Latine_Contextualizing_Stigma
Melissa-Ann Lagunas
2023-11-20
# import data
library(readr)
d <- read_csv("/Users/melissalagunas/Desktop/Lab/Contextualizing\ Stigma\ and\ Trauma/LatineData_Subscales.csv")## New names:
## Rows: 259 Columns: 16
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (16): ...1, SSRPH, SSOSH, MHSAS, PR_PF, PR_A, PR_D, PR_SR, PR_PI, ANG, C...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# import data
library(readr)
dat <- read_csv("/Users/melissalagunas/Desktop/Lab/Contextualizing\ Stigma\ and\ Trauma/LatineData2.csv")## New names:
## Rows: 259 Columns: 151
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): AHELP, GEN_8_TEXT, ETH_7_TEXT, BORN_1 dbl (146): ...1, Progress,
## Duration..in.seconds., Consent, SSRPH_1, SSRPH_2,... lgl (1): EDU_11_TEXT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# remove NA variables
d <- na.omit(d)#data set with only variables of interest
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniond <- data.frame(d$PHFS, d$MHSAS, d$SSOSH, d$SSRPH)
d <- rename(d, familism = d.PHFS, help_seeking_attitudes = d.MHSAS, self_stigma = d.SSOSH, public_stigma = d.SSRPH)#loading packages
library(lavaan)## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.library(semPlot)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(psych)##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
##
## The following object is masked from 'package:lavaan':
##
## cor2covlibrary(jtools)
library(interactions)
library(ggplot2)
library(tidySEM)## Loading required package: OpenMx
## OpenMx may run faster if it is compiled to take advantage of multiple cores.
##
## Attaching package: 'OpenMx'
##
## The following object is masked from 'package:psych':
##
## tr
##
## Registered S3 method overwritten by 'tidySEM':
## method from
## predict.MxModel OpenMx
##
## Attaching package: 'tidySEM'
##
## The following object is masked from 'package:jtools':
##
## get_data# Correlation Analysis
table <- apaTables::apa.cor.table(d, table.number = 1, show.sig.stars = TRUE,
landscape = TRUE, filename = "table.doc")## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtoolsprint(table)##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3
## 1. familism 3.68 0.99
##
## 2. help_seeking_attitudes 5.54 1.16 .21**
## [.09, .32]
##
## 3. self_stigma 2.32 0.83 -.18** -.55**
## [-.30, -.06] [-.63, -.46]
##
## 4. public_stigma 2.22 0.67 -.12 -.40** .56**
## [-.24, .00] [-.50, -.29] [.46, .63]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## psych::pairs.panels(d)
# Checking the table of means, standard deviations, and correlations
table <- apaTables::apa.cor.table(d, table.number = 1, show.sig.stars = TRUE,
landscape = TRUE, filename = "Lewis_Corr.doc")
print(table)##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3
## 1. familism 3.68 0.99
##
## 2. help_seeking_attitudes 5.54 1.16 .21**
## [.09, .32]
##
## 3. self_stigma 2.32 0.83 -.18** -.55**
## [-.30, -.06] [-.63, -.46]
##
## 4. public_stigma 2.22 0.67 -.12 -.40** .56**
## [-.24, .00] [-.50, -.29] [.46, .63]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## # quick peak at data
psych::describe(d)## vars n mean sd median trimmed mad min max range
## familism 1 257 3.68 0.99 3.80 3.77 0.89 1 5.0 4.0
## help_seeking_attitudes 2 257 5.54 1.16 5.78 5.66 0.99 1 7.0 6.0
## self_stigma 3 257 2.32 0.83 2.10 2.25 0.74 1 4.9 3.9
## public_stigma 4 257 2.22 0.67 2.20 2.21 0.59 1 4.0 3.0
## skew kurtosis se
## familism -0.76 0.13 0.06
## help_seeking_attitudes -1.13 1.45 0.07
## self_stigma 0.73 -0.05 0.05
## public_stigma 0.19 -0.05 0.04Working the Moderated Mediation
Piecewise assemly of the moderated mediation
Analysis 1: A simple moderation
# Y = self-stigma; X = public stigma; W = familism
Mod_a_path <- lm(self_stigma ~ public_stigma * familism, data = d)
jtools::summ(Mod_a_path, digits = 3)## MODEL INFO:
## Observations: 257
## Dependent Variable: self_stigma
## Type: OLS linear regression
##
## MODEL FIT:
## F(3,253) = 43.036, p = 0.000
## R² = 0.338
## Adj. R² = 0.330
##
## Standard errors: OLS
## --------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- -------- ------- -------- -------
## (Intercept) -0.088 0.556 -0.159 0.874
## public_stigma 1.227 0.230 5.343 0.000
## familism 0.243 0.143 1.698 0.091
## public_stigma:familism -0.149 0.060 -2.496 0.013
## --------------------------------------------------------------#graph interaction
interactions::interact_plot(Mod_a_path, pred = public_stigma, modx = familism) +
ylim(1, 10)
#probing the interaction with simple slopes
interactions::sim_slopes(Mod_a_path, pred = public_stigma, modx = familism)## JOHNSON-NEYMAN INTERVAL
##
## When familism is OUTSIDE the interval [6.12, 25.27], the slope of
## public_stigma is p < .05.
##
## Note: The range of observed values of familism is [1.00, 5.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of public_stigma when familism = 2.686265 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.83 0.09 9.41 0.00
##
## Slope of public_stigma when familism = 3.676265 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.68 0.06 10.53 0.00
##
## Slope of public_stigma when familism = 4.666264 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.53 0.09 6.08 0.00 # The Johnson-Neyman is a floodlight approach and provides an indication of the places in the distribution of W (moderator) that X has an effect on Y that is different than zero. The analysis of simple slopes approach is thought of as a spotlight approach because probes the distribution at specific values (often the M +/- 1SD).In this model that is, overall, statistically significant, we account for about 33% of the variance in self-stigma. Looking at these results we can see that there is a statistically significant main effect of public stigma on self-stigma as well as a statistically significant interaction effect (public stigma:familism [XM])
Analysis 2: Another simple moderation
# Y = help-seeking attitudes; X = public stigma; W = familism
Mod_b_path <- lm(help_seeking_attitudes ~ public_stigma * familism, data = d)
jtools::summ(Mod_b_path, digits = 3)## MODEL INFO:
## Observations: 257
## Dependent Variable: help_seeking_attitudes
## Type: OLS linear regression
##
## MODEL FIT:
## F(3,253) = 19.231, p = 0.000
## R² = 0.186
## Adj. R² = 0.176
##
## Standard errors: OLS
## --------------------------------------------------------------
## Est. S.E. t val. p
## ---------------------------- -------- ------- -------- -------
## (Intercept) 6.621 0.861 7.692 0.000
## public_stigma -0.807 0.356 -2.271 0.024
## familism 0.103 0.221 0.467 0.641
## public_stigma:familism 0.040 0.093 0.435 0.664
## --------------------------------------------------------------#graph interaction
interactions::interact_plot(Mod_b_path, pred = public_stigma, modx = familism) +
ylim(1, 10)
#probing the interaction with simple slopes
interactions::sim_slopes(Mod_b_path, pred = public_stigma, modx = familism)## JOHNSON-NEYMAN INTERVAL
##
## When familism is INSIDE the interval [-0.79, 6.48], the slope of
## public_stigma is p < .05.
##
## Note: The range of observed values of familism is [1.00, 5.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of public_stigma when familism = 2.686265 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.70 0.14 -5.14 0.00
##
## Slope of public_stigma when familism = 3.676265 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.66 0.10 -6.62 0.00
##
## Slope of public_stigma when familism = 4.666264 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.62 0.13 -4.59 0.00 # The Johnson-Neyman is a floodlight approach and provides an indication of the places in the distribution of W (moderator) that X has an effect on Y that is different than zero. The analysis of simple slopes approach is thought of as a spotlight approach because probes the distribution at specific values (often the M +/- 1SD).Looking at these results we can see that the predictors account for about 18.6% of variance in help-seeking attitudes. Only the independent variable (X), public stigma is a significant predictor. Neither the moderator (familism), nor its interaction with public stigma (public stigma:familism [XW]) are signficant.
Analysis 3: A simple mediation
# Y = help-seeking attitudes; X = public_stigma; M = self_stigma
LMedModel <- "
help_seeking_attitudes ~ b*self_stigma + c_p*public_stigma
self_stigma ~a*public_stigma
#intercepts
self_stigma ~ self_stigma.mean*1
help_seeking_attitudes ~ help_seeking_attitudes.mean*1
indirect := a*b
direct := c_p
total_c := c_p + (a*b)
"
set.seed(230925) #required for reproducible results because lavaan introduces randomness into the calculations
LMed_fit <- lavaan::sem(LMedModel, data = d, se = "bootstrap", missing = "fiml")
LMed_Sum <- lavaan::summary(LMed_fit, standardized = T, rsq = T, ci = TRUE)
LMed_ParEsts <- lavaan::parameterEstimates(LMed_fit, boot.ci.type = "bca.simple",
standardized = TRUE)
LMed_Sum## lavaan 0.6.16 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 257
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## help_seeking_attitudes ~
## slf_stgm (b) -0.659 0.101 -6.530 0.000 -0.864
## pblc_stg (c_p) -0.236 0.123 -1.921 0.055 -0.484
## self_stigma ~
## pblc_stg (a) 0.694 0.075 9.219 0.000 0.545
## ci.upper Std.lv Std.all
##
## -0.463 -0.659 -0.472
## -0.002 -0.236 -0.135
##
## 0.840 0.694 0.555
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .slf_stg (sl_.) 0.780 0.154 5.080 0.000 0.480 1.072
## .hlp_sk_ (h__.) 7.587 0.225 33.737 0.000 7.145 8.021
## Std.lv Std.all
## 0.780 0.938
## 7.587 6.537
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .hlp_skng_tttds 0.927 0.125 7.397 0.000 0.679 1.158
## .self_stigma 0.478 0.042 11.483 0.000 0.396 0.559
## Std.lv Std.all
## 0.927 0.688
## 0.478 0.692
##
## R-Square:
## Estimate
## hlp_skng_tttds 0.312
## self_stigma 0.308
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## indirect -0.457 0.090 -5.069 0.000 -0.648 -0.289
## direct -0.236 0.123 -1.920 0.055 -0.484 -0.002
## total_c -0.693 0.106 -6.528 0.000 -0.888 -0.462
## Std.lv Std.all
## -0.457 -0.262
## -0.236 -0.135
## -0.693 -0.397LMed_ParEsts## lhs op rhs label
## 1 help_seeking_attitudes ~ self_stigma b
## 2 help_seeking_attitudes ~ public_stigma c_p
## 3 self_stigma ~ public_stigma a
## 4 self_stigma ~1 self_stigma.mean
## 5 help_seeking_attitudes ~1 help_seeking_attitudes.mean
## 6 help_seeking_attitudes ~~ help_seeking_attitudes
## 7 self_stigma ~~ self_stigma
## 8 public_stigma ~~ public_stigma
## 9 public_stigma ~1
## 10 indirect := a*b indirect
## 11 direct := c_p direct
## 12 total_c := c_p+(a*b) total_c
## est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 -0.659 0.101 -6.530 0.000 -0.853 -0.458 -0.659 -0.472 -0.472
## 2 -0.236 0.123 -1.921 0.055 -0.504 -0.016 -0.236 -0.135 -0.204
## 3 0.694 0.075 9.219 0.000 0.544 0.840 0.694 0.555 0.835
## 4 0.780 0.154 5.080 0.000 0.483 1.073 0.780 0.938 0.938
## 5 7.587 0.225 33.737 0.000 7.125 8.014 7.587 6.537 6.537
## 6 0.927 0.125 7.397 0.000 0.716 1.211 0.927 0.688 0.688
## 7 0.478 0.042 11.483 0.000 0.399 0.565 0.478 0.692 0.692
## 8 0.443 0.000 NA NA 0.443 0.443 0.443 1.000 0.443
## 9 2.216 0.000 NA NA 2.216 2.216 2.216 3.332 2.216
## 10 -0.457 0.090 -5.069 0.000 -0.641 -0.286 -0.457 -0.262 -0.394
## 11 -0.236 0.123 -1.920 0.055 -0.504 -0.016 -0.236 -0.135 -0.204
## 12 -0.693 0.106 -6.528 0.000 -0.884 -0.460 -0.693 -0.397 -0.597The a path (public_stigma -> self_stigma) is statistically significant The b path (self_stigma -> help_seeking_attitudes) is statistically significant The total effect (public_stigma -> help_seeking_attitudes) is statistically significant The direct effect (public_stigma -> help_seeking_attitudes when self_stigma is in the model) is marginally significant The indirect effect (ab) is statistically significant The model accounts for 31% of the variance in help-seeking attitudes (DV) and 30% of the variance in self-stigma
Analysis 4: The moderated mediation - a combined analysis
Combined <- '
#equations
self_stigma ~ a1*public_stigma + a2*familism + a3*public_stigma:familism
help_seeking_attitudes ~ c_p1*public_stigma + c_p2*familism + c_p3*public_stigma:familism + b*self_stigma
#intercepts
self_stigma ~ self_stigma.mean*1
help_seeking_attitudes ~ help_seeking_attitudes.mean*1
#means, variances of W for simple slopes
familism ~ familism.mean*1
familism ~~ familism.var*familism
#index of moderated mediation, there will be an a and b path in the product
#if the a and/or b path is moderated, select the label that represents the moderation
imm := a3*b
#Note that we first create the indirect product, then add to it the product of the imm and the W level
indirect.SDbelow := a1*b + imm*(familism.mean - sqrt(familism.var))
indirect.mean := a1*b + imm*(familism.mean)
indirect.SDabove := a1*b + imm*(familism.mean + sqrt(familism.var))
#direct effect is also moderated so calculate with c_p1 + c_p3
direct.SDbelow := c_p1 + c_p3*(familism.mean - sqrt(familism.var))
direct.Smean := c_p1 + c_p3*(familism.mean)
direct.SDabove := c_p1 + c_p3*(familism.mean + sqrt(familism.var))
'
set.seed(230925) #required for reproducible results because lavaan introduces randomness into the calculations
Combined_fit <- lavaan::sem(Combined, data = d, se = "bootstrap", missing = 'fiml', bootstrap = 1000)## Warning in lav_partable_vnames(FLAT, "ov.x", warn = TRUE): lavaan WARNING:
## model syntax contains variance/covariance/intercept formulas
## involving (an) exogenous variable(s): [familism]; These variables
## will now be treated as random introducing additional free
## parameters. If you wish to treat those variables as fixed, remove
## these formulas from the model syntax. Otherwise, consider adding
## the fixed.x = FALSE option.cFITsum <- lavaan::summary(Combined_fit, standardized = TRUE, rsq=T, ci=TRUE)
cParamEsts <- lavaan::parameterEstimates(Combined_fit, boot.ci.type = "bca.simple", standardized=TRUE)
cFITsum## lavaan 0.6.16 ended normally after 5 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 13
##
## Number of observations 257
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 616.759
## Degrees of freedom 2
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## self_stigma ~
## pblc_st (a1) 1.227 0.225 5.455 0.000 0.829
## familsm (a2) 0.243 0.139 1.744 0.081 -0.025
## pblc_s: (a3) -0.149 0.067 -2.237 0.025 -0.284
## help_seeking_attitudes ~
## pblc_st (c_p1) -0.020 0.401 -0.050 0.960 -0.674
## familsm (c_p2) 0.259 0.222 1.168 0.243 -0.144
## pblc_s: (c_p3) -0.056 0.104 -0.537 0.591 -0.293
## slf_stg (b) -0.642 0.099 -6.505 0.000 -0.843
## ci.upper Std.lv Std.all
##
## 1.717 1.227 0.872
## 0.532 0.243 0.256
## -0.025 -0.149 -0.511
##
## 0.957 -0.020 -0.012
## 0.733 0.259 0.223
## 0.126 -0.056 -0.155
## -0.454 -0.642 -0.524
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .slf_stg (sl_.) -0.088 0.490 -0.180 0.857 -1.141 0.852
## .hlp_sk_ (h__.) 6.564 0.896 7.324 0.000 4.476 8.132
## familsm (fml.) 3.676 0.063 58.363 0.000 3.558 3.797
## Std.lv Std.all
## -0.088 -0.094
## 6.564 5.719
## 3.676 3.721
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## familsm (fml.) 0.976 0.088 11.070 0.000 0.799 1.149
## .slf_stg 0.457 0.040 11.314 0.000 0.371 0.536
## .hlp_sk_ 0.908 0.125 7.294 0.000 0.659 1.141
## Std.lv Std.all
## 0.976 1.000
## 0.457 0.521
## 0.908 0.689
##
## R-Square:
## Estimate
## self_stigma 0.479
## hlp_skng_tttds 0.311
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## imm 0.096 0.045 2.144 0.032 0.015 0.191
## indirect.SDblw -0.530 0.096 -5.502 0.000 -0.730 -0.358
## indirect.mean -0.435 0.086 -5.068 0.000 -0.609 -0.279
## indirect.SDabv -0.340 0.097 -3.521 0.000 -0.547 -0.167
## direct.SDbelow -0.170 0.158 -1.074 0.283 -0.438 0.165
## direct.Smean -0.224 0.121 -1.860 0.063 -0.459 0.022
## direct.SDabove -0.279 0.157 -1.782 0.075 -0.606 0.008
## Std.lv Std.all
## 0.096 0.267
## -0.530 0.271
## -0.435 0.539
## -0.340 0.806
## -0.170 -0.433
## -0.224 -0.588
## -0.279 -0.744cParamEsts## lhs op rhs
## 1 self_stigma ~ public_stigma
## 2 self_stigma ~ familism
## 3 self_stigma ~ public_stigma:familism
## 4 help_seeking_attitudes ~ public_stigma
## 5 help_seeking_attitudes ~ familism
## 6 help_seeking_attitudes ~ public_stigma:familism
## 7 help_seeking_attitudes ~ self_stigma
## 8 self_stigma ~1
## 9 help_seeking_attitudes ~1
## 10 familism ~1
## 11 familism ~~ familism
## 12 self_stigma ~~ self_stigma
## 13 help_seeking_attitudes ~~ help_seeking_attitudes
## 14 public_stigma ~~ public_stigma
## 15 public_stigma ~~ public_stigma:familism
## 16 public_stigma:familism ~~ public_stigma:familism
## 17 public_stigma ~1
## 18 public_stigma:familism ~1
## 19 imm := a3*b
## 20 indirect.SDbelow := a1*b+imm*(familism.mean-sqrt(familism.var))
## 21 indirect.mean := a1*b+imm*(familism.mean)
## 22 indirect.SDabove := a1*b+imm*(familism.mean+sqrt(familism.var))
## 23 direct.SDbelow := c_p1+c_p3*(familism.mean-sqrt(familism.var))
## 24 direct.Smean := c_p1+c_p3*(familism.mean)
## 25 direct.SDabove := c_p1+c_p3*(familism.mean+sqrt(familism.var))
## label est se z pvalue ci.lower ci.upper
## 1 a1 1.227 0.225 5.455 0.000 0.827 1.713
## 2 a2 0.243 0.139 1.744 0.081 -0.032 0.528
## 3 a3 -0.149 0.067 -2.237 0.025 -0.283 -0.018
## 4 c_p1 -0.020 0.401 -0.050 0.960 -0.710 0.860
## 5 c_p2 0.259 0.222 1.168 0.243 -0.166 0.711
## 6 c_p3 -0.056 0.104 -0.537 0.591 -0.276 0.140
## 7 b -0.642 0.099 -6.505 0.000 -0.827 -0.431
## 8 self_stigma.mean -0.088 0.490 -0.180 0.857 -1.130 0.853
## 9 help_seeking_attitudes.mean 6.564 0.896 7.324 0.000 4.760 8.300
## 10 familism.mean 3.676 0.063 58.363 0.000 3.558 3.796
## 11 familism.var 0.976 0.088 11.070 0.000 0.825 1.187
## 12 0.457 0.040 11.314 0.000 0.388 0.550
## 13 0.908 0.125 7.294 0.000 0.706 1.191
## 14 0.443 0.000 NA NA 0.443 0.443
## 15 1.454 0.000 NA NA 1.454 1.454
## 16 10.259 0.000 NA NA 10.259 10.259
## 17 2.216 0.000 NA NA 2.216 2.216
## 18 8.070 0.000 NA NA 8.070 8.070
## 19 imm 0.096 0.045 2.144 0.032 0.019 0.193
## 20 indirect.SDbelow -0.530 0.096 -5.502 0.000 -0.716 -0.341
## 21 indirect.mean -0.435 0.086 -5.068 0.000 -0.603 -0.274
## 22 indirect.SDabove -0.340 0.097 -3.521 0.000 -0.546 -0.166
## 23 direct.SDbelow -0.170 0.158 -1.074 0.283 -0.458 0.128
## 24 direct.Smean -0.224 0.121 -1.860 0.063 -0.484 -0.010
## 25 direct.SDabove -0.279 0.157 -1.782 0.075 -0.611 0.002
## std.lv std.all std.nox
## 1 1.227 0.872 1.310
## 2 0.243 0.256 0.172
## 3 -0.149 -0.511 -0.159
## 4 -0.020 -0.012 -0.018
## 5 0.259 0.223 0.150
## 6 -0.056 -0.155 -0.048
## 7 -0.642 -0.524 -0.524
## 8 -0.088 -0.094 -0.094
## 9 6.564 5.719 5.719
## 10 3.676 3.721 3.721
## 11 0.976 1.000 1.000
## 12 0.457 0.521 0.521
## 13 0.908 0.689 0.689
## 14 0.443 1.000 0.443
## 15 1.454 0.682 1.454
## 16 10.259 1.000 10.259
## 17 2.216 3.332 2.216
## 18 8.070 2.520 8.070
## 19 0.096 0.267 0.083
## 20 -0.530 0.271 -0.459
## 21 -0.435 0.539 -0.375
## 22 -0.340 0.806 -0.292
## 23 -0.170 -0.433 -0.149
## 24 -0.224 -0.588 -0.198
## 25 -0.279 -0.744 -0.246