## .extraW2S1 0.066 NA 0.066 0.035
## .extraW2S2 0.106 NA 0.106 0.067
## .extraW2P1 0.288 NA 0.288 0.149
## .extraW2P2 0.203 NA 0.203 0.119
## .extraW3S1 0.110 NA 0.110 0.059
## .extraW3S2 0.173 NA 0.173 0.108
## .extraW3P1 0.158 NA 0.158 0.091
## .extraW3P2 0.183 NA 0.183 0.112
## .extraW4S1 0.151 NA 0.151 0.075
## .extraW4S2 0.108 NA 0.108 0.067
## .extraW4P1 0.138 NA 0.138 0.077
## .extraW4P2 0.189 NA 0.189 0.111
## .extra1 0.662 NA 0.285 0.285
## .extra2 0.084 NA 0.046 0.046
## .extra3 -0.031 NA -0.018 -0.018
## .extra4 0.061 NA 0.033 0.033
## interc 1.656 NA 1.000 1.000
## slope -0.001 NA NA NAsemPaths(lgmExtra, what = "col", whatLabels = "est", intercepts = T)
LGM Neuroticism
with aspects as parcels
lgmNeuro <- '
# factor at each time point with same loading
neuro1 =~ volatW1S + a * withdW1S +
peer * volatW1P + aa * withdW1P
neuro2 =~ volatW2S + a * withdW2S +
peer * volatW2P + aa * withdW2P
neuro3 =~ volatW3S + a * withdW3S +
peer * volatW3P + aa * withdW3P
neuro4 =~ volatW4S + a * withdW4S +
peer * volatW4P + aa * withdW4P
# second order factor for intercept and slope
interc =~ 1*neuro1 + 1*neuro2 + 1*neuro3 + 1*neuro4
slope =~ 0*neuro1 + 6*neuro2 + 13*neuro3 + 19*neuro4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
volatW1S ~ 0*1
volatW2S ~ 0*1
volatW3S ~ 0*1
volatW4S ~ 0*1
# fix equal intercepts
withdW1S ~ b*1
withdW2S ~ b*1
withdW3S ~ b*1
withdW4S ~ b*1
volatW1P ~ c*1
volatW2P ~ c*1
volatW3P ~ c*1
volatW4P ~ c*1
withdW1P ~ d*1
withdW2P ~ d*1
withdW3P ~ d*1
withdW4P ~ d*1
# error covariance - similar aspects across waves and informants
volatW1S ~~ volatW2S + volatW3S + volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW2S ~~ volatW3S + volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW3S ~~ volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW4S ~~ volatW1P + volatW2P + volatW3P + volatW4P
withdW1S ~~ withdW2S + withdW3S + withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW2S ~~ withdW3S + withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW3S ~~ withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW4S ~~ withdW1P + withdW2P + withdW3P + withdW4P
volatW1P ~~ volatW2P + volatW3P + volatW4P
volatW2P ~~ volatW3P + volatW4P
volatW3P ~~ volatW4P
withdW1P ~~ withdW2P + withdW3P + withdW4P
withdW2P ~~ withdW3P + withdW4P
withdW3P ~~ withdW4P
'
lgmNeuro <- sem(lgmNeuro, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_object_post_check(object): lavaan WARNING: the covariance matrix of the residuals of the observed
## variables (theta) is not positive definite;
## use lavInspect(fit, "theta") to investigate.summary(lgmNeuro, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 243 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 105
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 218.197
## Degrees of freedom 65
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2496.820
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.936
## Tucker-Lewis Index (TLI) 0.881
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1658.077
## Loglikelihood unrestricted model (H1) -1548.979
##
## Akaike (AIC) 3490.155
## Bayesian (BIC) 3799.599
## Sample-size adjusted Bayesian (BIC) 3523.777
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.095
## 90 Percent confidence interval - lower 0.082
## 90 Percent confidence interval - upper 0.110
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.249
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro1 =~
## voltW1S 1.000 0.364 0.475
## wthdW1S (a) 1.226 0.972 1.261 0.207 0.446 0.636
## voltW1P (peer) -0.767 0.182 -4.222 0.000 -0.279 -0.370
## wthdW1P (aa) -0.825 0.673 -1.226 0.220 -0.300 -0.457
## neuro2 =~
## voltW2S 1.000 0.341 0.429
## wthdW2S (a) 1.226 0.972 1.261 0.207 0.418 0.607
## voltW2P (peer) -0.767 0.182 -4.222 0.000 -0.262 -0.343
## wthdW2P (aa) -0.825 0.673 -1.226 0.220 -0.282 -0.422
## neuro3 =~
## voltW3S 1.000 0.345 0.461
## wthdW3S (a) 1.226 0.972 1.261 0.207 0.423 0.640
## voltW3P (peer) -0.767 0.182 -4.222 0.000 -0.265 -0.364
## wthdW3P (aa) -0.825 0.673 -1.226 0.220 -0.285 -0.451
## neuro4 =~
## voltW4S 1.000 0.383 0.485
## wthdW4S (a) 1.226 0.972 1.261 0.207 0.469 0.694
## voltW4P (peer) -0.767 0.182 -4.222 0.000 -0.293 -0.381
## wthdW4P (aa) -0.825 0.673 -1.226 0.220 -0.316 -0.494
## interc =~
## neuro1 1.000 0.867 0.867
## neuro2 1.000 0.924 0.924
## neuro3 1.000 0.914 0.914
## neuro4 1.000 0.824 0.824
## slope =~
## neuro1 0.000 0.000 0.000
## neuro2 6.000 0.173 0.173
## neuro3 13.000 0.371 0.371
## neuro4 19.000 0.489 0.489
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.000 0.001 -0.495 0.621 -0.135 -0.135
## .volatW1S ~~
## .volatW2S 0.404 0.090 4.510 0.000 0.404 0.836
## .volatW3S 0.349 0.085 4.101 0.000 0.349 0.781
## .volatW4S 0.349 0.085 4.092 0.000 0.349 0.750
## .volatW1P 0.279 0.090 3.090 0.002 0.279 0.590
## .volatW2P 0.277 0.071 3.876 0.000 0.277 0.574
## .volatW3P 0.253 0.069 3.647 0.000 0.253 0.555
## .volatW4P 0.281 0.070 4.026 0.000 0.281 0.586
## .volatW2S ~~
## .volatW3S 0.424 0.092 4.626 0.000 0.424 0.891
## .volatW4S 0.400 0.094 4.266 0.000 0.400 0.808
## .volatW1P ~~
## .volatW2S 0.275 0.074 3.703 0.000 0.275 0.546
## .volatW2S ~~
## .volatW2P 0.337 0.085 3.992 0.000 0.337 0.656
## .volatW3P 0.286 0.074 3.863 0.000 0.286 0.590
## .volatW4P 0.290 0.077 3.768 0.000 0.290 0.569
## .volatW3S ~~
## .volatW4S 0.371 0.099 3.740 0.000 0.371 0.810
## .volatW1P ~~
## .volatW3S 0.256 0.070 3.664 0.000 0.256 0.551
## .volatW2P ~~
## .volatW3S 0.290 0.074 3.922 0.000 0.290 0.611
## .volatW3S ~~
## .volatW3P 0.251 0.083 3.036 0.002 0.251 0.560
## .volatW4P 0.247 0.081 3.046 0.002 0.247 0.522
## .volatW1P ~~
## .volatW4S 0.248 0.071 3.482 0.000 0.248 0.512
## .volatW2P ~~
## .volatW4S 0.255 0.076 3.358 0.001 0.255 0.515
## .volatW3P ~~
## .volatW4S 0.225 0.079 2.863 0.004 0.225 0.482
## .volatW4S ~~
## .volatW4P 0.223 0.102 2.187 0.029 0.223 0.453
## .withdW1S ~~
## .withdW2S 0.225 0.125 1.802 0.072 0.225 0.760
## .withdW3S 0.208 0.120 1.729 0.084 0.208 0.758
## .withdW4S 0.212 0.118 1.792 0.073 0.212 0.807
## .withdW1P 0.263 0.112 2.360 0.018 0.263 0.834
## .withdW2P 0.264 0.085 3.109 0.002 0.264 0.809
## .withdW3P 0.236 0.082 2.874 0.004 0.236 0.775
## .withdW4P 0.232 0.082 2.831 0.005 0.232 0.773
## .withdW2S ~~
## .withdW3S 0.212 0.127 1.673 0.094 0.212 0.762
## .withdW4S 0.198 0.128 1.542 0.123 0.198 0.744
## .withdW1P ~~
## .withdW2S 0.220 0.086 2.567 0.010 0.220 0.690
## .withdW2S ~~
## .withdW2P 0.275 0.100 2.739 0.006 0.275 0.832
## .withdW3P 0.219 0.086 2.541 0.011 0.219 0.712
## .withdW4P 0.236 0.088 2.683 0.007 0.236 0.776
## .withdW3S ~~
## .withdW4S 0.192 0.139 1.376 0.169 0.192 0.775
## .withdW1P ~~
## .withdW3S 0.199 0.082 2.421 0.015 0.199 0.671
## .withdW2P ~~
## .withdW3S 0.237 0.086 2.756 0.006 0.237 0.773
## .withdW3S ~~
## .withdW3P 0.247 0.101 2.457 0.014 0.247 0.864
## .withdW4P 0.226 0.095 2.387 0.017 0.226 0.801
## .withdW1P ~~
## .withdW4S 0.194 0.081 2.406 0.016 0.194 0.683
## .withdW2P ~~
## .withdW4S 0.242 0.087 2.788 0.005 0.242 0.824
## .withdW3P ~~
## .withdW4S 0.230 0.094 2.444 0.015 0.230 0.840
## .withdW4S ~~
## .withdW4P 0.274 0.123 2.219 0.026 0.274 1.011
## .volatW1P ~~
## .volatW2P 0.386 0.071 5.425 0.000 0.386 0.769
## .volatW3P 0.344 0.069 5.017 0.000 0.344 0.726
## .volatW4P 0.327 0.071 4.620 0.000 0.327 0.656
## .volatW2P ~~
## .volatW3P 0.400 0.074 5.436 0.000 0.400 0.827
## .volatW4P 0.423 0.079 5.323 0.000 0.423 0.830
## .volatW3P ~~
## .volatW4P 0.376 0.080 4.672 0.000 0.376 0.781
## .withdW1P ~~
## .withdW2P 0.280 0.068 4.113 0.000 0.280 0.793
## .withdW3P 0.274 0.067 4.110 0.000 0.274 0.832
## .withdW4P 0.225 0.065 3.456 0.001 0.225 0.693
## .withdW2P ~~
## .withdW3P 0.280 0.070 4.031 0.000 0.280 0.824
## .withdW4P 0.276 0.071 3.906 0.000 0.276 0.823
## .withdW3P ~~
## .withdW4P 0.264 0.076 3.479 0.001 0.264 0.842
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 2.784 0.047 59.529 0.000 8.831 8.831
## slope -0.002 0.002 -1.147 0.251 -0.228 -0.228
## .volatW1S 0.000 0.000 0.000
## .volatW2S 0.000 0.000 0.000
## .volatW3S 0.000 0.000 0.000
## .volatW4S 0.000 0.000 0.000
## .withdW1S (b) -0.397 2.690 -0.147 0.883 -0.397 -0.566
## .withdW2S (b) -0.397 2.690 -0.147 0.883 -0.397 -0.576
## .withdW3S (b) -0.397 2.690 -0.147 0.883 -0.397 -0.600
## .withdW4S (b) -0.397 2.690 -0.147 0.883 -0.397 -0.587
## .volatW1P (c) 4.725 0.506 9.342 0.000 4.725 6.262
## .volatW2P (c) 4.725 0.506 9.342 0.000 4.725 6.198
## .volatW3P (c) 4.725 0.506 9.342 0.000 4.725 6.506
## .volatW4P (c) 4.725 0.506 9.342 0.000 4.725 6.140
## .withdW1P (d) 4.864 1.864 2.610 0.009 4.864 7.405
## .withdW2P (d) 4.864 1.864 2.610 0.009 4.864 7.298
## .withdW3P (d) 4.864 1.864 2.610 0.009 4.864 7.709
## .withdW4P (d) 4.864 1.864 2.610 0.009 4.864 7.609
## .neuro1 0.000 0.000 0.000
## .neuro2 0.000 0.000 0.000
## .neuro3 0.000 0.000 0.000
## .neuro4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S 0.453 0.115 3.951 0.000 0.453 0.774
## .withdW1S 0.292 0.166 1.759 0.079 0.292 0.595
## .volatW1P 0.492 0.088 5.583 0.000 0.492 0.863
## .withdW1P 0.342 0.087 3.920 0.000 0.342 0.791
## .volatW2S 0.515 0.107 4.799 0.000 0.515 0.816
## .withdW2S 0.299 0.149 2.012 0.044 0.299 0.631
## .volatW2P 0.513 0.085 6.006 0.000 0.513 0.882
## .withdW2P 0.365 0.080 4.534 0.000 0.365 0.822
## .volatW3S 0.440 0.106 4.159 0.000 0.440 0.787
## .withdW3S 0.258 0.150 1.719 0.086 0.258 0.591
## .volatW3P 0.457 0.083 5.491 0.000 0.457 0.867
## .withdW3P 0.317 0.082 3.890 0.000 0.317 0.796
## .volatW4S 0.477 0.128 3.722 0.000 0.477 0.765
## .withdW4S 0.237 0.185 1.279 0.201 0.237 0.519
## .volatW4P 0.506 0.105 4.836 0.000 0.506 0.855
## .withdW4P 0.309 0.097 3.181 0.001 0.309 0.756
## .neuro1 0.033 0.028 1.169 0.242 0.248 0.248
## .neuro2 0.018 0.016 1.167 0.243 0.159 0.159
## .neuro3 0.014 0.013 1.115 0.265 0.118 0.118
## .neuro4 0.028 0.025 1.135 0.257 0.190 0.190
## interc 0.099 0.081 1.229 0.219 1.000 1.000
## slope 0.000 0.000 1.041 0.298 1.000 1.000semPaths(lgmNeuro, what = "col", whatLabels = "est", intercepts = T)
with random parcels
lgmNeuro <- '
# factor at each time point with same loading
neuro1 =~ neuroW1S1 + a * neuroW1S2 +
peer * neuroW1P1 + aa * neuroW1P2
neuro2 =~ neuroW2S1 + a * neuroW2S2 +
peer * neuroW2P1 + aa * neuroW2P2
neuro3 =~ neuroW3S1 + a * neuroW3S2 +
peer * neuroW3P1 + aa * neuroW3P2
neuro4 =~ neuroW4S1 + a * neuroW4S2 +
peer * neuroW4P1 + aa * neuroW4P2
# second order factor for intercept and slope
interc =~ 1*neuro1 + 1*neuro2 + 1*neuro3 + 1*neuro4
slope =~ 0*neuro1 + 6*neuro2 + 13*neuro3 + 19*neuro4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
neuroW1S1 ~ 0*1
neuroW2S1 ~ 0*1
neuroW3S1 ~ 0*1
neuroW4S1 ~ 0*1
# fix equal intercepts
neuroW1S2 ~ b*1
neuroW2S2 ~ b*1
neuroW3S2 ~ b*1
neuroW4S2 ~ b*1
neuroW1P1 ~ c*1
neuroW2P1 ~ c*1
neuroW3P1 ~ c*1
neuroW4P1 ~ c*1
neuroW1P2 ~ d*1
neuroW2P2 ~ d*1
neuroW3P2 ~ d*1
neuroW4P2 ~ d*1
# error covariance - similar parcels across waves
neuroW1S1 ~~ neuroW2S1 + neuroW3S1 + neuroW4S1
neuroW2S1 ~~ neuroW3S1 + neuroW4S1
neuroW3S1 ~~ neuroW4S1
neuroW1S2 ~~ neuroW2S2 + neuroW3S2 + neuroW4S2
neuroW2S2 ~~ neuroW3S2 + neuroW4S2
neuroW3S2 ~~ neuroW4S2
neuroW1P1 ~~ neuroW2P1 + neuroW3P1 + neuroW4P1
neuroW2P1 ~~ neuroW3P1 + neuroW4P1
neuroW3P1 ~~ neuroW4P1
neuroW1P2 ~~ neuroW2P2 + neuroW3P2 + neuroW4P2
neuroW2P2 ~~ neuroW3P2 + neuroW4P2
neuroW3P2 ~~ neuroW4P2
# error covariance - same method at one wave
neuroW1S1 ~~ neuroW1S2
neuroW1P1 ~~ neuroW1P2
neuroW2S1 ~~ neuroW2S2
neuroW2P1 ~~ neuroW2P2
neuroW3S1 ~~ neuroW3S2
neuroW3P1 ~~ neuroW3P2
neuroW4S1 ~~ neuroW4S2
neuroW4P1 ~~ neuroW4P2
'
lgmNeuro <- sem(lgmNeuro, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmNeuro, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 318 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro1 =~
## nerW1S1 1.000 0.774 0.844
## nerW1S2 (a) 0.900 NA 0.696 0.800
## nerW1P1 (peer) 0.807 NA 0.624 0.813
## nerW1P2 (aa) 0.736 NA 0.570 0.851
## neuro2 =~
## nerW2S1 1.000 0.763 0.886
## nerW2S2 (a) 0.900 NA 0.686 0.902
## nerW2P1 (peer) 0.807 NA 0.616 0.750
## nerW2P2 (aa) 0.736 NA 0.562 0.809
## neuro3 =~
## nerW3S1 1.000 0.761 0.879
## nerW3S2 (a) 0.900 NA 0.685 0.874
## nerW3P1 (peer) 0.807 NA 0.614 0.690
## nerW3P2 (aa) 0.736 NA 0.560 0.812
## neuro4 =~
## nerW4S1 1.000 0.767 0.859
## nerW4S2 (a) 0.900 NA 0.690 0.812
## nerW4P1 (peer) 0.807 NA 0.619 0.763
## nerW4P2 (aa) 0.736 NA 0.565 0.831
## interc =~
## neuro1 1.000 0.976 0.976
## neuro2 1.000 0.990 0.990
## neuro3 1.000 0.992 0.992
## neuro4 1.000 0.985 0.985
## slope =~
## neuro1 0.000 NA NA
## neuro2 6.000 NA NA
## neuro3 13.000 NA NA
## neuro4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.001 NA 0.136 0.136
## .neuroW1S1 ~~
## .neuroW2S1 -0.003 NA -0.003 -0.016
## .neuroW3S1 0.078 NA 0.078 0.383
## .neuroW4S1 0.044 NA 0.044 0.194
## .neuroW2S1 ~~
## .neuroW3S1 0.050 NA 0.050 0.301
## .neuroW4S1 -0.024 NA -0.024 -0.132
## .neuroW3S1 ~~
## .neuroW4S1 0.042 NA 0.042 0.223
## .neuroW1S2 ~~
## .neuroW2S2 0.055 NA 0.055 0.319
## .neuroW3S2 0.022 NA 0.022 0.111
## .neuroW4S2 0.045 NA 0.045 0.175
## .neuroW2S2 ~~
## .neuroW3S2 0.010 NA 0.010 0.082
## .neuroW4S2 0.048 NA 0.048 0.295
## .neuroW3S2 ~~
## .neuroW4S2 -0.001 NA -0.001 -0.006
## .neuroW1P1 ~~
## .neuroW2P1 0.117 NA 0.117 0.482
## .neuroW3P1 0.069 NA 0.069 0.240
## .neuroW4P1 0.046 NA 0.046 0.197
## .neuroW2P1 ~~
## .neuroW3P1 0.134 NA 0.134 0.384
## .neuroW4P1 0.169 NA 0.169 0.596
## .neuroW3P1 ~~
## .neuroW4P1 0.290 NA 0.290 0.861
## .neuroW1P2 ~~
## .neuroW2P2 0.149 NA 0.149 1.035
## .neuroW3P2 0.144 NA 0.144 1.021
## .neuroW4P2 0.137 NA 0.137 1.031
## .neuroW2P2 ~~
## .neuroW3P2 0.165 NA 0.165 1.002
## .neuroW4P2 0.155 NA 0.155 1.004
## .neuroW3P2 ~~
## .neuroW4P2 0.153 NA 0.153 1.006
## .neuroW1S1 ~~
## .neuroW1S2 0.166 NA 0.166 0.644
## .neuroW1P1 ~~
## .neuroW1P2 0.000 NA 0.000 0.003
## .neuroW2S1 ~~
## .neuroW2S2 0.058 NA 0.058 0.446
## .neuroW2P1 ~~
## .neuroW2P2 0.000 NA 0.000 0.000
## .neuroW3S1 ~~
## .neuroW3S2 0.071 NA 0.071 0.450
## .neuroW3P1 ~~
## .neuroW3P2 -0.000 NA -0.000 -0.000
## .neuroW4S1 ~~
## .neuroW4S2 0.116 NA 0.116 0.512
## .neuroW4P1 ~~
## .neuroW4P2 -0.000 NA -0.000 -0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 2.997 NA 3.970 3.970
## slope 0.000 NA NA NA
## .neuroW1S1 0.000 0.000 0.000
## .neuroW2S1 0.000 0.000 0.000
## .neuroW3S1 0.000 0.000 0.000
## .neuroW4S1 0.000 0.000 0.000
## .neuroW1S2 (b) 0.271 NA 0.271 0.311
## .neuroW2S2 (b) 0.271 NA 0.271 0.356
## .neuroW3S2 (b) 0.271 NA 0.271 0.346
## .neuroW4S2 (b) 0.271 NA 0.271 0.319
## .neuroW1P1 (c) 0.143 NA 0.143 0.187
## .neuroW2P1 (c) 0.143 NA 0.143 0.175
## .neuroW3P1 (c) 0.143 NA 0.143 0.161
## .neuroW4P1 (c) 0.143 NA 0.143 0.177
## .neuroW1P2 (d) 0.467 NA 0.467 0.697
## .neuroW2P2 (d) 0.467 NA 0.467 0.672
## .neuroW3P2 (d) 0.467 NA 0.467 0.676
## .neuroW4P2 (d) 0.467 NA 0.467 0.687
## .neuro1 0.000 0.000 0.000
## .neuro2 0.000 0.000 0.000
## .neuro3 0.000 0.000 0.000
## .neuro4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .neuroW1S1 0.242 NA 0.242 0.288
## .neuroW1S2 0.273 NA 0.273 0.360
## .neuroW1P1 0.200 NA 0.200 0.339
## .neuroW1P2 0.124 NA 0.124 0.276
## .neuroW2S1 0.160 NA 0.160 0.215
## .neuroW2S2 0.107 NA 0.107 0.186
## .neuroW2P1 0.295 NA 0.295 0.437
## .neuroW2P2 0.167 NA 0.167 0.346
## .neuroW3S1 0.171 NA 0.171 0.228
## .neuroW3S2 0.146 NA 0.146 0.237
## .neuroW3P1 0.414 NA 0.414 0.523
## .neuroW3P2 0.162 NA 0.162 0.340
## .neuroW4S1 0.209 NA 0.209 0.262
## .neuroW4S2 0.246 NA 0.246 0.341
## .neuroW4P1 0.274 NA 0.274 0.417
## .neuroW4P2 0.143 NA 0.143 0.310
## .neuro1 0.028 NA 0.047 0.047
## .neuro2 0.005 NA 0.009 0.009
## .neuro3 -0.001 NA -0.002 -0.002
## .neuro4 0.008 NA 0.013 0.013
## interc 0.570 NA 1.000 1.000
## slope -0.000 NA NA NAsemPaths(lgmNeuro, what = "col", whatLabels = "est", intercepts = T)
LGM Openness domain
with aspects as parcels
lgmOpend <- '
# factor at each time point with same loading
opend1 =~ intelW1S + a * openaW1S +
peer * intelW1P + aa * openaW1P
opend2 =~ intelW2S + a * openaW2S +
peer * intelW2P + aa * openaW2P
opend3 =~ intelW3S + a * openaW3S +
peer * intelW3P + aa * openaW3P
opend4 =~ intelW4S + a * openaW4S +
peer * intelW4P + aa * openaW4P
# second order factor for intercept and slope
interc =~ 1*opend1 + 1*opend2 + 1*opend3 + 1*opend4
slope =~ 0*opend1 + 6*opend2 + 13*opend3 + 19*opend4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
intelW1S ~ 0*1
intelW2S ~ 0*1
intelW3S ~ 0*1
intelW4S ~ 0*1
# fix equal intercepts
openaW1S ~ b*1
openaW2S ~ b*1
openaW3S ~ b*1
openaW4S ~ b*1
intelW1P ~ c*1
intelW2P ~ c*1
intelW3P ~ c*1
intelW4P ~ c*1
openaW1P ~ d*1
openaW2P ~ d*1
openaW3P ~ d*1
openaW4P ~ d*1
# error covariance - similar aspects across waves and informants
intelW1S ~~ intelW2S + intelW3S + intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW2S ~~ intelW3S + intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW3S ~~ intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW4S ~~ intelW1P + intelW2P + intelW3P + intelW4P
openaW1S ~~ openaW2S + openaW3S + openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW2S ~~ openaW3S + openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW3S ~~ openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW4S ~~ openaW1P + openaW2P + openaW3P + openaW4P
intelW1P ~~ intelW2P + intelW3P + intelW4P
intelW2P ~~ intelW3P + intelW4P
intelW3P ~~ intelW4P
openaW1P ~~ openaW2P + openaW3P + openaW4P
openaW2P ~~ openaW3P + openaW4P
openaW3P ~~ openaW4P
'
lgmOpend <- sem(lgmOpend, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativesummary(lgmOpend, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 1125 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 105
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 137.418
## Degrees of freedom 65
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2293.599
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967
## Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1055.056
## Loglikelihood unrestricted model (H1) -986.347
##
## Akaike (AIC) 2284.111
## Bayesian (BIC) 2593.555
## Sample-size adjusted Bayesian (BIC) 2317.734
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.066
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.081
## P-value RMSEA <= 0.05 0.047
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend1 =~
## intlW1S 1.000 7.927 14.493
## openW1S (a) 0.001 0.000 4.375 0.000 0.005 0.009
## intlW1P (peer) 1.979 0.000 6819.914 0.000 15.686 28.182
## openW1P (aa) 0.001 0.000 5.896 0.000 0.008 0.016
## opend2 =~
## intlW2S 1.000 7.860 14.587
## openW2S (a) 0.001 0.000 4.631 0.000 0.005 0.008
## intlW2P (peer) 1.979 0.000 6089.929 0.000 15.552 30.974
## openW2P (aa) 0.001 0.000 6.981 0.000 0.008 0.015
## opend3 =~
## intlW3S 1.000 7.582 13.888
## openW3S (a) 0.001 0.000 4.084 0.000 0.005 0.008
## intlW3P (peer) 1.979 0.000 7322.770 0.000 15.001 26.138
## openW3P (aa) 0.001 0.000 5.966 0.000 0.008 0.014
## opend4 =~
## intlW4S 1.000 8.245 15.806
## openW4S (a) 0.001 0.000 4.133 0.000 0.006 0.009
## intlW4P (peer) 1.979 0.000 7802.412 0.000 16.314 28.893
## openW4P (aa) 0.001 0.000 6.245 0.000 0.008 0.015
## interc =~
## opend1 1.000 0.862 0.862
## opend2 1.000 0.869 0.869
## opend3 1.000 0.901 0.901
## opend4 1.000 0.829 0.829
## slope =~
## opend1 0.000 0.000 0.000
## opend2 6.000 0.042 0.042
## opend3 13.000 0.095 0.095
## opend4 19.000 0.128 0.128
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.339 NA 0.897 0.897
## .intelW1S ~~
## .intelW2S -48.505 0.018 -2731.739 0.000 -48.505 -0.782
## .intelW3S -50.880 0.018 -2905.535 0.000 -50.880 -0.851
## .intelW4S -52.931 0.017 -3066.015 0.000 -52.931 -0.813
## .intelW1P -124.251 0.014 -8658.906 0.000 -124.251 -1.002
## .intelW2P -96.368 0.017 -5596.464 0.000 -96.368 -0.784
## .intelW3P -101.045 0.016 -6138.351 0.000 -101.045 -0.852
## .intelW4P -105.114 0.020 -5224.111 0.000 -105.114 -0.815
## .intelW2S ~~
## .intelW3S -53.145 0.018 -2882.772 0.000 -53.145 -0.896
## .intelW4S -55.307 0.018 -3041.130 0.000 -55.307 -0.857
## .intelW1P ~~
## .intelW2S -96.344 0.012 -8262.344 0.000 -96.344 -0.784
## .intelW2S ~~
## .intelW2P -122.180 0.022 -5502.392 0.000 -122.180 -1.002
## .intelW3P -105.552 0.019 -5455.181 0.000 -105.552 -0.898
## .intelW4P -109.836 0.018 -6069.986 0.000 -109.836 -0.859
## .intelW3S ~~
## .intelW4S -58.076 0.018 -3216.416 0.000 -58.076 -0.933
## .intelW1P ~~
## .intelW3S -101.014 0.016 -6513.441 0.000 -101.014 -0.852
## .intelW2P ~~
## .intelW3S -105.554 0.015 -6941.398 0.000 -105.554 -0.898
## .intelW3S ~~
## .intelW3P -113.654 0.023 -4887.992 0.000 -113.654 -1.003
## .intelW4P -115.336 0.015 -7485.934 0.000 -115.336 -0.935
## .intelW1P ~~
## .intelW4S -105.068 0.016 -6767.585 0.000 -105.068 -0.815
## .intelW2P ~~
## .intelW4S -109.815 0.012 -9036.228 0.000 -109.815 -0.859
## .intelW3P ~~
## .intelW4S -115.312 0.023 -5033.405 0.000 -115.312 -0.935
## .intelW4S ~~
## .intelW4P -134.479 0.021 -6491.536 0.000 -134.479 -1.002
## .openaW1S ~~
## .openaW2S 0.330 0.033 10.130 0.000 0.330 0.818
## .openaW3S 0.325 0.032 10.224 0.000 0.325 0.840
## .openaW4S 0.319 0.033 9.727 0.000 0.319 0.809
## .openaW1P 0.163 0.025 6.612 0.000 0.163 0.505
## .openaW2P 0.182 0.025 7.280 0.000 0.182 0.535
## .openaW3P 0.196 0.028 7.109 0.000 0.196 0.557
## .openaW4P 0.178 0.028 6.343 0.000 0.178 0.512
## .openaW2S ~~
## .openaW3S 0.348 0.034 10.182 0.000 0.348 0.871
## .openaW4S 0.343 0.035 9.717 0.000 0.343 0.841
## .openaW1P ~~
## .openaW2S 0.158 0.026 6.087 0.000 0.158 0.474
## .openaW2S ~~
## .openaW2P 0.177 0.027 6.494 0.000 0.177 0.506
## .openaW3P 0.195 0.029 6.709 0.000 0.195 0.537
## .openaW4P 0.181 0.030 6.056 0.000 0.181 0.505
## .openaW3S ~~
## .openaW4S 0.336 0.035 9.714 0.000 0.336 0.860
## .openaW1P ~~
## .openaW3S 0.150 0.025 6.008 0.000 0.150 0.470
## .openaW2P ~~
## .openaW3S 0.164 0.026 6.315 0.000 0.164 0.488
## .openaW3S ~~
## .openaW3P 0.187 0.028 6.610 0.000 0.187 0.538
## .openaW4P 0.167 0.029 5.806 0.000 0.167 0.487
## .openaW1P ~~
## .openaW4S 0.158 0.026 5.957 0.000 0.158 0.485
## .openaW2P ~~
## .openaW4S 0.178 0.027 6.501 0.000 0.178 0.519
## .openaW3P ~~
## .openaW4S 0.199 0.030 6.717 0.000 0.199 0.561
## .openaW4S ~~
## .openaW4P 0.198 0.031 6.336 0.000 0.198 0.566
## .intelW1P ~~
## .intelW2P -190.571 0.021 -9203.643 0.000 -190.571 -0.782
## .intelW3P -199.840 NA -199.840 -0.850
## .intelW4P -207.828 0.015 -13495.314 0.000 -207.828 -0.813
## .intelW2P ~~
## .intelW3P -208.744 0.025 -8354.334 0.000 -208.744 -0.896
## .intelW4P -217.188 NA -217.188 -0.857
## .intelW3P ~~
## .intelW4P -228.037 0.015 -14730.101 0.000 -228.037 -0.933
## .openaW1P ~~
## .openaW2P 0.203 0.025 8.263 0.000 0.203 0.726
## .openaW3P 0.218 0.026 8.246 0.000 0.218 0.755
## .openaW4P 0.230 0.027 8.487 0.000 0.230 0.803
## .openaW2P ~~
## .openaW3P 0.246 0.028 8.745 0.000 0.246 0.805
## .openaW4P 0.236 0.028 8.465 0.000 0.236 0.782
## .openaW3P ~~
## .openaW4P 0.274 0.031 8.853 0.000 0.274 0.878
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.657 0.032 112.552 0.000 0.535 0.535
## slope -0.001 0.001 -1.866 0.062 -0.026 -0.026
## .intelW1S 0.000 0.000 0.000
## .intelW2S 0.000 0.000 0.000
## .intelW3S 0.000 0.000 0.000
## .intelW4S 0.000 0.000 0.000
## .openaW1S (b) 3.800 0.037 102.232 0.000 3.800 6.081
## .openaW2S (b) 3.800 0.037 102.232 0.000 3.800 5.886
## .openaW3S (b) 3.800 0.037 102.232 0.000 3.800 6.145
## .openaW4S (b) 3.800 0.037 102.232 0.000 3.800 6.021
## .intelW1P (c) -3.258 0.066 -49.564 0.000 -3.258 -5.854
## .intelW2P (c) -3.258 0.066 -49.564 0.000 -3.258 -6.489
## .intelW3P (c) -3.258 0.066 -49.564 0.000 -3.258 -5.677
## .intelW4P (c) -3.258 0.066 -49.564 0.000 -3.258 -5.770
## .openaW1P (d) 3.592 0.036 100.226 0.000 3.592 6.977
## .openaW2P (d) 3.592 0.036 100.226 0.000 3.592 6.609
## .openaW3P (d) 3.592 0.036 100.226 0.000 3.592 6.387
## .openaW4P (d) 3.592 0.036 100.226 0.000 3.592 6.465
## .opend1 0.000 0.000 0.000
## .opend2 0.000 0.000 0.000
## .opend3 0.000 0.000 0.000
## .opend4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S -62.545 0.020 -3061.926 0.000 -62.545 -209.055
## .openaW1S 0.390 0.034 11.508 0.000 0.390 1.000
## .intelW1P -245.728 0.010 -25094.945 0.000 -245.728 -793.202
## .openaW1P 0.265 0.030 8.869 0.000 0.265 1.000
## .intelW2S -61.487 0.026 -2378.583 0.000 -61.487 -211.788
## .openaW2S 0.417 0.039 10.743 0.000 0.417 1.000
## .intelW2P -241.610 NA -241.610 -958.390
## .openaW2P 0.295 0.031 9.416 0.000 0.295 1.000
## .intelW3S -57.183 0.025 -2305.401 0.000 -57.183 -191.889
## .openaW3S 0.382 0.036 10.671 0.000 0.382 1.000
## .intelW3P -224.711 NA -224.711 -682.198
## .openaW3P 0.316 0.034 9.232 0.000 0.316 1.000
## .intelW4S -67.707 0.020 -3388.325 0.000 -67.707 -248.837
## .openaW4S 0.398 0.040 9.920 0.000 0.398 1.000
## .intelW4P -265.821 0.015 -18176.586 0.000 -265.821 -833.797
## .openaW4P 0.309 0.036 8.614 0.000 0.309 1.000
## .opend1 16.148 NA 0.257 0.257
## .opend2 10.899 0.015 727.654 0.000 0.176 0.176
## .opend3 1.445 0.016 91.167 0.000 0.025 0.025
## .opend4 7.283 NA 0.107 0.107
## interc 46.696 0.013 3678.942 0.000 1.000 1.000
## slope 0.003 NA 1.000 1.000semPaths(lgmOpend, what = "col", whatLabels = "est", intercepts = T)
with random parcels
lgmOpend <- '
# factor at each time point with same loading
opend1 =~ opendW1S1 + a * opendW1S2 +
peer * opendW1P1 + aa * opendW1P2
opend2 =~ opendW2S1 + a * opendW2S2 +
peer * opendW2P1 + aa * opendW2P2
opend3 =~ opendW3S1 + a * opendW3S2 +
peer * opendW3P1 + aa * opendW3P2
opend4 =~ opendW4S1 + a * opendW4S2 +
peer * opendW4P1 + aa * opendW4P2
# second order factor for intercept and slope
interc =~ 1*opend1 + 1*opend2 + 1*opend3 + 1*opend4
slope =~ 0*opend1 + 6*opend2 + 13*opend3 + 19*opend4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
opendW1S1 ~ 0*1
opendW2S1 ~ 0*1
opendW3S1 ~ 0*1
opendW4S1 ~ 0*1
# fix equal intercepts
opendW1S2 ~ b*1
opendW2S2 ~ b*1
opendW3S2 ~ b*1
opendW4S2 ~ b*1
opendW1P1 ~ c*1
opendW2P1 ~ c*1
opendW3P1 ~ c*1
opendW4P1 ~ c*1
opendW1P2 ~ d*1
opendW2P2 ~ d*1
opendW3P2 ~ d*1
opendW4P2 ~ d*1
# error covariance - similar parcels across waves
opendW1S1 ~~ opendW2S1 + opendW3S1 + opendW4S1
opendW2S1 ~~ opendW3S1 + opendW4S1
opendW3S1 ~~ opendW4S1
opendW1S2 ~~ opendW2S2 + opendW3S2 + opendW4S2
opendW2S2 ~~ opendW3S2 + opendW4S2
opendW3S2 ~~ opendW4S2
opendW1P1 ~~ opendW2P1 + opendW3P1 + opendW4P1
opendW2P1 ~~ opendW3P1 + opendW4P1
opendW3P1 ~~ opendW4P1
opendW1P2 ~~ opendW2P2 + opendW3P2 + opendW4P2
opendW2P2 ~~ opendW3P2 + opendW4P2
opendW3P2 ~~ opendW4P2
# error covariance - same method at one wave
opendW1S1 ~~ opendW1S2
opendW1P1 ~~ opendW1P2
opendW2S1 ~~ opendW2S2
opendW2P1 ~~ opendW2P2
opendW3S1 ~~ opendW3S2
opendW3P1 ~~ opendW3P2
opendW4S1 ~~ opendW4S2
opendW4P1 ~~ opendW4P2
'
lgmOpend <- sem(lgmOpend, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lgmOpend, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 524 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend1 =~
## opnW1S1 1.000 1.300 0.922
## opnW1S2 (a) 0.908 NA 1.180 0.935
## opnW1P1 (peer) 0.952 NA 1.238 0.971
## opnW1P2 (aa) 0.935 NA 1.216 0.994
## opend2 =~
## opnW2S1 1.000 1.159 0.959
## opnW2S2 (a) 0.908 NA 1.052 0.957
## opnW2P1 (peer) 0.952 NA 1.103 0.957
## opnW2P2 (aa) 0.935 NA 1.084 0.940
## opend3 =~
## opnW3S1 1.000 1.146 0.965
## opnW3S2 (a) 0.908 NA 1.041 0.952
## opnW3P1 (peer) 0.952 NA 1.091 0.903
## opnW3P2 (aa) 0.935 NA 1.072 0.943
## opend4 =~
## opnW4S1 1.000 1.148 0.942
## opnW4S2 (a) 0.908 NA 1.042 0.952
## opnW4P1 (peer) 0.952 NA 1.093 0.924
## opnW4P2 (aa) 0.935 NA 1.074 0.944
## interc =~
## opend1 1.000 0.883 0.883
## opend2 1.000 0.990 0.990
## opend3 1.000 1.001 1.001
## opend4 1.000 1.000 1.000
## slope =~
## opend1 0.000 NA NA
## opend2 6.000 NA NA
## opend3 13.000 NA NA
## opend4 19.000 NA NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.001 NA 0.085 0.085
## .opendW1S1 ~~
## .opendW2S1 0.026 NA 0.026 0.140
## .opendW3S1 0.037 NA 0.037 0.220
## .opendW4S1 0.042 NA 0.042 0.190
## .opendW2S1 ~~
## .opendW3S1 0.030 NA 0.030 0.280
## .opendW4S1 0.055 NA 0.055 0.391
## .opendW3S1 ~~
## .opendW4S1 0.051 NA 0.051 0.399
## .opendW1S2 ~~
## .opendW2S2 0.065 NA 0.065 0.454
## .opendW3S2 0.061 NA 0.061 0.405
## .opendW4S2 0.021 NA 0.021 0.143
## .opendW2S2 ~~
## .opendW3S2 0.057 NA 0.057 0.529
## .opendW4S2 0.018 NA 0.018 0.173
## .opendW3S2 ~~
## .opendW4S2 0.027 NA 0.027 0.239
## .opendW1P1 ~~
## .opendW2P1 0.022 NA 0.022 0.220
## .opendW3P1 0.085 NA 0.085 0.540
## .opendW4P1 0.053 NA 0.053 0.388
## .opendW2P1 ~~
## .opendW3P1 0.072 NA 0.072 0.413
## .opendW4P1 0.074 NA 0.074 0.488
## .opendW3P1 ~~
## .opendW4P1 0.153 NA 0.153 0.655
## .opendW1P2 ~~
## .opendW2P2 0.063 NA 0.063 1.218
## .opendW3P2 0.060 NA 0.060 1.209
## .opendW4P2 0.059 NA 0.059 1.206
## .opendW2P2 ~~
## .opendW3P2 0.149 NA 0.149 0.998
## .opendW4P2 0.147 NA 0.147 0.999
## .opendW3P2 ~~
## .opendW4P2 0.141 NA 0.141 1.000
## .opendW1S1 ~~
## .opendW1S2 0.158 NA 0.158 0.644
## .opendW1P1 ~~
## .opendW1P2 0.002 NA 0.002 0.061
## .opendW2S1 ~~
## .opendW2S2 0.012 NA 0.012 0.107
## .opendW2P1 ~~
## .opendW2P2 0.000 NA 0.000 0.002
## .opendW3S1 ~~
## .opendW3S2 0.039 NA 0.039 0.376
## .opendW3P1 ~~
## .opendW3P2 -0.000 NA -0.000 -0.000
## .opendW4S1 ~~
## .opendW4S2 0.048 NA 0.048 0.350
## .opendW4P1 ~~
## .opendW4P2 -0.000 NA -0.000 -0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.766 NA 3.283 3.283
## slope 0.000 NA NA NA
## .opendW1S1 0.000 0.000 0.000
## .opendW2S1 0.000 0.000 0.000
## .opendW3S1 0.000 0.000 0.000
## .opendW4S1 0.000 0.000 0.000
## .opendW1S2 (b) 0.208 NA 0.208 0.165
## .opendW2S2 (b) 0.208 NA 0.208 0.189
## .opendW3S2 (b) 0.208 NA 0.208 0.191
## .opendW4S2 (b) 0.208 NA 0.208 0.190
## .opendW1P1 (c) 0.171 NA 0.171 0.134
## .opendW2P1 (c) 0.171 NA 0.171 0.148
## .opendW3P1 (c) 0.171 NA 0.171 0.142
## .opendW4P1 (c) 0.171 NA 0.171 0.145
## .opendW1P2 (d) 0.365 NA 0.365 0.298
## .opendW2P2 (d) 0.365 NA 0.365 0.316
## .opendW3P2 (d) 0.365 NA 0.365 0.321
## .opendW4P2 (d) 0.365 NA 0.365 0.321
## .opend1 0.000 0.000 0.000
## .opend2 0.000 0.000 0.000
## .opend3 0.000 0.000 0.000
## .opend4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .opendW1S1 0.299 NA 0.299 0.150
## .opendW1S2 0.201 NA 0.201 0.126
## .opendW1P1 0.092 NA 0.092 0.057
## .opendW1P2 0.017 NA 0.017 0.012
## .opendW2S1 0.118 NA 0.118 0.081
## .opendW2S2 0.102 NA 0.102 0.084
## .opendW2P1 0.112 NA 0.112 0.084
## .opendW2P2 0.155 NA 0.155 0.117
## .opendW3S1 0.097 NA 0.097 0.068
## .opendW3S2 0.112 NA 0.112 0.094
## .opendW3P1 0.268 NA 0.268 0.184
## .opendW3P2 0.142 NA 0.142 0.110
## .opendW4S1 0.166 NA 0.166 0.112
## .opendW4S2 0.111 NA 0.111 0.093
## .opendW4P1 0.204 NA 0.204 0.146
## .opendW4P2 0.140 NA 0.140 0.108
## .opend1 0.373 NA 0.221 0.221
## .opend2 0.017 NA 0.012 0.012
## .opend3 -0.007 NA -0.006 -0.006
## .opend4 0.014 NA 0.010 0.010
## interc 1.316 NA 1.000 1.000
## slope -0.000 NA NA NAsemPaths(lgmOpend, what = "col", whatLabels = "est", intercepts = T)
LGM Assertiveness
lgmAssert <- '
# factor at each time point with same loading
assert1 =~ assertW1S1 + a * assertW1S2 +
peer * assertW1P1 + aa * assertW1P2
assert2 =~ assertW2S1 + a * assertW2S2 +
peer * assertW2P1 + aa * assertW2P2
assert3 =~ assertW3S1 + a * assertW3S2 +
peer * assertW3P1 + aa * assertW3P2
assert4 =~ assertW4S1 + a * assertW4S2 +
peer * assertW4P1 + aa * assertW4P2
# second order factor for intercept and slope
interc =~ 1*assert1 + 1*assert2 + 1*assert3 + 1*assert4
slope =~ 0*assert1 + 6*assert2 + 13*assert3 + 19*assert4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
assertW1S1 ~ 0*1
assertW2S1 ~ 0*1
assertW3S1 ~ 0*1
assertW4S1 ~ 0*1
# fix equal intercepts
assertW1S2 ~ b*1
assertW2S2 ~ b*1
assertW3S2 ~ b*1
assertW4S2 ~ b*1
assertW1P1 ~ c*1
assertW2P1 ~ c*1
assertW3P1 ~ c*1
assertW4P1 ~ c*1
assertW1P2 ~ d*1
assertW2P2 ~ d*1
assertW3P2 ~ d*1
assertW4P2 ~ d*1
# error covariance - similar parcels across waves
assertW1S1 ~~ assertW2S1 + assertW3S1 + assertW4S1
assertW2S1 ~~ assertW3S1 + assertW4S1
assertW3S1 ~~ assertW4S1
assertW1S2 ~~ assertW2S2 + assertW3S2 + assertW4S2
assertW2S2 ~~ assertW3S2 + assertW4S2
assertW3S2 ~~ assertW4S2
assertW1P1 ~~ assertW2P1 + assertW3P1 + assertW4P1
assertW2P1 ~~ assertW3P1 + assertW4P1
assertW3P1 ~~ assertW4P1
assertW1P2 ~~ assertW2P2 + assertW3P2 + assertW4P2
assertW2P2 ~~ assertW3P2 + assertW4P2
assertW3P2 ~~ assertW4P2
# error covariance - same method at one wave
assertW1S1 ~~ assertW1S2
assertW1P1 ~~ assertW1P2
assertW2S1 ~~ assertW2S2
assertW2P1 ~~ assertW2P2
assertW3S1 ~~ assertW3S2
assertW3P1 ~~ assertW3P2
assertW4S1 ~~ assertW4S2
assertW4P1 ~~ assertW4P2
'
lgmAssert <- sem(lgmAssert, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmAssert, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 188 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 299.533
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2668.403
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.917
## Tucker-Lewis Index (TLI) 0.889
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1412.291
## Loglikelihood unrestricted model (H1) -1262.524
##
## Akaike (AIC) 2950.581
## Bayesian (BIC) 3174.662
## Sample-size adjusted Bayesian (BIC) 2974.929
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.096
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.108
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.181
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## assert1 =~
## assW1S1 1.000 0.570 0.825
## assW1S2 (a) 1.066 0.067 15.917 0.000 0.608 0.806
## assW1P1 (peer) 0.377 0.069 5.478 0.000 0.215 0.370
## assW1P2 (aa) 0.525 0.073 7.213 0.000 0.299 0.476
## assert2 =~
## assW2S1 1.000 0.570 0.810
## assW2S2 (a) 1.066 0.067 15.917 0.000 0.608 0.830
## assW2P1 (peer) 0.377 0.069 5.478 0.000 0.215 0.406
## assW2P2 (aa) 0.525 0.073 7.213 0.000 0.299 0.483
## assert3 =~
## assW3S1 1.000 0.576 0.845
## assW3S2 (a) 1.066 0.067 15.917 0.000 0.614 0.860
## assW3P1 (peer) 0.377 0.069 5.478 0.000 0.217 0.390
## assW3P2 (aa) 0.525 0.073 7.213 0.000 0.302 0.441
## assert4 =~
## assW4S1 1.000 0.618 0.890
## assW4S2 (a) 1.066 0.067 15.917 0.000 0.659 0.907
## assW4P1 (peer) 0.377 0.069 5.478 0.000 0.233 0.436
## assW4P2 (aa) 0.525 0.073 7.213 0.000 0.324 0.507
## interc =~
## assert1 1.000 1.027 1.027
## assert2 1.000 1.027 1.027
## assert3 1.000 1.017 1.017
## assert4 1.000 0.947 0.947
## slope =~
## assert1 0.000 0.000 0.000
## assert2 6.000 0.137 0.137
## assert3 13.000 0.295 0.295
## assert4 19.000 0.401 0.401
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.001 0.001 -1.270 0.204 -0.193 -0.193
## .assertW1S1 ~~
## .assertW2S1 0.043 0.021 2.049 0.040 0.043 0.269
## .assertW3S1 0.035 0.019 1.813 0.070 0.035 0.245
## .assertW4S1 0.022 0.019 1.156 0.248 0.022 0.178
## .assertW2S1 ~~
## .assertW3S1 0.065 0.021 3.114 0.002 0.065 0.433
## .assertW4S1 0.055 0.021 2.629 0.009 0.055 0.418
## .assertW3S1 ~~
## .assertW4S1 0.057 0.020 2.853 0.004 0.057 0.494
## .assertW1S2 ~~
## .assertW2S2 0.069 0.024 2.900 0.004 0.069 0.379
## .assertW3S2 0.059 0.022 2.721 0.007 0.059 0.362
## .assertW4S2 0.054 0.022 2.443 0.015 0.054 0.396
## .assertW2S2 ~~
## .assertW3S2 0.055 0.022 2.471 0.013 0.055 0.370
## .assertW4S2 0.070 0.024 2.942 0.003 0.070 0.559
## .assertW3S2 ~~
## .assertW4S2 0.047 0.023 2.105 0.035 0.047 0.425
## .assertW1P1 ~~
## .assertW2P1 0.162 0.028 5.830 0.000 0.162 0.618
## .assertW3P1 0.171 0.032 5.332 0.000 0.171 0.615
## .assertW4P1 0.137 0.028 4.891 0.000 0.137 0.525
## .assertW2P1 ~~
## .assertW3P1 0.129 0.029 4.397 0.000 0.129 0.518
## .assertW4P1 0.133 0.027 4.991 0.000 0.133 0.572
## .assertW3P1 ~~
## .assertW4P1 0.163 0.032 5.115 0.000 0.163 0.660
## .assertW1P2 ~~
## .assertW2P2 0.170 0.032 5.262 0.000 0.170 0.566
## .assertW3P2 0.243 0.038 6.397 0.000 0.243 0.715
## .assertW4P2 0.208 0.033 6.207 0.000 0.208 0.681
## .assertW2P2 ~~
## .assertW3P2 0.268 0.040 6.647 0.000 0.268 0.802
## .assertW4P2 0.219 0.035 6.176 0.000 0.219 0.731
## .assertW3P2 ~~
## .assertW4P2 0.254 0.042 6.050 0.000 0.254 0.747
## .assertW1S1 ~~
## .assertW1S2 0.063 0.027 2.329 0.020 0.063 0.364
## .assertW1P1 ~~
## .assertW1P2 0.071 0.017 4.227 0.000 0.071 0.236
## .assertW2S1 ~~
## .assertW2S2 0.041 0.023 1.813 0.070 0.041 0.245
## .assertW2P1 ~~
## .assertW2P2 0.057 0.015 3.745 0.000 0.057 0.215
## .assertW3S1 ~~
## .assertW3S2 0.014 0.019 0.780 0.435 0.014 0.109
## .assertW3P1 ~~
## .assertW3P2 -0.003 0.018 -0.186 0.853 -0.003 -0.010
## .assertW4S1 ~~
## .assertW4S2 -0.005 0.031 -0.156 0.876 -0.005 -0.050
## .assertW4P1 ~~
## .assertW4P2 0.040 0.016 2.521 0.012 0.040 0.150
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.381 0.042 81.208 0.000 5.774 5.774
## slope -0.001 0.002 -0.433 0.665 -0.053 -0.053
## .assertW1S1 0.000 0.000 0.000
## .assertW2S1 0.000 0.000 0.000
## .assertW3S1 0.000 0.000 0.000
## .assertW4S1 0.000 0.000 0.000
## .assertW1S2 (b) -0.104 0.227 -0.458 0.647 -0.104 -0.138
## .assertW2S2 (b) -0.104 0.227 -0.458 0.647 -0.104 -0.142
## .assertW3S2 (b) -0.104 0.227 -0.458 0.647 -0.104 -0.146
## .assertW4S2 (b) -0.104 0.227 -0.458 0.647 -0.104 -0.143
## .assertW1P1 (c) 2.429 0.233 10.431 0.000 2.429 4.175
## .assertW2P1 (c) 2.429 0.233 10.431 0.000 2.429 4.583
## .assertW3P1 (c) 2.429 0.233 10.431 0.000 2.429 4.361
## .assertW4P1 (c) 2.429 0.233 10.431 0.000 2.429 4.544
## .assertW1P2 (d) 1.753 0.248 7.066 0.000 1.753 2.787
## .assertW2P2 (d) 1.753 0.248 7.066 0.000 1.753 2.826
## .assertW3P2 (d) 1.753 0.248 7.066 0.000 1.753 2.557
## .assertW4P2 (d) 1.753 0.248 7.066 0.000 1.753 2.738
## .assert1 0.000 0.000 0.000
## .assert2 0.000 0.000 0.000
## .assert3 0.000 0.000 0.000
## .assert4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S1 0.152 0.031 4.922 0.000 0.152 0.319
## .assertW1S2 0.199 0.040 4.921 0.000 0.199 0.350
## .assertW1P1 0.292 0.034 8.565 0.000 0.292 0.863
## .assertW1P2 0.306 0.036 8.524 0.000 0.306 0.774
## .assertW2S1 0.171 0.033 5.253 0.000 0.171 0.345
## .assertW2S2 0.167 0.035 4.814 0.000 0.167 0.311
## .assertW2P1 0.235 0.030 7.894 0.000 0.235 0.835
## .assertW2P2 0.295 0.037 7.979 0.000 0.295 0.767
## .assertW3S1 0.133 0.027 4.927 0.000 0.133 0.287
## .assertW3S2 0.133 0.031 4.287 0.000 0.133 0.261
## .assertW3P1 0.263 0.037 7.183 0.000 0.263 0.848
## .assertW3P2 0.378 0.051 7.421 0.000 0.378 0.806
## .assertW4S1 0.101 0.038 2.649 0.008 0.101 0.209
## .assertW4S2 0.094 0.042 2.206 0.027 0.094 0.177
## .assertW4P1 0.231 0.033 6.973 0.000 0.231 0.810
## .assertW4P2 0.305 0.044 6.956 0.000 0.305 0.743
## .assert1 -0.018 0.024 -0.727 0.467 -0.055 -0.055
## .assert2 -0.006 0.020 -0.304 0.761 -0.018 -0.018
## .assert3 -0.002 0.016 -0.118 0.906 -0.006 -0.006
## .assert4 0.034 0.029 1.156 0.248 0.089 0.089
## interc 0.343 0.043 7.945 0.000 1.000 1.000
## slope 0.000 0.000 2.488 0.013 1.000 1.000semPaths(lgmAssert, what = "col", whatLabels = "est", intercepts = T)
LGM Compassion
lgmCompa <- '
# factor at each time point with same loading
compa1 =~ compaW1S1 + a * compaW1S2 +
peer * compaW1P1 + aa * compaW1P2
compa2 =~ compaW2S1 + a * compaW2S2 +
peer * compaW2P1 + aa * compaW2P2
compa3 =~ compaW3S1 + a * compaW3S2 +
peer * compaW3P1 + aa * compaW3P2
compa4 =~ compaW4S1 + a * compaW4S2 +
peer * compaW4P1 + aa * compaW4P2
# second order factor for intercept and slope
interc =~ 1*compa1 + 1*compa2 + 1*compa3 + 1*compa4
slope =~ 0*compa1 + 6*compa2 + 13*compa3 + 19*compa4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
compaW1S1 ~ 0*1
compaW2S1 ~ 0*1
compaW3S1 ~ 0*1
compaW4S1 ~ 0*1
# fix equal intercepts
compaW1S2 ~ b*1
compaW2S2 ~ b*1
compaW3S2 ~ b*1
compaW4S2 ~ b*1
compaW1P1 ~ c*1
compaW2P1 ~ c*1
compaW3P1 ~ c*1
compaW4P1 ~ c*1
compaW1P2 ~ d*1
compaW2P2 ~ d*1
compaW3P2 ~ d*1
compaW4P2 ~ d*1
# error covariance - similar parcels across waves
compaW1S1 ~~ compaW2S1 + compaW3S1 + compaW4S1
compaW2S1 ~~ compaW3S1 + compaW4S1
compaW3S1 ~~ compaW4S1
compaW1S2 ~~ compaW2S2 + compaW3S2 + compaW4S2
compaW2S2 ~~ compaW3S2 + compaW4S2
compaW3S2 ~~ compaW4S2
compaW1P1 ~~ compaW2P1 + compaW3P1 + compaW4P1
compaW2P1 ~~ compaW3P1 + compaW4P1
compaW3P1 ~~ compaW4P1
compaW1P2 ~~ compaW2P2 + compaW3P2 + compaW4P2
compaW2P2 ~~ compaW3P2 + compaW4P2
compaW3P2 ~~ compaW4P2
# error covariance - same method at one wave
compaW1S1 ~~ compaW1S2
compaW1P1 ~~ compaW1P2
compaW2S1 ~~ compaW2S2
compaW2P1 ~~ compaW2P2
compaW3S1 ~~ compaW3S2
compaW3P1 ~~ compaW3P2
compaW4S1 ~~ compaW4S2
compaW4P1 ~~ compaW4P2
'
lgmCompa <- sem(lgmCompa, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmCompa, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 180 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 302.256
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2272.548
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.901
## Tucker-Lewis Index (TLI) 0.866
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1038.587
## Loglikelihood unrestricted model (H1) -887.459
##
## Akaike (AIC) 2203.174
## Bayesian (BIC) 2427.254
## Sample-size adjusted Bayesian (BIC) 2227.521
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.096
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.108
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.180
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## compa1 =~
## cmpW1S1 1.000 0.258 0.524
## cmpW1S2 (a) 1.092 0.057 19.068 0.000 0.281 0.530
## cmpW1P1 (peer) 1.014 0.136 7.451 0.000 0.261 0.426
## cmpW1P2 (aa) 0.977 0.133 7.369 0.000 0.252 0.452
## compa2 =~
## cmpW2S1 1.000 0.280 0.581
## cmpW2S2 (a) 1.092 0.057 19.068 0.000 0.305 0.594
## cmpW2P1 (peer) 1.014 0.136 7.451 0.000 0.284 0.502
## cmpW2P2 (aa) 0.977 0.133 7.369 0.000 0.273 0.460
## compa3 =~
## cmpW3S1 1.000 0.304 0.620
## cmpW3S2 (a) 1.092 0.057 19.068 0.000 0.333 0.635
## cmpW3P1 (peer) 1.014 0.136 7.451 0.000 0.309 0.537
## cmpW3P2 (aa) 0.977 0.133 7.369 0.000 0.298 0.559
## compa4 =~
## cmpW4S1 1.000 0.305 0.628
## cmpW4S2 (a) 1.092 0.057 19.068 0.000 0.333 0.626
## cmpW4P1 (peer) 1.014 0.136 7.451 0.000 0.309 0.500
## cmpW4P2 (aa) 0.977 0.133 7.369 0.000 0.298 0.492
## interc =~
## compa1 1.000 1.302 1.302
## compa2 1.000 1.199 1.199
## compa3 1.000 1.102 1.102
## compa4 1.000 1.101 1.101
## slope =~
## compa1 0.000 0.000 0.000
## compa2 6.000 0.209 0.209
## compa3 13.000 0.417 0.417
## compa4 19.000 0.608 0.608
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.000 0.001 0.571 0.568 0.104 0.104
## .compaW1S1 ~~
## .compaW2S1 -0.004 0.009 -0.472 0.637 -0.004 -0.025
## .compaW3S1 0.006 0.008 0.698 0.485 0.006 0.036
## .compaW4S1 0.008 0.008 1.127 0.260 0.008 0.054
## .compaW2S1 ~~
## .compaW3S1 0.026 0.009 2.812 0.005 0.026 0.172
## .compaW4S1 0.020 0.008 2.414 0.016 0.020 0.133
## .compaW3S1 ~~
## .compaW4S1 0.022 0.008 2.657 0.008 0.022 0.152
## .compaW1S2 ~~
## .compaW2S2 0.022 0.010 2.253 0.024 0.022 0.117
## .compaW3S2 0.016 0.009 1.720 0.086 0.016 0.085
## .compaW4S2 -0.003 0.008 -0.335 0.738 -0.003 -0.015
## .compaW2S2 ~~
## .compaW3S2 0.031 0.010 3.101 0.002 0.031 0.186
## .compaW4S2 0.026 0.009 2.899 0.004 0.026 0.152
## .compaW3S2 ~~
## .compaW4S2 0.017 0.009 1.871 0.061 0.017 0.099
## .compaW1P1 ~~
## .compaW2P1 0.000 0.013 0.003 0.997 0.000 0.000
## .compaW3P1 0.000 0.016 0.018 0.986 0.000 0.001
## .compaW4P1 0.010 0.015 0.684 0.494 0.010 0.035
## .compaW2P1 ~~
## .compaW3P1 0.024 0.013 1.899 0.058 0.024 0.103
## .compaW4P1 0.021 0.013 1.576 0.115 0.021 0.080
## .compaW3P1 ~~
## .compaW4P1 0.005 0.017 0.313 0.754 0.005 0.021
## .compaW1P2 ~~
## .compaW2P2 0.025 0.013 1.880 0.060 0.025 0.094
## .compaW3P2 0.036 0.012 2.948 0.003 0.036 0.164
## .compaW4P2 0.019 0.015 1.256 0.209 0.019 0.074
## .compaW2P2 ~~
## .compaW3P2 0.001 0.012 0.052 0.959 0.001 0.003
## .compaW4P2 -0.005 0.014 -0.319 0.750 -0.005 -0.016
## .compaW3P2 ~~
## .compaW4P2 0.025 0.013 1.978 0.048 0.025 0.108
## .compaW1S1 ~~
## .compaW1S2 0.125 0.021 5.867 0.000 0.125 0.663
## .compaW1P1 ~~
## .compaW1P2 0.201 0.033 6.110 0.000 0.201 0.727
## .compaW2S1 ~~
## .compaW2S2 0.088 0.018 4.830 0.000 0.088 0.541
## .compaW2P1 ~~
## .compaW2P2 0.209 0.033 6.417 0.000 0.209 0.810
## .compaW3S1 ~~
## .compaW3S2 0.089 0.017 5.351 0.000 0.089 0.572
## .compaW3P1 ~~
## .compaW3P2 0.143 0.027 5.334 0.000 0.143 0.669
## .compaW4S1 ~~
## .compaW4S2 0.116 0.024 4.833 0.000 0.116 0.742
## .compaW4P1 ~~
## .compaW4P2 0.242 0.043 5.608 0.000 0.242 0.859
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 4.191 0.027 157.851 0.000 12.500 12.500
## slope 0.001 0.001 0.782 0.434 0.106 0.106
## .compaW1S1 0.000 0.000 0.000
## .compaW2S1 0.000 0.000 0.000
## .compaW3S1 0.000 0.000 0.000
## .compaW4S1 0.000 0.000 0.000
## .compaW1S2 (b) -0.486 0.241 -2.016 0.044 -0.486 -0.915
## .compaW2S2 (b) -0.486 0.241 -2.016 0.044 -0.486 -0.944
## .compaW3S2 (b) -0.486 0.241 -2.016 0.044 -0.486 -0.928
## .compaW4S2 (b) -0.486 0.241 -2.016 0.044 -0.486 -0.914
## .compaW1P1 (c) -0.330 0.575 -0.574 0.566 -0.330 -0.537
## .compaW2P1 (c) -0.330 0.575 -0.574 0.566 -0.330 -0.584
## .compaW3P1 (c) -0.330 0.575 -0.574 0.566 -0.330 -0.574
## .compaW4P1 (c) -0.330 0.575 -0.574 0.566 -0.330 -0.534
## .compaW1P2 (d) -0.116 0.560 -0.207 0.836 -0.116 -0.208
## .compaW2P2 (d) -0.116 0.560 -0.207 0.836 -0.116 -0.195
## .compaW3P2 (d) -0.116 0.560 -0.207 0.836 -0.116 -0.218
## .compaW4P2 (d) -0.116 0.560 -0.207 0.836 -0.116 -0.191
## .compa1 0.000 0.000 0.000
## .compa2 0.000 0.000 0.000
## .compa3 0.000 0.000 0.000
## .compa4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S1 0.176 0.022 7.992 0.000 0.176 0.726
## .compaW1S2 0.203 0.024 8.349 0.000 0.203 0.719
## .compaW1P1 0.309 0.041 7.552 0.000 0.309 0.819
## .compaW1P2 0.247 0.031 8.062 0.000 0.247 0.796
## .compaW2S1 0.153 0.019 7.987 0.000 0.153 0.663
## .compaW2S2 0.171 0.021 8.044 0.000 0.171 0.648
## .compaW2P1 0.239 0.032 7.377 0.000 0.239 0.748
## .compaW2P2 0.279 0.037 7.559 0.000 0.279 0.789
## .compaW3S1 0.148 0.018 8.390 0.000 0.148 0.615
## .compaW3S2 0.163 0.020 8.336 0.000 0.163 0.596
## .compaW3P1 0.235 0.032 7.294 0.000 0.235 0.711
## .compaW3P2 0.195 0.026 7.426 0.000 0.195 0.688
## .compaW4S1 0.142 0.023 6.280 0.000 0.142 0.606
## .compaW4S2 0.172 0.028 6.240 0.000 0.172 0.608
## .compaW4P1 0.286 0.046 6.180 0.000 0.286 0.750
## .compaW4P2 0.277 0.043 6.422 0.000 0.277 0.758
## .compa1 -0.046 0.016 -2.916 0.004 -0.694 -0.694
## .compa2 -0.042 0.013 -3.284 0.001 -0.535 -0.535
## .compa3 -0.045 0.011 -3.995 0.000 -0.482 -0.482
## .compa4 -0.067 0.019 -3.570 0.000 -0.721 -0.721
## interc 0.112 0.017 6.487 0.000 1.000 1.000
## slope 0.000 0.000 1.890 0.059 1.000 1.000semPaths(lgmCompa, what = "col", whatLabels = "est", intercepts = T)
LGM Enthusiasm
lgmEnthu <- '
# factor at each time point with same loading
enthu1 =~ enthuW1S1 + a * enthuW1S2 +
peer * enthuW1P1 + aa * enthuW1P2
enthu2 =~ enthuW2S1 + a * enthuW2S2 +
peer * enthuW2P1 + aa * enthuW2P2
enthu3 =~ enthuW3S1 + a * enthuW3S2 +
peer * enthuW3P1 + aa * enthuW3P2
enthu4 =~ enthuW4S1 + a * enthuW4S2 +
peer * enthuW4P1 + aa * enthuW4P2
# second order factor for intercept and slope
interc =~ 1*enthu1 + 1*enthu2 + 1*enthu3 + 1*enthu4
slope =~ 0*enthu1 + 6*enthu2 + 13*enthu3 + 19*enthu4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
enthuW1S1 ~ 0*1
enthuW2S1 ~ 0*1
enthuW3S1 ~ 0*1
enthuW4S1 ~ 0*1
# fix equal intercepts
enthuW1S2 ~ b*1
enthuW2S2 ~ b*1
enthuW3S2 ~ b*1
enthuW4S2 ~ b*1
enthuW1P1 ~ c*1
enthuW2P1 ~ c*1
enthuW3P1 ~ c*1
enthuW4P1 ~ c*1
enthuW1P2 ~ d*1
enthuW2P2 ~ d*1
enthuW3P2 ~ d*1
enthuW4P2 ~ d*1
# error covariance - similar parcels across waves
enthuW1S1 ~~ enthuW2S1 + enthuW3S1 + enthuW4S1
enthuW2S1 ~~ enthuW3S1 + enthuW4S1
enthuW3S1 ~~ enthuW4S1
enthuW1S2 ~~ enthuW2S2 + enthuW3S2 + enthuW4S2
enthuW2S2 ~~ enthuW3S2 + enthuW4S2
enthuW3S2 ~~ enthuW4S2
enthuW1P1 ~~ enthuW2P1 + enthuW3P1 + enthuW4P1
enthuW2P1 ~~ enthuW3P1 + enthuW4P1
enthuW3P1 ~~ enthuW4P1
enthuW1P2 ~~ enthuW2P2 + enthuW3P2 + enthuW4P2
enthuW2P2 ~~ enthuW3P2 + enthuW4P2
enthuW3P2 ~~ enthuW4P2
# error covariance - same method at one wave
enthuW1S1 ~~ enthuW1S2
enthuW1P1 ~~ enthuW1P2
enthuW2S1 ~~ enthuW2S2
enthuW2P1 ~~ enthuW2P2
enthuW3S1 ~~ enthuW3S2
enthuW3P1 ~~ enthuW3P2
enthuW4S1 ~~ enthuW4S2
enthuW4P1 ~~ enthuW4P2
'
lgmEnthu <- sem(lgmEnthu, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, : lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
## but not all elements of the gradient are (near) zero;
## the optimizer may not have found a local solution
## use check.gradient = FALSE to skip this check.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmEnthu, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 246 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 244.983
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2328.338
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.929
## Tucker-Lewis Index (TLI) 0.905
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1513.362
## Loglikelihood unrestricted model (H1) -1390.870
##
## Akaike (AIC) 3152.724
## Bayesian (BIC) 3376.804
## Sample-size adjusted Bayesian (BIC) 3177.071
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.082
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.095
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.146
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## enthu1 =~
## entW1S1 1.000 0.380 0.590
## entW1S2 (a) 1.194 0.141 8.494 0.000 0.454 0.617
## entW1P1 (peer) 1.461 0.252 5.795 0.000 0.555 0.813
## entW1P2 (aa) 1.244 0.216 5.754 0.000 0.473 0.762
## enthu2 =~
## entW2S1 1.000 0.347 0.552
## entW2S2 (a) 1.194 0.141 8.494 0.000 0.414 0.575
## entW2P1 (peer) 1.461 0.252 5.795 0.000 0.506 0.776
## entW2P2 (aa) 1.244 0.216 5.754 0.000 0.431 0.741
## enthu3 =~
## entW3S1 1.000 0.325 0.532
## entW3S2 (a) 1.194 0.141 8.494 0.000 0.387 0.539
## entW3P1 (peer) 1.461 0.252 5.795 0.000 0.474 0.776
## entW3P2 (aa) 1.244 0.216 5.754 0.000 0.404 0.717
## enthu4 =~
## entW4S1 1.000 0.315 0.523
## entW4S2 (a) 1.194 0.141 8.494 0.000 0.376 0.524
## entW4P1 (peer) 1.461 0.252 5.795 0.000 0.460 0.747
## entW4P2 (aa) 1.244 0.216 5.754 0.000 0.392 0.700
## interc =~
## enthu1 1.000 1.020 1.020
## enthu2 1.000 1.118 1.118
## enthu3 1.000 1.194 1.194
## enthu4 1.000 1.231 1.231
## slope =~
## enthu1 0.000 0.000 0.000
## enthu2 6.000 0.156 0.156
## enthu3 13.000 0.362 0.362
## enthu4 19.000 0.545 0.545
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.002 0.001 -2.625 0.009 -0.509 -0.509
## .enthuW1S1 ~~
## .enthuW2S1 0.156 0.028 5.572 0.000 0.156 0.573
## .enthuW3S1 0.142 0.028 5.140 0.000 0.142 0.529
## .enthuW4S1 0.128 0.027 4.722 0.000 0.128 0.479
## .enthuW2S1 ~~
## .enthuW3S1 0.146 0.032 4.580 0.000 0.146 0.541
## .enthuW4S1 0.162 0.032 5.025 0.000 0.162 0.604
## .enthuW3S1 ~~
## .enthuW4S1 0.157 0.034 4.615 0.000 0.157 0.589
## .enthuW1S2 ~~
## .enthuW2S2 0.173 0.035 4.875 0.000 0.173 0.507
## .enthuW3S2 0.149 0.036 4.192 0.000 0.149 0.426
## .enthuW4S2 0.166 0.036 4.648 0.000 0.166 0.468
## .enthuW2S2 ~~
## .enthuW3S2 0.223 0.042 5.256 0.000 0.223 0.625
## .enthuW4S2 0.232 0.042 5.513 0.000 0.232 0.646
## .enthuW3S2 ~~
## .enthuW4S2 0.239 0.043 5.519 0.000 0.239 0.645
## .enthuW1P1 ~~
## .enthuW2P1 0.046 0.022 2.080 0.038 0.046 0.281
## .enthuW3P1 0.036 0.022 1.658 0.097 0.036 0.233
## .enthuW4P1 0.043 0.021 2.074 0.038 0.043 0.267
## .enthuW2P1 ~~
## .enthuW3P1 0.051 0.022 2.324 0.020 0.051 0.323
## .enthuW4P1 0.030 0.025 1.196 0.232 0.030 0.176
## .enthuW3P1 ~~
## .enthuW4P1 0.040 0.023 1.755 0.079 0.040 0.256
## .enthuW1P2 ~~
## .enthuW2P2 0.054 0.017 3.186 0.001 0.054 0.343
## .enthuW3P2 0.067 0.017 3.991 0.000 0.067 0.427
## .enthuW4P2 0.037 0.018 2.034 0.042 0.037 0.233
## .enthuW2P2 ~~
## .enthuW3P2 0.046 0.018 2.479 0.013 0.046 0.297
## .enthuW4P2 0.025 0.018 1.352 0.176 0.025 0.160
## .enthuW3P2 ~~
## .enthuW4P2 0.044 0.020 2.254 0.024 0.044 0.280
## .enthuW1S1 ~~
## .enthuW1S2 0.088 0.018 4.862 0.000 0.088 0.292
## .enthuW1P1 ~~
## .enthuW1P2 0.053 0.022 2.367 0.018 0.053 0.332
## .enthuW2S1 ~~
## .enthuW2S2 0.044 0.017 2.658 0.008 0.044 0.144
## .enthuW2P1 ~~
## .enthuW2P2 0.060 0.021 2.853 0.004 0.060 0.374
## .enthuW3S1 ~~
## .enthuW3S2 0.055 0.019 2.824 0.005 0.055 0.175
## .enthuW3P1 ~~
## .enthuW3P2 0.060 0.023 2.537 0.011 0.060 0.394
## .enthuW4S1 ~~
## .enthuW4S2 0.064 0.019 3.366 0.001 0.064 0.205
## .enthuW4P1 ~~
## .enthuW4P2 0.074 0.030 2.511 0.012 0.074 0.454
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.891 0.038 103.250 0.000 10.042 10.042
## slope -0.000 0.001 -0.257 0.797 -0.034 -0.034
## .enthuW1S1 0.000 0.000 0.000
## .enthuW2S1 0.000 0.000 0.000
## .enthuW3S1 0.000 0.000 0.000
## .enthuW4S1 0.000 0.000 0.000
## .enthuW1S2 (b) -1.125 0.549 -2.047 0.041 -1.125 -1.528
## .enthuW2S2 (b) -1.125 0.549 -2.047 0.041 -1.125 -1.563
## .enthuW3S2 (b) -1.125 0.549 -2.047 0.041 -1.125 -1.564
## .enthuW4S2 (b) -1.125 0.549 -2.047 0.041 -1.125 -1.569
## .enthuW1P1 (c) -1.977 0.984 -2.009 0.045 -1.977 -2.896
## .enthuW2P1 (c) -1.977 0.984 -2.009 0.045 -1.977 -3.029
## .enthuW3P1 (c) -1.977 0.984 -2.009 0.045 -1.977 -3.237
## .enthuW4P1 (c) -1.977 0.984 -2.009 0.045 -1.977 -3.214
## .enthuW1P2 (d) -0.880 0.844 -1.044 0.297 -0.880 -1.419
## .enthuW2P2 (d) -0.880 0.844 -1.044 0.297 -0.880 -1.513
## .enthuW3P2 (d) -0.880 0.844 -1.044 0.297 -0.880 -1.563
## .enthuW4P2 (d) -0.880 0.844 -1.044 0.297 -0.880 -1.574
## .enthu1 0.000 0.000 0.000
## .enthu2 0.000 0.000 0.000
## .enthu3 0.000 0.000 0.000
## .enthu4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .enthuW1S1 0.270 0.027 9.927 0.000 0.270 0.651
## .enthuW1S2 0.336 0.038 8.874 0.000 0.336 0.620
## .enthuW1P1 0.158 0.036 4.346 0.000 0.158 0.339
## .enthuW1P2 0.161 0.027 5.948 0.000 0.161 0.419
## .enthuW2S1 0.274 0.033 8.414 0.000 0.274 0.695
## .enthuW2S2 0.347 0.042 8.242 0.000 0.347 0.669
## .enthuW2P1 0.170 0.033 5.201 0.000 0.170 0.398
## .enthuW2P2 0.153 0.027 5.688 0.000 0.153 0.451
## .enthuW3S1 0.267 0.033 8.059 0.000 0.267 0.717
## .enthuW3S2 0.367 0.042 8.712 0.000 0.367 0.710
## .enthuW3P1 0.148 0.036 4.107 0.000 0.148 0.397
## .enthuW3P2 0.154 0.027 5.765 0.000 0.154 0.486
## .enthuW4S1 0.264 0.035 7.501 0.000 0.264 0.727
## .enthuW4S2 0.373 0.046 8.038 0.000 0.373 0.725
## .enthuW4P1 0.167 0.037 4.547 0.000 0.167 0.441
## .enthuW4P2 0.160 0.036 4.493 0.000 0.160 0.510
## .enthu1 -0.006 0.009 -0.623 0.533 -0.040 -0.040
## .enthu2 -0.012 0.008 -1.362 0.173 -0.096 -0.096
## .enthu3 -0.012 0.011 -1.162 0.245 -0.117 -0.117
## .enthu4 -0.013 0.010 -1.276 0.202 -0.130 -0.130
## interc 0.150 0.039 3.878 0.000 1.000 1.000
## slope 0.000 0.000 1.994 0.046 1.000 1.000semPaths(lgmEnthu, what = "col", whatLabels = "est", intercepts = T)
LGM Industriousness
lgmIndus <- '
# factor at each time point with same loading
indus1 =~ indusW1S1 + a * indusW1S2 +
peer * indusW1P1 + aa * indusW1P2
indus2 =~ indusW2S1 + a * indusW2S2 +
peer * indusW2P1 + aa * indusW2P2
indus3 =~ indusW3S1 + a * indusW3S2 +
peer * indusW3P1 + aa * indusW3P2
indus4 =~ indusW4S1 + a * indusW4S2 +
peer * indusW4P1 + aa * indusW4P2
# second order factor for intercept and slope
interc =~ 1*indus1 + 1*indus2 + 1*indus3 + 1*indus4
slope =~ 0*indus1 + 6*indus2 + 13*indus3 + 19*indus4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
indusW1S1 ~ 0*1
indusW2S1 ~ 0*1
indusW3S1 ~ 0*1
indusW4S1 ~ 0*1
# fix equal intercepts
indusW1S2 ~ b*1
indusW2S2 ~ b*1
indusW3S2 ~ b*1
indusW4S2 ~ b*1
indusW1P1 ~ c*1
indusW2P1 ~ c*1
indusW3P1 ~ c*1
indusW4P1 ~ c*1
indusW1P2 ~ d*1
indusW2P2 ~ d*1
indusW3P2 ~ d*1
indusW4P2 ~ d*1
# error covariance - similar parcels across waves
indusW1S1 ~~ indusW2S1 + indusW3S1 + indusW4S1
indusW2S1 ~~ indusW3S1 + indusW4S1
indusW3S1 ~~ indusW4S1
indusW1S2 ~~ indusW2S2 + indusW3S2 + indusW4S2
indusW2S2 ~~ indusW3S2 + indusW4S2
indusW3S2 ~~ indusW4S2
indusW1P1 ~~ indusW2P1 + indusW3P1 + indusW4P1
indusW2P1 ~~ indusW3P1 + indusW4P1
indusW3P1 ~~ indusW4P1
indusW1P2 ~~ indusW2P2 + indusW3P2 + indusW4P2
indusW2P2 ~~ indusW3P2 + indusW4P2
indusW3P2 ~~ indusW4P2
# error covariance - same method at one wave
indusW1S1 ~~ indusW1S2
indusW1P1 ~~ indusW1P2
indusW2S1 ~~ indusW2S2
indusW2P1 ~~ indusW2P2
indusW3S1 ~~ indusW3S2
indusW3P1 ~~ indusW3P2
indusW4S1 ~~ indusW4S2
indusW4P1 ~~ indusW4P2
'
lgmIndus <- sem(lgmIndus, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmIndus, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 197 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 245.959
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1960.526
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.915
## Tucker-Lewis Index (TLI) 0.885
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1638.292
## Loglikelihood unrestricted model (H1) -1515.313
##
## Akaike (AIC) 3402.585
## Bayesian (BIC) 3626.665
## Sample-size adjusted Bayesian (BIC) 3426.932
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.083
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.095
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.173
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indus1 =~
## indW1S1 1.000 0.425 0.707
## indW1S2 (a) 1.393 0.125 11.148 0.000 0.592 0.790
## indW1P1 (peer) 0.695 0.121 5.766 0.000 0.295 0.465
## indW1P2 (aa) 0.515 0.140 3.676 0.000 0.219 0.342
## indus2 =~
## indW2S1 1.000 0.420 0.719
## indW2S2 (a) 1.393 0.125 11.148 0.000 0.585 0.826
## indW2P1 (peer) 0.695 0.121 5.766 0.000 0.292 0.491
## indW2P2 (aa) 0.515 0.140 3.676 0.000 0.216 0.353
## indus3 =~
## indW3S1 1.000 0.420 0.683
## indW3S2 (a) 1.393 0.125 11.148 0.000 0.585 0.801
## indW3P1 (peer) 0.695 0.121 5.766 0.000 0.292 0.469
## indW3P2 (aa) 0.515 0.140 3.676 0.000 0.217 0.370
## indus4 =~
## indW4S1 1.000 0.423 0.742
## indW4S2 (a) 1.393 0.125 11.148 0.000 0.589 0.834
## indW4P1 (peer) 0.695 0.121 5.766 0.000 0.294 0.444
## indW4P2 (aa) 0.515 0.140 3.676 0.000 0.218 0.376
## interc =~
## indus1 1.000 0.937 0.937
## indus2 1.000 0.948 0.948
## indus3 1.000 0.947 0.947
## indus4 1.000 0.941 0.941
## slope =~
## indus1 0.000 0.000 0.000
## indus2 6.000 0.089 0.089
## indus3 13.000 0.193 0.193
## indus4 19.000 0.280 0.280
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.000 0.001 0.582 0.561 0.193 0.193
## .indusW1S1 ~~
## .indusW2S1 0.079 0.017 4.570 0.000 0.079 0.456
## .indusW3S1 0.075 0.018 4.176 0.000 0.075 0.396
## .indusW4S1 0.057 0.017 3.340 0.001 0.057 0.352
## .indusW2S1 ~~
## .indusW3S1 0.081 0.019 4.221 0.000 0.081 0.446
## .indusW4S1 0.057 0.018 3.163 0.002 0.057 0.367
## .indusW3S1 ~~
## .indusW4S1 0.089 0.020 4.530 0.000 0.089 0.519
## .indusW1S2 ~~
## .indusW2S2 0.072 0.032 2.279 0.023 0.072 0.392
## .indusW3S2 0.045 0.031 1.463 0.143 0.045 0.224
## .indusW4S2 0.024 0.032 0.745 0.456 0.024 0.131
## .indusW2S2 ~~
## .indusW3S2 0.051 0.032 1.589 0.112 0.051 0.294
## .indusW4S2 0.017 0.033 0.528 0.597 0.017 0.112
## .indusW3S2 ~~
## .indusW4S2 0.051 0.036 1.425 0.154 0.051 0.298
## .indusW1P1 ~~
## .indusW2P1 0.141 0.032 4.353 0.000 0.141 0.485
## .indusW3P1 0.133 0.034 3.902 0.000 0.133 0.430
## .indusW4P1 0.175 0.038 4.631 0.000 0.175 0.525
## .indusW2P1 ~~
## .indusW3P1 0.178 0.037 4.767 0.000 0.178 0.627
## .indusW4P1 0.186 0.039 4.838 0.000 0.186 0.606
## .indusW3P1 ~~
## .indusW4P1 0.233 0.044 5.296 0.000 0.233 0.713
## .indusW1P2 ~~
## .indusW2P2 0.167 0.037 4.516 0.000 0.167 0.484
## .indusW3P2 0.163 0.036 4.480 0.000 0.163 0.499
## .indusW4P2 0.162 0.042 3.851 0.000 0.162 0.501
## .indusW2P2 ~~
## .indusW3P2 0.178 0.041 4.326 0.000 0.178 0.569
## .indusW4P2 0.194 0.040 4.858 0.000 0.194 0.629
## .indusW3P2 ~~
## .indusW4P2 0.189 0.039 4.815 0.000 0.189 0.646
## .indusW1S1 ~~
## .indusW1S2 0.042 0.034 1.235 0.217 0.042 0.216
## .indusW1P1 ~~
## .indusW1P2 0.118 0.026 4.538 0.000 0.118 0.350
## .indusW2S1 ~~
## .indusW2S2 0.008 0.026 0.299 0.765 0.008 0.049
## .indusW2P1 ~~
## .indusW2P2 0.070 0.022 3.205 0.001 0.070 0.235
## .indusW3S1 ~~
## .indusW3S2 0.020 0.025 0.792 0.428 0.020 0.100
## .indusW3P1 ~~
## .indusW3P2 0.073 0.024 3.069 0.002 0.073 0.243
## .indusW4S1 ~~
## .indusW4S2 0.026 0.026 1.022 0.307 0.026 0.175
## .indusW4P1 ~~
## .indusW4P2 0.031 0.023 1.356 0.175 0.031 0.098
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.325 0.035 96.267 0.000 8.358 8.358
## slope 0.000 0.001 0.092 0.927 0.020 0.020
## .indusW1S1 0.000 0.000 0.000
## .indusW2S1 0.000 0.000 0.000
## .indusW3S1 0.000 0.000 0.000
## .indusW4S1 0.000 0.000 0.000
## .indusW1S2 (b) -1.615 0.418 -3.868 0.000 -1.615 -2.157
## .indusW2S2 (b) -1.615 0.418 -3.868 0.000 -1.615 -2.281
## .indusW3S2 (b) -1.615 0.418 -3.868 0.000 -1.615 -2.211
## .indusW4S2 (b) -1.615 0.418 -3.868 0.000 -1.615 -2.287
## .indusW1P1 (c) 1.432 0.402 3.566 0.000 1.432 2.256
## .indusW2P1 (c) 1.432 0.402 3.566 0.000 1.432 2.410
## .indusW3P1 (c) 1.432 0.402 3.566 0.000 1.432 2.303
## .indusW4P1 (c) 1.432 0.402 3.566 0.000 1.432 2.162
## .indusW1P2 (d) 1.888 0.466 4.052 0.000 1.888 2.953
## .indusW2P2 (d) 1.888 0.466 4.052 0.000 1.888 3.081
## .indusW3P2 (d) 1.888 0.466 4.052 0.000 1.888 3.223
## .indusW4P2 (d) 1.888 0.466 4.052 0.000 1.888 3.255
## .indus1 0.000 0.000 0.000
## .indus2 0.000 0.000 0.000
## .indus3 0.000 0.000 0.000
## .indus4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S1 0.180 0.031 5.761 0.000 0.180 0.500
## .indusW1S2 0.211 0.060 3.540 0.000 0.211 0.376
## .indusW1P1 0.316 0.038 8.253 0.000 0.316 0.784
## .indusW1P2 0.361 0.042 8.623 0.000 0.361 0.883
## .indusW2S1 0.165 0.027 6.073 0.000 0.165 0.483
## .indusW2S2 0.160 0.054 2.956 0.003 0.160 0.318
## .indusW2P1 0.268 0.034 7.839 0.000 0.268 0.759
## .indusW2P2 0.329 0.046 7.199 0.000 0.329 0.875
## .indusW3S1 0.202 0.030 6.666 0.000 0.202 0.533
## .indusW3S2 0.191 0.048 3.952 0.000 0.191 0.358
## .indusW3P1 0.302 0.041 7.431 0.000 0.302 0.780
## .indusW3P2 0.296 0.038 7.743 0.000 0.296 0.863
## .indusW4S1 0.146 0.027 5.486 0.000 0.146 0.450
## .indusW4S2 0.152 0.050 3.019 0.003 0.152 0.304
## .indusW4P1 0.353 0.051 6.952 0.000 0.353 0.803
## .indusW4P2 0.289 0.041 6.961 0.000 0.289 0.859
## .indus1 0.022 0.023 0.944 0.345 0.122 0.122
## .indus2 0.011 0.018 0.603 0.546 0.061 0.061
## .indus3 -0.001 0.016 -0.044 0.965 -0.004 -0.004
## .indus4 -0.012 0.018 -0.647 0.518 -0.065 -0.065
## interc 0.158 0.027 5.806 0.000 1.000 1.000
## slope 0.000 0.000 0.617 0.537 1.000 1.000semPaths(lgmIndus, what = "col", whatLabels = "est", intercepts = T)
LGM Intellect
lgmIntel <- '
# factor at each time point with same loading
intel1 =~ intelW1S1 + a * intelW1S2 +
peer * intelW1P1 + aa * intelW1P2
intel2 =~ intelW2S1 + a * intelW2S2 +
peer * intelW2P1 + aa * intelW2P2
intel3 =~ intelW3S1 + a * intelW3S2 +
peer * intelW3P1 + aa * intelW3P2
intel4 =~ intelW4S1 + a * intelW4S2 +
peer * intelW4P1 + aa * intelW4P2
# second order factor for intercept and slope
interc =~ 1*intel1 + 1*intel2 + 1*intel3 + 1*intel4
slope =~ 0*intel1 + 6*intel2 + 13*intel3 + 19*intel4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
intelW1S1 ~ 0*1
intelW2S1 ~ 0*1
intelW3S1 ~ 0*1
intelW4S1 ~ 0*1
# fix equal intercepts
intelW1S2 ~ b*1
intelW2S2 ~ b*1
intelW3S2 ~ b*1
intelW4S2 ~ b*1
intelW1P1 ~ c*1
intelW2P1 ~ c*1
intelW3P1 ~ c*1
intelW4P1 ~ c*1
intelW1P2 ~ d*1
intelW2P2 ~ d*1
intelW3P2 ~ d*1
intelW4P2 ~ d*1
# error covariance - similar parcels across waves
intelW1S1 ~~ intelW2S1 + intelW3S1 + intelW4S1
intelW2S1 ~~ intelW3S1 + intelW4S1
intelW3S1 ~~ intelW4S1
intelW1S2 ~~ intelW2S2 + intelW3S2 + intelW4S2
intelW2S2 ~~ intelW3S2 + intelW4S2
intelW3S2 ~~ intelW4S2
intelW1P1 ~~ intelW2P1 + intelW3P1 + intelW4P1
intelW2P1 ~~ intelW3P1 + intelW4P1
intelW3P1 ~~ intelW4P1
intelW1P2 ~~ intelW2P2 + intelW3P2 + intelW4P2
intelW2P2 ~~ intelW3P2 + intelW4P2
intelW3P2 ~~ intelW4P2
# error covariance - same method at one wave
intelW1S1 ~~ intelW1S2
intelW1P1 ~~ intelW1P2
intelW2S1 ~~ intelW2S2
intelW2P1 ~~ intelW2P2
intelW3S1 ~~ intelW3S2
intelW3P1 ~~ intelW3P2
intelW4S1 ~~ intelW4S2
intelW4P1 ~~ intelW4P2
'
lgmIntel <- sem(lgmIntel, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmIntel, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 186 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 238.564
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2029.552
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.922
## Tucker-Lewis Index (TLI) 0.894
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1330.312
## Loglikelihood unrestricted model (H1) -1211.030
##
## Akaike (AIC) 2786.624
## Bayesian (BIC) 3010.704
## Sample-size adjusted Bayesian (BIC) 2810.971
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.081
## 90 Percent confidence interval - lower 0.068
## 90 Percent confidence interval - upper 0.093
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.172
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intel1 =~
## intW1S1 1.000 0.437 0.757
## intW1S2 (a) 1.096 0.108 10.165 0.000 0.479 0.747
## intW1P1 (peer) 0.576 0.110 5.227 0.000 0.252 0.457
## intW1P2 (aa) 0.416 0.108 3.856 0.000 0.182 0.332
## intel2 =~
## intW2S1 1.000 0.407 0.721
## intW2S2 (a) 1.096 0.108 10.165 0.000 0.446 0.708
## intW2P1 (peer) 0.576 0.110 5.227 0.000 0.234 0.425
## intW2P2 (aa) 0.416 0.108 3.856 0.000 0.169 0.356
## intel3 =~
## intW3S1 1.000 0.406 0.700
## intW3S2 (a) 1.096 0.108 10.165 0.000 0.446 0.718
## intW3P1 (peer) 0.576 0.110 5.227 0.000 0.234 0.393
## intW3P2 (aa) 0.416 0.108 3.856 0.000 0.169 0.292
## intel4 =~
## intW4S1 1.000 0.365 0.646
## intW4S2 (a) 1.096 0.108 10.165 0.000 0.400 0.680
## intW4P1 (peer) 0.576 0.110 5.227 0.000 0.210 0.328
## intW4P2 (aa) 0.416 0.108 3.856 0.000 0.152 0.297
## interc =~
## intel1 1.000 0.967 0.967
## intel2 1.000 1.039 1.039
## intel3 1.000 1.040 1.040
## intel4 1.000 1.158 1.158
## slope =~
## intel1 0.000 0.000 0.000
## intel2 6.000 0.014 0.014
## intel3 13.000 0.030 0.030
## intel4 19.000 0.049 0.049
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.000 0.001 -0.050 0.960 -0.103 -0.103
## .intelW1S1 ~~
## .intelW2S1 0.061 0.019 3.251 0.001 0.061 0.411
## .intelW3S1 0.060 0.019 3.088 0.002 0.060 0.382
## .intelW4S1 0.041 0.018 2.244 0.025 0.041 0.255
## .intelW2S1 ~~
## .intelW3S1 0.072 0.020 3.514 0.000 0.072 0.442
## .intelW4S1 0.043 0.019 2.244 0.025 0.043 0.257
## .intelW3S1 ~~
## .intelW4S1 0.053 0.021 2.585 0.010 0.053 0.297
## .intelW1S2 ~~
## .intelW2S2 0.069 0.023 3.050 0.002 0.069 0.362
## .intelW3S2 0.049 0.023 2.176 0.030 0.049 0.267
## .intelW4S2 0.051 0.022 2.322 0.020 0.051 0.278
## .intelW2S2 ~~
## .intelW3S2 0.087 0.025 3.482 0.000 0.087 0.453
## .intelW4S2 0.075 0.024 3.143 0.002 0.075 0.391
## .intelW3S2 ~~
## .intelW4S2 0.062 0.024 2.599 0.009 0.062 0.335
## .intelW1P1 ~~
## .intelW2P1 0.132 0.029 4.522 0.000 0.132 0.541
## .intelW3P1 0.187 0.032 5.807 0.000 0.187 0.699
## .intelW4P1 0.150 0.031 4.784 0.000 0.150 0.505
## .intelW2P1 ~~
## .intelW3P1 0.189 0.035 5.398 0.000 0.189 0.692
## .intelW4P1 0.153 0.036 4.275 0.000 0.153 0.507
## .intelW3P1 ~~
## .intelW4P1 0.172 0.041 4.192 0.000 0.172 0.520
## .intelW1P2 ~~
## .intelW2P2 0.166 0.025 6.529 0.000 0.166 0.720
## .intelW3P2 0.181 0.033 5.445 0.000 0.181 0.634
## .intelW4P2 0.135 0.028 4.798 0.000 0.135 0.535
## .intelW2P2 ~~
## .intelW3P2 0.158 0.030 5.325 0.000 0.158 0.639
## .intelW4P2 0.126 0.024 5.266 0.000 0.126 0.579
## .intelW3P2 ~~
## .intelW4P2 0.155 0.034 4.620 0.000 0.155 0.576
## .intelW1S1 ~~
## .intelW1S2 0.035 0.023 1.497 0.135 0.035 0.217
## .intelW1P1 ~~
## .intelW1P2 0.058 0.014 4.250 0.000 0.058 0.228
## .intelW2S1 ~~
## .intelW2S2 0.043 0.019 2.345 0.019 0.043 0.250
## .intelW2P1 ~~
## .intelW2P2 0.030 0.013 2.206 0.027 0.030 0.133
## .intelW3S1 ~~
## .intelW3S2 0.028 0.021 1.311 0.190 0.028 0.154
## .intelW3P1 ~~
## .intelW3P2 0.029 0.019 1.522 0.128 0.029 0.096
## .intelW4S1 ~~
## .intelW4S2 0.070 0.033 2.145 0.032 0.070 0.377
## .intelW4P1 ~~
## .intelW4P2 0.123 0.029 4.232 0.000 0.123 0.417
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.686 0.034 107.977 0.000 8.723 8.723
## slope -0.001 0.001 -0.475 0.635 -0.650 -0.650
## .intelW1S1 0.000 0.000 0.000
## .intelW2S1 0.000 0.000 0.000
## .intelW3S1 0.000 0.000 0.000
## .intelW4S1 0.000 0.000 0.000
## .intelW1S2 (b) -0.414 0.398 -1.042 0.298 -0.414 -0.647
## .intelW2S2 (b) -0.414 0.398 -1.042 0.298 -0.414 -0.658
## .intelW3S2 (b) -0.414 0.398 -1.042 0.298 -0.414 -0.668
## .intelW4S2 (b) -0.414 0.398 -1.042 0.298 -0.414 -0.705
## .intelW1P1 (c) 1.841 0.404 4.552 0.000 1.841 3.346
## .intelW2P1 (c) 1.841 0.404 4.552 0.000 1.841 3.336
## .intelW3P1 (c) 1.841 0.404 4.552 0.000 1.841 3.092
## .intelW4P1 (c) 1.841 0.404 4.552 0.000 1.841 2.873
## .intelW1P2 (d) 2.461 0.398 6.185 0.000 2.461 4.491
## .intelW2P2 (d) 2.461 0.398 6.185 0.000 2.461 5.168
## .intelW3P2 (d) 2.461 0.398 6.185 0.000 2.461 4.252
## .intelW4P2 (d) 2.461 0.398 6.185 0.000 2.461 4.820
## .intel1 0.000 0.000 0.000
## .intel2 0.000 0.000 0.000
## .intel3 0.000 0.000 0.000
## .intel4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S1 0.142 0.030 4.739 0.000 0.142 0.427
## .intelW1S2 0.181 0.034 5.281 0.000 0.181 0.442
## .intelW1P1 0.239 0.030 7.890 0.000 0.239 0.791
## .intelW1P2 0.267 0.032 8.383 0.000 0.267 0.890
## .intelW2S1 0.153 0.025 6.206 0.000 0.153 0.480
## .intelW2S2 0.198 0.034 5.781 0.000 0.198 0.499
## .intelW2P1 0.250 0.033 7.452 0.000 0.250 0.820
## .intelW2P2 0.198 0.025 7.998 0.000 0.198 0.874
## .intelW3S1 0.172 0.031 5.587 0.000 0.172 0.510
## .intelW3S2 0.186 0.032 5.804 0.000 0.186 0.484
## .intelW3P1 0.300 0.040 7.412 0.000 0.300 0.845
## .intelW3P2 0.306 0.041 7.444 0.000 0.306 0.915
## .intelW4S1 0.186 0.037 5.033 0.000 0.186 0.583
## .intelW4S2 0.186 0.039 4.770 0.000 0.186 0.537
## .intelW4P1 0.367 0.048 7.683 0.000 0.367 0.892
## .intelW4P2 0.238 0.030 8.005 0.000 0.238 0.912
## .intel1 0.012 0.019 0.634 0.526 0.065 0.065
## .intel2 -0.013 0.015 -0.862 0.388 -0.076 -0.076
## .intel3 -0.012 0.017 -0.754 0.451 -0.075 -0.075
## .intel4 -0.044 0.027 -1.646 0.100 -0.332 -0.332
## interc 0.179 0.031 5.770 0.000 1.000 1.000
## slope 0.000 0.000 0.017 0.987 1.000 1.000semPaths(lgmIntel, what = "col", whatLabels = "est", intercepts = T)
LGM Openness aspect
lgmOpena <- '
# factor at each time point with same loading
opena1 =~ openaW1S1 + a * openaW1S2 +
peer * openaW1P1 + aa * openaW1P2
opena2 =~ openaW2S1 + a * openaW2S2 +
peer * openaW2P1 + aa * openaW2P2
opena3 =~ openaW3S1 + a * openaW3S2 +
peer * openaW3P1 + aa * openaW3P2
opena4 =~ openaW4S1 + a * openaW4S2 +
peer * openaW4P1 + aa * openaW4P2
# second order factor for intercept and slope
interc =~ 1*opena1 + 1*opena2 + 1*opena3 + 1*opena4
slope =~ 0*opena1 + 6*opena2 + 13*opena3 + 19*opena4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
openaW1S1 ~ 0*1
openaW2S1 ~ 0*1
openaW3S1 ~ 0*1
openaW4S1 ~ 0*1
# fix equal intercepts
openaW1S2 ~ b*1
openaW2S2 ~ b*1
openaW3S2 ~ b*1
openaW4S2 ~ b*1
openaW1P1 ~ c*1
openaW2P1 ~ c*1
openaW3P1 ~ c*1
openaW4P1 ~ c*1
openaW1P2 ~ d*1
openaW2P2 ~ d*1
openaW3P2 ~ d*1
openaW4P2 ~ d*1
# error covariance - similar parcels across waves
openaW1S1 ~~ openaW2S1 + openaW3S1 + openaW4S1
openaW2S1 ~~ openaW3S1 + openaW4S1
openaW3S1 ~~ openaW4S1
openaW1S2 ~~ openaW2S2 + openaW3S2 + openaW4S2
openaW2S2 ~~ openaW3S2 + openaW4S2
openaW3S2 ~~ openaW4S2
openaW1P1 ~~ openaW2P1 + openaW3P1 + openaW4P1
openaW2P1 ~~ openaW3P1 + openaW4P1
openaW3P1 ~~ openaW4P1
openaW1P2 ~~ openaW2P2 + openaW3P2 + openaW4P2
openaW2P2 ~~ openaW3P2 + openaW4P2
openaW3P2 ~~ openaW4P2
# error covariance - same method at one wave
openaW1S1 ~~ openaW1S2
openaW1P1 ~~ openaW1P2
openaW2S1 ~~ openaW2S2
openaW2P1 ~~ openaW2P2
openaW3S1 ~~ openaW3S2
openaW3P1 ~~ openaW3P2
openaW4S1 ~~ openaW4S2
openaW4P1 ~~ openaW4P2
'
lgmOpena <- sem(lgmOpena, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmOpena, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 172 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 170.483
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2368.492
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964
## Tucker-Lewis Index (TLI) 0.951
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1152.281
## Loglikelihood unrestricted model (H1) -1067.039
##
## Akaike (AIC) 2430.561
## Bayesian (BIC) 2654.642
## Sample-size adjusted Bayesian (BIC) 2454.909
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent confidence interval - lower 0.046
## 90 Percent confidence interval - upper 0.073
## P-value RMSEA <= 0.05 0.122
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.118
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opena1 =~
## opnW1S1 1.000 0.501 0.751
## opnW1S2 (a) 0.913 0.064 14.259 0.000 0.457 0.780
## opnW1P1 (peer) 0.632 0.079 8.028 0.000 0.316 0.573
## opnW1P2 (aa) 0.505 0.078 6.505 0.000 0.253 0.514
## opena2 =~
## opnW2S1 1.000 0.506 0.770
## opnW2S2 (a) 0.913 0.064 14.259 0.000 0.462 0.781
## opnW2P1 (peer) 0.632 0.079 8.028 0.000 0.320 0.577
## opnW2P2 (aa) 0.505 0.078 6.505 0.000 0.256 0.462
## opena3 =~
## opnW3S1 1.000 0.499 0.782
## opnW3S2 (a) 0.913 0.064 14.259 0.000 0.455 0.818
## opnW3P1 (peer) 0.632 0.079 8.028 0.000 0.315 0.590
## opnW3P2 (aa) 0.505 0.078 6.505 0.000 0.252 0.487
## opena4 =~
## opnW4S1 1.000 0.502 0.732
## opnW4S2 (a) 0.913 0.064 14.259 0.000 0.458 0.759
## opnW4P1 (peer) 0.632 0.079 8.028 0.000 0.317 0.567
## opnW4P2 (aa) 0.505 0.078 6.505 0.000 0.254 0.506
## interc =~
## opena1 1.000 1.004 1.004
## opena2 1.000 0.993 0.993
## opena3 1.000 1.007 1.007
## opena4 1.000 1.001 1.001
## slope =~
## opena1 0.000 0.000 0.000
## opena2 6.000 0.079 0.079
## opena3 13.000 0.173 0.173
## opena4 19.000 0.252 0.252
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.000 0.001 0.363 0.717 0.097 0.097
## .openaW1S1 ~~
## .openaW2S1 0.108 0.019 5.606 0.000 0.108 0.586
## .openaW3S1 0.086 0.018 4.880 0.000 0.086 0.491
## .openaW4S1 0.102 0.020 5.082 0.000 0.102 0.494
## .openaW2S1 ~~
## .openaW3S1 0.086 0.018 4.711 0.000 0.086 0.518
## .openaW4S1 0.096 0.020 4.684 0.000 0.096 0.489
## .openaW3S1 ~~
## .openaW4S1 0.085 0.020 4.328 0.000 0.085 0.455
## .openaW1S2 ~~
## .openaW2S2 0.035 0.015 2.297 0.022 0.035 0.258
## .openaW3S2 0.038 0.014 2.681 0.007 0.038 0.320
## .openaW4S2 0.040 0.016 2.442 0.015 0.040 0.274
## .openaW2S2 ~~
## .openaW3S2 0.052 0.015 3.507 0.000 0.052 0.442
## .openaW4S2 0.051 0.017 2.983 0.003 0.051 0.352
## .openaW3S2 ~~
## .openaW4S2 0.051 0.016 3.204 0.001 0.051 0.409
## .openaW1P1 ~~
## .openaW2P1 0.092 0.022 4.274 0.000 0.092 0.451
## .openaW3P1 0.094 0.022 4.263 0.000 0.094 0.479
## .openaW4P1 0.022 0.027 0.813 0.416 0.022 0.105
## .openaW2P1 ~~
## .openaW3P1 0.128 0.025 5.102 0.000 0.128 0.656
## .openaW4P1 0.075 0.029 2.579 0.010 0.075 0.358
## .openaW3P1 ~~
## .openaW4P1 0.101 0.030 3.335 0.001 0.101 0.508
## .openaW1P2 ~~
## .openaW2P2 0.122 0.022 5.598 0.000 0.122 0.588
## .openaW3P2 0.089 0.022 4.030 0.000 0.089 0.469
## .openaW4P2 0.106 0.021 5.018 0.000 0.106 0.582
## .openaW2P2 ~~
## .openaW3P2 0.138 0.027 5.140 0.000 0.138 0.620
## .openaW4P2 0.126 0.027 4.719 0.000 0.126 0.594
## .openaW3P2 ~~
## .openaW4P2 0.130 0.029 4.415 0.000 0.130 0.663
## .openaW1S1 ~~
## .openaW1S2 0.040 0.017 2.430 0.015 0.040 0.250
## .openaW1P1 ~~
## .openaW1P2 0.050 0.013 3.714 0.000 0.050 0.260
## .openaW2S1 ~~
## .openaW2S2 0.036 0.016 2.213 0.027 0.036 0.234
## .openaW2P1 ~~
## .openaW2P2 0.033 0.014 2.301 0.021 0.033 0.148
## .openaW3S1 ~~
## .openaW3S2 0.031 0.013 2.384 0.017 0.031 0.241
## .openaW3P1 ~~
## .openaW3P2 0.027 0.012 2.156 0.031 0.027 0.137
## .openaW4S1 ~~
## .openaW4S2 0.062 0.023 2.693 0.007 0.062 0.338
## .openaW4P1 ~~
## .openaW4P2 0.047 0.022 2.111 0.035 0.047 0.237
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.840 0.040 96.496 0.000 7.643 7.643
## slope 0.000 0.001 0.165 0.869 0.033 0.033
## .openaW1S1 0.000 0.000 0.000
## .openaW2S1 0.000 0.000 0.000
## .openaW3S1 0.000 0.000 0.000
## .openaW4S1 0.000 0.000 0.000
## .openaW1S2 (b) 0.401 0.247 1.619 0.105 0.401 0.684
## .openaW2S2 (b) 0.401 0.247 1.619 0.105 0.401 0.678
## .openaW3S2 (b) 0.401 0.247 1.619 0.105 0.401 0.720
## .openaW4S2 (b) 0.401 0.247 1.619 0.105 0.401 0.664
## .openaW1P1 (c) 1.386 0.305 4.545 0.000 1.386 2.509
## .openaW2P1 (c) 1.386 0.305 4.545 0.000 1.386 2.501
## .openaW3P1 (c) 1.386 0.305 4.545 0.000 1.386 2.593
## .openaW4P1 (c) 1.386 0.305 4.545 0.000 1.386 2.476
## .openaW1P2 (d) 1.707 0.300 5.686 0.000 1.707 3.467
## .openaW2P2 (d) 1.707 0.300 5.686 0.000 1.707 3.082
## .openaW3P2 (d) 1.707 0.300 5.686 0.000 1.707 3.300
## .openaW4P2 (d) 1.707 0.300 5.686 0.000 1.707 3.405
## .opena1 0.000 0.000 0.000
## .opena2 0.000 0.000 0.000
## .opena3 0.000 0.000 0.000
## .opena4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .openaW1S1 0.194 0.027 7.288 0.000 0.194 0.436
## .openaW1S2 0.135 0.023 5.893 0.000 0.135 0.392
## .openaW1P1 0.205 0.026 7.919 0.000 0.205 0.672
## .openaW1P2 0.178 0.021 8.310 0.000 0.178 0.736
## .openaW2S1 0.175 0.027 6.407 0.000 0.175 0.407
## .openaW2S2 0.136 0.024 5.787 0.000 0.136 0.390
## .openaW2P1 0.205 0.028 7.249 0.000 0.205 0.667
## .openaW2P2 0.241 0.030 8.065 0.000 0.241 0.787
## .openaW3S1 0.158 0.023 6.832 0.000 0.158 0.388
## .openaW3S2 0.103 0.018 5.569 0.000 0.103 0.331
## .openaW3P1 0.186 0.027 7.002 0.000 0.186 0.652
## .openaW3P2 0.204 0.030 6.795 0.000 0.204 0.763
## .openaW4S1 0.218 0.035 6.166 0.000 0.218 0.464
## .openaW4S2 0.154 0.028 5.556 0.000 0.154 0.424
## .openaW4P1 0.213 0.033 6.491 0.000 0.213 0.679
## .openaW4P2 0.187 0.027 6.885 0.000 0.187 0.744
## .opena1 -0.002 0.017 -0.103 0.918 -0.007 -0.007
## .opena2 -0.002 0.015 -0.125 0.901 -0.008 -0.008
## .opena3 -0.019 0.012 -1.649 0.099 -0.078 -0.078
## .opena4 -0.029 0.022 -1.321 0.187 -0.115 -0.115
## interc 0.252 0.037 6.759 0.000 1.000 1.000
## slope 0.000 0.000 0.880 0.379 1.000 1.000semPaths(lgmOpena, what = "col", whatLabels = "est", intercepts = T)
LGM Orderliness
lgmOrder <- '
# factor at each time point with same loading
order1 =~ orderW1S1 + a * orderW1S2 +
peer * orderW1P1 + aa * orderW1P2
order2 =~ orderW2S1 + a * orderW2S2 +
peer * orderW2P1 + aa * orderW2P2
order3 =~ orderW3S1 + a * orderW3S2 +
peer * orderW3P1 + aa * orderW3P2
order4 =~ orderW4S1 + a * orderW4S2 +
peer * orderW4P1 + aa * orderW4P2
# second order factor for intercept and slope
interc =~ 1*order1 + 1*order2 + 1*order3 + 1*order4
slope =~ 0*order1 + 6*order2 + 13*order3 + 19*order4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
orderW1S1 ~ 0*1
orderW2S1 ~ 0*1
orderW3S1 ~ 0*1
orderW4S1 ~ 0*1
# fix equal intercepts
orderW1S2 ~ b*1
orderW2S2 ~ b*1
orderW3S2 ~ b*1
orderW4S2 ~ b*1
orderW1P1 ~ c*1
orderW2P1 ~ c*1
orderW3P1 ~ c*1
orderW4P1 ~ c*1
orderW1P2 ~ d*1
orderW2P2 ~ d*1
orderW3P2 ~ d*1
orderW4P2 ~ d*1
# error covariance - similar parcels across waves
orderW1S1 ~~ orderW2S1 + orderW3S1 + orderW4S1
orderW2S1 ~~ orderW3S1 + orderW4S1
orderW3S1 ~~ orderW4S1
orderW1S2 ~~ orderW2S2 + orderW3S2 + orderW4S2
orderW2S2 ~~ orderW3S2 + orderW4S2
orderW3S2 ~~ orderW4S2
orderW1P1 ~~ orderW2P1 + orderW3P1 + orderW4P1
orderW2P1 ~~ orderW3P1 + orderW4P1
orderW3P1 ~~ orderW4P1
orderW1P2 ~~ orderW2P2 + orderW3P2 + orderW4P2
orderW2P2 ~~ orderW3P2 + orderW4P2
orderW3P2 ~~ orderW4P2
# error covariance - same method at one wave
orderW1S1 ~~ orderW1S2
orderW1P1 ~~ orderW1P2
orderW2S1 ~~ orderW2S2
orderW2P1 ~~ orderW2P2
orderW3S1 ~~ orderW3S2
orderW3P1 ~~ orderW3P2
orderW4S1 ~~ orderW4S2
orderW4P1 ~~ orderW4P2
'
lgmOrder <- sem(lgmOrder, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmOrder, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 149 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 177.467
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2131.446
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1607.660
## Loglikelihood unrestricted model (H1) -1518.927
##
## Akaike (AIC) 3341.321
## Bayesian (BIC) 3565.401
## Sample-size adjusted Bayesian (BIC) 3365.668
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062
## 90 Percent confidence interval - lower 0.049
## 90 Percent confidence interval - upper 0.075
## P-value RMSEA <= 0.05 0.070
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.110
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## order1 =~
## ordW1S1 1.000 0.534 0.761
## ordW1S2 (a) 0.782 0.053 14.783 0.000 0.417 0.656
## ordW1P1 (peer) 0.788 0.096 8.206 0.000 0.421 0.577
## ordW1P2 (aa) 0.565 0.076 7.407 0.000 0.301 0.494
## order2 =~
## ordW2S1 1.000 0.538 0.799
## ordW2S2 (a) 0.782 0.053 14.783 0.000 0.421 0.691
## ordW2P1 (peer) 0.788 0.096 8.206 0.000 0.424 0.624
## ordW2P2 (aa) 0.565 0.076 7.407 0.000 0.304 0.558
## order3 =~
## ordW3S1 1.000 0.524 0.777
## ordW3S2 (a) 0.782 0.053 14.783 0.000 0.410 0.693
## ordW3P1 (peer) 0.788 0.096 8.206 0.000 0.413 0.545
## ordW3P2 (aa) 0.565 0.076 7.407 0.000 0.296 0.527
## order4 =~
## ordW4S1 1.000 0.587 0.822
## ordW4S2 (a) 0.782 0.053 14.783 0.000 0.459 0.732
## ordW4P1 (peer) 0.788 0.096 8.206 0.000 0.463 0.606
## ordW4P2 (aa) 0.565 0.076 7.407 0.000 0.332 0.565
## interc =~
## order1 1.000 1.013 1.013
## order2 1.000 1.005 1.005
## order3 1.000 1.031 1.031
## order4 1.000 0.920 0.920
## slope =~
## order1 0.000 0.000 0.000
## order2 6.000 0.101 0.101
## order3 13.000 0.225 0.225
## order4 19.000 0.294 0.294
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.000 0.001 0.336 0.737 0.089 0.089
## .orderW1S1 ~~
## .orderW2S1 0.001 0.022 0.049 0.961 0.001 0.006
## .orderW3S1 0.029 0.021 1.353 0.176 0.029 0.149
## .orderW4S1 0.006 0.023 0.260 0.795 0.006 0.032
## .orderW2S1 ~~
## .orderW3S1 0.032 0.023 1.379 0.168 0.032 0.183
## .orderW4S1 -0.003 0.023 -0.122 0.903 -0.003 -0.017
## .orderW3S1 ~~
## .orderW4S1 0.037 0.024 1.540 0.124 0.037 0.216
## .orderW1S2 ~~
## .orderW2S2 0.089 0.016 5.399 0.000 0.089 0.420
## .orderW3S2 0.060 0.015 3.892 0.000 0.060 0.291
## .orderW4S2 0.061 0.017 3.617 0.000 0.061 0.296
## .orderW2S2 ~~
## .orderW3S2 0.073 0.016 4.432 0.000 0.073 0.388
## .orderW4S2 0.075 0.018 4.275 0.000 0.075 0.402
## .orderW3S2 ~~
## .orderW4S2 0.081 0.016 4.917 0.000 0.081 0.444
## .orderW1P1 ~~
## .orderW2P1 0.150 0.037 4.091 0.000 0.150 0.473
## .orderW3P1 0.176 0.045 3.924 0.000 0.176 0.464
## .orderW4P1 0.182 0.042 4.353 0.000 0.182 0.504
## .orderW2P1 ~~
## .orderW3P1 0.205 0.041 5.024 0.000 0.205 0.607
## .orderW4P1 0.203 0.038 5.325 0.000 0.203 0.628
## .orderW3P1 ~~
## .orderW4P1 0.262 0.047 5.576 0.000 0.262 0.680
## .orderW1P2 ~~
## .orderW2P2 0.124 0.027 4.628 0.000 0.124 0.520
## .orderW3P2 0.123 0.029 4.239 0.000 0.123 0.485
## .orderW4P2 0.087 0.028 3.129 0.002 0.087 0.340
## .orderW2P2 ~~
## .orderW3P2 0.124 0.026 4.802 0.000 0.124 0.575
## .orderW4P2 0.102 0.025 4.015 0.000 0.102 0.467
## .orderW3P2 ~~
## .orderW4P2 0.080 0.030 2.707 0.007 0.080 0.347
## .orderW1S1 ~~
## .orderW1S2 0.088 0.023 3.767 0.000 0.088 0.404
## .orderW1P1 ~~
## .orderW1P2 0.088 0.026 3.428 0.001 0.088 0.278
## .orderW2S1 ~~
## .orderW2S2 0.010 0.016 0.604 0.546 0.010 0.055
## .orderW2P1 ~~
## .orderW2P2 0.036 0.016 2.340 0.019 0.036 0.152
## .orderW3S1 ~~
## .orderW3S2 0.043 0.017 2.554 0.011 0.043 0.240
## .orderW3P1 ~~
## .orderW3P2 0.071 0.023 3.130 0.002 0.071 0.233
## .orderW4S1 ~~
## .orderW4S2 0.056 0.030 1.861 0.063 0.056 0.320
## .orderW4P1 ~~
## .orderW4P2 0.103 0.029 3.536 0.000 0.103 0.351
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.450 0.040 85.745 0.000 6.383 6.383
## slope 0.002 0.002 0.959 0.338 0.195 0.195
## .orderW1S1 0.000 0.000 0.000
## .orderW2S1 0.000 0.000 0.000
## .orderW3S1 0.000 0.000 0.000
## .orderW4S1 0.000 0.000 0.000
## .orderW1S2 (b) 1.053 0.185 5.697 0.000 1.053 1.656
## .orderW2S2 (b) 1.053 0.185 5.697 0.000 1.053 1.730
## .orderW3S2 (b) 1.053 0.185 5.697 0.000 1.053 1.780
## .orderW4S2 (b) 1.053 0.185 5.697 0.000 1.053 1.679
## .orderW1P1 (c) 0.552 0.336 1.643 0.100 0.552 0.756
## .orderW2P1 (c) 0.552 0.336 1.643 0.100 0.552 0.811
## .orderW3P1 (c) 0.552 0.336 1.643 0.100 0.552 0.727
## .orderW4P1 (c) 0.552 0.336 1.643 0.100 0.552 0.722
## .orderW1P2 (d) 1.553 0.266 5.839 0.000 1.553 2.546
## .orderW2P2 (d) 1.553 0.266 5.839 0.000 1.553 2.853
## .orderW3P2 (d) 1.553 0.266 5.839 0.000 1.553 2.765
## .orderW4P2 (d) 1.553 0.266 5.839 0.000 1.553 2.643
## .order1 0.000 0.000 0.000
## .order2 0.000 0.000 0.000
## .order3 0.000 0.000 0.000
## .order4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .orderW1S1 0.208 0.036 5.689 0.000 0.208 0.421
## .orderW1S2 0.230 0.026 8.886 0.000 0.230 0.570
## .orderW1P1 0.355 0.044 8.141 0.000 0.355 0.667
## .orderW1P2 0.281 0.033 8.400 0.000 0.281 0.756
## .orderW2S1 0.164 0.031 5.357 0.000 0.164 0.362
## .orderW2S2 0.193 0.023 8.255 0.000 0.193 0.522
## .orderW2P1 0.282 0.039 7.326 0.000 0.282 0.611
## .orderW2P2 0.204 0.028 7.201 0.000 0.204 0.688
## .orderW3S1 0.181 0.030 6.062 0.000 0.181 0.396
## .orderW3S2 0.182 0.022 8.392 0.000 0.182 0.520
## .orderW3P1 0.404 0.052 7.767 0.000 0.404 0.703
## .orderW3P2 0.228 0.030 7.653 0.000 0.228 0.722
## .orderW4S1 0.166 0.047 3.528 0.000 0.166 0.325
## .orderW4S2 0.182 0.031 5.966 0.000 0.182 0.464
## .orderW4P1 0.369 0.051 7.276 0.000 0.369 0.632
## .orderW4P2 0.235 0.034 6.890 0.000 0.235 0.681
## .order1 -0.007 0.024 -0.298 0.765 -0.025 -0.025
## .order2 -0.011 0.015 -0.734 0.463 -0.037 -0.037
## .order3 -0.042 0.016 -2.580 0.010 -0.154 -0.154
## .order4 0.006 0.034 0.190 0.849 0.019 0.019
## interc 0.292 0.040 7.253 0.000 1.000 1.000
## slope 0.000 0.000 0.913 0.361 1.000 1.000semPaths(lgmOrder, what = "col", whatLabels = "est", intercepts = T)
LGM Politeness
lgmPolit <- '
# factor at each time point with same loading
polit1 =~ politW1S1 + a * politW1S2 +
peer * politW1P1 + aa * politW1P2
polit2 =~ politW2S1 + a * politW2S2 +
peer * politW2P1 + aa * politW2P2
polit3 =~ politW3S1 + a * politW3S2 +
peer * politW3P1 + aa * politW3P2
polit4 =~ politW4S1 + a * politW4S2 +
peer * politW4P1 + aa * politW4P2
# second polit factor for intercept and slope
interc =~ 1*polit1 + 1*polit2 + 1*polit3 + 1*polit4
slope =~ 0*polit1 + 6*polit2 + 13*polit3 + 19*polit4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
politW1S1 ~ 0*1
politW2S1 ~ 0*1
politW3S1 ~ 0*1
politW4S1 ~ 0*1
# fix equal intercepts
politW1S2 ~ b*1
politW2S2 ~ b*1
politW3S2 ~ b*1
politW4S2 ~ b*1
politW1P1 ~ c*1
politW2P1 ~ c*1
politW3P1 ~ c*1
politW4P1 ~ c*1
politW1P2 ~ d*1
politW2P2 ~ d*1
politW3P2 ~ d*1
politW4P2 ~ d*1
# error covariance - similar parcels across waves
politW1S1 ~~ politW2S1 + politW3S1 + politW4S1
politW2S1 ~~ politW3S1 + politW4S1
politW3S1 ~~ politW4S1
politW1S2 ~~ politW2S2 + politW3S2 + politW4S2
politW2S2 ~~ politW3S2 + politW4S2
politW3S2 ~~ politW4S2
politW1P1 ~~ politW2P1 + politW3P1 + politW4P1
politW2P1 ~~ politW3P1 + politW4P1
politW3P1 ~~ politW4P1
politW1P2 ~~ politW2P2 + politW3P2 + politW4P2
politW2P2 ~~ politW3P2 + politW4P2
politW3P2 ~~ politW4P2
# error covariance - same method at one wave
politW1S1 ~~ politW1S2
politW1P1 ~~ politW1P2
politW2S1 ~~ politW2S2
politW2P1 ~~ politW2P2
politW3S1 ~~ politW3S2
politW3P1 ~~ politW3P2
politW4S1 ~~ politW4S2
politW4P1 ~~ politW4P2
'
lgmPolit <- sem(lgmPolit, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmPolit, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 158 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 170.613
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1979.092
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1509.512
## Loglikelihood unrestricted model (H1) -1424.206
##
## Akaike (AIC) 3145.025
## Bayesian (BIC) 3369.105
## Sample-size adjusted Bayesian (BIC) 3169.372
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060
## 90 Percent confidence interval - lower 0.046
## 90 Percent confidence interval - upper 0.073
## P-value RMSEA <= 0.05 0.120
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.095
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## polit1 =~
## pltW1S1 1.000 0.392 0.603
## pltW1S2 (a) 0.851 0.093 9.178 0.000 0.334 0.584
## pltW1P1 (peer) 1.110 0.126 8.807 0.000 0.435 0.699
## pltW1P2 (aa) 1.273 0.142 8.980 0.000 0.500 0.786
## polit2 =~
## pltW2S1 1.000 0.391 0.587
## pltW2S2 (a) 0.851 0.093 9.178 0.000 0.333 0.581
## pltW2P1 (peer) 1.110 0.126 8.807 0.000 0.434 0.707
## pltW2P2 (aa) 1.273 0.142 8.980 0.000 0.498 0.774
## polit3 =~
## pltW3S1 1.000 0.411 0.622
## pltW3S2 (a) 0.851 0.093 9.178 0.000 0.350 0.580
## pltW3P1 (peer) 1.110 0.126 8.807 0.000 0.456 0.697
## pltW3P2 (aa) 1.273 0.142 8.980 0.000 0.524 0.783
## polit4 =~
## pltW4S1 1.000 0.441 0.702
## pltW4S2 (a) 0.851 0.093 9.178 0.000 0.375 0.593
## pltW4P1 (peer) 1.110 0.126 8.807 0.000 0.489 0.792
## pltW4P2 (aa) 1.273 0.142 8.980 0.000 0.561 0.758
## interc =~
## polit1 1.000 0.980 0.980
## polit2 1.000 0.983 0.983
## polit3 1.000 0.935 0.935
## polit4 1.000 0.873 0.873
## slope =~
## polit1 0.000 0.000 0.000
## polit2 6.000 0.172 0.172
## polit3 13.000 0.354 0.354
## polit4 19.000 0.484 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope 0.000 0.001 0.492 0.623 0.086 0.086
## .politW1S1 ~~
## .politW2S1 0.189 0.027 7.088 0.000 0.189 0.676
## .politW3S1 0.160 0.025 6.338 0.000 0.160 0.594
## .politW4S1 0.137 0.024 5.779 0.000 0.137 0.590
## .politW2S1 ~~
## .politW3S1 0.194 0.029 6.661 0.000 0.194 0.692
## .politW4S1 0.144 0.027 5.340 0.000 0.144 0.596
## .politW3S1 ~~
## .politW4S1 0.142 0.028 5.102 0.000 0.142 0.614
## .politW1S2 ~~
## .politW2S2 0.089 0.020 4.405 0.000 0.089 0.409
## .politW3S2 0.102 0.020 5.029 0.000 0.102 0.449
## .politW4S2 0.093 0.023 4.051 0.000 0.093 0.392
## .politW2S2 ~~
## .politW3S2 0.127 0.023 5.525 0.000 0.127 0.554
## .politW4S2 0.131 0.025 5.236 0.000 0.131 0.550
## .politW3S2 ~~
## .politW4S2 0.158 0.028 5.743 0.000 0.158 0.633
## .politW1P1 ~~
## .politW2P1 0.077 0.019 4.071 0.000 0.077 0.399
## .politW3P1 0.064 0.018 3.545 0.000 0.064 0.306
## .politW4P1 0.069 0.021 3.326 0.001 0.069 0.408
## .politW2P1 ~~
## .politW3P1 0.051 0.018 2.816 0.005 0.051 0.250
## .politW4P1 0.063 0.022 2.843 0.004 0.063 0.387
## .politW3P1 ~~
## .politW4P1 0.041 0.020 2.003 0.045 0.041 0.230
## .politW1P2 ~~
## .politW2P2 0.005 0.021 0.254 0.799 0.005 0.033
## .politW3P2 -0.005 0.018 -0.282 0.778 -0.005 -0.031
## .politW4P2 -0.004 0.032 -0.135 0.893 -0.004 -0.023
## .politW2P2 ~~
## .politW3P2 0.032 0.020 1.594 0.111 0.032 0.189
## .politW4P2 0.049 0.027 1.829 0.067 0.049 0.247
## .politW3P2 ~~
## .politW4P2 0.075 0.026 2.906 0.004 0.075 0.374
## .politW1S1 ~~
## .politW1S2 0.043 0.014 3.064 0.002 0.043 0.178
## .politW1P1 ~~
## .politW1P2 0.069 0.023 3.007 0.003 0.069 0.392
## .politW2S1 ~~
## .politW2S2 0.023 0.013 1.832 0.067 0.023 0.091
## .politW2P1 ~~
## .politW2P2 0.043 0.022 1.966 0.049 0.043 0.241
## .politW3S1 ~~
## .politW3S2 0.034 0.013 2.563 0.010 0.034 0.135
## .politW3P1 ~~
## .politW3P2 0.098 0.027 3.702 0.000 0.098 0.502
## .politW4S1 ~~
## .politW4S2 0.008 0.016 0.505 0.613 0.008 0.036
## .politW4P1 ~~
## .politW4P2 0.041 0.029 1.416 0.157 0.041 0.225
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.829 0.039 98.639 0.000 9.955 9.955
## slope 0.001 0.001 0.491 0.623 0.062 0.062
## .politW1S1 0.000 0.000 0.000
## .politW2S1 0.000 0.000 0.000
## .politW3S1 0.000 0.000 0.000
## .politW4S1 0.000 0.000 0.000
## .politW1S2 (b) 0.362 0.357 1.013 0.311 0.362 0.633
## .politW2S2 (b) 0.362 0.357 1.013 0.311 0.362 0.631
## .politW3S2 (b) 0.362 0.357 1.013 0.311 0.362 0.599
## .politW4S2 (b) 0.362 0.357 1.013 0.311 0.362 0.572
## .politW1P1 (c) -0.638 0.486 -1.312 0.189 -0.638 -1.024
## .politW2P1 (c) -0.638 0.486 -1.312 0.189 -0.638 -1.040
## .politW3P1 (c) -0.638 0.486 -1.312 0.189 -0.638 -0.974
## .politW4P1 (c) -0.638 0.486 -1.312 0.189 -0.638 -1.034
## .politW1P2 (d) -0.905 0.546 -1.656 0.098 -0.905 -1.425
## .politW2P2 (d) -0.905 0.546 -1.656 0.098 -0.905 -1.407
## .politW3P2 (d) -0.905 0.546 -1.656 0.098 -0.905 -1.354
## .politW4P2 (d) -0.905 0.546 -1.656 0.098 -0.905 -1.223
## .polit1 0.000 0.000 0.000
## .polit2 0.000 0.000 0.000
## .polit3 0.000 0.000 0.000
## .polit4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .politW1S1 0.270 0.029 9.363 0.000 0.270 0.636
## .politW1S2 0.215 0.024 9.072 0.000 0.215 0.658
## .politW1P1 0.199 0.030 6.669 0.000 0.199 0.512
## .politW1P2 0.154 0.032 4.883 0.000 0.154 0.381
## .politW2S1 0.292 0.034 8.664 0.000 0.292 0.656
## .politW2S2 0.218 0.026 8.444 0.000 0.218 0.663
## .politW2P1 0.188 0.029 6.520 0.000 0.188 0.500
## .politW2P2 0.166 0.032 5.217 0.000 0.166 0.401
## .politW3S1 0.268 0.032 8.426 0.000 0.268 0.613
## .politW3S2 0.242 0.028 8.631 0.000 0.242 0.664
## .politW3P1 0.221 0.033 6.662 0.000 0.221 0.515
## .politW3P2 0.173 0.033 5.164 0.000 0.173 0.386
## .politW4S1 0.200 0.031 6.428 0.000 0.200 0.507
## .politW4S2 0.259 0.034 7.546 0.000 0.259 0.649
## .politW4P1 0.142 0.032 4.434 0.000 0.142 0.372
## .politW4P2 0.233 0.050 4.629 0.000 0.233 0.426
## .polit1 0.006 0.012 0.511 0.609 0.039 0.039
## .polit2 -0.004 0.010 -0.406 0.685 -0.025 -0.025
## .polit3 -0.010 0.010 -0.911 0.362 -0.056 -0.056
## .polit4 -0.013 0.016 -0.819 0.413 -0.068 -0.068
## interc 0.148 0.030 4.912 0.000 1.000 1.000
## slope 0.000 0.000 2.219 0.026 1.000 1.000semPaths(lgmPolit, what = "col", whatLabels = "est", intercepts = T)
LGM Volatility
lgmVolat <- '
# factor at each time point with same loading
volat1 =~ volatW1S1 + a * volatW1S2 +
peer * volatW1P1 + aa * volatW1P2
volat2 =~ volatW2S1 + a * volatW2S2 +
peer * volatW2P1 + aa * volatW2P2
volat3 =~ volatW3S1 + a * volatW3S2 +
peer * volatW3P1 + aa * volatW3P2
volat4 =~ volatW4S1 + a * volatW4S2 +
peer * volatW4P1 + aa * volatW4P2
# second volat factor for intercept and slope
interc =~ 1*volat1 + 1*volat2 + 1*volat3 + 1*volat4
slope =~ 0*volat1 + 6*volat2 + 13*volat3 + 19*volat4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
volatW1S1 ~ 0*1
volatW2S1 ~ 0*1
volatW3S1 ~ 0*1
volatW4S1 ~ 0*1
# fix equal intercepts
volatW1S2 ~ b*1
volatW2S2 ~ b*1
volatW3S2 ~ b*1
volatW4S2 ~ b*1
volatW1P1 ~ c*1
volatW2P1 ~ c*1
volatW3P1 ~ c*1
volatW4P1 ~ c*1
volatW1P2 ~ d*1
volatW2P2 ~ d*1
volatW3P2 ~ d*1
volatW4P2 ~ d*1
# error covariance - similar parcels across waves
volatW1S1 ~~ volatW2S1 + volatW3S1 + volatW4S1
volatW2S1 ~~ volatW3S1 + volatW4S1
volatW3S1 ~~ volatW4S1
volatW1S2 ~~ volatW2S2 + volatW3S2 + volatW4S2
volatW2S2 ~~ volatW3S2 + volatW4S2
volatW3S2 ~~ volatW4S2
volatW1P1 ~~ volatW2P1 + volatW3P1 + volatW4P1
volatW2P1 ~~ volatW3P1 + volatW4P1
volatW3P1 ~~ volatW4P1
volatW1P2 ~~ volatW2P2 + volatW3P2 + volatW4P2
volatW2P2 ~~ volatW3P2 + volatW4P2
volatW3P2 ~~ volatW4P2
# error covariance - same method at one wave
volatW1S1 ~~ volatW1S2
volatW1P1 ~~ volatW1P2
volatW2S1 ~~ volatW2S2
volatW2P1 ~~ volatW2P2
volatW3S1 ~~ volatW3S2
volatW3P1 ~~ volatW3P2
volatW4S1 ~~ volatW4S2
volatW4P1 ~~ volatW4P2
'
lgmVolat <- sem(lgmVolat, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmVolat, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 143 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 342.462
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2829.311
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.906
## Tucker-Lewis Index (TLI) 0.874
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1825.531
## Loglikelihood unrestricted model (H1) -1654.300
##
## Akaike (AIC) 3777.062
## Bayesian (BIC) 4001.142
## Sample-size adjusted Bayesian (BIC) 3801.409
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.105
## 90 Percent confidence interval - lower 0.093
## 90 Percent confidence interval - upper 0.117
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.192
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## volat1 =~
## vltW1S1 1.000 0.656 0.754
## vltW1S2 (a) 0.877 0.039 22.529 0.000 0.575 0.732
## vltW1P1 (peer) 0.621 0.062 10.084 0.000 0.407 0.502
## vltW1P2 (aa) 0.622 0.062 10.033 0.000 0.408 0.503
## volat2 =~
## vltW2S1 1.000 0.672 0.806
## vltW2S2 (a) 0.877 0.039 22.529 0.000 0.589 0.800
## vltW2P1 (peer) 0.621 0.062 10.084 0.000 0.417 0.576
## vltW2P2 (aa) 0.622 0.062 10.033 0.000 0.418 0.573
## volat3 =~
## vltW3S1 1.000 0.592 0.758
## vltW3S2 (a) 0.877 0.039 22.529 0.000 0.519 0.695
## vltW3P1 (peer) 0.621 0.062 10.084 0.000 0.367 0.433
## vltW3P2 (aa) 0.622 0.062 10.033 0.000 0.368 0.481
## volat4 =~
## vltW4S1 1.000 0.565 0.643
## vltW4S2 (a) 0.877 0.039 22.529 0.000 0.495 0.643
## vltW4P1 (peer) 0.621 0.062 10.084 0.000 0.350 0.468
## vltW4P2 (aa) 0.622 0.062 10.033 0.000 0.351 0.471
## interc =~
## volat1 1.000 1.102 1.102
## volat2 1.000 1.076 1.076
## volat3 1.000 1.220 1.220
## volat4 1.000 1.280 1.280
## slope =~
## volat1 0.000 0.000 0.000
## volat2 6.000 0.114 0.114
## volat3 13.000 0.280 0.280
## volat4 19.000 0.429 0.429
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.002 0.002 -1.321 0.186 -0.258 -0.258
## .volatW1S1 ~~
## .volatW2S1 0.032 0.021 1.517 0.129 0.032 0.113
## .volatW3S1 0.000 0.018 0.025 0.980 0.000 0.002
## .volatW4S1 0.039 0.024 1.632 0.103 0.039 0.101
## .volatW2S1 ~~
## .volatW3S1 0.033 0.019 1.744 0.081 0.033 0.129
## .volatW4S1 0.018 0.024 0.759 0.448 0.018 0.054
## .volatW3S1 ~~
## .volatW4S1 0.024 0.021 1.119 0.263 0.024 0.070
## .volatW1S2 ~~
## .volatW2S2 0.043 0.017 2.614 0.009 0.043 0.183
## .volatW3S2 0.059 0.017 3.566 0.000 0.059 0.205
## .volatW4S2 0.033 0.019 1.774 0.076 0.033 0.106
## .volatW2S2 ~~
## .volatW3S2 0.059 0.016 3.625 0.000 0.059 0.247
## .volatW4S2 0.033 0.017 1.945 0.052 0.033 0.128
## .volatW3S2 ~~
## .volatW4S2 0.040 0.017 2.339 0.019 0.040 0.125
## .volatW1P1 ~~
## .volatW2P1 0.029 0.022 1.276 0.202 0.029 0.069
## .volatW3P1 -0.038 0.025 -1.499 0.134 -0.038 -0.070
## .volatW4P1 0.028 0.025 1.136 0.256 0.028 0.060
## .volatW2P1 ~~
## .volatW3P1 0.027 0.025 1.100 0.271 0.027 0.060
## .volatW4P1 0.052 0.025 2.087 0.037 0.052 0.132
## .volatW3P1 ~~
## .volatW4P1 -0.051 0.030 -1.690 0.091 -0.051 -0.101
## .volatW1P2 ~~
## .volatW2P2 0.048 0.021 2.341 0.019 0.048 0.115
## .volatW3P2 0.069 0.020 3.385 0.001 0.069 0.147
## .volatW4P2 0.048 0.024 2.001 0.045 0.048 0.104
## .volatW2P2 ~~
## .volatW3P2 0.019 0.021 0.904 0.366 0.019 0.048
## .volatW4P2 0.046 0.024 1.903 0.057 0.046 0.116
## .volatW3P2 ~~
## .volatW4P2 0.096 0.022 4.281 0.000 0.096 0.216
## .volatW1S1 ~~
## .volatW1S2 0.172 0.046 3.742 0.000 0.172 0.561
## .volatW1P1 ~~
## .volatW1P2 0.398 0.057 6.941 0.000 0.398 0.808
## .volatW2S1 ~~
## .volatW2S2 0.087 0.040 2.196 0.028 0.087 0.399
## .volatW2P1 ~~
## .volatW2P2 0.247 0.045 5.531 0.000 0.247 0.698
## .volatW3S1 ~~
## .volatW3S2 0.176 0.040 4.361 0.000 0.176 0.643
## .volatW3P1 ~~
## .volatW3P2 0.428 0.066 6.480 0.000 0.428 0.832
## .volatW4S1 ~~
## .volatW4S2 0.274 0.064 4.294 0.000 0.274 0.690
## .volatW4P1 ~~
## .volatW4P2 0.342 0.062 5.552 0.000 0.342 0.785
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 2.842 0.051 56.127 0.000 3.933 3.933
## slope 0.000 0.002 0.100 0.920 0.016 0.016
## .volatW1S1 0.000 0.000 0.000
## .volatW2S1 0.000 0.000 0.000
## .volatW3S1 0.000 0.000 0.000
## .volatW4S1 0.000 0.000 0.000
## .volatW1S2 (b) 0.213 0.113 1.891 0.059 0.213 0.271
## .volatW2S2 (b) 0.213 0.113 1.891 0.059 0.213 0.289
## .volatW3S2 (b) 0.213 0.113 1.891 0.059 0.213 0.285
## .volatW4S2 (b) 0.213 0.113 1.891 0.059 0.213 0.277
## .volatW1P1 (c) 0.849 0.179 4.741 0.000 0.849 1.048
## .volatW2P1 (c) 0.849 0.179 4.741 0.000 0.849 1.174
## .volatW3P1 (c) 0.849 0.179 4.741 0.000 0.849 1.000
## .volatW4P1 (c) 0.849 0.179 4.741 0.000 0.849 1.135
## .volatW1P2 (d) 0.800 0.181 4.422 0.000 0.800 0.985
## .volatW2P2 (d) 0.800 0.181 4.422 0.000 0.800 1.096
## .volatW3P2 (d) 0.800 0.181 4.422 0.000 0.800 1.045
## .volatW4P2 (d) 0.800 0.181 4.422 0.000 0.800 1.072
## .volat1 0.000 0.000 0.000
## .volat2 0.000 0.000 0.000
## .volat3 0.000 0.000 0.000
## .volat4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S1 0.327 0.055 5.971 0.000 0.327 0.432
## .volatW1S2 0.287 0.045 6.313 0.000 0.287 0.464
## .volatW1P1 0.491 0.063 7.845 0.000 0.491 0.748
## .volatW1P2 0.493 0.059 8.315 0.000 0.493 0.747
## .volatW2S1 0.243 0.049 4.984 0.000 0.243 0.350
## .volatW2S2 0.195 0.039 5.047 0.000 0.195 0.360
## .volatW2P1 0.350 0.047 7.436 0.000 0.350 0.668
## .volatW2P2 0.358 0.047 7.644 0.000 0.358 0.672
## .volatW3S1 0.260 0.047 5.501 0.000 0.260 0.426
## .volatW3S2 0.290 0.040 7.232 0.000 0.290 0.518
## .volatW3P1 0.586 0.084 7.005 0.000 0.586 0.813
## .volatW3P2 0.450 0.059 7.591 0.000 0.450 0.768
## .volatW4S1 0.452 0.076 5.927 0.000 0.452 0.586
## .volatW4S2 0.347 0.059 5.848 0.000 0.347 0.586
## .volatW4P1 0.437 0.067 6.517 0.000 0.437 0.781
## .volatW4P2 0.433 0.061 7.076 0.000 0.433 0.778
## .volat1 -0.092 0.047 -1.954 0.051 -0.214 -0.214
## .volat2 -0.048 0.039 -1.225 0.220 -0.107 -0.107
## .volat3 -0.137 0.040 -3.439 0.001 -0.391 -0.391
## .volat4 -0.172 0.060 -2.874 0.004 -0.538 -0.538
## interc 0.522 0.060 8.693 0.000 1.000 1.000
## slope 0.000 0.000 1.406 0.160 1.000 1.000semPaths(lgmVolat, what = "col", whatLabels = "est", intercepts = T)
LGM Withdrawal
lgmWithd <- '
# factor at each time point with same loading
withd1 =~ withdW1S1 + a * withdW1S2 +
peer * withdW1P1 + aa * withdW1P2
withd2 =~ withdW2S1 + a * withdW2S2 +
peer * withdW2P1 + aa * withdW2P2
withd3 =~ withdW3S1 + a * withdW3S2 +
peer * withdW3P1 + aa * withdW3P2
withd4 =~ withdW4S1 + a * withdW4S2 +
peer * withdW4P1 + aa * withdW4P2
# second withd factor for intercept and slope
interc =~ 1*withd1 + 1*withd2 + 1*withd3 + 1*withd4
slope =~ 0*withd1 + 6*withd2 + 13*withd3 + 19*withd4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
withdW1S1 ~ 0*1
withdW2S1 ~ 0*1
withdW3S1 ~ 0*1
withdW4S1 ~ 0*1
# fix equal intercepts
withdW1S2 ~ b*1
withdW2S2 ~ b*1
withdW3S2 ~ b*1
withdW4S2 ~ b*1
withdW1P1 ~ c*1
withdW2P1 ~ c*1
withdW3P1 ~ c*1
withdW4P1 ~ c*1
withdW1P2 ~ d*1
withdW2P2 ~ d*1
withdW3P2 ~ d*1
withdW4P2 ~ d*1
# error covariance - similar parcels across waves
withdW1S1 ~~ withdW2S1 + withdW3S1 + withdW4S1
withdW2S1 ~~ withdW3S1 + withdW4S1
withdW3S1 ~~ withdW4S1
withdW1S2 ~~ withdW2S2 + withdW3S2 + withdW4S2
withdW2S2 ~~ withdW3S2 + withdW4S2
withdW3S2 ~~ withdW4S2
withdW1P1 ~~ withdW2P1 + withdW3P1 + withdW4P1
withdW2P1 ~~ withdW3P1 + withdW4P1
withdW3P1 ~~ withdW4P1
withdW1P2 ~~ withdW2P2 + withdW3P2 + withdW4P2
withdW2P2 ~~ withdW3P2 + withdW4P2
withdW3P2 ~~ withdW4P2
# error covariance - same method at one wave
withdW1S1 ~~ withdW1S2
withdW1P1 ~~ withdW1P2
withdW2S1 ~~ withdW2S2
withdW2P1 ~~ withdW2P2
withdW3S1 ~~ withdW3S2
withdW3P1 ~~ withdW3P2
withdW4S1 ~~ withdW4S2
withdW4P1 ~~ withdW4P2
'
lgmWithd <- sem(lgmWithd, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmWithd, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 160 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 322.713
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2333.668
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.894
## Tucker-Lewis Index (TLI) 0.858
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1793.040
## Loglikelihood unrestricted model (H1) -1631.683
##
## Akaike (AIC) 3712.080
## Bayesian (BIC) 3936.160
## Sample-size adjusted Bayesian (BIC) 3736.427
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.101
## 90 Percent confidence interval - lower 0.089
## 90 Percent confidence interval - upper 0.113
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.167
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## withd1 =~
## wthW1S1 1.000 0.559 0.732
## wthW1S2 (a) 0.926 0.067 13.863 0.000 0.518 0.710
## wthW1P1 (peer) 0.684 0.139 4.924 0.000 0.382 0.565
## wthW1P2 (aa) 0.648 0.141 4.598 0.000 0.362 0.508
## withd2 =~
## wthW2S1 1.000 0.578 0.766
## wthW2S2 (a) 0.926 0.067 13.863 0.000 0.535 0.751
## wthW2P1 (peer) 0.684 0.139 4.924 0.000 0.395 0.592
## wthW2P2 (aa) 0.648 0.141 4.598 0.000 0.374 0.572
## withd3 =~
## wthW3S1 1.000 0.566 0.745
## wthW3S2 (a) 0.926 0.067 13.863 0.000 0.524 0.757
## wthW3P1 (peer) 0.684 0.139 4.924 0.000 0.387 0.546
## wthW3P2 (aa) 0.648 0.141 4.598 0.000 0.366 0.546
## withd4 =~
## wthW4S1 1.000 0.549 0.724
## wthW4S2 (a) 0.926 0.067 13.863 0.000 0.508 0.754
## wthW4P1 (peer) 0.684 0.139 4.924 0.000 0.375 0.553
## wthW4P2 (aa) 0.648 0.141 4.598 0.000 0.355 0.550
## interc =~
## withd1 1.000 1.053 1.053
## withd2 1.000 1.019 1.019
## withd3 1.000 1.041 1.041
## withd4 1.000 1.073 1.073
## slope =~
## withd1 0.000 0.000 0.000
## withd2 6.000 0.131 0.131
## withd3 13.000 0.291 0.291
## withd4 19.000 0.438 0.438
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.003 0.002 -1.711 0.087 -0.354 -0.354
## .withdW1S1 ~~
## .withdW2S1 0.058 0.027 2.154 0.031 0.058 0.231
## .withdW3S1 0.067 0.029 2.294 0.022 0.067 0.254
## .withdW4S1 0.078 0.029 2.646 0.008 0.078 0.285
## .withdW2S1 ~~
## .withdW3S1 0.092 0.035 2.633 0.008 0.092 0.374
## .withdW4S1 0.075 0.033 2.261 0.024 0.075 0.295
## .withdW3S1 ~~
## .withdW4S1 0.101 0.038 2.660 0.008 0.101 0.381
## .withdW1S2 ~~
## .withdW2S2 0.097 0.025 3.818 0.000 0.097 0.401
## .withdW3S2 0.106 0.024 4.342 0.000 0.106 0.459
## .withdW4S2 0.085 0.025 3.404 0.001 0.085 0.373
## .withdW2S2 ~~
## .withdW3S2 0.100 0.025 3.934 0.000 0.100 0.468
## .withdW4S2 0.091 0.026 3.503 0.000 0.091 0.436
## .withdW3S2 ~~
## .withdW4S2 0.108 0.027 4.027 0.000 0.108 0.539
## .withdW1P1 ~~
## .withdW2P1 0.143 0.052 2.756 0.006 0.143 0.477
## .withdW3P1 0.187 0.063 2.950 0.003 0.187 0.566
## .withdW4P1 0.165 0.052 3.162 0.002 0.165 0.525
## .withdW2P1 ~~
## .withdW3P1 0.209 0.057 3.631 0.000 0.209 0.652
## .withdW4P1 0.189 0.053 3.563 0.000 0.189 0.620
## .withdW3P1 ~~
## .withdW4P1 0.206 0.066 3.116 0.002 0.206 0.615
## .withdW1P2 ~~
## .withdW2P2 0.193 0.052 3.719 0.000 0.193 0.586
## .withdW3P2 0.217 0.061 3.582 0.000 0.217 0.632
## .withdW4P2 0.158 0.056 2.800 0.005 0.158 0.478
## .withdW2P2 ~~
## .withdW3P2 0.211 0.055 3.809 0.000 0.211 0.699
## .withdW4P2 0.126 0.050 2.507 0.012 0.126 0.436
## .withdW3P2 ~~
## .withdW4P2 0.197 0.059 3.343 0.001 0.197 0.649
## .withdW1S1 ~~
## .withdW1S2 0.108 0.033 3.232 0.001 0.108 0.404
## .withdW1P1 ~~
## .withdW1P2 0.096 0.027 3.590 0.000 0.096 0.281
## .withdW2S1 ~~
## .withdW2S2 0.060 0.026 2.346 0.019 0.060 0.263
## .withdW2P1 ~~
## .withdW2P2 0.032 0.020 1.565 0.118 0.032 0.109
## .withdW3S1 ~~
## .withdW3S2 0.034 0.022 1.538 0.124 0.034 0.151
## .withdW3P1 ~~
## .withdW3P2 0.021 0.027 0.803 0.422 0.021 0.064
## .withdW4S1 ~~
## .withdW4S2 0.045 0.031 1.446 0.148 0.045 0.192
## .withdW4P1 ~~
## .withdW4P2 0.093 0.031 3.041 0.002 0.093 0.306
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 2.973 0.045 66.207 0.000 5.050 5.050
## slope -0.000 0.002 -0.034 0.973 -0.005 -0.005
## .withdW1S1 0.000 0.000 0.000
## .withdW2S1 0.000 0.000 0.000
## .withdW3S1 0.000 0.000 0.000
## .withdW4S1 0.000 0.000 0.000
## .withdW1S2 (b) 0.265 0.201 1.316 0.188 0.265 0.363
## .withdW2S2 (b) 0.265 0.201 1.316 0.188 0.265 0.371
## .withdW3S2 (b) 0.265 0.201 1.316 0.188 0.265 0.382
## .withdW4S2 (b) 0.265 0.201 1.316 0.188 0.265 0.392
## .withdW1P1 (c) 0.580 0.417 1.392 0.164 0.580 0.858
## .withdW2P1 (c) 0.580 0.417 1.392 0.164 0.580 0.868
## .withdW3P1 (c) 0.580 0.417 1.392 0.164 0.580 0.819
## .withdW4P1 (c) 0.580 0.417 1.392 0.164 0.580 0.855
## .withdW1P2 (d) 0.620 0.421 1.473 0.141 0.620 0.871
## .withdW2P2 (d) 0.620 0.421 1.473 0.141 0.620 0.947
## .withdW3P2 (d) 0.620 0.421 1.473 0.141 0.620 0.925
## .withdW4P2 (d) 0.620 0.421 1.473 0.141 0.620 0.960
## .withd1 0.000 0.000 0.000
## .withd2 0.000 0.000 0.000
## .withd3 0.000 0.000 0.000
## .withd4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .withdW1S1 0.271 0.047 5.748 0.000 0.271 0.464
## .withdW1S2 0.263 0.042 6.232 0.000 0.263 0.495
## .withdW1P1 0.311 0.048 6.452 0.000 0.311 0.680
## .withdW1P2 0.376 0.056 6.720 0.000 0.376 0.742
## .withdW2S1 0.235 0.046 5.128 0.000 0.235 0.412
## .withdW2S2 0.222 0.039 5.619 0.000 0.222 0.436
## .withdW2P1 0.290 0.054 5.420 0.000 0.290 0.650
## .withdW2P2 0.289 0.055 5.289 0.000 0.289 0.673
## .withdW3S1 0.256 0.053 4.871 0.000 0.256 0.445
## .withdW3S2 0.204 0.036 5.701 0.000 0.204 0.427
## .withdW3P1 0.352 0.067 5.270 0.000 0.352 0.702
## .withdW3P2 0.315 0.061 5.194 0.000 0.315 0.701
## .withdW4S1 0.273 0.055 4.943 0.000 0.273 0.476
## .withdW4S2 0.197 0.041 4.852 0.000 0.197 0.432
## .withdW4P1 0.319 0.055 5.787 0.000 0.319 0.694
## .withdW4P2 0.291 0.050 5.771 0.000 0.291 0.697
## .withd1 -0.034 0.028 -1.202 0.229 -0.109 -0.109
## .withd2 0.013 0.022 0.612 0.540 0.040 0.040
## .withd3 0.015 0.019 0.784 0.433 0.045 0.045
## .withd4 -0.003 0.031 -0.102 0.919 -0.010 -0.010
## interc 0.347 0.057 6.128 0.000 1.000 1.000
## slope 0.000 0.000 1.601 0.109 1.000 1.000semPaths(lgmWithd, what = "col", whatLabels = "est", intercepts = T)
LGM Confusion
lgmConfu <- '
# factor at each time point with same loading
confu1 =~ confuW1S1 + a * confuW1S2 +
peer * confuW1P1 + aa * confuW1P2
confu2 =~ confuW2S1 + a * confuW2S2 +
peer * confuW2P1 + aa * confuW2P2
confu3 =~ confuW3S1 + a * confuW3S2 +
peer * confuW3P1 + aa * confuW3P2
confu4 =~ confuW4S1 + a * confuW4S2 +
peer * confuW4P1 + aa * confuW4P2
# second confu factor for intercept and slope
interc =~ 1*confu1 + 1*confu2 + 1*confu3 + 1*confu4
slope =~ 0*confu1 + 6*confu2 + 13*confu3 + 19*confu4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
confuW1S1 ~ 0*1
confuW2S1 ~ 0*1
confuW3S1 ~ 0*1
confuW4S1 ~ 0*1
# fix equal intercepts
confuW1S2 ~ b*1
confuW2S2 ~ b*1
confuW3S2 ~ b*1
confuW4S2 ~ b*1
confuW1P1 ~ c*1
confuW2P1 ~ c*1
confuW3P1 ~ c*1
confuW4P1 ~ c*1
confuW1P2 ~ d*1
confuW2P2 ~ d*1
confuW3P2 ~ d*1
confuW4P2 ~ d*1
# error covariance - similar parcels across waves
confuW1S1 ~~ confuW2S1 + confuW3S1 + confuW4S1
confuW2S1 ~~ confuW3S1 + confuW4S1
confuW3S1 ~~ confuW4S1
confuW1S2 ~~ confuW2S2 + confuW3S2 + confuW4S2
confuW2S2 ~~ confuW3S2 + confuW4S2
confuW3S2 ~~ confuW4S2
confuW1P1 ~~ confuW2P1 + confuW3P1 + confuW4P1
confuW2P1 ~~ confuW3P1 + confuW4P1
confuW3P1 ~~ confuW4P1
confuW1P2 ~~ confuW2P2 + confuW3P2 + confuW4P2
confuW2P2 ~~ confuW3P2 + confuW4P2
confuW3P2 ~~ confuW4P2
# error covariance - same method at one wave
confuW1S1 ~~ confuW1S2
confuW1P1 ~~ confuW1P2
confuW2S1 ~~ confuW2S2
confuW2P1 ~~ confuW2P2
confuW3S1 ~~ confuW3S2
confuW3P1 ~~ confuW3P2
confuW4S1 ~~ confuW4S2
confuW4P1 ~~ confuW4P2
'
lgmConfu <- sem(lgmConfu, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmConfu, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 135 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 55
##
## Model Test User Model:
##
## Test statistic 181.006
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1406.278
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.928
## Tucker-Lewis Index (TLI) 0.904
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2233.611
## Loglikelihood unrestricted model (H1) -2143.108
##
## Akaike (AIC) 4593.222
## Bayesian (BIC) 4817.302
## Sample-size adjusted Bayesian (BIC) 4617.570
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.076
## P-value RMSEA <= 0.05 0.051
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.108
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## confu1 =~
## cnfW1S1 1.000 0.409 0.526
## cnfW1S2 (a) 1.092 0.114 9.607 0.000 0.446 0.574
## cnfW1P1 (peer) 0.997 0.171 5.813 0.000 0.407 0.482
## cnfW1P2 (aa) 0.777 0.149 5.223 0.000 0.317 0.490
## confu2 =~
## cnfW2S1 1.000 0.440 0.590
## cnfW2S2 (a) 1.092 0.114 9.607 0.000 0.480 0.609
## cnfW2P1 (peer) 0.997 0.171 5.813 0.000 0.438 0.604
## cnfW2P2 (aa) 0.777 0.149 5.223 0.000 0.341 0.512
## confu3 =~
## cnfW3S1 1.000 0.450 0.577
## cnfW3S2 (a) 1.092 0.114 9.607 0.000 0.491 0.593
## cnfW3P1 (peer) 0.997 0.171 5.813 0.000 0.448 0.631
## cnfW3P2 (aa) 0.777 0.149 5.223 0.000 0.349 0.521
## confu4 =~
## cnfW4S1 1.000 0.448 0.587
## cnfW4S2 (a) 1.092 0.114 9.607 0.000 0.489 0.615
## cnfW4P1 (peer) 0.997 0.171 5.813 0.000 0.447 0.597
## cnfW4P2 (aa) 0.777 0.149 5.223 0.000 0.348 0.474
## interc =~
## confu1 1.000 1.186 1.186
## confu2 1.000 1.103 1.103
## confu3 1.000 1.078 1.078
## confu4 1.000 1.082 1.082
## slope =~
## confu1 0.000 0.000 0.000
## confu2 6.000 0.222 0.222
## confu3 13.000 0.470 0.470
## confu4 19.000 0.689 0.689
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.003 0.002 -1.739 0.082 -0.356 -0.356
## .confuW1S1 ~~
## .confuW2S1 0.087 0.032 2.730 0.006 0.087 0.221
## .confuW3S1 0.088 0.032 2.778 0.005 0.088 0.210
## .confuW4S1 0.130 0.034 3.776 0.000 0.130 0.320
## .confuW2S1 ~~
## .confuW3S1 0.102 0.034 2.985 0.003 0.102 0.268
## .confuW4S1 0.115 0.035 3.267 0.001 0.115 0.311
## .confuW3S1 ~~
## .confuW4S1 0.126 0.037 3.449 0.001 0.126 0.322
## .confuW1S2 ~~
## .confuW2S2 0.077 0.036 2.120 0.034 0.077 0.193
## .confuW3S2 0.065 0.034 1.934 0.053 0.065 0.153
## .confuW4S2 0.037 0.037 1.019 0.308 0.037 0.094
## .confuW2S2 ~~
## .confuW3S2 0.157 0.040 3.951 0.000 0.157 0.378
## .confuW4S2 0.101 0.043 2.321 0.020 0.101 0.257
## .confuW3S2 ~~
## .confuW4S2 0.141 0.046 3.064 0.002 0.141 0.337
## .confuW1P1 ~~
## .confuW2P1 0.166 0.054 3.067 0.002 0.166 0.388
## .confuW3P1 0.195 0.052 3.723 0.000 0.195 0.477
## .confuW4P1 0.161 0.061 2.624 0.009 0.161 0.363
## .confuW2P1 ~~
## .confuW3P1 0.124 0.051 2.413 0.016 0.124 0.388
## .confuW4P1 0.138 0.057 2.428 0.015 0.138 0.397
## .confuW3P1 ~~
## .confuW4P1 0.160 0.056 2.865 0.004 0.160 0.485
## .confuW1P2 ~~
## .confuW2P2 0.148 0.039 3.751 0.000 0.148 0.455
## .confuW3P2 0.114 0.038 2.993 0.003 0.114 0.353
## .confuW4P2 0.220 0.047 4.709 0.000 0.220 0.603
## .confuW2P2 ~~
## .confuW3P2 0.181 0.043 4.215 0.000 0.181 0.552
## .confuW4P2 0.215 0.048 4.502 0.000 0.215 0.579
## .confuW3P2 ~~
## .confuW4P2 0.184 0.048 3.825 0.000 0.184 0.497
## .confuW1S1 ~~
## .confuW1S2 0.157 0.039 3.989 0.000 0.157 0.375
## .confuW1P1 ~~
## .confuW1P2 0.116 0.035 3.293 0.001 0.116 0.278
## .confuW2S1 ~~
## .confuW2S2 0.083 0.035 2.414 0.016 0.083 0.222
## .confuW2P1 ~~
## .confuW2P2 0.059 0.029 2.010 0.044 0.059 0.179
## .confuW3S1 ~~
## .confuW3S2 0.154 0.040 3.831 0.000 0.154 0.364
## .confuW3P1 ~~
## .confuW3P2 0.068 0.032 2.127 0.033 0.068 0.216
## .confuW4S1 ~~
## .confuW4S2 0.093 0.048 1.940 0.052 0.093 0.239
## .confuW4P1 ~~
## .confuW4P2 0.066 0.041 1.612 0.107 0.066 0.169
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 2.852 0.043 65.956 0.000 5.884 5.884
## slope -0.004 0.002 -1.728 0.084 -0.232 -0.232
## .confuW1S1 0.000 0.000 0.000
## .confuW2S1 0.000 0.000 0.000
## .confuW3S1 0.000 0.000 0.000
## .confuW4S1 0.000 0.000 0.000
## .confuW1S2 (b) -0.207 0.323 -0.641 0.522 -0.207 -0.266
## .confuW2S2 (b) -0.207 0.323 -0.641 0.522 -0.207 -0.262
## .confuW3S2 (b) -0.207 0.323 -0.641 0.522 -0.207 -0.250
## .confuW4S2 (b) -0.207 0.323 -0.641 0.522 -0.207 -0.260
## .confuW1P1 (c) -0.378 0.489 -0.773 0.439 -0.378 -0.447
## .confuW2P1 (c) -0.378 0.489 -0.773 0.439 -0.378 -0.521
## .confuW3P1 (c) -0.378 0.489 -0.773 0.439 -0.378 -0.532
## .confuW4P1 (c) -0.378 0.489 -0.773 0.439 -0.378 -0.505
## .confuW1P2 (d) 0.294 0.423 0.697 0.486 0.294 0.454
## .confuW2P2 (d) 0.294 0.423 0.697 0.486 0.294 0.441
## .confuW3P2 (d) 0.294 0.423 0.697 0.486 0.294 0.440
## .confuW4P2 (d) 0.294 0.423 0.697 0.486 0.294 0.401
## .confu1 0.000 0.000 0.000
## .confu2 0.000 0.000 0.000
## .confu3 0.000 0.000 0.000
## .confu4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .confuW1S1 0.436 0.050 8.710 0.000 0.436 0.723
## .confuW1S2 0.404 0.051 7.906 0.000 0.404 0.670
## .confuW1P1 0.549 0.075 7.368 0.000 0.549 0.768
## .confuW1P2 0.320 0.043 7.435 0.000 0.320 0.760
## .confuW2S1 0.361 0.047 7.678 0.000 0.361 0.651
## .confuW2S2 0.390 0.053 7.300 0.000 0.390 0.629
## .confuW2P1 0.334 0.056 5.961 0.000 0.334 0.635
## .confuW2P2 0.328 0.046 7.214 0.000 0.328 0.738
## .confuW3S1 0.404 0.055 7.409 0.000 0.404 0.667
## .confuW3S2 0.445 0.058 7.646 0.000 0.445 0.649
## .confuW3P1 0.304 0.054 5.583 0.000 0.304 0.602
## .confuW3P2 0.327 0.049 6.636 0.000 0.327 0.728
## .confuW4S1 0.381 0.056 6.851 0.000 0.381 0.655
## .confuW4S2 0.394 0.080 4.935 0.000 0.394 0.622
## .confuW4P1 0.360 0.069 5.188 0.000 0.360 0.643
## .confuW4P2 0.418 0.069 6.085 0.000 0.418 0.776
## .confu1 -0.068 0.028 -2.434 0.015 -0.407 -0.407
## .confu2 -0.018 0.021 -0.835 0.403 -0.091 -0.091
## .confu3 -0.005 0.024 -0.186 0.852 -0.022 -0.022
## .confu4 -0.023 0.036 -0.642 0.521 -0.114 -0.114
## interc 0.235 0.048 4.846 0.000 1.000 1.000
## slope 0.000 0.000 2.098 0.036 1.000 1.000semPaths(lgmConfu, what = "col", whatLabels = "est", intercepts = T)
LGM Coherence
lgmCoher <- '
# factor at each time point with same loading
coher1 =~ coherW1S1 + a * coherW1S2 +
peer * coherW1P1 + aa * coherW1P2
coher2 =~ coherW2S1 + a * coherW2S2 +
peer * coherW2P1 + aa * coherW2P2
coher3 =~ coherW3S1 + a * coherW3S2 +
peer * coherW3P1 + aa * coherW3P2
coher4 =~ coherW4S1 + a * coherW4S2 +
peer * coherW4P1 + aa * coherW4P2
# second coher factor for intercept and slope
interc =~ 1*coher1 + 1*coher2 + 1*coher3 + 1*coher4
slope =~ 0*coher1 + 6*coher2 + 13*coher3 + 19*coher4
interc ~~ slope
interc ~ 1
slope ~ 1
# fix zero intercepts
coherW1S1 ~ 0*1
coherW2S1 ~ 0*1
coherW3S1 ~ 0*1
coherW4S1 ~ 0*1
# fix equal intercepts
coherW1S2 ~ b*1
coherW2S2 ~ b*1
coherW3S2 ~ b*1
coherW4S2 ~ b*1
coherW1P1 ~ c*1
coherW2P1 ~ c*1
coherW3P1 ~ c*1
coherW4P1 ~ c*1
coherW1P2 ~ d*1
coherW2P2 ~ d*1
coherW3P2 ~ d*1
coherW4P2 ~ d*1
# error covariance - similar parcels across waves
coherW1S1 ~~ coherW2S1 + coherW3S1 + coherW4S1
coherW2S1 ~~ coherW3S1 + coherW4S1
coherW3S1 ~~ coherW4S1
coherW1S2 ~~ coherW2S2 + coherW3S2 + coherW4S2
coherW2S2 ~~ coherW3S2 + coherW4S2
coherW3S2 ~~ coherW4S2
coherW1P1 ~~ coherW2P1 + coherW3P1 + coherW4P1
coherW2P1 ~~ coherW3P1 + coherW4P1
coherW3P1 ~~ coherW4P1
coherW1P2 ~~ coherW2P2 + coherW3P2 + coherW4P2
coherW2P2 ~~ coherW3P2 + coherW4P2
coherW3P2 ~~ coherW4P2
# error covariance - same method at one wave
coherW1S1 ~~ coherW1S2
coherW1P1 ~~ coherW1P2
coherW2S1 ~~ coherW2S2
coherW2P1 ~~ coherW2P2
coherW3S1 ~~ coherW3S2
coherW3P1 ~~ coherW3P2
coherW4S1 ~~ coherW4S2
coherW4P1 ~~ coherW4P2
'
lgmCoher <- sem(lgmCoher, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lgmCoher, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 189 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 18
##
## Number of observations 259
## Number of missing patterns 55
##
## Model Test User Model:
##
## Test statistic 192.352
## Degrees of freedom 89
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1489.355
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.925
## Tucker-Lewis Index (TLI) 0.898
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1727.529
## Loglikelihood unrestricted model (H1) -1631.353
##
## Akaike (AIC) 3581.058
## Bayesian (BIC) 3805.138
## Sample-size adjusted Bayesian (BIC) 3605.405
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.080
## P-value RMSEA <= 0.05 0.017
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.134
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## coher1 =~
## chrW1S1 1.000 0.222 0.330
## chrW1S2 (a) 0.914 0.174 5.247 0.000 0.203 0.365
## chrW1P1 (peer) 1.822 0.431 4.225 0.000 0.405 0.581
## chrW1P2 (aa) 1.965 0.495 3.972 0.000 0.436 0.682
## coher2 =~
## chrW2S1 1.000 0.221 0.329
## chrW2S2 (a) 0.914 0.174 5.247 0.000 0.202 0.354
## chrW2P1 (peer) 1.822 0.431 4.225 0.000 0.403 0.637
## chrW2P2 (aa) 1.965 0.495 3.972 0.000 0.434 0.693
## coher3 =~
## chrW3S1 1.000 0.238 0.325
## chrW3S2 (a) 0.914 0.174 5.247 0.000 0.217 0.379
## chrW3P1 (peer) 1.822 0.431 4.225 0.000 0.433 0.684
## chrW3P2 (aa) 1.965 0.495 3.972 0.000 0.467 0.752
## coher4 =~
## chrW4S1 1.000 0.222 0.342
## chrW4S2 (a) 0.914 0.174 5.247 0.000 0.202 0.354
## chrW4P1 (peer) 1.822 0.431 4.225 0.000 0.404 0.641
## chrW4P2 (aa) 1.965 0.495 3.972 0.000 0.435 0.707
## interc =~
## coher1 1.000 1.080 1.080
## coher2 1.000 1.085 1.085
## coher3 1.000 1.009 1.009
## coher4 1.000 1.082 1.082
## slope =~
## coher1 0.000 0.000 0.000
## coher2 6.000 0.201 0.201
## coher3 13.000 0.406 0.406
## coher4 19.000 0.636 0.636
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc ~~
## slope -0.000 0.000 -0.279 0.780 -0.076 -0.076
## .coherW1S1 ~~
## .coherW2S1 0.190 0.035 5.495 0.000 0.190 0.470
## .coherW3S1 0.160 0.036 4.388 0.000 0.160 0.365
## .coherW4S1 0.147 0.034 4.333 0.000 0.147 0.379
## .coherW2S1 ~~
## .coherW3S1 0.205 0.039 5.226 0.000 0.205 0.467
## .coherW4S1 0.170 0.036 4.709 0.000 0.170 0.440
## .coherW3S1 ~~
## .coherW4S1 0.191 0.041 4.706 0.000 0.191 0.454
## .coherW1S2 ~~
## .coherW2S2 0.145 0.025 5.830 0.000 0.145 0.524
## .coherW3S2 0.140 0.024 5.736 0.000 0.140 0.508
## .coherW4S2 0.126 0.026 4.894 0.000 0.126 0.456
## .coherW2S2 ~~
## .coherW3S2 0.140 0.027 5.279 0.000 0.140 0.495
## .coherW4S2 0.139 0.027 5.201 0.000 0.139 0.488
## .coherW3S2 ~~
## .coherW4S2 0.151 0.029 5.165 0.000 0.151 0.532
## .coherW1P1 ~~
## .coherW2P1 0.068 0.024 2.820 0.005 0.068 0.246
## .coherW3P1 0.044 0.026 1.697 0.090 0.044 0.168
## .coherW4P1 0.086 0.027 3.228 0.001 0.086 0.315
## .coherW2P1 ~~
## .coherW3P1 0.056 0.022 2.570 0.010 0.056 0.248
## .coherW4P1 0.074 0.023 3.220 0.001 0.074 0.316
## .coherW3P1 ~~
## .coherW4P1 0.069 0.025 2.771 0.006 0.069 0.310
## .coherW1P2 ~~
## .coherW2P2 0.008 0.024 0.316 0.752 0.008 0.035
## .coherW3P2 0.025 0.024 1.012 0.311 0.025 0.128
## .coherW4P2 0.030 0.027 1.123 0.261 0.030 0.149
## .coherW2P2 ~~
## .coherW3P2 0.017 0.024 0.718 0.473 0.017 0.093
## .coherW4P2 0.003 0.028 0.108 0.914 0.003 0.015
## .coherW3P2 ~~
## .coherW4P2 0.018 0.030 0.606 0.545 0.018 0.100
## .coherW1S1 ~~
## .coherW1S2 0.053 0.019 2.848 0.004 0.053 0.161
## .coherW1P1 ~~
## .coherW1P2 0.153 0.042 3.636 0.000 0.153 0.578
## .coherW2S1 ~~
## .coherW2S2 0.064 0.020 3.220 0.001 0.064 0.188
## .coherW2P1 ~~
## .coherW2P2 0.135 0.045 3.029 0.002 0.135 0.612
## .coherW3S1 ~~
## .coherW3S2 0.078 0.022 3.556 0.000 0.078 0.213
## .coherW3P1 ~~
## .coherW3P2 0.095 0.042 2.292 0.022 0.095 0.505
## .coherW4S1 ~~
## .coherW4S2 0.076 0.023 3.266 0.001 0.076 0.235
## .coherW4P1 ~~
## .coherW4P2 0.109 0.050 2.170 0.030 0.109 0.518
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## interc 3.493 0.036 96.775 0.000 14.569 14.569
## slope -0.000 0.001 -0.294 0.768 -0.047 -0.047
## .coherW1S1 0.000 0.000 0.000
## .coherW2S1 0.000 0.000 0.000
## .coherW3S1 0.000 0.000 0.000
## .coherW4S1 0.000 0.000 0.000
## .coherW1S2 (b) 0.767 0.609 1.259 0.208 0.767 1.377
## .coherW2S2 (b) 0.767 0.609 1.259 0.208 0.767 1.345
## .coherW3S2 (b) 0.767 0.609 1.259 0.208 0.767 1.338
## .coherW4S2 (b) 0.767 0.609 1.259 0.208 0.767 1.342
## .coherW1P1 (c) -2.366 1.505 -1.571 0.116 -2.366 -3.396
## .coherW2P1 (c) -2.366 1.505 -1.571 0.116 -2.366 -3.741
## .coherW3P1 (c) -2.366 1.505 -1.571 0.116 -2.366 -3.740
## .coherW4P1 (c) -2.366 1.505 -1.571 0.116 -2.366 -3.757
## .coherW1P2 (d) -2.889 1.727 -1.673 0.094 -2.889 -4.515
## .coherW2P2 (d) -2.889 1.727 -1.673 0.094 -2.889 -4.607
## .coherW3P2 (d) -2.889 1.727 -1.673 0.094 -2.889 -4.652
## .coherW4P2 (d) -2.889 1.727 -1.673 0.094 -2.889 -4.691
## .coher1 0.000 0.000 0.000
## .coher2 0.000 0.000 0.000
## .coher3 0.000 0.000 0.000
## .coher4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .coherW1S1 0.404 0.038 10.546 0.000 0.404 0.891
## .coherW1S2 0.269 0.026 10.226 0.000 0.269 0.867
## .coherW1P1 0.322 0.055 5.801 0.000 0.322 0.663
## .coherW1P2 0.219 0.046 4.717 0.000 0.219 0.535
## .coherW2S1 0.403 0.042 9.699 0.000 0.403 0.892
## .coherW2S2 0.284 0.030 9.440 0.000 0.284 0.874
## .coherW2P1 0.238 0.044 5.461 0.000 0.238 0.595
## .coherW2P2 0.205 0.057 3.574 0.000 0.205 0.520
## .coherW3S1 0.476 0.051 9.400 0.000 0.476 0.894
## .coherW3S2 0.281 0.030 9.338 0.000 0.281 0.856
## .coherW3P1 0.213 0.047 4.555 0.000 0.213 0.532
## .coherW3P2 0.168 0.049 3.441 0.001 0.168 0.435
## .coherW4S1 0.370 0.044 8.412 0.000 0.370 0.883
## .coherW4S2 0.286 0.034 8.439 0.000 0.286 0.874
## .coherW4P1 0.234 0.052 4.461 0.000 0.234 0.589
## .coherW4P2 0.190 0.062 3.065 0.002 0.190 0.500
## .coher1 -0.008 0.011 -0.746 0.456 -0.166 -0.166
## .coher2 -0.009 0.010 -0.870 0.384 -0.184 -0.184
## .coher3 -0.007 0.009 -0.773 0.440 -0.121 -0.121
## .coher4 -0.023 0.015 -1.498 0.134 -0.470 -0.470
## interc 0.057 0.025 2.283 0.022 1.000 1.000
## slope 0.000 0.000 1.198 0.231 1.000 1.000semPaths(lgmCoher, what = "col", whatLabels = "est", intercepts = T)
Latent stability model
LSM Agreeableness
with aspects as parcels
lsmAgree <- '
# factor at each time point with same loading
agree1 =~ compaW1S + a * politW1S +
peer * compaW1P + aa * politW1P
agree2 =~ compaW2S + a * politW2S +
peer * compaW2P + aa * politW2P
agree3 =~ compaW3S + a * politW3S +
peer * compaW3P + aa * politW3P
agree4 =~ compaW4S + a * politW4S +
peer * compaW4P + aa * politW4P
# structural paths between time points
agree4 ~ agree3
agree3 ~ agree2
agree2 ~ agree1
# error covariance - similar aspects across waves and informants
compaW1S ~~ compaW2S + compaW3S + compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW2S ~~ compaW3S + compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW3S ~~ compaW4S +
compaW1P + compaW2P + compaW3P + compaW4P
compaW4S ~~ compaW1P + compaW2P + compaW3P + compaW4P
politW1S ~~ politW2S + politW3S + politW4S +
politW1P + politW2P + politW3P + politW4P
politW2S ~~ politW3S + politW4S +
politW1P + politW2P + politW3P + politW4P
politW3S ~~ politW4S +
politW1P + politW2P + politW3P + politW4P
politW4S ~~ politW1P + politW2P + politW3P + politW4P
compaW1P ~~ compaW2P + compaW3P + compaW4P
compaW2P ~~ compaW3P + compaW4P
compaW3P ~~ compaW4P
politW1P ~~ politW2P + politW3P + politW4P
politW2P ~~ politW3P + politW4P
politW3P ~~ politW4P
'
lsmAgree <- sem(lsmAgree, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: the covariance matrix of the residuals of the observed
## variables (theta) is not positive definite;
## use lavInspect(fit, "theta") to investigate.summary(lsmAgree, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 237 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 107
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 126.710
## Degrees of freedom 54
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2012.112
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962
## Tucker-Lewis Index (TLI) 0.915
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1072.147
## Loglikelihood unrestricted model (H1) -1008.792
##
## Akaike (AIC) 2340.294
## Bayesian (BIC) 2688.863
## Sample-size adjusted Bayesian (BIC) 2378.168
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.072
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.088
## P-value RMSEA <= 0.05 0.014
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.102
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree1 =~
## compW1S 1.000 0.153 0.327
## poltW1S (a) 1.322 NA 0.202 0.378
## compW1P (peer) 3.363 1.062 3.167 0.002 0.514 0.887
## poltW1P (aa) 2.567 NA 0.392 0.666
## agree2 =~
## compW2S 1.000 0.156 0.323
## poltW2S (a) 1.322 NA 0.206 0.382
## compW2P (peer) 3.363 1.062 3.167 0.002 0.523 0.911
## poltW2P (aa) 2.567 NA 0.399 0.706
## agree3 =~
## compW3S 1.000 0.158 0.330
## poltW3S (a) 1.322 NA 0.208 0.378
## compW3P (peer) 3.363 1.062 3.167 0.002 0.530 0.954
## poltW3P (aa) 2.567 NA 0.405 0.642
## agree4 =~
## compW4S 1.000 0.169 0.349
## poltW4S (a) 1.322 NA 0.223 0.416
## compW4P (peer) 3.363 1.062 3.167 0.002 0.568 0.981
## poltW4P (aa) 2.567 NA 0.433 0.694
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree4 ~
## agree3 1.014 0.069 14.791 0.000 0.947 0.947
## agree3 ~
## agree2 0.943 0.074 12.657 0.000 0.930 0.930
## agree2 ~
## agree1 0.844 0.077 10.995 0.000 0.829 0.829
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S ~~
## .compaW2S 0.123 NA 0.123 0.608
## .compaW3S 0.114 NA 0.114 0.573
## .compaW4S 0.116 NA 0.116 0.580
## .compaW1P 0.006 NA 0.006 0.049
## .compaW2P 0.036 NA 0.036 0.344
## .compaW3P 0.035 NA 0.035 0.479
## .compaW4P 0.008 NA 0.008 0.159
## .compaW2S ~~
## .compaW3S 0.154 NA 0.154 0.750
## .compaW4S 0.155 NA 0.155 0.751
## .compaW1P ~~
## .compaW2S 0.028 NA 0.028 0.230
## .compaW2S ~~
## .compaW2P -0.006 NA -0.006 -0.055
## .compaW3P 0.017 NA 0.017 0.228
## .compaW4P 0.010 NA 0.010 0.200
## .compaW3S ~~
## .compaW4S 0.171 NA 0.171 0.835
## .compaW1P ~~
## .compaW3S 0.014 NA 0.014 0.115
## .compaW2P ~~
## .compaW3S -0.007 NA -0.007 -0.063
## .compaW3S ~~
## .compaW3P -0.008 NA -0.008 -0.107
## .compaW4P -0.023 NA -0.023 -0.451
## .compaW1P ~~
## .compaW4S 0.002 NA 0.002 0.019
## .compaW2P ~~
## .compaW4S -0.010 NA -0.010 -0.091
## .compaW3P ~~
## .compaW4S 0.003 NA 0.003 0.041
## .compaW4S ~~
## .compaW4P -0.026 NA -0.026 -0.515
## .politW1S ~~
## .politW2S 0.183 NA 0.183 0.742
## .politW3S 0.167 NA 0.167 0.663
## .politW4S 0.165 NA 0.165 0.684
## .politW1P 0.085 NA 0.085 0.389
## .politW2P 0.058 NA 0.058 0.293
## .politW3P 0.061 NA 0.061 0.254
## .politW4P 0.065 NA 0.065 0.293
## .politW2S ~~
## .politW3S 0.199 NA 0.199 0.786
## .politW4S 0.183 NA 0.183 0.754
## .politW1P ~~
## .politW2S 0.089 NA 0.089 0.405
## .politW2S ~~
## .politW2P 0.057 NA 0.057 0.287
## .politW3P 0.033 NA 0.033 0.139
## .politW4P 0.053 NA 0.053 0.235
## .politW3S ~~
## .politW4S 0.209 NA 0.209 0.841
## .politW1P ~~
## .politW3S 0.095 NA 0.095 0.422
## .politW2P ~~
## .politW3S 0.063 NA 0.063 0.306
## .politW3S ~~
## .politW3P 0.040 NA 0.040 0.162
## .politW4P 0.057 NA 0.057 0.246
## .politW1P ~~
## .politW4S 0.091 NA 0.091 0.426
## .politW2P ~~
## .politW4S 0.073 NA 0.073 0.373
## .politW3P ~~
## .politW4S 0.056 NA 0.056 0.236
## .politW4S ~~
## .politW4P 0.069 NA 0.069 0.315
## .compaW1P ~~
## .compaW2P -0.016 NA -0.016 -0.247
## .compaW3P 0.015 NA 0.015 0.332
## .compaW4P 0.000 NA 0.000 0.002
## .compaW2P ~~
## .compaW3P -0.010 NA -0.010 -0.263
## .compaW4P -0.007 NA -0.007 -0.262
## .compaW3P ~~
## .compaW4P -0.014 NA -0.014 -0.761
## .politW1P ~~
## .politW2P 0.114 NA 0.114 0.644
## .politW3P 0.110 NA 0.110 0.517
## .politW4P 0.130 NA 0.130 0.657
## .politW2P ~~
## .politW3P 0.123 NA 0.123 0.636
## .politW4P 0.124 NA 0.124 0.688
## .politW3P ~~
## .politW4P 0.167 NA 0.167 0.769
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S 4.146 0.029 142.461 0.000 4.146 8.861
## .politW1S 3.698 0.033 111.332 0.000 3.698 6.923
## .compaW1P 3.932 0.045 87.826 0.000 3.932 6.787
## .politW1P 3.826 0.044 86.323 0.000 3.826 6.490
## .compaW2S 4.126 0.032 127.505 0.000 4.126 8.577
## .politW2S 3.734 0.036 105.155 0.000 3.734 6.934
## .compaW2P 3.978 0.045 88.720 0.000 3.978 6.926
## .politW2P 3.802 0.044 87.285 0.000 3.802 6.719
## .compaW3S 4.133 0.033 127.091 0.000 4.133 8.646
## .politW3S 3.733 0.037 101.172 0.000 3.733 6.778
## .compaW3P 3.974 0.043 91.389 0.000 3.974 7.154
## .politW3P 3.796 0.050 75.211 0.000 3.796 6.022
## .compaW4S 4.196 0.034 122.690 0.000 4.196 8.669
## .politW4S 3.770 0.037 101.218 0.000 3.770 7.031
## .compaW4P 3.891 0.048 80.829 0.000 3.891 6.723
## .politW4P 3.707 0.051 72.835 0.000 3.707 5.932
## agree1 0.000 0.000 0.000
## .agree2 0.000 0.000 0.000
## .agree3 0.000 0.000 0.000
## .agree4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S 0.196 NA 0.196 0.893
## .politW1S 0.245 NA 0.245 0.857
## .compaW1P 0.071 NA 0.071 0.213
## .politW1P 0.194 NA 0.194 0.557
## .compaW2S 0.207 NA 0.207 0.896
## .politW2S 0.248 NA 0.248 0.854
## .compaW2P 0.056 NA 0.056 0.171
## .politW2P 0.161 NA 0.161 0.502
## .compaW3S 0.204 NA 0.204 0.891
## .politW3S 0.260 NA 0.260 0.857
## .compaW3P 0.028 NA 0.028 0.089
## .politW3P 0.234 NA 0.234 0.588
## .compaW4S 0.206 NA 0.206 0.878
## .politW4S 0.238 NA 0.238 0.827
## .compaW4P 0.013 NA 0.013 0.037
## .politW4P 0.203 NA 0.203 0.519
## agree1 0.023 NA 1.000 1.000
## .agree2 0.008 NA 0.312 0.312
## .agree3 0.003 NA 0.135 0.135
## .agree4 0.003 NA 0.104 0.104semPaths(lsmAgree, what = "col", whatLabels = "est", structural = T, layout = "spring")
with random parcels
lsmAgree <- '
# factor at each time point with same loading
agree1 =~ agreeW1S1 + a * agreeW1S2 +
peer * agreeW1P1 + aa * agreeW1P2
agree2 =~ agreeW2S1 + a * agreeW2S2 +
peer * agreeW2P1 + aa * agreeW2P2
agree3 =~ agreeW3S1 + a * agreeW3S2 +
peer * agreeW3P1 + aa * agreeW3P2
agree4 =~ agreeW4S1 + a * agreeW4S2 +
peer * agreeW4P1 + aa * agreeW4P2
# structural paths between time points
agree4 ~ agree3
agree3 ~ agree2
agree2 ~ agree1
# error covariance - similar parcels across waves
agreeW1S1 ~~ agreeW2S1 + agreeW3S1 + agreeW4S1
agreeW2S1 ~~ agreeW3S1 + agreeW4S1
agreeW3S1 ~~ agreeW4S1
agreeW1S2 ~~ agreeW2S2 + agreeW3S2 + agreeW4S2
agreeW2S2 ~~ agreeW3S2 + agreeW4S2
agreeW3S2 ~~ agreeW4S2
agreeW1P1 ~~ agreeW2P1 + agreeW3P1 + agreeW4P1
agreeW2P1 ~~ agreeW3P1 + agreeW4P1
agreeW3P1 ~~ agreeW4P1
agreeW1P2 ~~ agreeW2P2 + agreeW3P2 + agreeW4P2
agreeW2P2 ~~ agreeW3P2 + agreeW4P2
agreeW3P2 ~~ agreeW4P2
# error covariance - same method at one wave
agreeW1S1 ~~ agreeW1S2
agreeW1P1 ~~ agreeW1P2
agreeW2S1 ~~ agreeW2S2
agreeW2P1 ~~ agreeW2P2
agreeW3S1 ~~ agreeW3S2
agreeW3P1 ~~ agreeW3P2
agreeW4S1 ~~ agreeW4S2
agreeW4P1 ~~ agreeW4P2
'
lsmAgree <- sem(lsmAgree, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lsmAgree, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 323 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree1 =~
## agrW1S1 1.000 2.022 0.944
## agrW1S2 (a) 1.115 NA 2.255 0.948
## agrW1P1 (peer) 1.074 NA 2.172 0.927
## agrW1P2 (aa) 0.985 NA 1.991 0.948
## agree2 =~
## agrW2S1 1.000 3.116 0.996
## agrW2S2 (a) 1.115 NA 3.475 0.992
## agrW2P1 (peer) 1.074 NA 3.347 0.983
## agrW2P2 (aa) 0.985 NA 3.068 0.981
## agree3 =~
## agrW3S1 1.000 3.116 0.988
## agrW3S2 (a) 1.115 NA 3.476 0.989
## agrW3P1 (peer) 1.074 NA 3.347 0.981
## agrW3P2 (aa) 0.985 NA 3.069 0.981
## agree4 =~
## agrW4S1 1.000 3.152 0.990
## agrW4S2 (a) 1.115 NA 3.516 0.984
## agrW4P1 (peer) 1.074 NA 3.386 0.979
## agrW4P2 (aa) 0.985 NA 3.104 0.979
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## agree4 ~
## agree3 1.011 NA 0.999 0.999
## agree3 ~
## agree2 1.000 NA 1.000 1.000
## agree2 ~
## agree1 1.522 NA 0.988 0.988
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .agreeW1S1 ~~
## .agreeW2S1 0.036 NA 0.036 0.193
## .agreeW3S1 0.049 NA 0.049 0.143
## .agreeW4S1 -0.013 NA -0.013 -0.041
## .agreeW2S1 ~~
## .agreeW3S1 0.030 NA 0.030 0.230
## .agreeW4S1 0.022 NA 0.022 0.180
## .agreeW3S1 ~~
## .agreeW4S1 0.029 NA 0.029 0.135
## .agreeW1S2 ~~
## .agreeW2S2 0.121 NA 0.121 0.361
## .agreeW3S2 0.111 NA 0.111 0.286
## .agreeW4S2 0.151 NA 0.151 0.311
## .agreeW2S2 ~~
## .agreeW3S2 0.079 NA 0.079 0.344
## .agreeW4S2 0.056 NA 0.056 0.197
## .agreeW3S2 ~~
## .agreeW4S2 0.121 NA 0.121 0.366
## .agreeW1P1 ~~
## .agreeW2P1 -0.036 NA -0.036 -0.065
## .agreeW3P1 0.051 NA 0.051 0.088
## .agreeW4P1 0.090 NA 0.090 0.146
## .agreeW2P1 ~~
## .agreeW3P1 0.041 NA 0.041 0.098
## .agreeW4P1 0.154 NA 0.154 0.349
## .agreeW3P1 ~~
## .agreeW4P1 0.222 NA 0.222 0.481
## .agreeW1P2 ~~
## .agreeW2P2 0.291 NA 0.291 0.711
## .agreeW3P2 0.291 NA 0.291 0.711
## .agreeW4P2 0.314 NA 0.314 0.715
## .agreeW2P2 ~~
## .agreeW3P2 0.370 NA 0.370 1.000
## .agreeW4P2 0.403 NA 0.403 1.012
## .agreeW3P2 ~~
## .agreeW4P2 0.403 NA 0.403 1.012
## .agreeW1S1 ~~
## .agreeW1S2 0.417 NA 0.417 0.778
## .agreeW1P1 ~~
## .agreeW1P2 0.415 NA 0.415 0.703
## .agreeW2S1 ~~
## .agreeW2S2 0.036 NA 0.036 0.302
## .agreeW2P1 ~~
## .agreeW2P2 0.000 NA 0.000 0.000
## .agreeW3S1 ~~
## .agreeW3S2 0.148 NA 0.148 0.598
## .agreeW3P1 ~~
## .agreeW3P2 0.000 NA 0.000 0.000
## .agreeW4S1 ~~
## .agreeW4S2 0.211 NA 0.211 0.729
## .agreeW4P1 ~~
## .agreeW4P2 -0.005 NA -0.005 -0.011
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .agreeW1S1 1.551 NA 1.551 0.724
## .agreeW1S2 1.463 NA 1.463 0.615
## .agreeW1P1 1.029 NA 1.029 0.439
## .agreeW1P2 1.197 NA 1.197 0.570
## .agreeW2S1 0.348 NA 0.348 0.111
## .agreeW2S2 0.072 NA 0.072 0.021
## .agreeW2P1 0.079 NA 0.079 0.023
## .agreeW2P2 0.139 NA 0.139 0.045
## .agreeW3S1 0.419 NA 0.419 0.133
## .agreeW3S2 0.096 NA 0.096 0.027
## .agreeW3P1 0.089 NA 0.089 0.026
## .agreeW3P2 0.139 NA 0.139 0.044
## .agreeW4S1 0.381 NA 0.381 0.120
## .agreeW4S2 0.074 NA 0.074 0.021
## .agreeW4P1 -0.052 NA -0.052 -0.015
## .agreeW4P2 0.103 NA 0.103 0.033
## agree1 0.000 0.000 0.000
## .agree2 0.000 0.000 0.000
## .agree3 0.000 0.000 0.000
## .agree4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .agreeW1S1 0.503 NA 0.503 0.110
## .agreeW1S2 0.570 NA 0.570 0.101
## .agreeW1P1 0.775 NA 0.775 0.141
## .agreeW1P2 0.451 NA 0.451 0.102
## .agreeW2S1 0.071 NA 0.071 0.007
## .agreeW2S2 0.198 NA 0.198 0.016
## .agreeW2P1 0.398 NA 0.398 0.034
## .agreeW2P2 0.370 NA 0.370 0.038
## .agreeW3S1 0.232 NA 0.232 0.023
## .agreeW3S2 0.266 NA 0.266 0.022
## .agreeW3P1 0.435 NA 0.435 0.037
## .agreeW3P2 0.370 NA 0.370 0.038
## .agreeW4S1 0.205 NA 0.205 0.020
## .agreeW4S2 0.410 NA 0.410 0.032
## .agreeW4P1 0.489 NA 0.489 0.041
## .agreeW4P2 0.427 NA 0.427 0.042
## agree1 4.087 NA 1.000 1.000
## .agree2 0.237 NA 0.024 0.024
## .agree3 0.000 NA 0.000 0.000
## .agree4 0.015 NA 0.002 0.002semPaths(lsmAgree, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Conscientiousness
with aspects as parcels
lsmConsci <- '
# factor at each time point with same loading
consci1 =~ indusW1S + a * orderW1S +
peer * indusW1P + aa * orderW1P
consci2 =~ indusW2S + a * orderW2S +
peer * indusW2P + aa * orderW2P
consci3 =~ indusW3S + a * orderW3S +
peer * indusW3P + aa * orderW3P
consci4 =~ indusW4S + a * orderW4S +
peer * indusW4P + aa * orderW4P
# structural paths between time points
consci4 ~ consci3
consci3 ~ consci2
consci2 ~ consci1
# error covariance - similar aspects across waves and informants
indusW1S ~~ indusW2S + indusW3S + indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW2S ~~ indusW3S + indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW3S ~~ indusW4S +
indusW1P + indusW2P + indusW3P + indusW4P
indusW4S ~~ indusW1P + indusW2P + indusW3P + indusW4P
orderW1S ~~ orderW2S + orderW3S + orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW2S ~~ orderW3S + orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW3S ~~ orderW4S +
orderW1P + orderW2P + orderW3P + orderW4P
orderW4S ~~ orderW1P + orderW2P + orderW3P + orderW4P
indusW1P ~~ indusW2P + indusW3P + indusW4P
indusW2P ~~ indusW3P + indusW4P
indusW3P ~~ indusW4P
orderW1P ~~ orderW2P + orderW3P + orderW4P
orderW2P ~~ orderW3P + orderW4P
orderW3P ~~ orderW4P
'
lsmConsci <- sem(lsmConsci, data = data, missing = "ML")
summary(lsmConsci, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 221 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 107
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 111.782
## Degrees of freedom 54
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2107.030
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.971
## Tucker-Lewis Index (TLI) 0.935
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1335.933
## Loglikelihood unrestricted model (H1) -1280.042
##
## Akaike (AIC) 2867.866
## Bayesian (BIC) 3216.436
## Sample-size adjusted Bayesian (BIC) 2905.740
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.081
## P-value RMSEA <= 0.05 0.081
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.079
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci1 =~
## indsW1S 1.000 0.219 0.364
## ordrW1S (a) 0.820 NA 0.179 0.289
## indsW1P (peer) 2.433 0.866 2.810 0.005 0.532 0.861
## ordrW1P (aa) 1.695 NA 0.371 0.584
## consci2 =~
## indsW2S 1.000 0.207 0.334
## ordrW2S (a) 0.820 NA 0.170 0.304
## indsW2P (peer) 2.433 0.866 2.810 0.005 0.504 0.828
## ordrW2P (aa) 1.695 NA 0.351 0.611
## consci3 =~
## indsW3S 1.000 0.191 0.326
## ordrW3S (a) 0.820 NA 0.157 0.269
## indsW3P (peer) 2.433 0.866 2.810 0.005 0.465 0.739
## ordrW3P (aa) 1.695 NA 0.324 0.514
## consci4 =~
## indsW4S 1.000 0.188 0.318
## ordrW4S (a) 0.820 NA 0.154 0.247
## indsW4P (peer) 2.433 0.866 2.810 0.005 0.457 0.790
## ordrW4P (aa) 1.695 NA 0.318 0.528
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci4 ~
## consci3 0.871 0.105 8.324 0.000 0.886 0.886
## consci3 ~
## consci2 0.870 0.088 9.838 0.000 0.943 0.943
## consci2 ~
## consci1 0.759 0.081 9.354 0.000 0.801 0.801
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S ~~
## .indusW2S 0.250 NA 0.250 0.765
## .indusW3S 0.213 NA 0.213 0.686
## .indusW4S 0.219 NA 0.219 0.699
## .indusW1P 0.005 NA 0.005 0.029
## .indusW2P 0.032 NA 0.032 0.165
## .indusW3P 0.041 NA 0.041 0.174
## .indusW4P 0.019 NA 0.019 0.095
## .indusW2S ~~
## .indusW3S 0.257 NA 0.257 0.792
## .indusW4S 0.268 NA 0.268 0.820
## .indusW1P ~~
## .indusW2S 0.010 NA 0.010 0.052
## .indusW2S ~~
## .indusW2P 0.035 NA 0.035 0.177
## .indusW3P 0.058 NA 0.058 0.233
## .indusW4P 0.036 NA 0.036 0.174
## .indusW3S ~~
## .indusW4S 0.263 NA 0.263 0.846
## .indusW1P ~~
## .indusW3S 0.006 NA 0.006 0.033
## .indusW2P ~~
## .indusW3S 0.030 NA 0.030 0.161
## .indusW3S ~~
## .indusW3P 0.016 NA 0.016 0.068
## .indusW4P 0.002 NA 0.002 0.009
## .indusW1P ~~
## .indusW4S 0.036 NA 0.036 0.203
## .indusW2P ~~
## .indusW4S 0.053 NA 0.053 0.280
## .indusW3P ~~
## .indusW4S 0.026 NA 0.026 0.111
## .indusW4S ~~
## .indusW4P 0.011 NA 0.011 0.055
## .orderW1S ~~
## .orderW2S 0.237 NA 0.237 0.750
## .orderW3S 0.252 NA 0.252 0.757
## .orderW4S 0.237 NA 0.237 0.661
## .orderW1P 0.077 NA 0.077 0.253
## .orderW2P 0.111 NA 0.111 0.412
## .orderW3P 0.082 NA 0.082 0.257
## .orderW4P 0.095 NA 0.095 0.314
## .orderW2S ~~
## .orderW3S 0.243 NA 0.243 0.816
## .orderW4S 0.238 NA 0.238 0.741
## .orderW1P ~~
## .orderW2S 0.094 NA 0.094 0.343
## .orderW2S ~~
## .orderW2P 0.100 NA 0.100 0.413
## .orderW3P 0.084 NA 0.084 0.291
## .orderW4P 0.085 NA 0.085 0.313
## .orderW3S ~~
## .orderW4S 0.274 NA 0.274 0.806
## .orderW1P ~~
## .orderW3S 0.088 NA 0.088 0.306
## .orderW2P ~~
## .orderW3S 0.123 NA 0.123 0.481
## .orderW3S ~~
## .orderW3P 0.074 NA 0.074 0.244
## .orderW4P 0.095 NA 0.095 0.330
## .orderW1P ~~
## .orderW4S 0.101 NA 0.101 0.326
## .orderW2P ~~
## .orderW4S 0.134 NA 0.134 0.485
## .orderW3P ~~
## .orderW4S 0.107 NA 0.107 0.326
## .orderW4S ~~
## .orderW4P 0.117 NA 0.117 0.377
## .indusW1P ~~
## .indusW2P 0.040 NA 0.040 0.373
## .indusW3P 0.080 NA 0.080 0.599
## .indusW4P 0.076 NA 0.076 0.684
## .indusW2P ~~
## .indusW3P 0.089 NA 0.089 0.619
## .indusW4P 0.086 NA 0.086 0.713
## .indusW3P ~~
## .indusW4P 0.120 NA 0.120 0.798
## .orderW1P ~~
## .orderW2P 0.168 NA 0.168 0.718
## .orderW3P 0.215 NA 0.215 0.774
## .orderW4P 0.164 NA 0.164 0.621
## .orderW2P ~~
## .orderW3P 0.190 NA 0.190 0.773
## .orderW4P 0.165 NA 0.165 0.707
## .orderW3P ~~
## .orderW4P 0.217 NA 0.217 0.782
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S 3.183 0.037 85.257 0.000 3.183 5.301
## .orderW1S 3.563 0.039 92.392 0.000 3.563 5.745
## .indusW1P 3.699 0.048 76.985 0.000 3.699 5.985
## .orderW1P 3.375 0.049 69.411 0.000 3.375 5.320
## .indusW2S 3.137 0.041 77.123 0.000 3.137 5.062
## .orderW2S 3.638 0.037 98.978 0.000 3.638 6.519
## .indusW2P 3.668 0.047 77.303 0.000 3.668 6.028
## .orderW2P 3.460 0.044 79.166 0.000 3.460 6.021
## .indusW3S 3.173 0.039 80.732 0.000 3.173 5.407
## .orderW3S 3.625 0.038 94.240 0.000 3.625 6.221
## .indusW3P 3.692 0.049 74.637 0.000 3.692 5.872
## .orderW3P 3.379 0.049 68.376 0.000 3.379 5.363
## .indusW4S 3.186 0.041 77.790 0.000 3.186 5.395
## .orderW4S 3.650 0.044 82.402 0.000 3.650 5.847
## .indusW4P 3.610 0.048 74.892 0.000 3.610 6.243
## .orderW4P 3.314 0.051 64.476 0.000 3.314 5.489
## consci1 0.000 0.000 0.000
## .consci2 0.000 0.000 0.000
## .consci3 0.000 0.000 0.000
## .consci4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S 0.313 NA 0.313 0.867
## .orderW1S 0.352 NA 0.352 0.916
## .indusW1P 0.099 NA 0.099 0.259
## .orderW1P 0.265 NA 0.265 0.659
## .indusW2S 0.341 NA 0.341 0.888
## .orderW2S 0.283 NA 0.283 0.907
## .indusW2P 0.117 NA 0.117 0.315
## .orderW2P 0.207 NA 0.207 0.627
## .indusW3S 0.308 NA 0.308 0.894
## .orderW3S 0.315 NA 0.315 0.928
## .indusW3P 0.179 NA 0.179 0.454
## .orderW3P 0.292 NA 0.292 0.736
## .indusW4S 0.314 NA 0.314 0.899
## .orderW4S 0.366 NA 0.366 0.939
## .indusW4P 0.126 NA 0.126 0.376
## .orderW4P 0.263 NA 0.263 0.722
## consci1 0.048 NA 1.000 1.000
## .consci2 0.015 NA 0.358 0.358
## .consci3 0.004 NA 0.111 0.111
## .consci4 0.008 NA 0.214 0.214semPaths(lsmConsci, what = "col", whatLabels = "est", structural = T, layout = "spring")
with random parcels
lsmconsci <- '
# factor at each time point with same loading
consci1 =~ consciW1S1 + a * consciW1S2 +
peer * consciW1P1 + aa * consciW1P2
consci2 =~ consciW2S1 + a * consciW2S2 +
peer * consciW2P1 + aa * consciW2P2
consci3 =~ consciW3S1 + a * consciW3S2 +
peer * consciW3P1 + aa * consciW3P2
consci4 =~ consciW4S1 + a * consciW4S2 +
peer * consciW4P1 + aa * consciW4P2
# structural paths between time points
consci4 ~ consci3
consci3 ~ consci2
consci2 ~ consci1
# error covariance - similar parcels across waves
consciW1S1 ~~ consciW2S1 + consciW3S1 + consciW4S1
consciW2S1 ~~ consciW3S1 + consciW4S1
consciW3S1 ~~ consciW4S1
consciW1S2 ~~ consciW2S2 + consciW3S2 + consciW4S2
consciW2S2 ~~ consciW3S2 + consciW4S2
consciW3S2 ~~ consciW4S2
consciW1P1 ~~ consciW2P1 + consciW3P1 + consciW4P1
consciW2P1 ~~ consciW3P1 + consciW4P1
consciW3P1 ~~ consciW4P1
consciW1P2 ~~ consciW2P2 + consciW3P2 + consciW4P2
consciW2P2 ~~ consciW3P2 + consciW4P2
consciW3P2 ~~ consciW4P2
# error covariance - same method at one wave
consciW1S1 ~~ consciW1S2
consciW1P1 ~~ consciW1P2
consciW2S1 ~~ consciW2S2
consciW2P1 ~~ consciW2P2
consciW3S1 ~~ consciW3S2
consciW3P1 ~~ consciW3P2
consciW4S1 ~~ consciW4S2
consciW4P1 ~~ consciW4P2
'
lsmconsci <- sem(lsmconsci, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lsmconsci, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 427 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci1 =~
## cnsW1S1 1.000 1.891 0.959
## cnsW1S2 (a) 1.038 NA 1.964 0.970
## cnsW1P1 (peer) 1.154 NA 2.182 0.929
## cnsW1P2 (aa) 0.976 NA 1.846 0.968
## consci2 =~
## cnsW2S1 1.000 2.814 0.985
## cnsW2S2 (a) 1.038 NA 2.922 0.993
## cnsW2P1 (peer) 1.154 NA 3.248 0.982
## cnsW2P2 (aa) 0.976 NA 2.746 0.976
## consci3 =~
## cnsW3S1 1.000 2.813 0.989
## cnsW3S2 (a) 1.038 NA 2.921 0.988
## cnsW3P1 (peer) 1.154 NA 3.246 0.985
## cnsW3P2 (aa) 0.976 NA 2.745 0.976
## consci4 =~
## cnsW4S1 1.000 2.839 0.982
## cnsW4S2 (a) 1.038 NA 2.948 0.987
## cnsW4P1 (peer) 1.154 NA 3.277 0.979
## cnsW4P2 (aa) 0.976 NA 2.771 0.976
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## consci4 ~
## consci3 1.009 NA 1.000 1.000
## consci3 ~
## consci2 0.999 NA 1.000 1.000
## consci2 ~
## consci1 1.491 NA 1.002 1.002
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .consciW1S1 ~~
## .consciW2S1 0.117 NA 0.117 0.430
## .consciW3S1 0.081 NA 0.081 0.342
## .consciW4S1 0.172 NA 0.172 0.560
## .consciW2S1 ~~
## .consciW3S1 0.084 NA 0.084 0.403
## .consciW4S1 0.199 NA 0.199 0.738
## .consciW3S1 ~~
## .consciW4S1 0.156 NA 0.156 0.663
## .consciW1S2 ~~
## .consciW2S2 0.030 NA 0.030 0.186
## .consciW3S2 0.092 NA 0.092 0.414
## .consciW4S2 0.085 NA 0.085 0.358
## .consciW2S2 ~~
## .consciW3S2 0.074 NA 0.074 0.486
## .consciW4S2 0.073 NA 0.073 0.451
## .consciW3S2 ~~
## .consciW4S2 0.160 NA 0.160 0.722
## .consciW1P1 ~~
## .consciW2P1 0.022 NA 0.022 0.041
## .consciW3P1 0.216 NA 0.216 0.442
## .consciW4P1 0.134 NA 0.134 0.227
## .consciW2P1 ~~
## .consciW3P1 0.030 NA 0.030 0.085
## .consciW4P1 0.297 NA 0.297 0.706
## .consciW3P1 ~~
## .consciW4P1 0.070 NA 0.070 0.183
## .consciW1P2 ~~
## .consciW2P2 0.223 NA 0.223 0.759
## .consciW3P2 0.223 NA 0.223 0.759
## .consciW4P2 0.225 NA 0.225 0.756
## .consciW2P2 ~~
## .consciW3P2 0.373 NA 0.373 1.000
## .consciW4P2 0.377 NA 0.377 1.001
## .consciW3P2 ~~
## .consciW4P2 0.377 NA 0.377 1.001
## .consciW1S1 ~~
## .consciW1S2 0.159 NA 0.159 0.585
## .consciW1P1 ~~
## .consciW1P2 0.212 NA 0.212 0.506
## .consciW2S1 ~~
## .consciW2S2 0.048 NA 0.048 0.294
## .consciW2P1 ~~
## .consciW2P2 -0.000 NA -0.000 -0.000
## .consciW3S1 ~~
## .consciW3S2 0.051 NA 0.051 0.261
## .consciW3P1 ~~
## .consciW3P2 -0.000 NA -0.000 -0.000
## .consciW4S1 ~~
## .consciW4S2 0.010 NA 0.010 0.036
## .consciW4P1 ~~
## .consciW4P2 0.002 NA 0.002 0.005
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .consciW1S1 1.187 NA 1.187 0.602
## .consciW1S2 1.307 NA 1.307 0.646
## .consciW1P1 1.163 NA 1.163 0.495
## .consciW1P2 1.232 NA 1.232 0.646
## .consciW2S1 0.163 NA 0.163 0.057
## .consciW2S2 0.214 NA 0.214 0.073
## .consciW2P1 0.104 NA 0.104 0.031
## .consciW2P2 0.245 NA 0.245 0.087
## .consciW3S1 0.177 NA 0.177 0.062
## .consciW3S2 0.214 NA 0.214 0.072
## .consciW3P1 0.244 NA 0.244 0.074
## .consciW3P2 0.247 NA 0.247 0.088
## .consciW4S1 0.162 NA 0.162 0.056
## .consciW4S2 0.188 NA 0.188 0.063
## .consciW4P1 -0.049 NA -0.049 -0.015
## .consciW4P2 0.216 NA 0.216 0.076
## consci1 0.000 0.000 0.000
## .consci2 0.000 0.000 0.000
## .consci3 0.000 0.000 0.000
## .consci4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .consciW1S1 0.309 NA 0.309 0.079
## .consciW1S2 0.239 NA 0.239 0.058
## .consciW1P1 0.755 NA 0.755 0.137
## .consciW1P2 0.232 NA 0.232 0.064
## .consciW2S1 0.239 NA 0.239 0.029
## .consciW2S2 0.112 NA 0.112 0.013
## .consciW2P1 0.385 NA 0.385 0.035
## .consciW2P2 0.374 NA 0.374 0.047
## .consciW3S1 0.182 NA 0.182 0.023
## .consciW3S2 0.207 NA 0.207 0.024
## .consciW3P1 0.315 NA 0.315 0.029
## .consciW3P2 0.373 NA 0.373 0.047
## .consciW4S1 0.305 NA 0.305 0.036
## .consciW4S2 0.236 NA 0.236 0.026
## .consciW4P1 0.462 NA 0.462 0.041
## .consciW4P2 0.381 NA 0.381 0.047
## consci1 3.576 NA 1.000 1.000
## .consci2 -0.027 NA -0.003 -0.003
## .consci3 -0.000 NA -0.000 -0.000
## .consci4 0.001 NA 0.000 0.000semPaths(lsmconsci, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Extraversion
with aspects as parcels
lsmExtra <- '
# factor at each time point with same loading
extra1 =~ assertW1S + a * enthuW1S +
peer * assertW1P + aa * enthuW1P
extra2 =~ assertW2S + a * enthuW2S +
peer * assertW2P + aa * enthuW2P
extra3 =~ assertW3S + a * enthuW3S +
peer * assertW3P + aa * enthuW3P
extra4 =~ assertW4S + a * enthuW4S +
peer * assertW4P + aa * enthuW4P
# structural paths between time points
extra4 ~ extra3
extra3 ~ extra2
extra2 ~ extra1
# error covariance - similar aspects across waves and informants
assertW1S ~~ assertW2S + assertW3S + assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW2S ~~ assertW3S + assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW3S ~~ assertW4S +
assertW1P + assertW2P + assertW3P + assertW4P
assertW4S ~~ assertW1P + assertW2P + assertW3P + assertW4P
enthuW1S ~~ enthuW2S + enthuW3S + enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW2S ~~ enthuW3S + enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW3S ~~ enthuW4S +
enthuW1P + enthuW2P + enthuW3P + enthuW4P
enthuW4S ~~ enthuW1P + enthuW2P + enthuW3P + enthuW4P
assertW1P ~~ assertW2P + assertW3P + assertW4P
assertW2P ~~ assertW3P + assertW4P
assertW3P ~~ assertW4P
enthuW1P ~~ enthuW2P + enthuW3P + enthuW4P
enthuW2P ~~ enthuW3P + enthuW4P
enthuW3P ~~ enthuW4P
'
lsmExtra <- sem(lsmExtra, data = data, missing = "ML")
summary(lsmExtra, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 254 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 107
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 118.553
## Degrees of freedom 54
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2412.811
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.972
## Tucker-Lewis Index (TLI) 0.937
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1261.787
## Loglikelihood unrestricted model (H1) -1202.510
##
## Akaike (AIC) 2719.573
## Bayesian (BIC) 3068.142
## Sample-size adjusted Bayesian (BIC) 2757.447
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.068
## 90 Percent confidence interval - lower 0.051
## 90 Percent confidence interval - upper 0.085
## P-value RMSEA <= 0.05 0.038
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.176
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## extra1 =~
## assrW1S 1.000 0.291 0.432
## enthW1S (a) 0.701 40.976 0.017 0.986 0.204 0.323
## assrW1P (peer) -0.907 0.242 -3.751 0.000 -0.264 -0.444
## enthW1P (aa) -0.561 32.784 -0.017 0.986 -0.163 -0.265
## extra2 =~
## assrW2S 1.000 0.304 0.447
## enthW2S (a) 0.701 40.976 0.017 0.986 0.213 0.336
## assrW2P (peer) -0.907 0.242 -3.751 0.000 -0.276 -0.482
## enthW2P (aa) -0.561 32.784 -0.017 0.986 -0.171 -0.286
## extra3 =~
## assrW3S 1.000 0.310 0.460
## enthW3S (a) 0.701 40.976 0.017 0.986 0.218 0.343
## assrW3P (peer) -0.907 0.242 -3.751 0.000 -0.282 -0.497
## enthW3P (aa) -0.561 32.784 -0.017 0.986 -0.174 -0.337
## extra4 =~
## assrW4S 1.000 0.329 0.480
## enthW4S (a) 0.701 40.976 0.017 0.986 0.231 0.354
## assrW4P (peer) -0.907 0.242 -3.751 0.000 -0.298 -0.574
## enthW4P (aa) -0.561 32.784 -0.017 0.986 -0.185 -0.376
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## extra4 ~
## extra3 0.978 0.101 9.645 0.000 0.922 0.922
## extra3 ~
## extra2 0.817 0.115 7.101 0.000 0.799 0.799
## extra2 ~
## extra1 0.811 0.112 7.210 0.000 0.776 0.776
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S ~~
## .assertW2S 0.315 4.002 0.079 0.937 0.315 0.854
## .assertW3S 0.318 3.268 0.097 0.922 0.318 0.875
## .assertW4S 0.296 3.194 0.093 0.926 0.296 0.812
## .assertW1P 0.247 4.478 0.055 0.956 0.247 0.766
## .assertW2P 0.213 3.629 0.059 0.953 0.213 0.702
## .assertW3P 0.195 2.964 0.066 0.948 0.195 0.653
## .assertW4P 0.169 2.897 0.058 0.953 0.169 0.654
## .assertW2S ~~
## .assertW3S 0.325 4.403 0.074 0.941 0.325 0.893
## .assertW4S 0.315 4.304 0.073 0.942 0.315 0.863
## .assertW1P ~~
## .assertW2S 0.237 3.629 0.065 0.948 0.237 0.732
## .assertW2S ~~
## .assertW2P 0.238 4.890 0.049 0.961 0.238 0.781
## .assertW3P 0.209 3.993 0.052 0.958 0.209 0.702
## .assertW4P 0.173 3.904 0.044 0.965 0.173 0.670
## .assertW3S ~~
## .assertW4S 0.321 5.502 0.058 0.954 0.321 0.888
## .assertW1P ~~
## .assertW3S 0.201 2.964 0.068 0.946 0.201 0.630
## .assertW2P ~~
## .assertW3S 0.200 3.993 0.050 0.960 0.200 0.664
## .assertW3S ~~
## .assertW3P 0.201 5.105 0.039 0.969 0.201 0.682
## .assertW4P 0.173 4.990 0.035 0.972 0.173 0.675
## .assertW1P ~~
## .assertW4S 0.203 2.897 0.070 0.944 0.203 0.635
## .assertW2P ~~
## .assertW4S 0.208 3.904 0.053 0.958 0.208 0.689
## .assertW3P ~~
## .assertW4S 0.197 4.990 0.040 0.968 0.197 0.668
## .assertW4S ~~
## .assertW4P 0.205 5.738 0.036 0.972 0.205 0.799
## .enthuW1S ~~
## .enthuW2S 0.281 1.969 0.143 0.887 0.281 0.788
## .enthuW3S 0.257 1.608 0.160 0.873 0.257 0.721
## .enthuW4S 0.269 1.572 0.171 0.864 0.269 0.738
## .enthuW1P 0.188 1.944 0.097 0.923 0.188 0.530
## .enthuW2P 0.175 1.576 0.111 0.912 0.175 0.514
## .enthuW3P 0.130 1.287 0.101 0.920 0.130 0.448
## .enthuW4P 0.083 1.258 0.066 0.947 0.083 0.307
## .enthuW2S ~~
## .enthuW3S 0.298 2.167 0.137 0.891 0.298 0.837
## .enthuW4S 0.297 2.118 0.140 0.888 0.297 0.816
## .enthuW1P ~~
## .enthuW2S 0.172 1.576 0.109 0.913 0.172 0.487
## .enthuW2S ~~
## .enthuW2P 0.183 2.123 0.086 0.931 0.183 0.536
## .enthuW3P 0.139 1.734 0.080 0.936 0.139 0.479
## .enthuW4P 0.067 1.695 0.039 0.969 0.067 0.245
## .enthuW3S ~~
## .enthuW4S 0.305 2.708 0.113 0.910 0.305 0.839
## .enthuW1P ~~
## .enthuW3S 0.190 1.287 0.148 0.882 0.190 0.539
## .enthuW2P ~~
## .enthuW3S 0.199 1.734 0.115 0.908 0.199 0.586
## .enthuW3S ~~
## .enthuW3P 0.147 2.216 0.066 0.947 0.147 0.506
## .enthuW4P 0.110 2.166 0.051 0.960 0.110 0.404
## .enthuW1P ~~
## .enthuW4S 0.196 1.258 0.156 0.876 0.196 0.542
## .enthuW2P ~~
## .enthuW4S 0.208 1.695 0.123 0.902 0.208 0.596
## .enthuW3P ~~
## .enthuW4S 0.149 2.166 0.069 0.945 0.149 0.503
## .enthuW4S ~~
## .enthuW4P 0.105 2.491 0.042 0.966 0.105 0.377
## .assertW1P ~~
## .assertW2P 0.207 3.292 0.063 0.950 0.207 0.777
## .assertW3P 0.214 2.688 0.080 0.936 0.214 0.820
## .assertW4P 0.179 2.628 0.068 0.946 0.179 0.789
## .assertW2P ~~
## .assertW3P 0.214 3.622 0.059 0.953 0.214 0.869
## .assertW4P 0.180 3.540 0.051 0.959 0.180 0.845
## .assertW3P ~~
## .assertW4P 0.167 4.526 0.037 0.971 0.167 0.797
## .enthuW1P ~~
## .enthuW2P 0.276 1.261 0.219 0.827 0.276 0.815
## .enthuW3P 0.223 1.030 0.216 0.829 0.223 0.772
## .enthuW4P 0.181 1.007 0.180 0.857 0.181 0.670
## .enthuW2P ~~
## .enthuW3P 0.219 1.387 0.158 0.875 0.219 0.788
## .enthuW4P 0.180 1.356 0.133 0.894 0.180 0.693
## .enthuW3P ~~
## .enthuW4P 0.174 1.733 0.101 0.920 0.174 0.788
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S 3.461 0.042 82.755 0.000 3.461 5.145
## .enthuW1S 3.729 0.039 95.071 0.000 3.729 5.912
## .assertW1P 3.587 0.045 79.736 0.000 3.587 6.039
## .enthuW1P 3.805 0.047 81.626 0.000 3.805 6.189
## .assertW2S 3.414 0.044 77.620 0.000 3.414 5.028
## .enthuW2S 3.697 0.041 89.163 0.000 3.697 5.836
## .assertW2P 3.649 0.044 83.362 0.000 3.649 6.381
## .enthuW2P 3.814 0.046 83.527 0.000 3.814 6.398
## .assertW3S 3.409 0.044 77.471 0.000 3.409 5.046
## .enthuW3S 3.675 0.042 87.074 0.000 3.675 5.792
## .assertW3P 3.613 0.044 82.141 0.000 3.613 6.384
## .enthuW3P 3.853 0.041 94.328 0.000 3.853 7.461
## .assertW4S 3.454 0.047 73.710 0.000 3.454 5.038
## .enthuW4S 3.734 0.045 82.923 0.000 3.734 5.724
## .assertW4P 3.619 0.043 84.431 0.000 3.619 6.958
## .enthuW4P 3.816 0.042 90.519 0.000 3.816 7.768
## extra1 0.000 0.000 0.000
## .extra2 0.000 0.000 0.000
## .extra3 0.000 0.000 0.000
## .extra4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S 0.368 4.937 0.075 0.941 0.368 0.813
## .enthuW1S 0.356 2.430 0.147 0.883 0.356 0.895
## .assertW1P 0.283 4.061 0.070 0.944 0.283 0.803
## .enthuW1P 0.351 1.556 0.226 0.821 0.351 0.930
## .assertW2S 0.369 5.392 0.068 0.945 0.369 0.800
## .enthuW2S 0.356 2.654 0.134 0.893 0.356 0.887
## .assertW2P 0.251 4.435 0.057 0.955 0.251 0.768
## .enthuW2P 0.326 1.699 0.192 0.848 0.326 0.918
## .assertW3S 0.360 5.628 0.064 0.949 0.360 0.789
## .enthuW3S 0.355 2.770 0.128 0.898 0.355 0.882
## .assertW3P 0.241 4.630 0.052 0.958 0.241 0.753
## .enthuW3P 0.236 1.773 0.133 0.894 0.236 0.886
## .assertW4S 0.362 6.327 0.057 0.954 0.362 0.770
## .enthuW4S 0.372 3.113 0.120 0.905 0.372 0.875
## .assertW4P 0.181 5.204 0.035 0.972 0.181 0.671
## .enthuW4P 0.207 1.993 0.104 0.917 0.207 0.859
## extra1 0.085 4.937 0.017 0.986 1.000 1.000
## .extra2 0.037 2.148 0.017 0.986 0.398 0.398
## .extra3 0.035 2.033 0.017 0.986 0.361 0.361
## .extra4 0.016 0.948 0.017 0.986 0.150 0.150semPaths(lsmExtra, what = "col", whatLabels = "est", structural = T, layout = "spring")
with random parcels
lsmExtra <- '
# factor at each time point with same loading
extra1 =~ extraW1S1 + a * extraW1S2 +
peer * extraW1P1 + aa * extraW1P2
extra2 =~ extraW2S1 + a * extraW2S2 +
peer * extraW2P1 + aa * extraW2P2
extra3 =~ extraW3S1 + a * extraW3S2 +
peer * extraW3P1 + aa * extraW3P2
extra4 =~ extraW4S1 + a * extraW4S2 +
peer * extraW4P1 + aa * extraW4P2
# structural paths between time points
extra4 ~ extra3
extra3 ~ extra2
extra2 ~ extra1
# error covariance - similar parcels across waves
extraW1S1 ~~ extraW2S1 + extraW3S1 + extraW4S1
extraW2S1 ~~ extraW3S1 + extraW4S1
extraW3S1 ~~ extraW4S1
extraW1S2 ~~ extraW2S2 + extraW3S2 + extraW4S2
extraW2S2 ~~ extraW3S2 + extraW4S2
extraW3S2 ~~ extraW4S2
extraW1P1 ~~ extraW2P1 + extraW3P1 + extraW4P1
extraW2P1 ~~ extraW3P1 + extraW4P1
extraW3P1 ~~ extraW4P1
extraW1P2 ~~ extraW2P2 + extraW3P2 + extraW4P2
extraW2P2 ~~ extraW3P2 + extraW4P2
extraW3P2 ~~ extraW4P2
# error covariance - same method at one wave
extraW1S1 ~~ extraW1S2
extraW1P1 ~~ extraW1P2
extraW2S1 ~~ extraW2S2
extraW2P1 ~~ extraW2P2
extraW3S1 ~~ extraW3S2
extraW3P1 ~~ extraW3P2
extraW4S1 ~~ extraW4S2
extraW4P1 ~~ extraW4P2
'
lsmExtra <- sem(lsmExtra, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Error in validObject(.Object): invalid class "Fit" object: invalid object for slot "fx.group" in class "Fit": got class "NULL", should be or extend class "numeric"summary(lsmExtra, fit.measures = T, standardized = T)## Length Class Mode
## 1 character charactersemPaths(lsmExtra, what = "col", whatLabels = "est", structural = T, layout = "spring")## Error in semPlotModel.default("\n\n# factor at each time point with same loading\nextra1 =~ extraW1S1 + a * extraW1S2 + \n peer * extraW1P1 + aa * extraW1P2\n\nextra2 =~ extraW2S1 + a * extraW2S2 + \n peer * extraW2P1 + aa * extraW2P2\n\nextra3 =~ extraW3S1 + a * extraW3S2 + \n peer * extraW3P1 + aa * extraW3P2\n \nextra4 =~ extraW4S1 + a * extraW4S2 + \n peer * extraW4P1 + aa * extraW4P2\n\n# structural paths between time points \nextra4 ~ extra3\nextra3 ~ extra2\nextra2 ~ extra1\n\n# error covariance - similar parcels across waves\nextraW1S1 ~~ extraW2S1 + extraW3S1 + extraW4S1\nextraW2S1 ~~ extraW3S1 + extraW4S1\nextraW3S1 ~~ extraW4S1\n\nextraW1S2 ~~ extraW2S2 + extraW3S2 + extraW4S2\nextraW2S2 ~~ extraW3S2 + extraW4S2\nextraW3S2 ~~ extraW4S2\n\nextraW1P1 ~~ extraW2P1 + extraW3P1 + extraW4P1\nextraW2P1 ~~ extraW3P1 + extraW4P1\nextraW3P1 ~~ extraW4P1\n\nextraW1P2 ~~ extraW2P2 + extraW3P2 + extraW4P2\nextraW2P2 ~~ extraW3P2 + extraW4P2\nextraW3P2 ~~ extraW4P2\n\n# error covariance - same method at one wave\nextraW1S1 ~~ extraW1S2\nextraW1P1 ~~ extraW1P2\nextraW2S1 ~~ extraW2S2\nextraW2P1 ~~ extraW2P2\nextraW3S1 ~~ extraW3S2\nextraW3P1 ~~ extraW3P2\nextraW4S1 ~~ extraW4S2\nextraW4P1 ~~ extraW4P2\n", : Input string neither an existing file or Lavaan model.LSM Neuroticism
with aspects as parcels
lsmNeuro <- '
# factor at each time point with same loading
neuro1 =~ volatW1S + a * withdW1S +
peer * volatW1P + aa * withdW1P
neuro2 =~ volatW2S + a * withdW2S +
peer * volatW2P + aa * withdW2P
neuro3 =~ volatW3S + a * withdW3S +
peer * volatW3P + aa * withdW3P
neuro4 =~ volatW4S + a * withdW4S +
peer * volatW4P + aa * withdW4P
# structural paths between time points
neuro4 ~ neuro3
neuro3 ~ neuro2
neuro2 ~ neuro1
# error covariance - similar aspects across waves and informants
volatW1S ~~ volatW2S + volatW3S + volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW2S ~~ volatW3S + volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW3S ~~ volatW4S +
volatW1P + volatW2P + volatW3P + volatW4P
volatW4S ~~ volatW1P + volatW2P + volatW3P + volatW4P
withdW1S ~~ withdW2S + withdW3S + withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW2S ~~ withdW3S + withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW3S ~~ withdW4S +
withdW1P + withdW2P + withdW3P + withdW4P
withdW4S ~~ withdW1P + withdW2P + withdW3P + withdW4P
volatW1P ~~ volatW2P + volatW3P + volatW4P
volatW2P ~~ volatW3P + volatW4P
volatW3P ~~ volatW4P
withdW1P ~~ withdW2P + withdW3P + withdW4P
withdW2P ~~ withdW3P + withdW4P
withdW3P ~~ withdW4P
'
lsmNeuro <- sem(lsmNeuro, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: the covariance matrix of the residuals of the observed
## variables (theta) is not positive definite;
## use lavInspect(fit, "theta") to investigate.summary(lsmNeuro, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 190 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 107
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 216.924
## Degrees of freedom 54
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2496.820
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.931
## Tucker-Lewis Index (TLI) 0.848
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1657.441
## Loglikelihood unrestricted model (H1) -1548.979
##
## Akaike (AIC) 3510.881
## Bayesian (BIC) 3859.450
## Sample-size adjusted Bayesian (BIC) 3548.755
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.108
## 90 Percent confidence interval - lower 0.093
## 90 Percent confidence interval - upper 0.123
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.133
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro1 =~
## voltW1S 1.000 0.571 0.750
## wthdW1S (a) 0.705 NA 0.402 0.579
## voltW1P (peer) 0.969 0.168 5.754 0.000 0.553 0.704
## wthdW1P (aa) 0.638 NA 0.364 0.537
## neuro2 =~
## voltW2S 1.000 0.603 0.763
## wthdW2S (a) 0.705 NA 0.425 0.605
## voltW2P (peer) 0.969 0.168 5.754 0.000 0.585 0.745
## wthdW2P (aa) 0.638 NA 0.385 0.620
## neuro3 =~
## voltW3S 1.000 0.553 0.742
## wthdW3S (a) 0.705 NA 0.390 0.567
## voltW3P (peer) 0.969 0.168 5.754 0.000 0.536 0.729
## wthdW3P (aa) 0.638 NA 0.353 0.553
## neuro4 =~
## voltW4S 1.000 0.589 0.752
## wthdW4S (a) 0.705 NA 0.415 0.612
## voltW4P (peer) 0.969 0.168 5.754 0.000 0.571 0.756
## wthdW4P (aa) 0.638 NA 0.376 0.589
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro4 ~
## neuro3 0.980 0.062 15.859 0.000 0.920 0.920
## neuro3 ~
## neuro2 0.870 0.043 20.428 0.000 0.949 0.949
## neuro2 ~
## neuro1 0.935 0.057 16.437 0.000 0.884 0.884
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S ~~
## .volatW2S 0.193 NA 0.193 0.748
## .volatW3S 0.172 NA 0.172 0.686
## .volatW4S 0.164 NA 0.164 0.630
## .volatW1P -0.114 NA -0.114 -0.406
## .volatW2P -0.042 NA -0.042 -0.160
## .volatW3P -0.079 NA -0.079 -0.314
## .volatW4P -0.010 NA -0.010 -0.040
## .volatW2S ~~
## .volatW3S 0.204 NA 0.204 0.801
## .volatW4S 0.178 NA 0.178 0.674
## .volatW1P ~~
## .volatW2S -0.113 NA -0.113 -0.398
## .volatW2S ~~
## .volatW2P -0.072 NA -0.072 -0.268
## .volatW3P -0.109 NA -0.109 -0.426
## .volatW4P -0.069 NA -0.069 -0.273
## .volatW3S ~~
## .volatW4S 0.170 NA 0.170 0.660
## .volatW1P ~~
## .volatW3S -0.092 NA -0.092 -0.329
## .volatW2P ~~
## .volatW3S -0.058 NA -0.058 -0.224
## .volatW3S ~~
## .volatW3P -0.143 NA -0.143 -0.570
## .volatW4P -0.096 NA -0.096 -0.390
## .volatW1P ~~
## .volatW4S -0.117 NA -0.117 -0.408
## .volatW2P ~~
## .volatW4S -0.123 NA -0.123 -0.456
## .volatW3P ~~
## .volatW4S -0.173 NA -0.173 -0.666
## .volatW4S ~~
## .volatW4P -0.175 NA -0.175 -0.685
## .withdW1S ~~
## .withdW2S 0.224 NA 0.224 0.707
## .withdW3S 0.232 NA 0.232 0.725
## .withdW4S 0.220 NA 0.220 0.725
## .withdW1P 0.009 NA 0.009 0.027
## .withdW2P 0.034 NA 0.034 0.125
## .withdW3P -0.005 NA -0.005 -0.017
## .withdW4P 0.000 NA 0.000 0.002
## .withdW2S ~~
## .withdW3S 0.234 NA 0.234 0.739
## .withdW4S 0.209 NA 0.209 0.697
## .withdW1P ~~
## .withdW2S 0.010 NA 0.010 0.031
## .withdW2S ~~
## .withdW2P 0.011 NA 0.011 0.040
## .withdW3P -0.034 NA -0.034 -0.113
## .withdW4P -0.024 NA -0.024 -0.082
## .withdW3S ~~
## .withdW4S 0.231 NA 0.231 0.760
## .withdW1P ~~
## .withdW3S 0.005 NA 0.005 0.014
## .withdW2P ~~
## .withdW3S 0.018 NA 0.018 0.066
## .withdW3S ~~
## .withdW3P -0.010 NA -0.010 -0.032
## .withdW4P -0.015 NA -0.015 -0.050
## .withdW1P ~~
## .withdW4S 0.026 NA 0.026 0.085
## .withdW2P ~~
## .withdW4S 0.035 NA 0.035 0.135
## .withdW3P ~~
## .withdW4S 0.006 NA 0.006 0.022
## .withdW4S ~~
## .withdW4P 0.011 NA 0.011 0.040
## .volatW1P ~~
## .volatW2P 0.193 NA 0.193 0.659
## .volatW3P 0.187 NA 0.187 0.666
## .volatW4P 0.161 NA 0.161 0.585
## .volatW2P ~~
## .volatW3P 0.179 NA 0.179 0.679
## .volatW4P 0.202 NA 0.202 0.780
## .volatW3P ~~
## .volatW4P 0.166 NA 0.166 0.669
## .withdW1P ~~
## .withdW2P 0.204 NA 0.204 0.729
## .withdW3P 0.244 NA 0.244 0.803
## .withdW4P 0.170 NA 0.170 0.576
## .withdW2P ~~
## .withdW3P 0.189 NA 0.189 0.729
## .withdW4P 0.179 NA 0.179 0.711
## .withdW3P ~~
## .withdW4P 0.213 NA 0.213 0.777
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S 2.780 0.047 58.711 0.000 2.780 3.650
## .withdW1S 2.994 0.043 69.352 0.000 2.994 4.312
## .volatW1P 2.537 0.060 42.428 0.000 2.537 3.230
## .withdW1P 2.561 0.052 49.665 0.000 2.561 3.772
## .volatW2S 2.788 0.051 54.405 0.000 2.788 3.525
## .withdW2S 3.030 0.046 65.615 0.000 3.030 4.309
## .volatW2P 2.601 0.059 44.057 0.000 2.601 3.312
## .withdW2P 2.569 0.047 54.263 0.000 2.569 4.132
## .volatW3S 2.751 0.049 56.247 0.000 2.751 3.694
## .withdW3S 3.001 0.045 66.119 0.000 3.001 4.365
## .volatW3P 2.598 0.057 45.265 0.000 2.598 3.536
## .withdW3P 2.598 0.049 52.543 0.000 2.598 4.074
## .volatW4S 2.772 0.055 50.567 0.000 2.772 3.540
## .withdW4S 2.964 0.047 63.139 0.000 2.964 4.370
## .volatW4P 2.669 0.061 43.656 0.000 2.669 3.534
## .withdW4P 2.608 0.053 48.823 0.000 2.608 4.088
## neuro1 0.000 0.000 0.000
## .neuro2 0.000 0.000 0.000
## .neuro3 0.000 0.000 0.000
## .neuro4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S 0.254 NA 0.254 0.438
## .withdW1S 0.320 NA 0.320 0.664
## .volatW1P 0.311 NA 0.311 0.504
## .withdW1P 0.328 NA 0.328 0.712
## .volatW2S 0.261 NA 0.261 0.418
## .withdW2S 0.314 NA 0.314 0.634
## .volatW2P 0.275 NA 0.275 0.446
## .withdW2P 0.238 NA 0.238 0.616
## .volatW3S 0.249 NA 0.249 0.449
## .withdW3S 0.321 NA 0.321 0.679
## .volatW3P 0.253 NA 0.253 0.468
## .withdW3P 0.282 NA 0.282 0.694
## .volatW4S 0.267 NA 0.267 0.435
## .withdW4S 0.288 NA 0.288 0.626
## .volatW4P 0.245 NA 0.245 0.429
## .withdW4P 0.266 NA 0.266 0.653
## neuro1 0.326 NA 1.000 1.000
## .neuro2 0.079 NA 0.218 0.218
## .neuro3 0.030 NA 0.099 0.099
## .neuro4 0.053 NA 0.154 0.154semPaths(lsmNeuro, what = "col", whatLabels = "est", structural = T, layout = "spring")
with random parcels
lsmNeuro <- '
# factor at each time point with same loading
neuro1 =~ neuroW1S1 + a * neuroW1S2 +
peer * neuroW1P1 + aa * neuroW1P2
neuro2 =~ neuroW2S1 + a * neuroW2S2 +
peer * neuroW2P1 + aa * neuroW2P2
neuro3 =~ neuroW3S1 + a * neuroW3S2 +
peer * neuroW3P1 + aa * neuroW3P2
neuro4 =~ neuroW4S1 + a * neuroW4S2 +
peer * neuroW4P1 + aa * neuroW4P2
# structural paths between time points
neuro4 ~ neuro3
neuro3 ~ neuro2
neuro2 ~ neuro1
# error covariance - similar parcels across waves
neuroW1S1 ~~ neuroW2S1 + neuroW3S1 + neuroW4S1
neuroW2S1 ~~ neuroW3S1 + neuroW4S1
neuroW3S1 ~~ neuroW4S1
neuroW1S2 ~~ neuroW2S2 + neuroW3S2 + neuroW4S2
neuroW2S2 ~~ neuroW3S2 + neuroW4S2
neuroW3S2 ~~ neuroW4S2
neuroW1P1 ~~ neuroW2P1 + neuroW3P1 + neuroW4P1
neuroW2P1 ~~ neuroW3P1 + neuroW4P1
neuroW3P1 ~~ neuroW4P1
neuroW1P2 ~~ neuroW2P2 + neuroW3P2 + neuroW4P2
neuroW2P2 ~~ neuroW3P2 + neuroW4P2
neuroW3P2 ~~ neuroW4P2
# error covariance - same method at one wave
neuroW1S1 ~~ neuroW1S2
neuroW1P1 ~~ neuroW1P2
neuroW2S1 ~~ neuroW2S2
neuroW2P1 ~~ neuroW2P2
neuroW3S1 ~~ neuroW3S2
neuroW3P1 ~~ neuroW3P2
neuroW4S1 ~~ neuroW4S2
neuroW4P1 ~~ neuroW4P2
'
lsmNeuro <- sem(lsmNeuro, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lsmNeuro, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 290 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro1 =~
## nerW1S1 1.000 1.693 0.927
## nerW1S2 (a) 0.983 NA 1.664 0.908
## nerW1P1 (peer) 0.835 NA 1.413 0.864
## nerW1P2 (aa) 0.678 NA 1.148 0.902
## neuro2 =~
## nerW2S1 1.000 2.444 0.975
## nerW2S2 (a) 0.983 NA 2.403 0.976
## nerW2P1 (peer) 0.835 NA 2.041 0.963
## nerW2P2 (aa) 0.678 NA 1.657 0.930
## neuro3 =~
## nerW3S1 1.000 2.523 0.973
## nerW3S2 (a) 0.983 NA 2.480 0.970
## nerW3P1 (peer) 0.835 NA 2.107 0.949
## nerW3P2 (aa) 0.678 NA 1.711 0.931
## neuro4 =~
## nerW4S1 1.000 2.522 0.964
## nerW4S2 (a) 0.983 NA 2.480 0.963
## nerW4P1 (peer) 0.835 NA 2.106 0.936
## nerW4P2 (aa) 0.678 NA 1.710 0.931
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## neuro4 ~
## neuro3 1.000 NA 1.000 1.000
## neuro3 ~
## neuro2 1.029 NA 0.997 0.997
## neuro2 ~
## neuro1 1.380 NA 0.955 0.955
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .neuroW1S1 ~~
## .neuroW2S1 0.037 NA 0.037 0.097
## .neuroW3S1 0.059 NA 0.059 0.145
## .neuroW4S1 0.043 NA 0.043 0.089
## .neuroW2S1 ~~
## .neuroW3S1 0.040 NA 0.040 0.121
## .neuroW4S1 0.020 NA 0.020 0.051
## .neuroW3S1 ~~
## .neuroW4S1 0.049 NA 0.049 0.118
## .neuroW1S2 ~~
## .neuroW2S2 -0.006 NA -0.006 -0.014
## .neuroW3S2 -0.004 NA -0.004 -0.009
## .neuroW4S2 0.015 NA 0.015 0.027
## .neuroW2S2 ~~
## .neuroW3S2 -0.014 NA -0.014 -0.042
## .neuroW4S2 0.005 NA 0.005 0.014
## .neuroW3S2 ~~
## .neuroW4S2 -0.019 NA -0.019 -0.045
## .neuroW1P1 ~~
## .neuroW2P1 0.062 NA 0.062 0.132
## .neuroW3P1 0.111 NA 0.111 0.193
## .neuroW4P1 0.162 NA 0.162 0.250
## .neuroW2P1 ~~
## .neuroW3P1 0.017 NA 0.017 0.044
## .neuroW4P1 0.255 NA 0.255 0.564
## .neuroW3P1 ~~
## .neuroW4P1 0.258 NA 0.258 0.469
## .neuroW1P2 ~~
## .neuroW2P2 0.298 NA 0.298 0.828
## .neuroW3P2 0.303 NA 0.303 0.822
## .neuroW4P2 0.303 NA 0.303 0.823
## .neuroW2P2 ~~
## .neuroW3P2 0.449 NA 0.449 1.018
## .neuroW4P2 0.449 NA 0.449 1.018
## .neuroW3P2 ~~
## .neuroW4P2 0.453 NA 0.453 1.000
## .neuroW1S1 ~~
## .neuroW1S2 0.450 NA 0.450 0.857
## .neuroW1P1 ~~
## .neuroW1P2 0.228 NA 0.228 0.506
## .neuroW2S1 ~~
## .neuroW2S2 0.236 NA 0.236 0.789
## .neuroW2P1 ~~
## .neuroW2P2 -0.012 NA -0.012 -0.033
## .neuroW3S1 ~~
## .neuroW3S2 0.306 NA 0.306 0.827
## .neuroW3P1 ~~
## .neuroW3P2 -0.000 NA -0.000 -0.000
## .neuroW4S1 ~~
## .neuroW4S2 0.406 NA 0.406 0.839
## .neuroW4P1 ~~
## .neuroW4P2 -0.000 NA -0.000 -0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .neuroW1S1 1.261 NA 1.261 0.691
## .neuroW1S2 1.217 NA 1.217 0.664
## .neuroW1P1 0.772 NA 0.772 0.472
## .neuroW1P2 1.036 NA 1.036 0.815
## .neuroW2S1 0.364 NA 0.364 0.145
## .neuroW2S2 0.346 NA 0.346 0.141
## .neuroW2P1 0.176 NA 0.176 0.083
## .neuroW2P2 0.332 NA 0.332 0.186
## .neuroW3S1 0.211 NA 0.211 0.081
## .neuroW3S2 0.199 NA 0.199 0.078
## .neuroW3P1 0.124 NA 0.124 0.056
## .neuroW3P2 0.260 NA 0.260 0.141
## .neuroW4S1 0.194 NA 0.194 0.074
## .neuroW4S2 0.165 NA 0.165 0.064
## .neuroW4P1 0.123 NA 0.123 0.055
## .neuroW4P2 0.260 NA 0.260 0.141
## neuro1 0.000 0.000 0.000
## .neuro2 0.000 0.000 0.000
## .neuro3 0.000 0.000 0.000
## .neuro4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .neuroW1S1 0.467 NA 0.467 0.140
## .neuroW1S2 0.589 NA 0.589 0.175
## .neuroW1P1 0.679 NA 0.679 0.254
## .neuroW1P2 0.301 NA 0.301 0.186
## .neuroW2S1 0.314 NA 0.314 0.050
## .neuroW2S2 0.285 NA 0.285 0.047
## .neuroW2P1 0.327 NA 0.327 0.073
## .neuroW2P2 0.430 NA 0.430 0.135
## .neuroW3S1 0.355 NA 0.355 0.053
## .neuroW3S2 0.386 NA 0.386 0.059
## .neuroW3P1 0.487 NA 0.487 0.099
## .neuroW3P2 0.453 NA 0.453 0.134
## .neuroW4S1 0.491 NA 0.491 0.072
## .neuroW4S2 0.477 NA 0.477 0.072
## .neuroW4P1 0.624 NA 0.624 0.123
## .neuroW4P2 0.453 NA 0.453 0.134
## neuro1 2.865 NA 1.000 1.000
## .neuro2 0.520 NA 0.087 0.087
## .neuro3 0.040 NA 0.006 0.006
## .neuro4 0.000 NA 0.000 0.000semPaths(lsmNeuro, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Openness domain
with aspects as parcels
lsmOpend <- '
# factor at each time point with same loading
opend1 =~ intelW1S + a * openaW1S +
peer * intelW1P + aa * openaW1P
opend2 =~ intelW2S + a * openaW2S +
peer * intelW2P + aa * openaW2P
opend3 =~ intelW3S + a * openaW3S +
peer * intelW3P + aa * openaW3P
opend4 =~ intelW4S + a * openaW4S +
peer * intelW4P + aa * openaW4P
# structural paths between time points
opend4 ~ opend3
opend3 ~ opend2
opend2 ~ opend1
# error covariance - similar aspects across waves and informants
intelW1S ~~ intelW2S + intelW3S + intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW2S ~~ intelW3S + intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW3S ~~ intelW4S +
intelW1P + intelW2P + intelW3P + intelW4P
intelW4S ~~ intelW1P + intelW2P + intelW3P + intelW4P
openaW1S ~~ openaW2S + openaW3S + openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW2S ~~ openaW3S + openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW3S ~~ openaW4S +
openaW1P + openaW2P + openaW3P + openaW4P
openaW4S ~~ openaW1P + openaW2P + openaW3P + openaW4P
intelW1P ~~ intelW2P + intelW3P + intelW4P
intelW2P ~~ intelW3P + intelW4P
intelW3P ~~ intelW4P
openaW1P ~~ openaW2P + openaW3P + openaW4P
openaW2P ~~ openaW3P + openaW4P
openaW3P ~~ openaW4P
'
lsmOpend <- sem(lsmOpend, data = data, missing = "ML")
summary(lsmOpend, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 266 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 107
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic 120.793
## Degrees of freedom 54
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2293.599
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.969
## Tucker-Lewis Index (TLI) 0.932
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1046.743
## Loglikelihood unrestricted model (H1) -986.347
##
## Akaike (AIC) 2289.487
## Bayesian (BIC) 2638.056
## Sample-size adjusted Bayesian (BIC) 2327.360
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.069
## 90 Percent confidence interval - lower 0.053
## 90 Percent confidence interval - upper 0.086
## P-value RMSEA <= 0.05 0.030
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.078
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend1 =~
## intlW1S 1.000 0.176 0.324
## openW1S (a) 1.626 NA 0.286 0.458
## intlW1P (peer) 1.495 0.370 4.044 0.000 0.263 0.496
## openW1P (aa) 1.928 NA 0.339 0.680
## opend2 =~
## intlW2S 1.000 0.216 0.397
## openW2S (a) 1.626 NA 0.352 0.534
## intlW2P (peer) 1.495 0.370 4.044 0.000 0.323 0.635
## openW2P (aa) 1.928 NA 0.417 0.747
## opend3 =~
## intlW3S 1.000 0.185 0.340
## openW3S (a) 1.626 NA 0.300 0.484
## intlW3P (peer) 1.495 0.370 4.044 0.000 0.276 0.482
## openW3P (aa) 1.928 NA 0.356 0.623
## opend4 =~
## intlW4S 1.000 0.192 0.366
## openW4S (a) 1.626 NA 0.312 0.494
## intlW4P (peer) 1.495 0.370 4.044 0.000 0.286 0.522
## openW4P (aa) 1.928 NA 0.369 0.665
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend4 ~
## opend3 0.959 0.095 10.127 0.000 0.925 0.925
## opend3 ~
## opend2 0.826 0.065 12.647 0.000 0.967 0.967
## opend2 ~
## opend1 1.011 0.107 9.405 0.000 0.822 0.822
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S ~~
## .intelW2S 0.199 NA 0.199 0.774
## .intelW3S 0.198 NA 0.198 0.753
## .intelW4S 0.184 NA 0.184 0.733
## .intelW1P 0.038 NA 0.038 0.162
## .intelW2P 0.007 NA 0.007 0.037
## .intelW3P 0.035 NA 0.035 0.134
## .intelW4P -0.002 NA -0.002 -0.008
## .intelW2S ~~
## .intelW3S 0.203 NA 0.203 0.793
## .intelW4S 0.191 NA 0.191 0.782
## .intelW1P ~~
## .intelW2S 0.036 NA 0.036 0.155
## .intelW2S ~~
## .intelW2P -0.003 NA -0.003 -0.016
## .intelW3P 0.026 NA 0.026 0.104
## .intelW4P -0.015 NA -0.015 -0.063
## .intelW3S ~~
## .intelW4S 0.203 NA 0.203 0.814
## .intelW1P ~~
## .intelW3S 0.057 NA 0.057 0.244
## .intelW2P ~~
## .intelW3S 0.013 NA 0.013 0.065
## .intelW3S ~~
## .intelW3P 0.026 NA 0.026 0.103
## .intelW4P -0.013 NA -0.013 -0.054
## .intelW1P ~~
## .intelW4S 0.038 NA 0.038 0.171
## .intelW2P ~~
## .intelW4S 0.002 NA 0.002 0.012
## .intelW3P ~~
## .intelW4S 0.019 NA 0.019 0.076
## .intelW4S ~~
## .intelW4P -0.027 NA -0.027 -0.119
## .openaW1S ~~
## .openaW2S 0.255 NA 0.255 0.825
## .openaW3S 0.256 NA 0.256 0.851
## .openaW4S 0.251 NA 0.251 0.825
## .openaW1P 0.059 NA 0.059 0.291
## .openaW2P 0.089 NA 0.089 0.433
## .openaW3P 0.115 NA 0.115 0.465
## .openaW4P 0.098 NA 0.098 0.424
## .openaW2S ~~
## .openaW3S 0.257 NA 0.257 0.852
## .openaW4S 0.255 NA 0.255 0.837
## .openaW1P ~~
## .openaW2S 0.064 NA 0.064 0.316
## .openaW2S ~~
## .openaW2P 0.051 NA 0.051 0.249
## .openaW3P 0.087 NA 0.087 0.350
## .openaW4P 0.075 NA 0.075 0.325
## .openaW3S ~~
## .openaW4S 0.250 NA 0.250 0.841
## .openaW1P ~~
## .openaW3S 0.064 NA 0.064 0.321
## .openaW2P ~~
## .openaW3S 0.057 NA 0.057 0.284
## .openaW3S ~~
## .openaW3P 0.083 NA 0.083 0.342
## .openaW4P 0.066 NA 0.066 0.291
## .openaW1P ~~
## .openaW4S 0.072 NA 0.072 0.358
## .openaW2P ~~
## .openaW4S 0.072 NA 0.072 0.353
## .openaW3P ~~
## .openaW4S 0.098 NA 0.098 0.400
## .openaW4S ~~
## .openaW4P 0.083 NA 0.083 0.365
## .intelW1P ~~
## .intelW2P 0.140 NA 0.140 0.771
## .intelW3P 0.170 NA 0.170 0.736
## .intelW4P 0.150 NA 0.150 0.696
## .intelW2P ~~
## .intelW3P 0.166 NA 0.166 0.842
## .intelW4P 0.124 NA 0.124 0.672
## .intelW3P ~~
## .intelW4P 0.171 NA 0.171 0.728
## .openaW1P ~~
## .openaW2P 0.089 NA 0.089 0.659
## .openaW3P 0.117 NA 0.117 0.717
## .openaW4P 0.124 NA 0.124 0.818
## .openaW2P ~~
## .openaW3P 0.118 NA 0.118 0.712
## .openaW4P 0.107 NA 0.107 0.696
## .openaW3P ~~
## .openaW4P 0.158 NA 0.158 0.852
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S 3.666 0.034 108.517 0.000 3.666 6.747
## .openaW1S 3.799 0.039 97.829 0.000 3.799 6.081
## .intelW1P 3.993 0.041 97.308 0.000 3.993 7.532
## .openaW1P 3.607 0.038 95.890 0.000 3.607 7.237
## .intelW2S 3.614 0.036 100.934 0.000 3.614 6.628
## .openaW2S 3.772 0.043 88.082 0.000 3.772 5.732
## .intelW2P 3.972 0.040 100.237 0.000 3.972 7.803
## .openaW2P 3.602 0.042 85.294 0.000 3.602 6.457
## .intelW3S 3.641 0.036 101.320 0.000 3.641 6.705
## .openaW3S 3.800 0.040 94.234 0.000 3.800 6.126
## .intelW3P 3.950 0.045 86.826 0.000 3.950 6.895
## .openaW3P 3.587 0.043 82.566 0.000 3.587 6.278
## .intelW4S 3.659 0.036 100.624 0.000 3.659 6.985
## .openaW4S 3.826 0.043 89.612 0.000 3.826 6.071
## .intelW4P 3.884 0.048 80.877 0.000 3.884 7.072
## .openaW4P 3.580 0.044 80.864 0.000 3.580 6.443
## opend1 0.000 0.000 0.000
## .opend2 0.000 0.000 0.000
## .opend3 0.000 0.000 0.000
## .opend4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S 0.264 NA 0.264 0.895
## .openaW1S 0.308 NA 0.308 0.791
## .intelW1P 0.212 NA 0.212 0.754
## .openaW1P 0.134 NA 0.134 0.538
## .intelW2S 0.250 NA 0.250 0.843
## .openaW2S 0.309 NA 0.309 0.714
## .intelW2P 0.155 NA 0.155 0.597
## .openaW2P 0.137 NA 0.137 0.442
## .intelW3S 0.261 NA 0.261 0.884
## .openaW3S 0.295 NA 0.295 0.765
## .intelW3P 0.252 NA 0.252 0.767
## .openaW3P 0.200 NA 0.200 0.611
## .intelW4S 0.238 NA 0.238 0.866
## .openaW4S 0.300 NA 0.300 0.756
## .intelW4P 0.219 NA 0.219 0.728
## .openaW4P 0.172 NA 0.172 0.558
## opend1 0.031 NA 1.000 1.000
## .opend2 0.015 NA 0.325 0.325
## .opend3 0.002 NA 0.065 0.065
## .opend4 0.005 NA 0.145 0.145semPaths(lsmOpend, what = "col", whatLabels = "est", structural = T, layout = "spring")
with random parcels
lsmOpend <- '
# factor at each time point with same loading
opend1 =~ opendW1S1 + a * opendW1S2 +
peer * opendW1P1 + aa * opendW1P2
opend2 =~ opendW2S1 + a * opendW2S2 +
peer * opendW2P1 + aa * opendW2P2
opend3 =~ opendW3S1 + a * opendW3S2 +
peer * opendW3P1 + aa * opendW3P2
opend4 =~ opendW4S1 + a * opendW4S2 +
peer * opendW4P1 + aa * opendW4P2
# structural paths between time points
opend4 ~ opend3
opend3 ~ opend2
opend2 ~ opend1
# error covariance - similar parcels across waves
opendW1S1 ~~ opendW2S1 + opendW3S1 + opendW4S1
opendW2S1 ~~ opendW3S1 + opendW4S1
opendW3S1 ~~ opendW4S1
opendW1S2 ~~ opendW2S2 + opendW3S2 + opendW4S2
opendW2S2 ~~ opendW3S2 + opendW4S2
opendW3S2 ~~ opendW4S2
opendW1P1 ~~ opendW2P1 + opendW3P1 + opendW4P1
opendW2P1 ~~ opendW3P1 + opendW4P1
opendW3P1 ~~ opendW4P1
opendW1P2 ~~ opendW2P2 + opendW3P2 + opendW4P2
opendW2P2 ~~ opendW3P2 + opendW4P2
opendW3P2 ~~ opendW4P2
# error covariance - same method at one wave
opendW1S1 ~~ opendW1S2
opendW1P1 ~~ opendW1P2
opendW2S1 ~~ opendW2S2
opendW2P1 ~~ opendW2P2
opendW3S1 ~~ opendW3S2
opendW3P1 ~~ opendW3P2
opendW4S1 ~~ opendW4S2
opendW4P1 ~~ opendW4P2
'
lsmOpend <- sem(lsmOpend, data = data, missing = "ML")## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(lsmOpend, fit.measures = T, standardized = T)## lavaan 0.6-7 did NOT end normally after 442 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 51
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend1 =~
## opnW1S1 1.000 1.938 0.937
## opnW1S2 (a) 1.008 NA 1.954 0.948
## opnW1P1 (peer) 1.055 NA 2.044 0.915
## opnW1P2 (aa) 0.960 NA 1.861 0.951
## opend2 =~
## opnW2S1 1.000 2.841 0.991
## opnW2S2 (a) 1.008 NA 2.865 0.992
## opnW2P1 (peer) 1.055 NA 2.996 0.982
## opnW2P2 (aa) 0.960 NA 2.727 0.976
## opend3 =~
## opnW3S1 1.000 2.842 0.990
## opnW3S2 (a) 1.008 NA 2.866 0.989
## opnW3P1 (peer) 1.055 NA 2.997 0.984
## opnW3P2 (aa) 0.960 NA 2.728 0.976
## opend4 =~
## opnW4S1 1.000 2.843 0.980
## opnW4S2 (a) 1.008 NA 2.867 0.981
## opnW4P1 (peer) 1.055 NA 2.999 0.971
## opnW4P2 (aa) 0.960 NA 2.729 0.970
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opend4 ~
## opend3 1.001 NA 1.000 1.000
## opend3 ~
## opend2 1.000 NA 1.000 1.000
## opend2 ~
## opend1 1.440 NA 0.982 0.982
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .opendW1S1 ~~
## .opendW2S1 0.047 NA 0.047 0.173
## .opendW3S1 0.065 NA 0.065 0.217
## .opendW4S1 0.027 NA 0.027 0.066
## .opendW2S1 ~~
## .opendW3S1 0.023 NA 0.023 0.149
## .opendW4S1 0.077 NA 0.077 0.363
## .opendW3S1 ~~
## .opendW4S1 0.021 NA 0.021 0.091
## .opendW1S2 ~~
## .opendW2S2 0.053 NA 0.053 0.225
## .opendW3S2 0.027 NA 0.027 0.095
## .opendW4S2 0.056 NA 0.056 0.152
## .opendW2S2 ~~
## .opendW3S2 0.060 NA 0.060 0.386
## .opendW4S2 0.046 NA 0.046 0.228
## .opendW3S2 ~~
## .opendW4S2 0.049 NA 0.049 0.199
## .opendW1P1 ~~
## .opendW2P1 0.075 NA 0.075 0.143
## .opendW3P1 0.110 NA 0.110 0.222
## .opendW4P1 0.076 NA 0.076 0.115
## .opendW2P1 ~~
## .opendW3P1 0.185 NA 0.185 0.581
## .opendW4P1 0.231 NA 0.231 0.537
## .opendW3P1 ~~
## .opendW4P1 0.093 NA 0.093 0.229
## .opendW1P2 ~~
## .opendW2P2 0.278 NA 0.278 0.754
## .opendW3P2 0.278 NA 0.278 0.754
## .opendW4P2 0.308 NA 0.308 0.741
## .opendW2P2 ~~
## .opendW3P2 0.376 NA 0.376 1.000
## .opendW4P2 0.421 NA 0.421 0.995
## .opendW3P2 ~~
## .opendW4P2 0.421 NA 0.421 0.995
## .opendW1S1 ~~
## .opendW1S2 0.405 NA 0.405 0.849
## .opendW1P1 ~~
## .opendW1P2 0.354 NA 0.354 0.652
## .opendW2S1 ~~
## .opendW2S2 0.043 NA 0.043 0.323
## .opendW2P1 ~~
## .opendW2P2 0.000 NA 0.000 0.000
## .opendW3S1 ~~
## .opendW3S2 0.106 NA 0.106 0.590
## .opendW3P1 ~~
## .opendW3P2 -0.000 NA -0.000 -0.000
## .opendW4S1 ~~
## .opendW4S2 0.224 NA 0.224 0.700
## .opendW4P1 ~~
## .opendW4P2 0.036 NA 0.036 0.070
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .opendW1S1 1.428 NA 1.428 0.690
## .opendW1S2 1.263 NA 1.263 0.612
## .opendW1P1 0.916 NA 0.916 0.410
## .opendW1P2 1.181 NA 1.181 0.604
## .opendW2S1 0.266 NA 0.266 0.093
## .opendW2S2 0.104 NA 0.104 0.036
## .opendW2P1 0.126 NA 0.126 0.041
## .opendW2P2 0.182 NA 0.182 0.065
## .opendW3S1 0.244 NA 0.244 0.085
## .opendW3S2 0.061 NA 0.061 0.021
## .opendW3P1 -0.013 NA -0.013 -0.004
## .opendW3P2 0.181 NA 0.181 0.065
## .opendW4S1 0.323 NA 0.323 0.111
## .opendW4S2 0.126 NA 0.126 0.043
## .opendW4P1 0.031 NA 0.031 0.010
## .opendW4P2 0.141 NA 0.141 0.050
## opend1 0.000 0.000 0.000
## .opend2 0.000 0.000 0.000
## .opend3 0.000 0.000 0.000
## .opend4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .opendW1S1 0.524 NA 0.524 0.122
## .opendW1S2 0.435 NA 0.435 0.102
## .opendW1P1 0.812 NA 0.812 0.163
## .opendW1P2 0.363 NA 0.363 0.095
## .opendW2S1 0.140 NA 0.140 0.017
## .opendW2S2 0.128 NA 0.128 0.015
## .opendW2P1 0.337 NA 0.337 0.036
## .opendW2P2 0.376 NA 0.376 0.048
## .opendW3S1 0.169 NA 0.169 0.021
## .opendW3S2 0.192 NA 0.192 0.023
## .opendW3P1 0.302 NA 0.302 0.033
## .opendW3P2 0.376 NA 0.376 0.048
## .opendW4S1 0.325 NA 0.325 0.039
## .opendW4S2 0.314 NA 0.314 0.037
## .opendW4P1 0.548 NA 0.548 0.057
## .opendW4P2 0.476 NA 0.476 0.060
## opend1 3.756 NA 1.000 1.000
## .opend2 0.281 NA 0.035 0.035
## .opend3 -0.000 NA -0.000 -0.000
## .opend4 -0.002 NA -0.000 -0.000semPaths(lsmOpend, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Assertiveness
lsmAssert <- '
# factor at each time point with same loading
assert1 =~ assertW1S1 + a * assertW1S2 +
peer * assertW1P1 + aa * assertW1P2
assert2 =~ assertW2S1 + a * assertW2S2 +
peer * assertW2P1 + aa * assertW2P2
assert3 =~ assertW3S1 + a * assertW3S2 +
peer * assertW3P1 + aa * assertW3P2
assert4 =~ assertW4S1 + a * assertW4S2 +
peer * assertW4P1 + aa * assertW4P2
# structural paths between time points
assert4 ~ assert3
assert3 ~ assert2
assert2 ~ assert1
# error covariance - similar parcels across waves
assertW1S1 ~~ assertW2S1 + assertW3S1 + assertW4S1
assertW2S1 ~~ assertW3S1 + assertW4S1
assertW3S1 ~~ assertW4S1
assertW1S2 ~~ assertW2S2 + assertW3S2 + assertW4S2
assertW2S2 ~~ assertW3S2 + assertW4S2
assertW3S2 ~~ assertW4S2
assertW1P1 ~~ assertW2P1 + assertW3P1 + assertW4P1
assertW2P1 ~~ assertW3P1 + assertW4P1
assertW3P1 ~~ assertW4P1
assertW1P2 ~~ assertW2P2 + assertW3P2 + assertW4P2
assertW2P2 ~~ assertW3P2 + assertW4P2
assertW3P2 ~~ assertW4P2
# error covariance - same method at one wave
assertW1S1 ~~ assertW1S2
assertW1P1 ~~ assertW1P2
assertW2S1 ~~ assertW2S2
assertW2P1 ~~ assertW2P2
assertW3S1 ~~ assertW3S2
assertW3P1 ~~ assertW3P2
assertW4S1 ~~ assertW4S2
assertW4P1 ~~ assertW4P2
'
lsmAssert <- sem(lsmAssert, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmAssert, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 172 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 286.637
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2668.403
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.918
## Tucker-Lewis Index (TLI) 0.874
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1405.842
## Loglikelihood unrestricted model (H1) -1262.524
##
## Akaike (AIC) 2959.685
## Bayesian (BIC) 3222.890
## Sample-size adjusted Bayesian (BIC) 2988.284
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.102
## 90 Percent confidence interval - lower 0.089
## 90 Percent confidence interval - upper 0.114
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.181
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## assert1 =~
## assW1S1 1.000 0.570 0.828
## assW1S2 (a) 1.069 0.068 15.789 0.000 0.609 0.811
## assW1P1 (peer) 0.384 0.067 5.753 0.000 0.219 0.375
## assW1P2 (aa) 0.505 0.073 6.897 0.000 0.288 0.461
## assert2 =~
## assW2S1 1.000 0.581 0.824
## assW2S2 (a) 1.069 0.068 15.789 0.000 0.621 0.848
## assW2P1 (peer) 0.384 0.067 5.753 0.000 0.223 0.422
## assW2P2 (aa) 0.505 0.073 6.897 0.000 0.293 0.476
## assert3 =~
## assW3S1 1.000 0.594 0.863
## assW3S2 (a) 1.069 0.068 15.789 0.000 0.635 0.882
## assW3P1 (peer) 0.384 0.067 5.753 0.000 0.228 0.406
## assW3P2 (aa) 0.505 0.073 6.897 0.000 0.300 0.437
## assert4 =~
## assW4S1 1.000 0.604 0.877
## assW4S2 (a) 1.069 0.068 15.789 0.000 0.645 0.895
## assW4P1 (peer) 0.384 0.067 5.753 0.000 0.232 0.436
## assW4P2 (aa) 0.505 0.073 6.897 0.000 0.305 0.480
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## assert4 ~
## assert3 0.959 0.046 20.938 0.000 0.944 0.944
## assert3 ~
## assert2 0.989 0.045 21.736 0.000 0.967 0.967
## assert2 ~
## assert1 1.019 0.082 12.497 0.000 1.000 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S1 ~~
## .assertW2S1 0.045 0.021 2.115 0.034 0.045 0.291
## .assertW3S1 0.036 0.019 1.846 0.065 0.036 0.268
## .assertW4S1 0.022 0.019 1.132 0.257 0.022 0.171
## .assertW2S1 ~~
## .assertW3S1 0.064 0.021 3.037 0.002 0.064 0.464
## .assertW4S1 0.056 0.021 2.686 0.007 0.056 0.425
## .assertW3S1 ~~
## .assertW4S1 0.059 0.020 2.889 0.004 0.059 0.510
## .assertW1S2 ~~
## .assertW2S2 0.069 0.024 2.880 0.004 0.069 0.402
## .assertW3S2 0.057 0.022 2.594 0.009 0.057 0.381
## .assertW4S2 0.052 0.022 2.323 0.020 0.052 0.371
## .assertW2S2 ~~
## .assertW3S2 0.053 0.023 2.361 0.018 0.053 0.404
## .assertW4S2 0.069 0.024 2.909 0.004 0.069 0.556
## .assertW3S2 ~~
## .assertW4S2 0.046 0.023 2.026 0.043 0.046 0.424
## .assertW1P1 ~~
## .assertW2P1 0.162 0.027 5.946 0.000 0.162 0.625
## .assertW3P1 0.173 0.032 5.477 0.000 0.173 0.622
## .assertW4P1 0.137 0.028 4.947 0.000 0.137 0.529
## .assertW2P1 ~~
## .assertW3P1 0.131 0.028 4.628 0.000 0.131 0.533
## .assertW4P1 0.137 0.026 5.232 0.000 0.137 0.596
## .assertW3P1 ~~
## .assertW4P1 0.163 0.031 5.201 0.000 0.163 0.662
## .assertW1P2 ~~
## .assertW2P2 0.172 0.032 5.357 0.000 0.172 0.574
## .assertW3P2 0.246 0.038 6.521 0.000 0.246 0.720
## .assertW4P2 0.210 0.034 6.260 0.000 0.210 0.683
## .assertW2P2 ~~
## .assertW3P2 0.270 0.040 6.793 0.000 0.270 0.808
## .assertW4P2 0.221 0.035 6.248 0.000 0.221 0.736
## .assertW3P2 ~~
## .assertW4P2 0.259 0.041 6.267 0.000 0.259 0.754
## .assertW1S1 ~~
## .assertW1S2 0.059 0.027 2.189 0.029 0.059 0.347
## .assertW1P1 ~~
## .assertW1P2 0.068 0.016 4.169 0.000 0.068 0.229
## .assertW2S1 ~~
## .assertW2S2 0.028 0.011 2.594 0.009 0.028 0.179
## .assertW2P1 ~~
## .assertW2P2 0.053 0.014 3.780 0.000 0.053 0.206
## .assertW3S1 ~~
## .assertW3S2 0.000 0.010 0.049 0.961 0.000 0.004
## .assertW3P1 ~~
## .assertW3P2 -0.004 0.017 -0.258 0.797 -0.004 -0.014
## .assertW4S1 ~~
## .assertW4S2 0.004 0.033 0.113 0.910 0.004 0.035
## .assertW4P1 ~~
## .assertW4P2 0.039 0.015 2.543 0.011 0.039 0.146
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S1 3.401 0.043 79.457 0.000 3.401 4.940
## .assertW1S2 3.521 0.047 75.385 0.000 3.521 4.687
## .assertW1P1 3.683 0.046 80.837 0.000 3.683 6.319
## .assertW1P2 3.496 0.048 72.989 0.000 3.496 5.606
## .assertW2S1 3.348 0.046 72.372 0.000 3.348 4.751
## .assertW2S2 3.479 0.048 72.923 0.000 3.479 4.750
## .assertW2P1 3.751 0.042 88.665 0.000 3.751 7.097
## .assertW2P2 3.560 0.048 74.565 0.000 3.560 5.786
## .assertW3S1 3.336 0.045 73.518 0.000 3.336 4.845
## .assertW3S2 3.474 0.047 73.572 0.000 3.474 4.821
## .assertW3P1 3.671 0.046 79.553 0.000 3.671 6.534
## .assertW3P2 3.537 0.054 66.004 0.000 3.537 5.153
## .assertW4S1 3.391 0.048 70.829 0.000 3.391 4.926
## .assertW4S2 3.508 0.050 70.782 0.000 3.508 4.866
## .assertW4P1 3.664 0.047 78.318 0.000 3.664 6.894
## .assertW4P2 3.551 0.053 67.028 0.000 3.551 5.600
## assert1 0.000 0.000 0.000
## .assert2 0.000 0.000 0.000
## .assert3 0.000 0.000 0.000
## .assert4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .assertW1S1 0.149 0.031 4.821 0.000 0.149 0.315
## .assertW1S2 0.193 0.040 4.817 0.000 0.193 0.342
## .assertW1P1 0.292 0.034 8.582 0.000 0.292 0.859
## .assertW1P2 0.306 0.036 8.567 0.000 0.306 0.787
## .assertW2S1 0.159 0.027 5.882 0.000 0.159 0.320
## .assertW2S2 0.151 0.029 5.290 0.000 0.151 0.281
## .assertW2P1 0.230 0.029 8.049 0.000 0.230 0.822
## .assertW2P2 0.293 0.036 8.021 0.000 0.293 0.773
## .assertW3S1 0.121 0.024 4.998 0.000 0.121 0.255
## .assertW3S2 0.116 0.027 4.350 0.000 0.116 0.223
## .assertW3P1 0.264 0.037 7.216 0.000 0.264 0.835
## .assertW3P2 0.381 0.051 7.503 0.000 0.381 0.809
## .assertW4S1 0.109 0.039 2.806 0.005 0.109 0.231
## .assertW4S2 0.103 0.044 2.337 0.019 0.103 0.198
## .assertW4P1 0.229 0.032 7.042 0.000 0.229 0.810
## .assertW4P2 0.309 0.044 6.989 0.000 0.309 0.769
## assert1 0.325 0.045 7.165 0.000 1.000 1.000
## .assert2 -0.000 0.025 -0.003 0.998 -0.000 -0.000
## .assert3 0.023 0.011 2.176 0.030 0.066 0.066
## .assert4 0.040 0.031 1.295 0.195 0.109 0.109semPaths(lsmAssert, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Compassion
lsmCompa <- '
# factor at each time point with same loading
compa1 =~ compaW1S1 + a * compaW1S2 +
peer * compaW1P1 + aa * compaW1P2
compa2 =~ compaW2S1 + a * compaW2S2 +
peer * compaW2P1 + aa * compaW2P2
compa3 =~ compaW3S1 + a * compaW3S2 +
peer * compaW3P1 + aa * compaW3P2
compa4 =~ compaW4S1 + a * compaW4S2 +
peer * compaW4P1 + aa * compaW4P2
# structural paths between time points
compa4 ~ compa3
compa3 ~ compa2
compa2 ~ compa1
# error covariance - similar parcels across waves
compaW1S1 ~~ compaW2S1 + compaW3S1 + compaW4S1
compaW2S1 ~~ compaW3S1 + compaW4S1
compaW3S1 ~~ compaW4S1
compaW1S2 ~~ compaW2S2 + compaW3S2 + compaW4S2
compaW2S2 ~~ compaW3S2 + compaW4S2
compaW3S2 ~~ compaW4S2
compaW1P1 ~~ compaW2P1 + compaW3P1 + compaW4P1
compaW2P1 ~~ compaW3P1 + compaW4P1
compaW3P1 ~~ compaW4P1
compaW1P2 ~~ compaW2P2 + compaW3P2 + compaW4P2
compaW2P2 ~~ compaW3P2 + compaW4P2
compaW3P2 ~~ compaW4P2
# error covariance - same method at one wave
compaW1S1 ~~ compaW1S2
compaW1P1 ~~ compaW1P2
compaW2S1 ~~ compaW2S2
compaW2P1 ~~ compaW2P2
compaW3S1 ~~ compaW3S2
compaW3P1 ~~ compaW3P2
compaW4S1 ~~ compaW4S2
compaW4P1 ~~ compaW4P2
'
lsmCompa <- sem(lsmCompa, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmCompa, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 204 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 314.754
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2272.548
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.890
## Tucker-Lewis Index (TLI) 0.831
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1044.836
## Loglikelihood unrestricted model (H1) -887.459
##
## Akaike (AIC) 2237.671
## Bayesian (BIC) 2500.876
## Sample-size adjusted Bayesian (BIC) 2266.270
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.108
## 90 Percent confidence interval - lower 0.096
## 90 Percent confidence interval - upper 0.121
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.202
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## compa1 =~
## cmpW1S1 1.000 0.269 0.551
## cmpW1S2 (a) 1.076 0.059 18.321 0.000 0.289 0.551
## cmpW1P1 (peer) 0.685 0.123 5.592 0.000 0.184 0.304
## cmpW1P2 (aa) 0.674 0.122 5.535 0.000 0.181 0.336
## compa2 =~
## cmpW2S1 1.000 0.389 0.784
## cmpW2S2 (a) 1.076 0.059 18.321 0.000 0.418 0.779
## cmpW2P1 (peer) 0.685 0.123 5.592 0.000 0.266 0.488
## cmpW2P2 (aa) 0.674 0.122 5.535 0.000 0.262 0.447
## compa3 =~
## cmpW3S1 1.000 0.395 0.797
## cmpW3S2 (a) 1.076 0.059 18.321 0.000 0.425 0.810
## cmpW3P1 (peer) 0.685 0.123 5.592 0.000 0.271 0.471
## cmpW3P2 (aa) 0.674 0.122 5.535 0.000 0.266 0.515
## compa4 =~
## cmpW4S1 1.000 0.346 0.705
## cmpW4S2 (a) 1.076 0.059 18.321 0.000 0.372 0.702
## cmpW4P1 (peer) 0.685 0.123 5.592 0.000 0.237 0.403
## cmpW4P2 (aa) 0.674 0.122 5.535 0.000 0.233 0.394
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## compa4 ~
## compa3 1.091 0.063 17.222 0.000 1.245 1.245
## compa3 ~
## compa2 0.965 0.076 12.643 0.000 0.950 0.950
## compa2 ~
## compa1 1.761 0.557 3.163 0.002 1.216 1.216
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S1 ~~
## .compaW2S1 -0.006 0.009 -0.637 0.524 -0.006 -0.045
## .compaW3S1 0.005 0.008 0.621 0.535 0.005 0.043
## .compaW4S1 0.007 0.008 0.830 0.406 0.007 0.049
## .compaW2S1 ~~
## .compaW3S1 0.023 0.009 2.458 0.014 0.023 0.248
## .compaW4S1 0.016 0.009 1.798 0.072 0.016 0.146
## .compaW3S1 ~~
## .compaW4S1 0.018 0.009 2.082 0.037 0.018 0.173
## .compaW1S2 ~~
## .compaW2S2 0.022 0.010 2.155 0.031 0.022 0.152
## .compaW3S2 0.012 0.009 1.370 0.171 0.012 0.092
## .compaW4S2 -0.004 0.009 -0.452 0.651 -0.004 -0.025
## .compaW2S2 ~~
## .compaW3S2 0.034 0.010 3.331 0.001 0.034 0.327
## .compaW4S2 0.025 0.010 2.549 0.011 0.025 0.196
## .compaW3S2 ~~
## .compaW4S2 0.019 0.009 2.040 0.041 0.019 0.162
## .compaW1P1 ~~
## .compaW2P1 -0.001 0.015 -0.065 0.949 -0.001 -0.004
## .compaW3P1 0.001 0.021 0.034 0.973 0.001 0.002
## .compaW4P1 0.014 0.016 0.871 0.384 0.014 0.045
## .compaW2P1 ~~
## .compaW3P1 0.033 0.016 2.061 0.039 0.033 0.137
## .compaW4P1 0.036 0.015 2.446 0.014 0.036 0.142
## .compaW3P1 ~~
## .compaW4P1 0.007 0.022 0.335 0.737 0.007 0.026
## .compaW1P2 ~~
## .compaW2P2 0.033 0.015 2.226 0.026 0.033 0.124
## .compaW3P2 0.047 0.014 3.326 0.001 0.047 0.208
## .compaW4P2 0.020 0.017 1.198 0.231 0.020 0.073
## .compaW2P2 ~~
## .compaW3P2 0.001 0.015 0.085 0.932 0.001 0.006
## .compaW4P2 -0.014 0.017 -0.859 0.390 -0.014 -0.050
## .compaW3P2 ~~
## .compaW4P2 0.037 0.015 2.471 0.013 0.037 0.153
## .compaW1S1 ~~
## .compaW1S2 0.114 0.028 4.128 0.000 0.114 0.643
## .compaW1P1 ~~
## .compaW1P2 0.213 0.035 6.155 0.000 0.213 0.729
## .compaW2S1 ~~
## .compaW2S2 0.031 0.011 2.859 0.004 0.031 0.295
## .compaW2P1 ~~
## .compaW2P2 0.199 0.032 6.198 0.000 0.199 0.795
## .compaW3S1 ~~
## .compaW3S2 0.027 0.009 3.083 0.002 0.027 0.295
## .compaW3P1 ~~
## .compaW3P2 0.147 0.032 4.524 0.000 0.147 0.652
## .compaW4S1 ~~
## .compaW4S2 0.093 0.030 3.155 0.002 0.093 0.708
## .compaW4P1 ~~
## .compaW4P2 0.252 0.045 5.622 0.000 0.252 0.860
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S1 4.197 0.030 138.342 0.000 4.197 8.606
## .compaW1S2 4.095 0.033 125.587 0.000 4.095 7.812
## .compaW1P1 3.945 0.049 79.933 0.000 3.945 6.523
## .compaW1P2 3.972 0.043 91.339 0.000 3.972 7.370
## .compaW2S1 4.182 0.034 123.841 0.000 4.182 8.435
## .compaW2S2 4.084 0.036 112.558 0.000 4.084 7.601
## .compaW2P1 3.982 0.046 87.339 0.000 3.982 7.296
## .compaW2P2 4.026 0.050 81.180 0.000 4.026 6.862
## .compaW3S1 4.206 0.034 123.996 0.000 4.206 8.485
## .compaW3S2 4.067 0.036 113.728 0.000 4.067 7.745
## .compaW3P1 3.982 0.051 78.282 0.000 3.982 6.922
## .compaW3P2 4.023 0.045 89.976 0.000 4.023 7.782
## .compaW4S1 4.237 0.035 121.782 0.000 4.237 8.625
## .compaW4S2 4.163 0.038 110.427 0.000 4.163 7.847
## .compaW4P1 3.840 0.056 68.271 0.000 3.840 6.529
## .compaW4P2 3.950 0.057 69.830 0.000 3.950 6.675
## compa1 0.000 0.000 0.000
## .compa2 0.000 0.000 0.000
## .compa3 0.000 0.000 0.000
## .compa4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .compaW1S1 0.166 0.028 5.853 0.000 0.166 0.697
## .compaW1S2 0.191 0.030 6.359 0.000 0.191 0.696
## .compaW1P1 0.332 0.044 7.578 0.000 0.332 0.907
## .compaW1P2 0.258 0.030 8.455 0.000 0.258 0.887
## .compaW2S1 0.095 0.015 6.203 0.000 0.095 0.385
## .compaW2S2 0.114 0.017 6.780 0.000 0.114 0.394
## .compaW2P1 0.227 0.031 7.361 0.000 0.227 0.762
## .compaW2P2 0.276 0.038 7.265 0.000 0.276 0.800
## .compaW3S1 0.089 0.014 6.514 0.000 0.089 0.364
## .compaW3S2 0.095 0.014 6.808 0.000 0.095 0.344
## .compaW3P1 0.258 0.039 6.601 0.000 0.258 0.778
## .compaW3P2 0.196 0.028 6.972 0.000 0.196 0.735
## .compaW4S1 0.121 0.028 4.302 0.000 0.121 0.503
## .compaW4S2 0.143 0.033 4.297 0.000 0.143 0.507
## .compaW4P1 0.290 0.047 6.152 0.000 0.290 0.837
## .compaW4P2 0.296 0.045 6.508 0.000 0.296 0.845
## compa1 0.072 0.026 2.749 0.006 1.000 1.000
## .compa2 -0.072 0.068 -1.059 0.290 -0.479 -0.479
## .compa3 0.015 0.010 1.583 0.114 0.098 0.098
## .compa4 -0.066 0.025 -2.591 0.010 -0.551 -0.551semPaths(lsmCompa, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Enthusiasm
lsmEnthu <- '
# factor at each time point with same loading
enthu1 =~ enthuW1S1 + a * enthuW1S2 +
peer * enthuW1P1 + aa * enthuW1P2
enthu2 =~ enthuW2S1 + a * enthuW2S2 +
peer * enthuW2P1 + aa * enthuW2P2
enthu3 =~ enthuW3S1 + a * enthuW3S2 +
peer * enthuW3P1 + aa * enthuW3P2
enthu4 =~ enthuW4S1 + a * enthuW4S2 +
peer * enthuW4P1 + aa * enthuW4P2
# structural paths between time points
enthu4 ~ enthu3
enthu3 ~ enthu2
enthu2 ~ enthu1
# error covariance - similar parcels across waves
enthuW1S1 ~~ enthuW2S1 + enthuW3S1 + enthuW4S1
enthuW2S1 ~~ enthuW3S1 + enthuW4S1
enthuW3S1 ~~ enthuW4S1
enthuW1S2 ~~ enthuW2S2 + enthuW3S2 + enthuW4S2
enthuW2S2 ~~ enthuW3S2 + enthuW4S2
enthuW3S2 ~~ enthuW4S2
enthuW1P1 ~~ enthuW2P1 + enthuW3P1 + enthuW4P1
enthuW2P1 ~~ enthuW3P1 + enthuW4P1
enthuW3P1 ~~ enthuW4P1
enthuW1P2 ~~ enthuW2P2 + enthuW3P2 + enthuW4P2
enthuW2P2 ~~ enthuW3P2 + enthuW4P2
enthuW3P2 ~~ enthuW4P2
# error covariance - same method at one wave
enthuW1S1 ~~ enthuW1S2
enthuW1P1 ~~ enthuW1P2
enthuW2S1 ~~ enthuW2S2
enthuW2P1 ~~ enthuW2P2
enthuW3S1 ~~ enthuW3S2
enthuW3P1 ~~ enthuW3P2
enthuW4S1 ~~ enthuW4S2
enthuW4P1 ~~ enthuW4P2
'
lsmEnthu <- sem(lsmEnthu, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmEnthu, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 165 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 239.458
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2328.338
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.927
## Tucker-Lewis Index (TLI) 0.888
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1510.599
## Loglikelihood unrestricted model (H1) -1390.870
##
## Akaike (AIC) 3169.198
## Bayesian (BIC) 3432.404
## Sample-size adjusted Bayesian (BIC) 3197.797
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.089
## 90 Percent confidence interval - lower 0.077
## 90 Percent confidence interval - upper 0.102
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.148
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## enthu1 =~
## entW1S1 1.000 0.466 0.708
## entW1S2 (a) 1.176 0.090 13.061 0.000 0.548 0.738
## entW1P1 (peer) 0.832 0.181 4.590 0.000 0.388 0.604
## entW1P2 (aa) 0.701 0.162 4.329 0.000 0.327 0.562
## enthu2 =~
## entW2S1 1.000 0.484 0.755
## entW2S2 (a) 1.176 0.090 13.061 0.000 0.570 0.771
## entW2P1 (peer) 0.832 0.181 4.590 0.000 0.403 0.631
## entW2P2 (aa) 0.701 0.162 4.329 0.000 0.340 0.600
## enthu3 =~
## entW3S1 1.000 0.448 0.729
## entW3S2 (a) 1.176 0.090 13.061 0.000 0.527 0.721
## entW3P1 (peer) 0.832 0.181 4.590 0.000 0.373 0.626
## entW3P2 (aa) 0.701 0.162 4.329 0.000 0.314 0.580
## enthu4 =~
## entW4S1 1.000 0.423 0.697
## entW4S2 (a) 1.176 0.090 13.061 0.000 0.498 0.681
## entW4P1 (peer) 0.832 0.181 4.590 0.000 0.352 0.597
## entW4P2 (aa) 0.701 0.162 4.329 0.000 0.297 0.530
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## enthu4 ~
## enthu3 0.995 0.063 15.671 0.000 1.052 1.052
## enthu3 ~
## enthu2 0.900 0.055 16.374 0.000 0.974 0.974
## enthu2 ~
## enthu1 1.042 0.086 12.138 0.000 1.002 1.002
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .enthuW1S1 ~~
## .enthuW2S1 0.096 0.022 4.318 0.000 0.096 0.489
## .enthuW3S1 0.085 0.023 3.739 0.000 0.085 0.433
## .enthuW4S1 0.068 0.022 3.156 0.002 0.068 0.337
## .enthuW2S1 ~~
## .enthuW3S1 0.066 0.026 2.500 0.012 0.066 0.373
## .enthuW4S1 0.084 0.027 3.161 0.002 0.084 0.458
## .enthuW3S1 ~~
## .enthuW4S1 0.080 0.028 2.869 0.004 0.080 0.436
## .enthuW1S2 ~~
## .enthuW2S2 0.087 0.032 2.726 0.006 0.087 0.369
## .enthuW3S2 0.070 0.031 2.271 0.023 0.070 0.277
## .enthuW4S2 0.081 0.032 2.555 0.011 0.081 0.302
## .enthuW2S2 ~~
## .enthuW3S2 0.131 0.035 3.755 0.000 0.131 0.548
## .enthuW4S2 0.130 0.035 3.694 0.000 0.130 0.518
## .enthuW3S2 ~~
## .enthuW4S2 0.152 0.037 4.124 0.000 0.152 0.561
## .enthuW1P1 ~~
## .enthuW2P1 0.135 0.045 2.989 0.003 0.135 0.534
## .enthuW3P1 0.126 0.043 2.952 0.003 0.126 0.530
## .enthuW4P1 0.108 0.039 2.769 0.006 0.108 0.446
## .enthuW2P1 ~~
## .enthuW3P1 0.147 0.043 3.403 0.001 0.147 0.637
## .enthuW4P1 0.100 0.042 2.402 0.016 0.100 0.428
## .enthuW3P1 ~~
## .enthuW4P1 0.106 0.041 2.608 0.009 0.106 0.483
## .enthuW1P2 ~~
## .enthuW2P2 0.122 0.038 3.214 0.001 0.122 0.559
## .enthuW3P2 0.126 0.035 3.632 0.000 0.126 0.594
## .enthuW4P2 0.091 0.033 2.709 0.007 0.091 0.397
## .enthuW2P2 ~~
## .enthuW3P2 0.113 0.035 3.176 0.001 0.113 0.563
## .enthuW4P2 0.090 0.034 2.671 0.008 0.090 0.418
## .enthuW3P2 ~~
## .enthuW4P2 0.102 0.034 3.025 0.002 0.102 0.486
## .enthuW1S1 ~~
## .enthuW1S2 0.074 0.024 3.148 0.002 0.074 0.318
## .enthuW1P1 ~~
## .enthuW1P2 0.066 0.019 3.399 0.001 0.066 0.269
## .enthuW2S1 ~~
## .enthuW2S2 0.028 0.012 2.335 0.020 0.028 0.143
## .enthuW2P1 ~~
## .enthuW2P2 0.045 0.017 2.714 0.007 0.045 0.199
## .enthuW3S1 ~~
## .enthuW3S2 0.035 0.013 2.639 0.008 0.035 0.163
## .enthuW3P1 ~~
## .enthuW3P2 0.041 0.015 2.717 0.007 0.041 0.200
## .enthuW4S1 ~~
## .enthuW4S2 0.069 0.024 2.832 0.005 0.069 0.296
## .enthuW4P1 ~~
## .enthuW4P2 0.088 0.028 3.132 0.002 0.088 0.389
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .enthuW1S1 3.922 0.041 95.861 0.000 3.922 5.961
## .enthuW1S2 3.535 0.046 76.599 0.000 3.535 4.764
## .enthuW1P1 3.688 0.049 75.553 0.000 3.688 5.753
## .enthuW1P2 3.942 0.045 88.483 0.000 3.942 6.784
## .enthuW2S1 3.848 0.042 90.970 0.000 3.848 5.996
## .enthuW2S2 3.544 0.049 72.512 0.000 3.544 4.796
## .enthuW2P1 3.669 0.049 74.434 0.000 3.669 5.745
## .enthuW2P2 3.976 0.044 89.683 0.000 3.976 7.017
## .enthuW3S1 3.841 0.041 93.434 0.000 3.841 6.255
## .enthuW3S2 3.504 0.049 71.168 0.000 3.504 4.796
## .enthuW3P1 3.736 0.047 79.374 0.000 3.736 6.275
## .enthuW3P2 3.961 0.043 91.199 0.000 3.961 7.315
## .enthuW4S1 3.902 0.043 90.499 0.000 3.902 6.420
## .enthuW4S2 3.539 0.051 68.765 0.000 3.539 4.841
## .enthuW4P1 3.694 0.052 71.735 0.000 3.694 6.258
## .enthuW4P2 3.917 0.050 77.838 0.000 3.917 6.989
## enthu1 0.000 0.000 0.000
## .enthu2 0.000 0.000 0.000
## .enthu3 0.000 0.000 0.000
## .enthu4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .enthuW1S1 0.216 0.029 7.406 0.000 0.216 0.499
## .enthuW1S2 0.251 0.047 5.385 0.000 0.251 0.455
## .enthuW1P1 0.261 0.046 5.699 0.000 0.261 0.635
## .enthuW1P2 0.231 0.038 6.142 0.000 0.231 0.684
## .enthuW2S1 0.177 0.031 5.646 0.000 0.177 0.430
## .enthuW2S2 0.222 0.042 5.330 0.000 0.222 0.406
## .enthuW2P1 0.245 0.048 5.059 0.000 0.245 0.602
## .enthuW2P2 0.206 0.040 5.083 0.000 0.206 0.641
## .enthuW3S1 0.177 0.033 5.329 0.000 0.177 0.469
## .enthuW3S2 0.257 0.041 6.188 0.000 0.257 0.481
## .enthuW3P1 0.216 0.042 5.134 0.000 0.216 0.608
## .enthuW3P2 0.195 0.033 5.983 0.000 0.195 0.664
## .enthuW4S1 0.190 0.035 5.469 0.000 0.190 0.515
## .enthuW4S2 0.286 0.047 6.092 0.000 0.286 0.536
## .enthuW4P1 0.224 0.036 6.163 0.000 0.224 0.644
## .enthuW4P2 0.226 0.036 6.200 0.000 0.226 0.719
## enthu1 0.217 0.041 5.286 0.000 1.000 1.000
## .enthu2 -0.001 0.018 -0.044 0.965 -0.003 -0.003
## .enthu3 0.010 0.010 1.076 0.282 0.052 0.052
## .enthu4 -0.019 0.017 -1.102 0.270 -0.106 -0.106semPaths(lsmEnthu, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Industriousness
lsmIndus <- '
# factor at each time point with same loading
indus1 =~ indusW1S1 + a * indusW1S2 +
peer * indusW1P1 + aa * indusW1P2
indus2 =~ indusW2S1 + a * indusW2S2 +
peer * indusW2P1 + aa * indusW2P2
indus3 =~ indusW3S1 + a * indusW3S2 +
peer * indusW3P1 + aa * indusW3P2
indus4 =~ indusW4S1 + a * indusW4S2 +
peer * indusW4P1 + aa * indusW4P2
# structural paths between time points
indus4 ~ indus3
indus3 ~ indus2
indus2 ~ indus1
# error covariance - similar parcels across waves
indusW1S1 ~~ indusW2S1 + indusW3S1 + indusW4S1
indusW2S1 ~~ indusW3S1 + indusW4S1
indusW3S1 ~~ indusW4S1
indusW1S2 ~~ indusW2S2 + indusW3S2 + indusW4S2
indusW2S2 ~~ indusW3S2 + indusW4S2
indusW3S2 ~~ indusW4S2
indusW1P1 ~~ indusW2P1 + indusW3P1 + indusW4P1
indusW2P1 ~~ indusW3P1 + indusW4P1
indusW3P1 ~~ indusW4P1
indusW1P2 ~~ indusW2P2 + indusW3P2 + indusW4P2
indusW2P2 ~~ indusW3P2 + indusW4P2
indusW3P2 ~~ indusW4P2
# error covariance - same method at one wave
indusW1S1 ~~ indusW1S2
indusW1P1 ~~ indusW1P2
indusW2S1 ~~ indusW2S2
indusW2P1 ~~ indusW2P2
indusW3S1 ~~ indusW3S2
indusW3P1 ~~ indusW3P2
indusW4S1 ~~ indusW4S2
indusW4P1 ~~ indusW4P2
'
lsmIndus <- sem(lsmIndus, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmIndus, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 142 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 224.996
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1960.526
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.920
## Tucker-Lewis Index (TLI) 0.877
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1627.811
## Loglikelihood unrestricted model (H1) -1515.313
##
## Akaike (AIC) 3403.621
## Bayesian (BIC) 3666.827
## Sample-size adjusted Bayesian (BIC) 3432.220
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.085
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.098
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.168
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indus1 =~
## indW1S1 1.000 0.401 0.684
## indW1S2 (a) 1.391 0.123 11.266 0.000 0.558 0.764
## indW1P1 (peer) 0.681 0.114 5.975 0.000 0.273 0.438
## indW1P2 (aa) 0.534 0.129 4.138 0.000 0.214 0.336
## indus2 =~
## indW2S1 1.000 0.455 0.748
## indW2S2 (a) 1.391 0.123 11.266 0.000 0.632 0.856
## indW2P1 (peer) 0.681 0.114 5.975 0.000 0.310 0.514
## indW2P2 (aa) 0.534 0.129 4.138 0.000 0.243 0.392
## indus3 =~
## indW3S1 1.000 0.406 0.668
## indW3S2 (a) 1.391 0.123 11.266 0.000 0.565 0.789
## indW3P1 (peer) 0.681 0.114 5.975 0.000 0.277 0.451
## indW3P2 (aa) 0.534 0.129 4.138 0.000 0.217 0.370
## indus4 =~
## indW4S1 1.000 0.418 0.737
## indW4S2 (a) 1.391 0.123 11.266 0.000 0.581 0.833
## indW4P1 (peer) 0.681 0.114 5.975 0.000 0.284 0.434
## indW4P2 (aa) 0.534 0.129 4.138 0.000 0.223 0.383
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indus4 ~
## indus3 1.047 0.066 15.933 0.000 1.018 1.018
## indus3 ~
## indus2 0.886 0.062 14.275 0.000 0.991 0.991
## indus2 ~
## indus1 1.038 0.154 6.725 0.000 0.915 0.915
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S1 ~~
## .indusW2S1 0.078 0.017 4.573 0.000 0.078 0.455
## .indusW3S1 0.077 0.018 4.359 0.000 0.077 0.398
## .indusW4S1 0.057 0.017 3.456 0.001 0.057 0.350
## .indusW2S1 ~~
## .indusW3S1 0.080 0.020 4.068 0.000 0.080 0.436
## .indusW4S1 0.056 0.018 3.031 0.002 0.056 0.360
## .indusW3S1 ~~
## .indusW4S1 0.092 0.019 4.784 0.000 0.092 0.532
## .indusW1S2 ~~
## .indusW2S2 0.069 0.031 2.206 0.027 0.069 0.383
## .indusW3S2 0.050 0.029 1.713 0.087 0.050 0.239
## .indusW4S2 0.026 0.030 0.877 0.381 0.026 0.145
## .indusW2S2 ~~
## .indusW3S2 0.044 0.032 1.367 0.172 0.044 0.265
## .indusW4S2 0.008 0.033 0.229 0.819 0.008 0.052
## .indusW3S2 ~~
## .indusW4S2 0.054 0.034 1.622 0.105 0.054 0.321
## .indusW1P1 ~~
## .indusW2P1 0.138 0.032 4.353 0.000 0.138 0.475
## .indusW3P1 0.131 0.033 3.974 0.000 0.131 0.428
## .indusW4P1 0.175 0.037 4.731 0.000 0.175 0.528
## .indusW2P1 ~~
## .indusW3P1 0.176 0.037 4.807 0.000 0.176 0.622
## .indusW4P1 0.184 0.038 4.864 0.000 0.184 0.601
## .indusW3P1 ~~
## .indusW4P1 0.235 0.042 5.545 0.000 0.235 0.728
## .indusW1P2 ~~
## .indusW2P2 0.165 0.036 4.584 0.000 0.165 0.483
## .indusW3P2 0.165 0.036 4.576 0.000 0.165 0.503
## .indusW4P2 0.169 0.041 4.176 0.000 0.169 0.525
## .indusW2P2 ~~
## .indusW3P2 0.176 0.040 4.429 0.000 0.176 0.567
## .indusW4P2 0.198 0.038 5.160 0.000 0.198 0.647
## .indusW3P2 ~~
## .indusW4P2 0.194 0.038 5.091 0.000 0.194 0.663
## .indusW1S1 ~~
## .indusW1S2 0.047 0.034 1.352 0.177 0.047 0.231
## .indusW1P1 ~~
## .indusW1P2 0.117 0.026 4.541 0.000 0.117 0.346
## .indusW2S1 ~~
## .indusW2S2 0.006 0.014 0.399 0.690 0.006 0.037
## .indusW2P1 ~~
## .indusW2P2 0.072 0.021 3.407 0.001 0.072 0.244
## .indusW3S1 ~~
## .indusW3S2 0.019 0.012 1.560 0.119 0.019 0.095
## .indusW3P1 ~~
## .indusW3P2 0.071 0.023 3.120 0.002 0.071 0.239
## .indusW4S1 ~~
## .indusW4S2 0.024 0.025 0.981 0.327 0.024 0.165
## .indusW4P1 ~~
## .indusW4P2 0.021 0.022 0.986 0.324 0.021 0.067
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S1 3.340 0.036 91.671 0.000 3.340 5.701
## .indusW1S2 3.025 0.045 66.613 0.000 3.025 4.143
## .indusW1P1 3.776 0.049 76.583 0.000 3.776 6.055
## .indusW1P2 3.627 0.051 71.395 0.000 3.627 5.691
## .indusW2S1 3.311 0.041 81.673 0.000 3.311 5.446
## .indusW2S2 2.966 0.049 60.694 0.000 2.966 4.015
## .indusW2P1 3.736 0.048 77.739 0.000 3.736 6.196
## .indusW2P2 3.628 0.050 72.099 0.000 3.628 5.856
## .indusW3S1 3.326 0.041 80.650 0.000 3.326 5.469
## .indusW3S2 3.027 0.048 62.650 0.000 3.027 4.230
## .indusW3P1 3.748 0.050 75.147 0.000 3.748 6.109
## .indusW3P2 3.617 0.049 73.888 0.000 3.617 6.160
## .indusW4S1 3.327 0.040 82.589 0.000 3.327 5.871
## .indusW4S2 3.058 0.050 61.677 0.000 3.058 4.384
## .indusW4P1 3.681 0.056 65.547 0.000 3.681 5.617
## .indusW4P2 3.538 0.051 69.715 0.000 3.538 6.078
## indus1 0.000 0.000 0.000
## .indus2 0.000 0.000 0.000
## .indus3 0.000 0.000 0.000
## .indus4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .indusW1S1 0.183 0.031 5.851 0.000 0.183 0.532
## .indusW1S2 0.222 0.058 3.811 0.000 0.222 0.417
## .indusW1P1 0.314 0.038 8.347 0.000 0.314 0.808
## .indusW1P2 0.360 0.042 8.665 0.000 0.360 0.887
## .indusW2S1 0.163 0.024 6.867 0.000 0.163 0.440
## .indusW2S2 0.146 0.042 3.442 0.001 0.146 0.267
## .indusW2P1 0.268 0.034 7.938 0.000 0.268 0.736
## .indusW2P2 0.325 0.044 7.337 0.000 0.325 0.846
## .indusW3S1 0.205 0.026 7.976 0.000 0.205 0.554
## .indusW3S2 0.193 0.037 5.183 0.000 0.193 0.377
## .indusW3P1 0.300 0.040 7.554 0.000 0.300 0.797
## .indusW3P2 0.298 0.038 7.840 0.000 0.298 0.863
## .indusW4S1 0.147 0.026 5.648 0.000 0.147 0.457
## .indusW4S2 0.149 0.048 3.081 0.002 0.149 0.307
## .indusW4P1 0.348 0.050 6.972 0.000 0.348 0.811
## .indusW4P2 0.289 0.041 7.029 0.000 0.289 0.853
## indus1 0.161 0.035 4.630 0.000 1.000 1.000
## .indus2 0.034 0.026 1.291 0.197 0.162 0.162
## .indus3 0.003 0.009 0.311 0.756 0.017 0.017
## .indus4 -0.006 0.016 -0.389 0.697 -0.036 -0.036semPaths(lsmIndus, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Intellect
lsmIntel <- '
# factor at each time point with same loading
intel1 =~ intelW1S1 + a * intelW1S2 +
peer * intelW1P1 + aa * intelW1P2
intel2 =~ intelW2S1 + a * intelW2S2 +
peer * intelW2P1 + aa * intelW2P2
intel3 =~ intelW3S1 + a * intelW3S2 +
peer * intelW3P1 + aa * intelW3P2
intel4 =~ intelW4S1 + a * intelW4S2 +
peer * intelW4P1 + aa * intelW4P2
# structural paths between time points
intel4 ~ intel3
intel3 ~ intel2
intel2 ~ intel1
# error covariance - similar parcels across waves
intelW1S1 ~~ intelW2S1 + intelW3S1 + intelW4S1
intelW2S1 ~~ intelW3S1 + intelW4S1
intelW3S1 ~~ intelW4S1
intelW1S2 ~~ intelW2S2 + intelW3S2 + intelW4S2
intelW2S2 ~~ intelW3S2 + intelW4S2
intelW3S2 ~~ intelW4S2
intelW1P1 ~~ intelW2P1 + intelW3P1 + intelW4P1
intelW2P1 ~~ intelW3P1 + intelW4P1
intelW3P1 ~~ intelW4P1
intelW1P2 ~~ intelW2P2 + intelW3P2 + intelW4P2
intelW2P2 ~~ intelW3P2 + intelW4P2
intelW3P2 ~~ intelW4P2
# error covariance - same method at one wave
intelW1S1 ~~ intelW1S2
intelW1P1 ~~ intelW1P2
intelW2S1 ~~ intelW2S2
intelW2P1 ~~ intelW2P2
intelW3S1 ~~ intelW3S2
intelW3P1 ~~ intelW3P2
intelW4S1 ~~ intelW4S2
intelW4P1 ~~ intelW4P2
'
lsmIntel <- sem(lsmIntel, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmIntel, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 178 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 218.233
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2029.552
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.927
## Tucker-Lewis Index (TLI) 0.887
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1320.147
## Loglikelihood unrestricted model (H1) -1211.030
##
## Akaike (AIC) 2788.293
## Bayesian (BIC) 3051.498
## Sample-size adjusted Bayesian (BIC) 2816.892
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.083
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.097
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.171
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intel1 =~
## intW1S1 1.000 0.433 0.751
## intW1S2 (a) 1.101 0.107 10.322 0.000 0.477 0.745
## intW1P1 (peer) 0.584 0.111 5.281 0.000 0.253 0.461
## intW1P2 (aa) 0.408 0.107 3.800 0.000 0.177 0.324
## intel2 =~
## intW2S1 1.000 0.418 0.742
## intW2S2 (a) 1.101 0.107 10.322 0.000 0.461 0.734
## intW2P1 (peer) 0.584 0.111 5.281 0.000 0.244 0.440
## intW2P2 (aa) 0.408 0.107 3.800 0.000 0.171 0.360
## intel3 =~
## intW3S1 1.000 0.434 0.739
## intW3S2 (a) 1.101 0.107 10.322 0.000 0.478 0.760
## intW3P1 (peer) 0.584 0.111 5.281 0.000 0.254 0.425
## intW3P2 (aa) 0.408 0.107 3.800 0.000 0.177 0.308
## intel4 =~
## intW4S1 1.000 0.358 0.642
## intW4S2 (a) 1.101 0.107 10.322 0.000 0.395 0.679
## intW4P1 (peer) 0.584 0.111 5.281 0.000 0.209 0.331
## intW4P2 (aa) 0.408 0.107 3.800 0.000 0.146 0.290
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intel4 ~
## intel3 0.945 0.066 14.424 0.000 1.146 1.146
## intel3 ~
## intel2 1.042 0.071 14.759 0.000 1.003 1.003
## intel2 ~
## intel1 0.941 0.099 9.507 0.000 0.975 0.975
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S1 ~~
## .intelW2S1 0.064 0.018 3.461 0.001 0.064 0.442
## .intelW3S1 0.060 0.019 3.060 0.002 0.060 0.395
## .intelW4S1 0.043 0.018 2.345 0.019 0.043 0.264
## .intelW2S1 ~~
## .intelW3S1 0.071 0.020 3.560 0.000 0.071 0.478
## .intelW4S1 0.045 0.019 2.412 0.016 0.045 0.280
## .intelW3S1 ~~
## .intelW4S1 0.054 0.020 2.676 0.007 0.054 0.320
## .intelW1S2 ~~
## .intelW2S2 0.067 0.022 3.031 0.002 0.067 0.370
## .intelW3S2 0.046 0.023 2.059 0.040 0.046 0.265
## .intelW4S2 0.050 0.022 2.291 0.022 0.050 0.274
## .intelW2S2 ~~
## .intelW3S2 0.084 0.025 3.390 0.001 0.084 0.483
## .intelW4S2 0.075 0.024 3.189 0.001 0.075 0.415
## .intelW3S2 ~~
## .intelW4S2 0.061 0.024 2.595 0.009 0.061 0.350
## .intelW1P1 ~~
## .intelW2P1 0.135 0.029 4.700 0.000 0.135 0.555
## .intelW3P1 0.183 0.031 5.831 0.000 0.183 0.696
## .intelW4P1 0.149 0.031 4.836 0.000 0.149 0.512
## .intelW2P1 ~~
## .intelW3P1 0.191 0.034 5.534 0.000 0.191 0.709
## .intelW4P1 0.155 0.036 4.347 0.000 0.155 0.520
## .intelW3P1 ~~
## .intelW4P1 0.173 0.040 4.282 0.000 0.173 0.537
## .intelW1P2 ~~
## .intelW2P2 0.165 0.025 6.635 0.000 0.165 0.725
## .intelW3P2 0.180 0.032 5.528 0.000 0.180 0.635
## .intelW4P2 0.136 0.028 4.912 0.000 0.136 0.546
## .intelW2P2 ~~
## .intelW3P2 0.158 0.029 5.470 0.000 0.158 0.650
## .intelW4P2 0.127 0.023 5.446 0.000 0.127 0.597
## .intelW3P2 ~~
## .intelW4P2 0.157 0.033 4.806 0.000 0.157 0.593
## .intelW1S1 ~~
## .intelW1S2 0.038 0.023 1.645 0.100 0.038 0.231
## .intelW1P1 ~~
## .intelW1P2 0.058 0.013 4.302 0.000 0.058 0.230
## .intelW2S1 ~~
## .intelW2S2 0.029 0.010 3.020 0.003 0.029 0.183
## .intelW2P1 ~~
## .intelW2P2 0.027 0.012 2.191 0.028 0.027 0.122
## .intelW3S1 ~~
## .intelW3S2 0.013 0.010 1.273 0.203 0.013 0.081
## .intelW3P1 ~~
## .intelW3P2 0.027 0.018 1.522 0.128 0.027 0.090
## .intelW4S1 ~~
## .intelW4S2 0.066 0.031 2.114 0.035 0.066 0.362
## .intelW4P1 ~~
## .intelW4P2 0.113 0.027 4.169 0.000 0.113 0.393
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S1 3.712 0.036 103.457 0.000 3.712 6.433
## .intelW1S2 3.620 0.040 90.878 0.000 3.620 5.652
## .intelW1P1 3.998 0.043 93.712 0.000 3.998 7.288
## .intelW1P2 4.002 0.043 93.515 0.000 4.002 7.334
## .intelW2S1 3.644 0.037 97.637 0.000 3.644 6.465
## .intelW2S2 3.595 0.042 86.044 0.000 3.595 5.727
## .intelW2P1 3.960 0.045 88.781 0.000 3.960 7.134
## .intelW2P2 4.026 0.038 106.690 0.000 4.026 8.492
## .intelW3S1 3.658 0.039 93.180 0.000 3.658 6.223
## .intelW3S2 3.631 0.042 85.934 0.000 3.631 5.770
## .intelW3P1 3.979 0.048 82.997 0.000 3.979 6.666
## .intelW3P2 3.936 0.048 82.087 0.000 3.936 6.832
## .intelW4S1 3.683 0.040 91.958 0.000 3.683 6.597
## .intelW4S2 3.644 0.041 89.607 0.000 3.644 6.274
## .intelW4P1 3.856 0.058 66.734 0.000 3.856 6.096
## .intelW4P2 3.910 0.046 85.924 0.000 3.910 7.763
## intel1 0.000 0.000 0.000
## .intel2 0.000 0.000 0.000
## .intel3 0.000 0.000 0.000
## .intel4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intelW1S1 0.145 0.029 4.947 0.000 0.145 0.436
## .intelW1S2 0.183 0.034 5.388 0.000 0.183 0.445
## .intelW1P1 0.237 0.030 7.975 0.000 0.237 0.787
## .intelW1P2 0.266 0.032 8.447 0.000 0.266 0.895
## .intelW2S1 0.143 0.022 6.420 0.000 0.143 0.449
## .intelW2S2 0.182 0.029 6.239 0.000 0.182 0.461
## .intelW2P1 0.248 0.034 7.383 0.000 0.248 0.806
## .intelW2P2 0.196 0.024 8.021 0.000 0.196 0.870
## .intelW3S1 0.157 0.025 6.161 0.000 0.157 0.454
## .intelW3S2 0.167 0.029 5.773 0.000 0.167 0.422
## .intelW3P1 0.292 0.039 7.427 0.000 0.292 0.819
## .intelW3P2 0.300 0.040 7.533 0.000 0.300 0.905
## .intelW4S1 0.183 0.035 5.181 0.000 0.183 0.588
## .intelW4S2 0.182 0.038 4.772 0.000 0.182 0.539
## .intelW4P1 0.356 0.047 7.658 0.000 0.356 0.891
## .intelW4P2 0.232 0.029 8.037 0.000 0.232 0.916
## intel1 0.188 0.036 5.234 0.000 1.000 1.000
## .intel2 0.009 0.017 0.515 0.607 0.050 0.050
## .intel3 -0.001 0.009 -0.145 0.884 -0.007 -0.007
## .intel4 -0.040 0.025 -1.606 0.108 -0.312 -0.312semPaths(lsmIntel, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Openness aspect
lsmOpena <- '
# factor at each time point with same loading
opena1 =~ openaW1S1 + a * openaW1S2 +
peer * openaW1P1 + aa * openaW1P2
opena2 =~ openaW2S1 + a * openaW2S2 +
peer * openaW2P1 + aa * openaW2P2
opena3 =~ openaW3S1 + a * openaW3S2 +
peer * openaW3P1 + aa * openaW3P2
opena4 =~ openaW4S1 + a * openaW4S2 +
peer * openaW4P1 + aa * openaW4P2
# structural paths between time points
opena4 ~ opena3
opena3 ~ opena2
opena2 ~ opena1
# error covariance - similar parcels across waves
openaW1S1 ~~ openaW2S1 + openaW3S1 + openaW4S1
openaW2S1 ~~ openaW3S1 + openaW4S1
openaW3S1 ~~ openaW4S1
openaW1S2 ~~ openaW2S2 + openaW3S2 + openaW4S2
openaW2S2 ~~ openaW3S2 + openaW4S2
openaW3S2 ~~ openaW4S2
openaW1P1 ~~ openaW2P1 + openaW3P1 + openaW4P1
openaW2P1 ~~ openaW3P1 + openaW4P1
openaW3P1 ~~ openaW4P1
openaW1P2 ~~ openaW2P2 + openaW3P2 + openaW4P2
openaW2P2 ~~ openaW3P2 + openaW4P2
openaW3P2 ~~ openaW4P2
# error covariance - same method at one wave
openaW1S1 ~~ openaW1S2
openaW1P1 ~~ openaW1P2
openaW2S1 ~~ openaW2S2
openaW2P1 ~~ openaW2P2
openaW3S1 ~~ openaW3S2
openaW3P1 ~~ openaW3P2
openaW4S1 ~~ openaW4S2
openaW4P1 ~~ openaW4P2
'
lsmOpena <- sem(lsmOpena, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmOpena, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 187 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 146.582
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2368.492
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.969
## Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1140.330
## Loglikelihood unrestricted model (H1) -1067.039
##
## Akaike (AIC) 2428.660
## Bayesian (BIC) 2691.866
## Sample-size adjusted Bayesian (BIC) 2457.259
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent confidence interval - lower 0.044
## 90 Percent confidence interval - upper 0.073
## P-value RMSEA <= 0.05 0.168
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.114
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opena1 =~
## opnW1S1 1.000 0.484 0.737
## opnW1S2 (a) 0.906 0.063 14.304 0.000 0.438 0.765
## opnW1P1 (peer) 0.628 0.076 8.318 0.000 0.304 0.558
## opnW1P2 (aa) 0.479 0.076 6.296 0.000 0.232 0.478
## opena2 =~
## opnW2S1 1.000 0.538 0.798
## opnW2S2 (a) 0.906 0.063 14.304 0.000 0.487 0.807
## opnW2P1 (peer) 0.628 0.076 8.318 0.000 0.338 0.598
## opnW2P2 (aa) 0.479 0.076 6.296 0.000 0.257 0.462
## opena3 =~
## opnW3S1 1.000 0.541 0.828
## opnW3S2 (a) 0.906 0.063 14.304 0.000 0.490 0.864
## opnW3P1 (peer) 0.628 0.076 8.318 0.000 0.340 0.618
## opnW3P2 (aa) 0.479 0.076 6.296 0.000 0.259 0.491
## opena4 =~
## opnW4S1 1.000 0.474 0.717
## opnW4S2 (a) 0.906 0.063 14.304 0.000 0.430 0.742
## opnW4P1 (peer) 0.628 0.076 8.318 0.000 0.298 0.548
## opnW4P2 (aa) 0.479 0.076 6.296 0.000 0.227 0.460
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## opena4 ~
## opena3 0.913 0.047 19.282 0.000 1.041 1.041
## opena3 ~
## opena2 1.003 0.046 21.721 0.000 0.997 0.997
## opena2 ~
## opena1 1.108 0.091 12.148 0.000 0.996 0.996
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .openaW1S1 ~~
## .openaW2S1 0.108 0.020 5.526 0.000 0.108 0.599
## .openaW3S1 0.086 0.018 4.851 0.000 0.086 0.531
## .openaW4S1 0.103 0.020 5.165 0.000 0.103 0.502
## .openaW2S1 ~~
## .openaW3S1 0.082 0.019 4.429 0.000 0.082 0.552
## .openaW4S1 0.095 0.021 4.632 0.000 0.095 0.507
## .openaW3S1 ~~
## .openaW4S1 0.089 0.019 4.562 0.000 0.089 0.526
## .openaW1S2 ~~
## .openaW2S2 0.033 0.015 2.152 0.031 0.033 0.249
## .openaW3S2 0.035 0.014 2.522 0.012 0.035 0.334
## .openaW4S2 0.040 0.016 2.480 0.013 0.040 0.279
## .openaW2S2 ~~
## .openaW3S2 0.049 0.015 3.223 0.001 0.049 0.479
## .openaW4S2 0.051 0.017 2.957 0.003 0.051 0.367
## .openaW3S2 ~~
## .openaW4S2 0.049 0.016 3.116 0.002 0.049 0.447
## .openaW1P1 ~~
## .openaW2P1 0.095 0.022 4.410 0.000 0.095 0.465
## .openaW3P1 0.101 0.022 4.622 0.000 0.101 0.516
## .openaW4P1 0.035 0.026 1.360 0.174 0.035 0.171
## .openaW2P1 ~~
## .openaW3P1 0.135 0.025 5.510 0.000 0.135 0.692
## .openaW4P1 0.083 0.028 3.011 0.003 0.083 0.403
## .openaW3P1 ~~
## .openaW4P1 0.110 0.029 3.798 0.000 0.110 0.559
## .openaW1P2 ~~
## .openaW2P2 0.129 0.022 5.874 0.000 0.129 0.614
## .openaW3P2 0.099 0.022 4.432 0.000 0.099 0.507
## .openaW4P2 0.111 0.021 5.225 0.000 0.111 0.597
## .openaW2P2 ~~
## .openaW3P2 0.149 0.027 5.499 0.000 0.149 0.654
## .openaW4P2 0.133 0.026 5.047 0.000 0.133 0.615
## .openaW3P2 ~~
## .openaW4P2 0.140 0.029 4.788 0.000 0.140 0.695
## .openaW1S1 ~~
## .openaW1S2 0.043 0.017 2.597 0.009 0.043 0.262
## .openaW1P1 ~~
## .openaW1P2 0.045 0.013 3.503 0.000 0.045 0.234
## .openaW2S1 ~~
## .openaW2S2 0.030 0.009 3.470 0.001 0.030 0.207
## .openaW2P1 ~~
## .openaW2P2 0.031 0.013 2.411 0.016 0.031 0.137
## .openaW3S1 ~~
## .openaW3S2 0.011 0.007 1.539 0.124 0.011 0.103
## .openaW3P1 ~~
## .openaW3P2 0.018 0.011 1.654 0.098 0.018 0.091
## .openaW4S1 ~~
## .openaW4S2 0.055 0.023 2.461 0.014 0.055 0.310
## .openaW4P1 ~~
## .openaW4P2 0.045 0.021 2.179 0.029 0.045 0.224
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .openaW1S1 3.865 0.041 94.717 0.000 3.865 5.888
## .openaW1S2 3.916 0.036 109.899 0.000 3.916 6.834
## .openaW1P1 3.859 0.042 91.185 0.000 3.859 7.088
## .openaW1P2 3.656 0.038 97.377 0.000 3.656 7.551
## .openaW2S1 3.812 0.044 87.125 0.000 3.812 5.658
## .openaW2S2 3.861 0.040 96.555 0.000 3.861 6.397
## .openaW2P1 3.827 0.044 87.519 0.000 3.827 6.779
## .openaW2P2 3.595 0.044 81.576 0.000 3.595 6.448
## .openaW3S1 3.825 0.043 89.604 0.000 3.825 5.857
## .openaW3S2 3.910 0.037 104.970 0.000 3.910 6.897
## .openaW3P1 3.837 0.043 89.734 0.000 3.837 6.981
## .openaW3P2 3.657 0.043 85.878 0.000 3.657 6.933
## .openaW4S1 3.869 0.046 84.932 0.000 3.869 5.847
## .openaW4S2 3.937 0.041 97.187 0.000 3.937 6.800
## .openaW4P1 3.728 0.048 77.391 0.000 3.728 6.853
## .openaW4P2 3.644 0.042 86.414 0.000 3.644 7.383
## opena1 0.000 0.000 0.000
## .opena2 0.000 0.000 0.000
## .opena3 0.000 0.000 0.000
## .opena4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .openaW1S1 0.197 0.027 7.426 0.000 0.197 0.457
## .openaW1S2 0.136 0.023 6.057 0.000 0.136 0.415
## .openaW1P1 0.204 0.026 7.988 0.000 0.204 0.688
## .openaW1P2 0.181 0.022 8.301 0.000 0.181 0.771
## .openaW2S1 0.165 0.024 6.941 0.000 0.165 0.364
## .openaW2S2 0.127 0.020 6.243 0.000 0.127 0.349
## .openaW2P1 0.205 0.028 7.308 0.000 0.205 0.642
## .openaW2P2 0.245 0.030 8.140 0.000 0.245 0.787
## .openaW3S1 0.134 0.021 6.427 0.000 0.134 0.314
## .openaW3S2 0.081 0.017 4.892 0.000 0.081 0.253
## .openaW3P1 0.187 0.027 6.868 0.000 0.187 0.618
## .openaW3P2 0.211 0.031 6.754 0.000 0.211 0.759
## .openaW4S1 0.213 0.035 6.137 0.000 0.213 0.486
## .openaW4S2 0.151 0.027 5.555 0.000 0.151 0.449
## .openaW4P1 0.207 0.031 6.586 0.000 0.207 0.700
## .openaW4P2 0.192 0.027 7.026 0.000 0.192 0.788
## opena1 0.234 0.039 6.036 0.000 1.000 1.000
## .opena2 0.002 0.019 0.110 0.912 0.007 0.007
## .opena3 0.002 0.008 0.250 0.803 0.007 0.007
## .opena4 -0.019 0.022 -0.858 0.391 -0.083 -0.083semPaths(lsmOpena, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Orderliness
lsmOrder <- '
# factor at each time point with same loading
order1 =~ orderW1S1 + a * orderW1S2 +
peer * orderW1P1 + aa * orderW1P2
order2 =~ orderW2S1 + a * orderW2S2 +
peer * orderW2P1 + aa * orderW2P2
order3 =~ orderW3S1 + a * orderW3S2 +
peer * orderW3P1 + aa * orderW3P2
order4 =~ orderW4S1 + a * orderW4S2 +
peer * orderW4P1 + aa * orderW4P2
# structural paths between time points
order4 ~ order3
order3 ~ order2
order2 ~ order1
# error covariance - similar parcels across waves
orderW1S1 ~~ orderW2S1 + orderW3S1 + orderW4S1
orderW2S1 ~~ orderW3S1 + orderW4S1
orderW3S1 ~~ orderW4S1
orderW1S2 ~~ orderW2S2 + orderW3S2 + orderW4S2
orderW2S2 ~~ orderW3S2 + orderW4S2
orderW3S2 ~~ orderW4S2
orderW1P1 ~~ orderW2P1 + orderW3P1 + orderW4P1
orderW2P1 ~~ orderW3P1 + orderW4P1
orderW3P1 ~~ orderW4P1
orderW1P2 ~~ orderW2P2 + orderW3P2 + orderW4P2
orderW2P2 ~~ orderW3P2 + orderW4P2
orderW3P2 ~~ orderW4P2
# error covariance - same method at one wave
orderW1S1 ~~ orderW1S2
orderW1P1 ~~ orderW1P2
orderW2S1 ~~ orderW2S2
orderW2P1 ~~ orderW2P2
orderW3S1 ~~ orderW3S2
orderW3P1 ~~ orderW3P2
orderW4S1 ~~ orderW4S2
orderW4P1 ~~ orderW4P2
'
lsmOrder <- sem(lsmOrder, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmOrder, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 136 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 162.667
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2131.446
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958
## Tucker-Lewis Index (TLI) 0.935
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1600.261
## Loglikelihood unrestricted model (H1) -1518.927
##
## Akaike (AIC) 3348.521
## Bayesian (BIC) 3611.727
## Sample-size adjusted Bayesian (BIC) 3377.120
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.065
## 90 Percent confidence interval - lower 0.051
## 90 Percent confidence interval - upper 0.079
## P-value RMSEA <= 0.05 0.043
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.110
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## order1 =~
## ordW1S1 1.000 0.533 0.762
## ordW1S2 (a) 0.790 0.054 14.610 0.000 0.421 0.662
## ordW1P1 (peer) 0.780 0.097 8.032 0.000 0.415 0.569
## ordW1P2 (aa) 0.558 0.075 7.410 0.000 0.297 0.489
## order2 =~
## ordW2S1 1.000 0.540 0.808
## ordW2S2 (a) 0.790 0.054 14.610 0.000 0.426 0.703
## ordW2P1 (peer) 0.780 0.097 8.032 0.000 0.421 0.619
## ordW2P2 (aa) 0.558 0.075 7.410 0.000 0.301 0.560
## order3 =~
## ordW3S1 1.000 0.566 0.830
## ordW3S2 (a) 0.790 0.054 14.610 0.000 0.447 0.749
## ordW3P1 (peer) 0.780 0.097 8.032 0.000 0.442 0.574
## ordW3P2 (aa) 0.558 0.075 7.410 0.000 0.316 0.555
## order4 =~
## ordW4S1 1.000 0.561 0.803
## ordW4S2 (a) 0.790 0.054 14.610 0.000 0.443 0.714
## ordW4P1 (peer) 0.780 0.097 8.032 0.000 0.437 0.585
## ordW4P2 (aa) 0.558 0.075 7.410 0.000 0.313 0.551
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## order4 ~
## order3 0.977 0.057 17.051 0.000 0.987 0.987
## order3 ~
## order2 1.060 0.053 19.928 0.000 1.010 1.010
## order2 ~
## order1 1.013 0.094 10.754 0.000 1.000 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .orderW1S1 ~~
## .orderW2S1 0.005 0.021 0.219 0.827 0.005 0.026
## .orderW3S1 0.027 0.022 1.249 0.212 0.027 0.157
## .orderW4S1 0.004 0.023 0.180 0.857 0.004 0.022
## .orderW2S1 ~~
## .orderW3S1 0.030 0.023 1.305 0.192 0.030 0.203
## .orderW4S1 -0.004 0.023 -0.188 0.851 -0.004 -0.026
## .orderW3S1 ~~
## .orderW4S1 0.041 0.024 1.718 0.086 0.041 0.260
## .orderW1S2 ~~
## .orderW2S2 0.088 0.016 5.397 0.000 0.088 0.428
## .orderW3S2 0.060 0.015 3.924 0.000 0.060 0.319
## .orderW4S2 0.060 0.017 3.523 0.000 0.060 0.291
## .orderW2S2 ~~
## .orderW3S2 0.073 0.016 4.417 0.000 0.073 0.426
## .orderW4S2 0.073 0.018 4.069 0.000 0.073 0.388
## .orderW3S2 ~~
## .orderW4S2 0.084 0.017 5.062 0.000 0.084 0.488
## .orderW1P1 ~~
## .orderW2P1 0.164 0.036 4.499 0.000 0.164 0.512
## .orderW3P1 0.187 0.044 4.219 0.000 0.187 0.493
## .orderW4P1 0.187 0.041 4.519 0.000 0.187 0.512
## .orderW2P1 ~~
## .orderW3P1 0.214 0.041 5.280 0.000 0.214 0.638
## .orderW4P1 0.212 0.038 5.578 0.000 0.212 0.656
## .orderW3P1 ~~
## .orderW4P1 0.280 0.047 5.974 0.000 0.280 0.732
## .orderW1P2 ~~
## .orderW2P2 0.124 0.026 4.713 0.000 0.124 0.526
## .orderW3P2 0.128 0.029 4.442 0.000 0.128 0.511
## .orderW4P2 0.095 0.027 3.529 0.000 0.095 0.377
## .orderW2P2 ~~
## .orderW3P2 0.129 0.025 5.056 0.000 0.129 0.611
## .orderW4P2 0.104 0.024 4.263 0.000 0.104 0.492
## .orderW3P2 ~~
## .orderW4P2 0.084 0.029 2.890 0.004 0.084 0.373
## .orderW1S1 ~~
## .orderW1S2 0.086 0.023 3.755 0.000 0.086 0.398
## .orderW1P1 ~~
## .orderW1P2 0.085 0.025 3.436 0.001 0.085 0.268
## .orderW2S1 ~~
## .orderW2S2 0.001 0.012 0.109 0.913 0.001 0.008
## .orderW2P1 ~~
## .orderW2P2 0.029 0.013 2.189 0.029 0.029 0.124
## .orderW3S1 ~~
## .orderW3S2 0.011 0.011 1.065 0.287 0.011 0.075
## .orderW3P1 ~~
## .orderW3P2 0.055 0.020 2.789 0.005 0.055 0.186
## .orderW4S1 ~~
## .orderW4S2 0.061 0.028 2.167 0.030 0.061 0.335
## .orderW4P1 ~~
## .orderW4P2 0.091 0.026 3.491 0.000 0.091 0.316
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .orderW1S1 3.397 0.044 78.094 0.000 3.397 4.857
## .orderW1S2 3.727 0.040 94.324 0.000 3.727 5.866
## .orderW1P1 3.240 0.056 58.032 0.000 3.240 4.436
## .orderW1P2 3.521 0.047 74.533 0.000 3.521 5.795
## .orderW2S1 3.488 0.045 77.797 0.000 3.488 5.225
## .orderW2S2 3.791 0.040 93.813 0.000 3.791 6.254
## .orderW2P1 3.342 0.052 64.184 0.000 3.342 4.921
## .orderW2P2 3.571 0.042 85.107 0.000 3.571 6.646
## .orderW3S1 3.489 0.046 76.583 0.000 3.489 5.113
## .orderW3S2 3.761 0.040 93.836 0.000 3.761 6.300
## .orderW3P1 3.274 0.060 54.203 0.000 3.274 4.255
## .orderW3P2 3.494 0.046 75.887 0.000 3.494 6.135
## .orderW4S1 3.518 0.050 69.865 0.000 3.518 5.037
## .orderW4S2 3.784 0.045 84.864 0.000 3.784 6.094
## .orderW4P1 3.210 0.061 52.223 0.000 3.210 4.292
## .orderW4P2 3.415 0.051 66.630 0.000 3.415 6.015
## order1 0.000 0.000 0.000
## .order2 0.000 0.000 0.000
## .order3 0.000 0.000 0.000
## .order4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .orderW1S1 0.206 0.036 5.771 0.000 0.206 0.420
## .orderW1S2 0.226 0.026 8.842 0.000 0.226 0.561
## .orderW1P1 0.361 0.044 8.205 0.000 0.361 0.677
## .orderW1P2 0.281 0.033 8.475 0.000 0.281 0.761
## .orderW2S1 0.155 0.027 5.621 0.000 0.155 0.347
## .orderW2S2 0.186 0.022 8.400 0.000 0.186 0.506
## .orderW2P1 0.284 0.039 7.365 0.000 0.284 0.616
## .orderW2P2 0.198 0.027 7.267 0.000 0.198 0.686
## .orderW3S1 0.145 0.029 5.018 0.000 0.145 0.311
## .orderW3S2 0.156 0.019 8.092 0.000 0.156 0.438
## .orderW3P1 0.397 0.053 7.466 0.000 0.397 0.671
## .orderW3P2 0.225 0.030 7.395 0.000 0.225 0.692
## .orderW4S1 0.173 0.044 3.964 0.000 0.173 0.355
## .orderW4S2 0.189 0.030 6.281 0.000 0.189 0.491
## .orderW4P1 0.368 0.050 7.329 0.000 0.368 0.658
## .orderW4P2 0.224 0.032 7.001 0.000 0.224 0.696
## order1 0.284 0.047 6.077 0.000 1.000 1.000
## .order2 -0.000 0.023 -0.005 0.996 -0.000 -0.000
## .order3 -0.006 0.012 -0.506 0.613 -0.019 -0.019
## .order4 0.008 0.031 0.267 0.789 0.027 0.027semPaths(lsmOrder, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Politeness
lsmPolit <- '
# factor at each time point with same loading
polit1 =~ politW1S1 + a * politW1S2 +
peer * politW1P1 + aa * politW1P2
polit2 =~ politW2S1 + a * politW2S2 +
peer * politW2P1 + aa * politW2P2
polit3 =~ politW3S1 + a * politW3S2 +
peer * politW3P1 + aa * politW3P2
polit4 =~ politW4S1 + a * politW4S2 +
peer * politW4P1 + aa * politW4P2
# structural paths between time points
polit4 ~ polit3
polit3 ~ polit2
polit2 ~ polit1
# error covariance - similar parcels across waves
politW1S1 ~~ politW2S1 + politW3S1 + politW4S1
politW2S1 ~~ politW3S1 + politW4S1
politW3S1 ~~ politW4S1
politW1S2 ~~ politW2S2 + politW3S2 + politW4S2
politW2S2 ~~ politW3S2 + politW4S2
politW3S2 ~~ politW4S2
politW1P1 ~~ politW2P1 + politW3P1 + politW4P1
politW2P1 ~~ politW3P1 + politW4P1
politW3P1 ~~ politW4P1
politW1P2 ~~ politW2P2 + politW3P2 + politW4P2
politW2P2 ~~ politW3P2 + politW4P2
politW3P2 ~~ politW4P2
# error covariance - same method at one wave
politW1S1 ~~ politW1S2
politW1P1 ~~ politW1P2
politW2S1 ~~ politW2S2
politW2P1 ~~ politW2P2
politW3S1 ~~ politW3S2
politW3P1 ~~ politW3P2
politW4S1 ~~ politW4S2
politW4P1 ~~ politW4P2
'
lsmPolit <- sem(lsmPolit, data = data, missing = "ML")
summary(lsmPolit, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 153 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 148.718
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1979.092
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1498.565
## Loglikelihood unrestricted model (H1) -1424.206
##
## Akaike (AIC) 3145.129
## Bayesian (BIC) 3408.334
## Sample-size adjusted Bayesian (BIC) 3173.728
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent confidence interval - lower 0.045
## 90 Percent confidence interval - upper 0.074
## P-value RMSEA <= 0.05 0.143
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.094
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## polit1 =~
## pltW1S1 1.000 0.385 0.593
## pltW1S2 (a) 0.851 0.097 8.768 0.000 0.328 0.576
## pltW1P1 (peer) 1.157 0.132 8.736 0.000 0.446 0.711
## pltW1P2 (aa) 1.330 0.148 8.962 0.000 0.512 0.802
## polit2 =~
## pltW2S1 1.000 0.381 0.575
## pltW2S2 (a) 0.851 0.097 8.768 0.000 0.324 0.567
## pltW2P1 (peer) 1.157 0.132 8.736 0.000 0.440 0.723
## pltW2P2 (aa) 1.330 0.148 8.962 0.000 0.506 0.792
## polit3 =~
## pltW3S1 1.000 0.423 0.633
## pltW3S2 (a) 0.851 0.097 8.768 0.000 0.360 0.592
## pltW3P1 (peer) 1.157 0.132 8.736 0.000 0.489 0.739
## pltW3P2 (aa) 1.330 0.148 8.962 0.000 0.562 0.831
## polit4 =~
## pltW4S1 1.000 0.437 0.699
## pltW4S2 (a) 0.851 0.097 8.768 0.000 0.372 0.590
## pltW4P1 (peer) 1.157 0.132 8.736 0.000 0.505 0.813
## pltW4P2 (aa) 1.330 0.148 8.962 0.000 0.581 0.810
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## polit4 ~
## polit3 1.022 0.061 16.729 0.000 0.990 0.990
## polit3 ~
## polit2 1.063 0.071 14.970 0.000 0.957 0.957
## polit2 ~
## polit1 0.934 0.080 11.721 0.000 0.945 0.945
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .politW1S1 ~~
## .politW2S1 0.198 0.027 7.416 0.000 0.198 0.698
## .politW3S1 0.168 0.025 6.647 0.000 0.168 0.622
## .politW4S1 0.143 0.024 5.985 0.000 0.143 0.610
## .politW2S1 ~~
## .politW3S1 0.205 0.029 6.983 0.000 0.205 0.732
## .politW4S1 0.153 0.027 5.697 0.000 0.153 0.633
## .politW3S1 ~~
## .politW4S1 0.156 0.028 5.655 0.000 0.156 0.675
## .politW1S2 ~~
## .politW2S2 0.097 0.020 4.797 0.000 0.097 0.442
## .politW3S2 0.109 0.020 5.350 0.000 0.109 0.480
## .politW4S2 0.098 0.023 4.258 0.000 0.098 0.413
## .politW2S2 ~~
## .politW3S2 0.136 0.023 5.903 0.000 0.136 0.591
## .politW4S2 0.138 0.025 5.503 0.000 0.138 0.576
## .politW3S2 ~~
## .politW4S2 0.165 0.028 5.963 0.000 0.165 0.662
## .politW1P1 ~~
## .politW2P1 0.078 0.019 4.102 0.000 0.078 0.418
## .politW3P1 0.064 0.018 3.480 0.001 0.064 0.325
## .politW4P1 0.067 0.021 3.210 0.001 0.067 0.418
## .politW2P1 ~~
## .politW3P1 0.051 0.018 2.783 0.005 0.051 0.269
## .politW4P1 0.063 0.022 2.853 0.004 0.063 0.411
## .politW3P1 ~~
## .politW4P1 0.044 0.020 2.157 0.031 0.044 0.272
## .politW1P2 ~~
## .politW2P2 0.004 0.021 0.214 0.831 0.004 0.030
## .politW3P2 -0.007 0.018 -0.403 0.687 -0.007 -0.052
## .politW4P2 -0.015 0.029 -0.513 0.608 -0.015 -0.093
## .politW2P2 ~~
## .politW3P2 0.030 0.020 1.488 0.137 0.030 0.205
## .politW4P2 0.045 0.025 1.768 0.077 0.045 0.274
## .politW3P2 ~~
## .politW4P2 0.068 0.025 2.775 0.006 0.068 0.431
## .politW1S1 ~~
## .politW1S2 0.041 0.014 3.053 0.002 0.041 0.170
## .politW1P1 ~~
## .politW1P2 0.063 0.023 2.793 0.005 0.063 0.375
## .politW2S1 ~~
## .politW2S2 0.015 0.010 1.481 0.139 0.015 0.060
## .politW2P1 ~~
## .politW2P2 0.031 0.017 1.858 0.063 0.031 0.190
## .politW3S1 ~~
## .politW3S2 0.023 0.010 2.353 0.019 0.023 0.091
## .politW3P1 ~~
## .politW3P2 0.071 0.021 3.398 0.001 0.071 0.422
## .politW4S1 ~~
## .politW4S2 0.000 0.014 0.005 0.996 0.000 0.000
## .politW4P1 ~~
## .politW4P2 0.021 0.024 0.854 0.393 0.021 0.137
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .politW1S1 3.809 0.040 94.241 0.000 3.809 5.860
## .politW1S2 3.588 0.035 101.262 0.000 3.588 6.299
## .politW1P1 3.642 0.048 76.434 0.000 3.642 5.814
## .politW1P2 4.009 0.048 82.736 0.000 4.009 6.275
## .politW2S1 3.822 0.043 88.068 0.000 3.822 5.774
## .politW2S2 3.647 0.039 94.454 0.000 3.647 6.383
## .politW2P1 3.626 0.048 75.990 0.000 3.626 5.951
## .politW2P2 3.974 0.050 80.002 0.000 3.974 6.220
## .politW3S1 3.810 0.044 85.893 0.000 3.810 5.703
## .politW3S2 3.658 0.041 88.948 0.000 3.658 6.018
## .politW3P1 3.609 0.053 67.517 0.000 3.609 5.450
## .politW3P2 3.982 0.053 75.567 0.000 3.982 5.883
## .politW4S1 3.880 0.043 89.437 0.000 3.880 6.213
## .politW4S2 3.670 0.045 80.973 0.000 3.670 5.825
## .politW4P1 3.598 0.052 69.446 0.000 3.598 5.792
## .politW4P2 3.827 0.060 64.087 0.000 3.827 5.337
## polit1 0.000 0.000 0.000
## .polit2 0.000 0.000 0.000
## .polit3 0.000 0.000 0.000
## .polit4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .politW1S1 0.274 0.029 9.505 0.000 0.274 0.649
## .politW1S2 0.217 0.024 9.195 0.000 0.217 0.669
## .politW1P1 0.194 0.030 6.491 0.000 0.194 0.494
## .politW1P2 0.146 0.031 4.653 0.000 0.146 0.357
## .politW2S1 0.293 0.034 8.686 0.000 0.293 0.669
## .politW2S2 0.221 0.026 8.400 0.000 0.221 0.678
## .politW2P1 0.177 0.026 6.771 0.000 0.177 0.478
## .politW2P2 0.152 0.029 5.248 0.000 0.152 0.372
## .politW3S1 0.267 0.033 8.221 0.000 0.267 0.599
## .politW3S2 0.240 0.028 8.433 0.000 0.240 0.649
## .politW3P1 0.199 0.031 6.392 0.000 0.199 0.454
## .politW3P2 0.142 0.028 5.029 0.000 0.142 0.310
## .politW4S1 0.199 0.030 6.596 0.000 0.199 0.511
## .politW4S2 0.259 0.035 7.489 0.000 0.259 0.652
## .politW4P1 0.131 0.030 4.375 0.000 0.131 0.339
## .politW4P2 0.177 0.043 4.096 0.000 0.177 0.345
## polit1 0.148 0.030 4.901 0.000 1.000 1.000
## .polit2 0.015 0.010 1.478 0.140 0.106 0.106
## .polit3 0.015 0.009 1.701 0.089 0.084 0.084
## .polit4 0.004 0.013 0.284 0.776 0.020 0.020semPaths(lsmPolit, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Volatility
lsmVolat <- '
# factor at each time point with same loading
volat1 =~ volatW1S1 + a * volatW1S2 +
peer * volatW1P1 + aa * volatW1P2
volat2 =~ volatW2S1 + a * volatW2S2 +
peer * volatW2P1 + aa * volatW2P2
volat3 =~ volatW3S1 + a * volatW3S2 +
peer * volatW3P1 + aa * volatW3P2
volat4 =~ volatW4S1 + a * volatW4S2 +
peer * volatW4P1 + aa * volatW4P2
# structural paths between time points
volat4 ~ volat3
volat3 ~ volat2
volat2 ~ volat1
# error covariance - similar parcels across waves
volatW1S1 ~~ volatW2S1 + volatW3S1 + volatW4S1
volatW2S1 ~~ volatW3S1 + volatW4S1
volatW3S1 ~~ volatW4S1
volatW1S2 ~~ volatW2S2 + volatW3S2 + volatW4S2
volatW2S2 ~~ volatW3S2 + volatW4S2
volatW3S2 ~~ volatW4S2
volatW1P1 ~~ volatW2P1 + volatW3P1 + volatW4P1
volatW2P1 ~~ volatW3P1 + volatW4P1
volatW3P1 ~~ volatW4P1
volatW1P2 ~~ volatW2P2 + volatW3P2 + volatW4P2
volatW2P2 ~~ volatW3P2 + volatW4P2
volatW3P2 ~~ volatW4P2
# error covariance - same method at one wave
volatW1S1 ~~ volatW1S2
volatW1P1 ~~ volatW1P2
volatW2S1 ~~ volatW2S2
volatW2P1 ~~ volatW2P2
volatW3S1 ~~ volatW3S2
volatW3P1 ~~ volatW3P2
volatW4S1 ~~ volatW4S2
volatW4P1 ~~ volatW4P2
'
lsmVolat <- sem(lsmVolat, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmVolat, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 133 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 331.954
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2829.311
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.906
## Tucker-Lewis Index (TLI) 0.856
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1820.277
## Loglikelihood unrestricted model (H1) -1654.300
##
## Akaike (AIC) 3788.554
## Bayesian (BIC) 4051.759
## Sample-size adjusted Bayesian (BIC) 3817.153
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.112
## 90 Percent confidence interval - lower 0.100
## 90 Percent confidence interval - upper 0.125
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.201
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## volat1 =~
## vltW1S1 1.000 0.641 0.760
## vltW1S2 (a) 0.894 0.039 22.999 0.000 0.572 0.748
## vltW1P1 (peer) 0.565 0.054 10.464 0.000 0.362 0.455
## vltW1P2 (aa) 0.555 0.055 10.163 0.000 0.356 0.450
## volat2 =~
## vltW2S1 1.000 0.786 0.890
## vltW2S2 (a) 0.894 0.039 22.999 0.000 0.703 0.899
## vltW2P1 (peer) 0.565 0.054 10.464 0.000 0.444 0.597
## vltW2P2 (aa) 0.555 0.055 10.163 0.000 0.436 0.587
## volat3 =~
## vltW3S1 1.000 0.697 0.894
## vltW3S2 (a) 0.894 0.039 22.999 0.000 0.623 0.831
## vltW3P1 (peer) 0.565 0.054 10.464 0.000 0.393 0.461
## vltW3P2 (aa) 0.555 0.055 10.163 0.000 0.387 0.502
## volat4 =~
## vltW4S1 1.000 0.568 0.650
## vltW4S2 (a) 0.894 0.039 22.999 0.000 0.508 0.664
## vltW4P1 (peer) 0.565 0.054 10.464 0.000 0.321 0.433
## vltW4P2 (aa) 0.555 0.055 10.163 0.000 0.315 0.433
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## volat4 ~
## volat3 0.991 0.057 17.528 0.000 1.216 1.216
## volat3 ~
## volat2 0.871 0.040 21.741 0.000 0.983 0.983
## volat2 ~
## volat1 1.289 0.168 7.687 0.000 1.050 1.050
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S1 ~~
## .volatW2S1 0.033 0.021 1.541 0.123 0.033 0.148
## .volatW3S1 0.009 0.018 0.485 0.627 0.009 0.046
## .volatW4S1 0.045 0.024 1.893 0.058 0.045 0.124
## .volatW2S1 ~~
## .volatW3S1 0.030 0.019 1.626 0.104 0.030 0.217
## .volatW4S1 0.022 0.024 0.886 0.375 0.022 0.081
## .volatW3S1 ~~
## .volatW4S1 0.026 0.021 1.253 0.210 0.026 0.114
## .volatW1S2 ~~
## .volatW2S2 0.037 0.017 2.216 0.027 0.037 0.213
## .volatW3S2 0.054 0.016 3.295 0.001 0.054 0.253
## .volatW4S2 0.023 0.019 1.246 0.213 0.023 0.080
## .volatW2S2 ~~
## .volatW3S2 0.055 0.016 3.432 0.001 0.055 0.388
## .volatW4S2 0.024 0.017 1.397 0.162 0.024 0.123
## .volatW3S2 ~~
## .volatW4S2 0.040 0.017 2.412 0.016 0.040 0.169
## .volatW1P1 ~~
## .volatW2P1 0.027 0.024 1.112 0.266 0.027 0.063
## .volatW3P1 -0.036 0.028 -1.292 0.196 -0.036 -0.068
## .volatW4P1 0.038 0.025 1.500 0.134 0.038 0.081
## .volatW2P1 ~~
## .volatW3P1 0.027 0.027 1.002 0.316 0.027 0.060
## .volatW4P1 0.050 0.026 1.937 0.053 0.050 0.127
## .volatW3P1 ~~
## .volatW4P1 -0.054 0.032 -1.674 0.094 -0.054 -0.108
## .volatW1P2 ~~
## .volatW2P2 0.052 0.022 2.351 0.019 0.052 0.122
## .volatW3P2 0.073 0.023 3.158 0.002 0.073 0.155
## .volatW4P2 0.039 0.026 1.516 0.130 0.039 0.085
## .volatW2P2 ~~
## .volatW3P2 0.018 0.023 0.774 0.439 0.018 0.045
## .volatW4P2 0.049 0.025 1.952 0.051 0.049 0.124
## .volatW3P2 ~~
## .volatW4P2 0.097 0.024 4.079 0.000 0.097 0.223
## .volatW1S1 ~~
## .volatW1S2 0.145 0.049 2.960 0.003 0.145 0.520
## .volatW1P1 ~~
## .volatW1P2 0.407 0.058 6.991 0.000 0.407 0.814
## .volatW2S1 ~~
## .volatW2S2 0.010 0.016 0.653 0.514 0.010 0.074
## .volatW2P1 ~~
## .volatW2P2 0.251 0.045 5.626 0.000 0.251 0.700
## .volatW3S1 ~~
## .volatW3S2 0.051 0.015 3.291 0.001 0.051 0.348
## .volatW3P1 ~~
## .volatW3P2 0.417 0.066 6.303 0.000 0.417 0.826
## .volatW4S1 ~~
## .volatW4S2 0.258 0.068 3.804 0.000 0.258 0.678
## .volatW4P1 ~~
## .volatW4P2 0.344 0.062 5.538 0.000 0.344 0.786
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S1 2.851 0.052 54.389 0.000 2.851 3.382
## .volatW1S2 2.708 0.048 56.894 0.000 2.708 3.538
## .volatW1P1 2.529 0.063 39.861 0.000 2.529 3.181
## .volatW1P2 2.531 0.063 40.117 0.000 2.531 3.200
## .volatW2S1 2.863 0.058 49.453 0.000 2.863 3.243
## .volatW2S2 2.718 0.051 53.253 0.000 2.718 3.479
## .volatW2P1 2.603 0.060 43.364 0.000 2.603 3.504
## .volatW2P2 2.575 0.060 42.830 0.000 2.575 3.461
## .volatW3S1 2.824 0.052 54.789 0.000 2.824 3.623
## .volatW3S2 2.670 0.050 53.778 0.000 2.670 3.563
## .volatW3P1 2.638 0.075 35.191 0.000 2.638 3.094
## .volatW3P2 2.555 0.066 38.650 0.000 2.555 3.314
## .volatW4S1 2.833 0.062 45.531 0.000 2.833 3.241
## .volatW4S2 2.719 0.054 49.983 0.000 2.719 3.558
## .volatW4P1 2.715 0.069 39.355 0.000 2.715 3.670
## .volatW4P2 2.651 0.067 39.723 0.000 2.651 3.643
## volat1 0.000 0.000 0.000
## .volat2 0.000 0.000 0.000
## .volat3 0.000 0.000 0.000
## .volat4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .volatW1S1 0.300 0.058 5.211 0.000 0.300 0.423
## .volatW1S2 0.258 0.048 5.367 0.000 0.258 0.441
## .volatW1P1 0.501 0.064 7.787 0.000 0.501 0.793
## .volatW1P2 0.499 0.059 8.391 0.000 0.499 0.798
## .volatW2S1 0.161 0.031 5.281 0.000 0.161 0.207
## .volatW2S2 0.117 0.023 5.185 0.000 0.117 0.191
## .volatW2P1 0.355 0.047 7.517 0.000 0.355 0.643
## .volatW2P2 0.363 0.047 7.686 0.000 0.363 0.656
## .volatW3S1 0.122 0.023 5.222 0.000 0.122 0.200
## .volatW3S2 0.174 0.024 7.206 0.000 0.174 0.309
## .volatW3P1 0.572 0.085 6.698 0.000 0.572 0.787
## .volatW3P2 0.445 0.060 7.400 0.000 0.445 0.748
## .volatW4S1 0.441 0.080 5.487 0.000 0.441 0.578
## .volatW4S2 0.327 0.063 5.175 0.000 0.327 0.559
## .volatW4P1 0.444 0.069 6.426 0.000 0.444 0.812
## .volatW4P2 0.430 0.060 7.130 0.000 0.430 0.812
## volat1 0.410 0.074 5.547 0.000 1.000 1.000
## .volat2 -0.064 0.084 -0.761 0.447 -0.103 -0.103
## .volat3 0.017 0.017 0.977 0.329 0.034 0.034
## .volat4 -0.154 0.066 -2.332 0.020 -0.479 -0.479semPaths(lsmVolat, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Withdrawal
lsmWithd <- '
# factor at each time point with same loading
withd1 =~ withdW1S1 + a * withdW1S2 +
peer * withdW1P1 + aa * withdW1P2
withd2 =~ withdW2S1 + a * withdW2S2 +
peer * withdW2P1 + aa * withdW2P2
withd3 =~ withdW3S1 + a * withdW3S2 +
peer * withdW3P1 + aa * withdW3P2
withd4 =~ withdW4S1 + a * withdW4S2 +
peer * withdW4P1 + aa * withdW4P2
# structural paths between time points
withd4 ~ withd3
withd3 ~ withd2
withd2 ~ withd1
# error covariance - similar parcels across waves
withdW1S1 ~~ withdW2S1 + withdW3S1 + withdW4S1
withdW2S1 ~~ withdW3S1 + withdW4S1
withdW3S1 ~~ withdW4S1
withdW1S2 ~~ withdW2S2 + withdW3S2 + withdW4S2
withdW2S2 ~~ withdW3S2 + withdW4S2
withdW3S2 ~~ withdW4S2
withdW1P1 ~~ withdW2P1 + withdW3P1 + withdW4P1
withdW2P1 ~~ withdW3P1 + withdW4P1
withdW3P1 ~~ withdW4P1
withdW1P2 ~~ withdW2P2 + withdW3P2 + withdW4P2
withdW2P2 ~~ withdW3P2 + withdW4P2
withdW3P2 ~~ withdW4P2
# error covariance - same method at one wave
withdW1S1 ~~ withdW1S2
withdW1P1 ~~ withdW1P2
withdW2S1 ~~ withdW2S2
withdW2P1 ~~ withdW2P2
withdW3S1 ~~ withdW3S2
withdW3P1 ~~ withdW3P2
withdW4S1 ~~ withdW4S2
withdW4P1 ~~ withdW4P2
'
lsmWithd <- sem(lsmWithd, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmWithd, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 145 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 52
##
## Model Test User Model:
##
## Test statistic 308.848
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2333.668
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.896
## Tucker-Lewis Index (TLI) 0.840
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1786.107
## Loglikelihood unrestricted model (H1) -1631.683
##
## Akaike (AIC) 3720.214
## Bayesian (BIC) 3983.420
## Sample-size adjusted Bayesian (BIC) 3748.813
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.107
## 90 Percent confidence interval - lower 0.095
## 90 Percent confidence interval - upper 0.120
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.165
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## withd1 =~
## wthW1S1 1.000 0.536 0.708
## wthW1S2 (a) 0.928 0.067 13.860 0.000 0.497 0.687
## wthW1P1 (peer) 0.712 0.252 2.822 0.005 0.381 0.570
## wthW1P2 (aa) 0.686 0.246 2.794 0.005 0.367 0.524
## withd2 =~
## wthW2S1 1.000 0.593 0.772
## wthW2S2 (a) 0.928 0.067 13.860 0.000 0.550 0.758
## wthW2P1 (peer) 0.712 0.252 2.822 0.005 0.422 0.626
## wthW2P2 (aa) 0.686 0.246 2.794 0.005 0.407 0.615
## withd3 =~
## wthW3S1 1.000 0.544 0.723
## wthW3S2 (a) 0.928 0.067 13.860 0.000 0.505 0.737
## wthW3P1 (peer) 0.712 0.252 2.822 0.005 0.388 0.556
## wthW3P2 (aa) 0.686 0.246 2.794 0.005 0.374 0.566
## withd4 =~
## wthW4S1 1.000 0.547 0.722
## wthW4S2 (a) 0.928 0.067 13.860 0.000 0.507 0.753
## wthW4P1 (peer) 0.712 0.252 2.822 0.005 0.389 0.578
## wthW4P2 (aa) 0.686 0.246 2.794 0.005 0.375 0.583
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## withd4 ~
## withd3 0.976 0.067 14.644 0.000 0.972 0.972
## withd3 ~
## withd2 0.885 0.056 15.946 0.000 0.964 0.964
## withd2 ~
## withd1 1.151 0.131 8.808 0.000 1.040 1.040
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .withdW1S1 ~~
## .withdW2S1 0.062 0.039 1.593 0.111 0.062 0.237
## .withdW3S1 0.073 0.044 1.654 0.098 0.073 0.263
## .withdW4S1 0.081 0.041 2.001 0.045 0.081 0.290
## .withdW2S1 ~~
## .withdW3S1 0.100 0.059 1.706 0.088 0.100 0.393
## .withdW4S1 0.080 0.052 1.546 0.122 0.080 0.312
## .withdW3S1 ~~
## .withdW4S1 0.108 0.060 1.788 0.074 0.108 0.397
## .withdW1S2 ~~
## .withdW2S2 0.100 0.038 2.658 0.008 0.100 0.404
## .withdW3S2 0.111 0.035 3.139 0.002 0.111 0.458
## .withdW4S2 0.092 0.035 2.627 0.009 0.092 0.393
## .withdW2S2 ~~
## .withdW3S2 0.102 0.038 2.671 0.008 0.102 0.464
## .withdW4S2 0.095 0.038 2.492 0.013 0.095 0.452
## .withdW3S2 ~~
## .withdW4S2 0.115 0.039 2.943 0.003 0.115 0.560
## .withdW1P1 ~~
## .withdW2P1 0.128 0.089 1.442 0.149 0.128 0.443
## .withdW3P1 0.168 0.111 1.510 0.131 0.168 0.528
## .withdW4P1 0.151 0.088 1.716 0.086 0.151 0.499
## .withdW2P1 ~~
## .withdW3P1 0.191 0.100 1.906 0.057 0.191 0.626
## .withdW4P1 0.171 0.089 1.920 0.055 0.171 0.590
## .withdW3P1 ~~
## .withdW4P1 0.184 0.115 1.602 0.109 0.184 0.577
## .withdW1P2 ~~
## .withdW2P2 0.178 0.085 2.089 0.037 0.178 0.572
## .withdW3P2 0.197 0.103 1.915 0.056 0.197 0.605
## .withdW4P2 0.134 0.094 1.428 0.153 0.134 0.429
## .withdW2P2 ~~
## .withdW3P2 0.192 0.095 2.028 0.043 0.192 0.675
## .withdW4P2 0.110 0.084 1.314 0.189 0.110 0.404
## .withdW3P2 ~~
## .withdW4P2 0.174 0.100 1.741 0.082 0.174 0.611
## .withdW1S1 ~~
## .withdW1S2 0.118 0.038 3.062 0.002 0.118 0.420
## .withdW1P1 ~~
## .withdW1P2 0.099 0.033 2.970 0.003 0.099 0.302
## .withdW2S1 ~~
## .withdW2S2 0.060 0.022 2.745 0.006 0.060 0.259
## .withdW2P1 ~~
## .withdW2P2 0.031 0.019 1.629 0.103 0.031 0.113
## .withdW3S1 ~~
## .withdW3S2 0.041 0.018 2.311 0.021 0.041 0.172
## .withdW3P1 ~~
## .withdW3P2 0.029 0.034 0.855 0.393 0.029 0.092
## .withdW4S1 ~~
## .withdW4S2 0.039 0.032 1.211 0.226 0.039 0.167
## .withdW4P1 ~~
## .withdW4P2 0.097 0.036 2.701 0.007 0.097 0.336
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .withdW1S1 2.970 0.047 63.127 0.000 2.970 3.926
## .withdW1S2 3.020 0.045 67.177 0.000 3.020 4.177
## .withdW1P1 2.587 0.052 50.128 0.000 2.587 3.864
## .withdW1P2 2.484 0.054 45.855 0.000 2.484 3.545
## .withdW2S1 3.015 0.051 58.761 0.000 3.015 3.926
## .withdW2S2 3.042 0.048 63.305 0.000 3.042 4.191
## .withdW2P1 2.602 0.052 49.642 0.000 2.602 3.854
## .withdW2P2 2.538 0.051 49.318 0.000 2.538 3.835
## .withdW3S1 2.950 0.051 57.887 0.000 2.950 3.919
## .withdW3S2 3.041 0.046 66.672 0.000 3.041 4.441
## .withdW3P1 2.613 0.056 46.731 0.000 2.613 3.749
## .withdW3P2 2.584 0.052 49.595 0.000 2.584 3.913
## .withdW4S1 2.942 0.055 53.698 0.000 2.942 3.886
## .withdW4S2 2.979 0.048 62.547 0.000 2.979 4.419
## .withdW4P1 2.641 0.058 45.178 0.000 2.641 3.921
## .withdW4P2 2.595 0.058 44.617 0.000 2.595 4.032
## withd1 0.000 0.000 0.000
## .withd2 0.000 0.000 0.000
## .withd3 0.000 0.000 0.000
## .withd4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .withdW1S1 0.285 0.060 4.721 0.000 0.285 0.499
## .withdW1S2 0.276 0.058 4.767 0.000 0.276 0.528
## .withdW1P1 0.303 0.071 4.277 0.000 0.303 0.676
## .withdW1P2 0.356 0.080 4.435 0.000 0.356 0.725
## .withdW2S1 0.238 0.066 3.603 0.000 0.238 0.404
## .withdW2S2 0.224 0.052 4.350 0.000 0.224 0.426
## .withdW2P1 0.277 0.088 3.151 0.002 0.277 0.609
## .withdW2P2 0.272 0.088 3.087 0.002 0.272 0.622
## .withdW3S1 0.270 0.077 3.516 0.000 0.270 0.477
## .withdW3S2 0.214 0.046 4.649 0.000 0.214 0.456
## .withdW3P1 0.336 0.109 3.077 0.002 0.336 0.691
## .withdW3P2 0.297 0.099 2.991 0.003 0.297 0.680
## .withdW4S1 0.274 0.075 3.633 0.000 0.274 0.478
## .withdW4S2 0.197 0.054 3.650 0.000 0.197 0.434
## .withdW4P1 0.302 0.083 3.658 0.000 0.302 0.666
## .withdW4P2 0.274 0.073 3.730 0.000 0.274 0.660
## withd1 0.287 0.083 3.457 0.001 1.000 1.000
## .withd2 -0.029 0.035 -0.806 0.420 -0.081 -0.081
## .withd3 0.021 0.016 1.258 0.208 0.070 0.070
## .withd4 0.017 0.030 0.549 0.583 0.056 0.056semPaths(lsmWithd, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Confusion
lsmConfu <- '
# factor at each time point with same loading
confu1 =~ confuW1S1 + a * confuW1S2 +
peer * confuW1P1 + aa * confuW1P2
confu2 =~ confuW2S1 + a * confuW2S2 +
peer * confuW2P1 + aa * confuW2P2
confu3 =~ confuW3S1 + a * confuW3S2 +
peer * confuW3P1 + aa * confuW3P2
confu4 =~ confuW4S1 + a * confuW4S2 +
peer * confuW4P1 + aa * confuW4P2
# structural paths between time points
confu4 ~ confu3
confu3 ~ confu2
confu2 ~ confu1
# error covariance - similar parcels across waves
confuW1S1 ~~ confuW2S1 + confuW3S1 + confuW4S1
confuW2S1 ~~ confuW3S1 + confuW4S1
confuW3S1 ~~ confuW4S1
confuW1S2 ~~ confuW2S2 + confuW3S2 + confuW4S2
confuW2S2 ~~ confuW3S2 + confuW4S2
confuW3S2 ~~ confuW4S2
confuW1P1 ~~ confuW2P1 + confuW3P1 + confuW4P1
confuW2P1 ~~ confuW3P1 + confuW4P1
confuW3P1 ~~ confuW4P1
confuW1P2 ~~ confuW2P2 + confuW3P2 + confuW4P2
confuW2P2 ~~ confuW3P2 + confuW4P2
confuW3P2 ~~ confuW4P2
# error covariance - same method at one wave
confuW1S1 ~~ confuW1S2
confuW1P1 ~~ confuW1P2
confuW2S1 ~~ confuW2S2
confuW2P1 ~~ confuW2P2
confuW3S1 ~~ confuW3S2
confuW3P1 ~~ confuW3P2
confuW4S1 ~~ confuW4S2
confuW4P1 ~~ confuW4P2
'
lsmConfu <- sem(lsmConfu, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmConfu, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 101 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 55
##
## Model Test User Model:
##
## Test statistic 154.307
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1406.278
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.941
## Tucker-Lewis Index (TLI) 0.909
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2220.262
## Loglikelihood unrestricted model (H1) -2143.108
##
## Akaike (AIC) 4588.523
## Bayesian (BIC) 4851.729
## Sample-size adjusted Bayesian (BIC) 4617.122
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.061
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.076
## P-value RMSEA <= 0.05 0.091
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.107
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## confu1 =~
## cnfW1S1 1.000 0.408 0.526
## cnfW1S2 (a) 1.093 0.112 9.753 0.000 0.446 0.577
## cnfW1P1 (peer) 0.975 0.167 5.824 0.000 0.397 0.471
## cnfW1P2 (aa) 0.756 0.145 5.209 0.000 0.308 0.476
## confu2 =~
## cnfW2S1 1.000 0.457 0.619
## cnfW2S2 (a) 1.093 0.112 9.753 0.000 0.500 0.639
## cnfW2P1 (peer) 0.975 0.167 5.824 0.000 0.446 0.619
## cnfW2P2 (aa) 0.756 0.145 5.209 0.000 0.346 0.520
## confu3 =~
## cnfW3S1 1.000 0.507 0.639
## cnfW3S2 (a) 1.093 0.112 9.753 0.000 0.554 0.654
## cnfW3P1 (peer) 0.975 0.167 5.824 0.000 0.494 0.676
## cnfW3P2 (aa) 0.756 0.145 5.209 0.000 0.383 0.560
## confu4 =~
## cnfW4S1 1.000 0.428 0.571
## cnfW4S2 (a) 1.093 0.112 9.753 0.000 0.468 0.601
## cnfW4P1 (peer) 0.975 0.167 5.824 0.000 0.417 0.572
## cnfW4P2 (aa) 0.756 0.145 5.209 0.000 0.324 0.449
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## confu4 ~
## confu3 0.835 0.084 9.972 0.000 0.990 0.990
## confu3 ~
## confu2 1.029 0.092 11.196 0.000 0.928 0.928
## confu2 ~
## confu1 1.283 0.214 5.994 0.000 1.143 1.143
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .confuW1S1 ~~
## .confuW2S1 0.087 0.032 2.722 0.006 0.087 0.226
## .confuW3S1 0.085 0.032 2.675 0.007 0.085 0.212
## .confuW4S1 0.125 0.034 3.644 0.000 0.125 0.309
## .confuW2S1 ~~
## .confuW3S1 0.101 0.034 2.936 0.003 0.101 0.285
## .confuW4S1 0.118 0.035 3.421 0.001 0.118 0.331
## .confuW3S1 ~~
## .confuW4S1 0.130 0.036 3.616 0.000 0.130 0.347
## .confuW1S2 ~~
## .confuW2S2 0.081 0.036 2.274 0.023 0.081 0.213
## .confuW3S2 0.062 0.034 1.832 0.067 0.062 0.154
## .confuW4S2 0.037 0.037 0.999 0.318 0.037 0.093
## .confuW2S2 ~~
## .confuW3S2 0.156 0.041 3.858 0.000 0.156 0.405
## .confuW4S2 0.107 0.043 2.497 0.013 0.107 0.285
## .confuW3S2 ~~
## .confuW4S2 0.140 0.045 3.089 0.002 0.140 0.351
## .confuW1P1 ~~
## .confuW2P1 0.187 0.053 3.504 0.000 0.187 0.444
## .confuW3P1 0.200 0.052 3.852 0.000 0.200 0.499
## .confuW4P1 0.158 0.060 2.627 0.009 0.158 0.354
## .confuW2P1 ~~
## .confuW3P1 0.140 0.050 2.833 0.005 0.140 0.460
## .confuW4P1 0.158 0.055 2.876 0.004 0.158 0.466
## .confuW3P1 ~~
## .confuW4P1 0.168 0.054 3.138 0.002 0.168 0.522
## .confuW1P2 ~~
## .confuW2P2 0.157 0.039 3.995 0.000 0.157 0.485
## .confuW3P2 0.121 0.038 3.168 0.002 0.121 0.375
## .confuW4P2 0.221 0.047 4.728 0.000 0.221 0.603
## .confuW2P2 ~~
## .confuW3P2 0.191 0.043 4.417 0.000 0.191 0.591
## .confuW4P2 0.225 0.047 4.754 0.000 0.225 0.615
## .confuW3P2 ~~
## .confuW4P2 0.187 0.048 3.914 0.000 0.187 0.510
## .confuW1S1 ~~
## .confuW1S2 0.155 0.039 3.933 0.000 0.155 0.373
## .confuW1P1 ~~
## .confuW1P2 0.121 0.035 3.477 0.001 0.121 0.286
## .confuW2S1 ~~
## .confuW2S2 0.057 0.026 2.175 0.030 0.057 0.162
## .confuW2P1 ~~
## .confuW2P2 0.032 0.023 1.421 0.155 0.032 0.100
## .confuW3S1 ~~
## .confuW3S2 0.124 0.032 3.832 0.000 0.124 0.318
## .confuW3P1 ~~
## .confuW3P2 0.055 0.027 2.016 0.044 0.055 0.178
## .confuW4S1 ~~
## .confuW4S2 0.095 0.046 2.064 0.039 0.095 0.248
## .confuW4P1 ~~
## .confuW4P2 0.058 0.039 1.501 0.133 0.058 0.151
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .confuW1S1 2.900 0.048 60.229 0.000 2.900 3.742
## .confuW1S2 2.954 0.048 61.510 0.000 2.954 3.822
## .confuW1P1 2.489 0.069 36.322 0.000 2.489 2.951
## .confuW1P2 2.463 0.052 47.096 0.000 2.463 3.801
## .confuW2S1 2.806 0.051 55.408 0.000 2.806 3.794
## .confuW2S2 2.871 0.053 53.773 0.000 2.871 3.666
## .confuW2P1 2.349 0.058 40.330 0.000 2.349 3.259
## .confuW2P2 2.464 0.054 45.575 0.000 2.464 3.702
## .confuW3S1 2.737 0.055 49.707 0.000 2.737 3.449
## .confuW3S2 2.836 0.059 48.346 0.000 2.836 3.346
## .confuW3P1 2.458 0.060 41.042 0.000 2.458 3.362
## .confuW3P2 2.536 0.058 43.750 0.000 2.536 3.701
## .confuW4S1 2.810 0.056 50.130 0.000 2.810 3.749
## .confuW4S2 2.734 0.059 46.302 0.000 2.734 3.510
## .confuW4P1 2.493 0.068 36.565 0.000 2.493 3.415
## .confuW4P2 2.525 0.066 38.220 0.000 2.525 3.500
## confu1 0.000 0.000 0.000
## .confu2 0.000 0.000 0.000
## .confu3 0.000 0.000 0.000
## .confu4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .confuW1S1 0.434 0.050 8.676 0.000 0.434 0.723
## .confuW1S2 0.399 0.051 7.867 0.000 0.399 0.667
## .confuW1P1 0.554 0.074 7.455 0.000 0.554 0.778
## .confuW1P2 0.325 0.043 7.478 0.000 0.325 0.774
## .confuW2S1 0.338 0.044 7.617 0.000 0.338 0.617
## .confuW2S2 0.363 0.051 7.117 0.000 0.363 0.592
## .confuW2P1 0.321 0.057 5.644 0.000 0.321 0.617
## .confuW2P2 0.323 0.047 6.914 0.000 0.323 0.730
## .confuW3S1 0.372 0.052 7.176 0.000 0.372 0.591
## .confuW3S2 0.411 0.056 7.286 0.000 0.411 0.572
## .confuW3P1 0.290 0.055 5.315 0.000 0.290 0.543
## .confuW3P2 0.322 0.050 6.420 0.000 0.322 0.687
## .confuW4S1 0.378 0.054 7.013 0.000 0.378 0.674
## .confuW4S2 0.388 0.077 5.054 0.000 0.388 0.639
## .confuW4P1 0.359 0.068 5.300 0.000 0.359 0.673
## .confuW4P2 0.416 0.068 6.081 0.000 0.416 0.799
## confu1 0.166 0.045 3.655 0.000 1.000 1.000
## .confu2 -0.064 0.042 -1.547 0.122 -0.307 -0.307
## .confu3 0.036 0.022 1.620 0.105 0.139 0.139
## .confu4 0.004 0.034 0.113 0.910 0.021 0.021semPaths(lsmConfu, what = "col", whatLabels = "est", structural = T, layout = "spring")
LSM Coherence
lsmCoher <- '
# factor at each time point with same loading
coher1 =~ coherW1S1 + a * coherW1S2 +
peer * coherW1P1 + aa * coherW1P2
coher2 =~ coherW2S1 + a * coherW2S2 +
peer * coherW2P1 + aa * coherW2P2
coher3 =~ coherW3S1 + a * coherW3S2 +
peer * coherW3P1 + aa * coherW3P2
coher4 =~ coherW4S1 + a * coherW4S2 +
peer * coherW4P1 + aa * coherW4P2
# structural paths between time points
coher4 ~ coher3
coher3 ~ coher2
coher2 ~ coher1
# error covariance - similar parcels across waves
coherW1S1 ~~ coherW2S1 + coherW3S1 + coherW4S1
coherW2S1 ~~ coherW3S1 + coherW4S1
coherW3S1 ~~ coherW4S1
coherW1S2 ~~ coherW2S2 + coherW3S2 + coherW4S2
coherW2S2 ~~ coherW3S2 + coherW4S2
coherW3S2 ~~ coherW4S2
coherW1P1 ~~ coherW2P1 + coherW3P1 + coherW4P1
coherW2P1 ~~ coherW3P1 + coherW4P1
coherW3P1 ~~ coherW4P1
coherW1P2 ~~ coherW2P2 + coherW3P2 + coherW4P2
coherW2P2 ~~ coherW3P2 + coherW4P2
coherW3P2 ~~ coherW4P2
# error covariance - same method at one wave
coherW1S1 ~~ coherW1S2
coherW1P1 ~~ coherW1P2
coherW2S1 ~~ coherW2S2
coherW2P1 ~~ coherW2P2
coherW3S1 ~~ coherW3S2
coherW3P1 ~~ coherW3P2
coherW4S1 ~~ coherW4S2
coherW4P1 ~~ coherW4P2
'
lsmCoher <- sem(lsmCoher, data = data, missing = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativesummary(lsmCoher, fit.measures = T, standardized = T)## lavaan 0.6-7 ended normally after 206 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 83
## Number of equality constraints 9
##
## Number of observations 259
## Number of missing patterns 55
##
## Model Test User Model:
##
## Test statistic 172.603
## Degrees of freedom 78
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1489.355
## Degrees of freedom 120
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.931
## Tucker-Lewis Index (TLI) 0.894
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1717.654
## Loglikelihood unrestricted model (H1) -1631.353
##
## Akaike (AIC) 3583.309
## Bayesian (BIC) 3846.514
## Sample-size adjusted Bayesian (BIC) 3611.907
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.068
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.082
## P-value RMSEA <= 0.05 0.015
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.129
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## coher1 =~
## chrW1S1 1.000 0.176 0.263
## chrW1S2 (a) 0.831 0.196 4.230 0.000 0.146 0.266
## chrW1P1 (peer) 2.148 0.485 4.425 0.000 0.377 0.551
## chrW1P2 (aa) 2.424 0.597 4.056 0.000 0.426 0.675
## coher2 =~
## chrW2S1 1.000 0.220 0.323
## chrW2S2 (a) 0.831 0.196 4.230 0.000 0.183 0.322
## chrW2P1 (peer) 2.148 0.485 4.425 0.000 0.474 0.730
## chrW2P2 (aa) 2.424 0.597 4.056 0.000 0.534 0.840
## coher3 =~
## chrW3S1 1.000 0.250 0.338
## chrW3S2 (a) 0.831 0.196 4.230 0.000 0.208 0.359
## chrW3P1 (peer) 2.148 0.485 4.425 0.000 0.537 0.811
## chrW3P2 (aa) 2.424 0.597 4.056 0.000 0.606 0.911
## coher4 =~
## chrW4S1 1.000 0.171 0.265
## chrW4S2 (a) 0.831 0.196 4.230 0.000 0.143 0.256
## chrW4P1 (peer) 2.148 0.485 4.425 0.000 0.368 0.609
## chrW4P2 (aa) 2.424 0.597 4.056 0.000 0.416 0.693
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## coher4 ~
## coher3 0.801 0.072 11.187 0.000 1.169 1.169
## coher3 ~
## coher2 1.130 0.119 9.492 0.000 0.996 0.996
## coher2 ~
## coher1 1.219 0.329 3.706 0.000 0.972 0.972
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .coherW1S1 ~~
## .coherW2S1 0.212 0.034 6.309 0.000 0.212 0.508
## .coherW3S1 0.181 0.036 5.081 0.000 0.181 0.402
## .coherW4S1 0.166 0.033 4.963 0.000 0.166 0.413
## .coherW2S1 ~~
## .coherW3S1 0.226 0.038 5.960 0.000 0.226 0.502
## .coherW4S1 0.195 0.035 5.572 0.000 0.195 0.485
## .coherW3S1 ~~
## .coherW4S1 0.219 0.039 5.581 0.000 0.219 0.504
## .coherW1S2 ~~
## .coherW2S2 0.160 0.024 6.703 0.000 0.160 0.560
## .coherW3S2 0.156 0.024 6.604 0.000 0.156 0.543
## .coherW4S2 0.143 0.025 5.707 0.000 0.143 0.501
## .coherW2S2 ~~
## .coherW3S2 0.155 0.026 6.071 0.000 0.155 0.531
## .coherW4S2 0.155 0.026 5.985 0.000 0.155 0.533
## .coherW3S2 ~~
## .coherW4S2 0.167 0.028 6.031 0.000 0.167 0.576
## .coherW1P1 ~~
## .coherW2P1 0.073 0.025 2.928 0.003 0.073 0.287
## .coherW3P1 0.053 0.027 1.966 0.049 0.053 0.241
## .coherW4P1 0.090 0.027 3.352 0.001 0.090 0.327
## .coherW2P1 ~~
## .coherW3P1 0.067 0.025 2.710 0.007 0.067 0.391
## .coherW4P1 0.081 0.024 3.322 0.001 0.081 0.382
## .coherW3P1 ~~
## .coherW4P1 0.071 0.026 2.776 0.006 0.071 0.381
## .coherW1P2 ~~
## .coherW2P2 -0.001 0.025 -0.023 0.981 -0.001 -0.004
## .coherW3P2 0.007 0.027 0.267 0.790 0.007 0.057
## .coherW4P2 0.023 0.028 0.822 0.411 0.023 0.113
## .coherW2P2 ~~
## .coherW3P2 -0.004 0.030 -0.145 0.885 -0.004 -0.045
## .coherW4P2 -0.011 0.029 -0.387 0.699 -0.011 -0.074
## .coherW3P2 ~~
## .coherW4P2 0.011 0.032 0.363 0.716 0.011 0.097
## .coherW1S1 ~~
## .coherW1S2 0.048 0.018 2.733 0.006 0.048 0.142
## .coherW1P1 ~~
## .coherW1P2 0.157 0.049 3.177 0.001 0.157 0.589
## .coherW2S1 ~~
## .coherW2S2 0.057 0.017 3.363 0.001 0.057 0.165
## .coherW2P1 ~~
## .coherW2P2 0.075 0.027 2.757 0.006 0.075 0.491
## .coherW3S1 ~~
## .coherW3S2 0.067 0.019 3.517 0.000 0.067 0.179
## .coherW3P1 ~~
## .coherW3P2 0.022 0.024 0.897 0.370 0.022 0.205
## .coherW4S1 ~~
## .coherW4S2 0.067 0.021 3.205 0.001 0.067 0.199
## .coherW4P1 ~~
## .coherW4P2 0.108 0.054 1.988 0.047 0.108 0.523
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .coherW1S1 3.467 0.042 83.407 0.000 3.467 5.183
## .coherW1S2 3.940 0.034 115.366 0.000 3.940 7.168
## .coherW1P1 4.029 0.058 69.705 0.000 4.029 5.879
## .coherW1P2 3.982 0.053 75.481 0.000 3.982 6.314
## .coherW2S1 3.513 0.047 75.235 0.000 3.513 5.149
## .coherW2S2 3.933 0.039 101.445 0.000 3.933 6.900
## .coherW2P1 4.022 0.053 75.518 0.000 4.022 6.198
## .coherW2P2 4.004 0.052 76.773 0.000 4.004 6.295
## .coherW3S1 3.504 0.052 67.753 0.000 3.504 4.737
## .coherW3S2 3.965 0.040 99.673 0.000 3.965 6.848
## .coherW3P1 3.956 0.055 71.591 0.000 3.956 5.970
## .coherW3P2 3.946 0.055 72.079 0.000 3.946 5.931
## .coherW4S1 3.518 0.049 72.067 0.000 3.518 5.448
## .coherW4S2 4.024 0.041 97.520 0.000 4.024 7.243
## .coherW4P1 3.932 0.054 72.396 0.000 3.932 6.509
## .coherW4P2 3.924 0.054 72.176 0.000 3.924 6.545
## coher1 0.000 0.000 0.000
## .coher2 0.000 0.000 0.000
## .coher3 0.000 0.000 0.000
## .coher4 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .coherW1S1 0.417 0.038 10.840 0.000 0.417 0.931
## .coherW1S2 0.281 0.026 10.765 0.000 0.281 0.929
## .coherW1P1 0.327 0.060 5.436 0.000 0.327 0.697
## .coherW1P2 0.216 0.055 3.942 0.000 0.216 0.544
## .coherW2S1 0.417 0.042 9.896 0.000 0.417 0.896
## .coherW2S2 0.291 0.030 9.742 0.000 0.291 0.897
## .coherW2P1 0.197 0.035 5.648 0.000 0.197 0.468
## .coherW2P2 0.119 0.043 2.780 0.005 0.119 0.294
## .coherW3S1 0.485 0.051 9.559 0.000 0.485 0.886
## .coherW3S2 0.292 0.031 9.565 0.000 0.292 0.871
## .coherW3P1 0.150 0.035 4.333 0.000 0.150 0.343
## .coherW3P2 0.075 0.043 1.730 0.084 0.075 0.169
## .coherW4S1 0.388 0.044 8.791 0.000 0.388 0.930
## .coherW4S2 0.288 0.033 8.797 0.000 0.288 0.934
## .coherW4P1 0.229 0.053 4.296 0.000 0.229 0.629
## .coherW4P2 0.187 0.069 2.714 0.007 0.187 0.520
## coher1 0.031 0.017 1.783 0.075 1.000 1.000
## .coher2 0.003 0.012 0.230 0.818 0.056 0.056
## .coher3 0.000 0.005 0.091 0.927 0.008 0.008
## .coher4 -0.011 0.010 -1.059 0.290 -0.366 -0.366semPaths(lsmCoher, what = "col", whatLabels = "est", structural = T, layout = "spring")