Dynamic Assortative Mating - Analytic Results

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
options(digits = 3)

libraries <- c("here",         # directory access
               "relativeVariability", # gh/seanchrismurphy
               "rempsyc",      # simple slope analysis
               "rties",        # coupled damped oscillator
               "psych",        # fisher's z
               "Hmisc",        # correlation matrix
               "rio",          # import export
               "tidyr",        # long to wide
               "forestplot",   # forest plot of actual vs perceived
               "ggplot2",      # plots
               "kableExtra",   # scroll box
               "dplyr",        # general wrangling
               "lavaan")       # APIM

lapply(libraries, require, character.only = TRUE)

bl_path   <- paste0(here(), "/data/baseline_cleaned.RDS")
esm_path  <- paste0(here(), "/data/esm_cleaned.RDS")
dict_path <- paste0(here(), "/data/dict.xlsx")

source(paste0(here(), "/script/00_helpers.R"))

Meta Data

This is the analytic report for the Dynamic Assortative Mating paper.

• Online access: This analytic report is published on RPubs.
• Preregistration document: OSF.
• Descriptives and exploratory data analysis: RPubs.

# load in data
baseline <- rio::import(bl_path)
esm <- rio::import(esm_path)
dict <- rio::import(dict_path)

# example data
head(baseline) %>% 
  knitr::kable(
    caption = "Example first 6 rows of the baseline dataframe"
  ) %>% 
  kableExtra::kable_styling() %>%
  scroll_box(height = "300px")

Example first 6 rows of the baseline dataframe

Couple_ID	Participant_ID	bfas_self_1	bfas_self_2	bfas_self_3	bfas_self_4	bfas_self_5	bfas_self_6	bfas_self_7	bfas_self_8	bfas_self_9	bfas_self_10	bfas_self_11	bfas_self_12	bfas_self_13	bfas_self_14	bfas_self_15	bfas_self_16	bfas_self_17	bfas_self_18	bfas_self_19	bfas_self_20	bfas_self_21	bfas_self_22	bfas_self_23	bfas_self_24	bfas_self_25	bfas_self_26	bfas_self_27	bfas_self_28	bfas_self_29	bfas_self_30	bfas_self_31	bfas_self_32	bfas_self_33	bfas_self_34	bfas_self_35	bfas_self_36	bfas_self_37	bfas_self_38	bfas_self_39	bfas_self_40	bfas_self_41	bfas_self_42	bfas_self_43	bfas_self_44	bfas_self_45	bfas_self_46	bfas_self_47	bfas_self_48	bfas_self_49	bfas_self_50	bfas_self_51	bfas_self_52	bfas_self_53	bfas_self_54	bfas_self_55	bfas_self_56	bfas_self_57	bfas_self_58	bfas_self_59	bfas_self_60	bfas_self_61	bfas_self_62	bfas_self_63	bfas_self_64	bfas_self_65	bfas_self_66	bfas_self_67	bfas_self_68	bfas_self_69	bfas_self_70	bfas_self_71	bfas_self_72	bfas_self_73	bfas_self_74	bfas_self_75	bfas_self_76	bfas_self_77	bfas_self_78	bfas_self_79	bfas_self_80	bfas_self_81	bfas_self_82	bfas_self_83	bfas_self_84	bfas_self_85	bfas_self_86	bfas_self_87	bfas_self_88	bfas_self_89	bfas_self_90	bfas_self_91	bfas_self_92	bfas_self_93	bfas_self_94	bfas_self_95	bfas_self_96	bfas_self_97	bfas_self_98	bfas_self_99	bfas_self_100	bfas_partner_1	bfas_partner_2	bfas_partner_3	bfas_partner_4	bfas_partner_5	bfas_partner_6	bfas_partner_7	bfas_partner_8	bfas_partner_9	bfas_partner_10	bfas_partner_11	bfas_partner_12	bfas_partner_13	bfas_partner_14	bfas_partner_15	bfas_partner_16	bfas_partner_17	bfas_partner_18	bfas_partner_19	bfas_partner_20	bfas_partner_21	bfas_partner_22	bfas_partner_23	bfas_partner_24	bfas_partner_25	bfas_partner_26	bfas_partner_27	bfas_partner_28	bfas_partner_29	bfas_partner_30	bfas_partner_31	bfas_partner_32	bfas_partner_33	bfas_partner_34	bfas_partner_35	bfas_partner_36	bfas_partner_37	bfas_partner_38	bfas_partner_39	bfas_partner_40	bfas_partner_41	bfas_partner_42	bfas_partner_43	bfas_partner_44	bfas_partner_45	bfas_partner_46	bfas_partner_47	bfas_partner_48	bfas_partner_49	bfas_partner_50	bfas_partner_51	bfas_partner_52	bfas_partner_53	bfas_partner_54	bfas_partner_55	bfas_partner_56	bfas_partner_57	bfas_partner_58	bfas_partner_59	bfas_partner_60	bfas_partner_61	bfas_partner_62	bfas_partner_63	bfas_partner_64	bfas_partner_65	bfas_partner_66	bfas_partner_67	bfas_partner_68	bfas_partner_69	bfas_partner_70	bfas_partner_71	bfas_partner_72	bfas_partner_73	bfas_partner_74	bfas_partner_75	bfas_partner_76	bfas_partner_77	bfas_partner_78	bfas_partner_79	bfas_partner_80	bfas_partner_81	bfas_partner_82	bfas_partner_83	bfas_partner_84	bfas_partner_85	bfas_partner_86	bfas_partner_87	bfas_partner_88	bfas_partner_89	bfas_partner_90	bfas_partner_91	bfas_partner_92	bfas_partner_93	bfas_partner_94	bfas_partner_95	bfas_partner_96	bfas_partner_97	bfas_partner_98	bfas_partner_99	bfas_partner_100	panas_self_1	panas_self_2	panas_self_3	panas_self_4	panas_self_5	panas_self_6	panas_self_7	panas_self_8	panas_self_9	panas_self_10	panas_self_11	panas_self_12	panas_self_13	panas_self_14	panas_self_15	panas_self_16	panas_self_17	panas_self_18	panas_self_19	panas_self_20	panas_partner_1	panas_partner_2	panas_partner_3	panas_partner_4	panas_partner_5	panas_partner_6	panas_partner_7	panas_partner_8	panas_partner_9	panas_partner_10	panas_partner_11	panas_partner_12	panas_partner_13	panas_partner_14	panas_partner_15	panas_partner_16	panas_partner_17	panas_partner_18	panas_partner_19	panas_partner_20	csi_happiness	csi_agreement_1	csi_agreement_2	csi_agreement_3	csi_frequency_1	csi_frequency_2	csi_general_1	csi_general_2	csi_general_3	csi_general_4	csi_general_5	csi_general_6	csi_general_7	csi_general_8	csi_general_9	csi_general_10	csi_general_11	csi_general_12	csi_satisfaction_1	csi_satisfaction_2	csi_satisfaction_3	csi_satisfaction_4	csi_compare	csi_enjoyment_1	csi_enjoyment_2	csi_feeling_1	csi_feeling_2	csi_feeling_3	csi_feeling_4	csi_feeling_5	csi_feeling_6	csi_feeling_7	age	sex	sex_3_TEXT	gender	gender_6_TEXT	sexuality	sexuality_5_TEXT	duration	race	edu	ses	politics	religion	race_cat	P_num	self_neuro	self_agree	self_consci	self_extra	self_open	partner_neuro	partner_agree	partner_consci	partner_extra	partner_open	self_pa	self_na	partner_pa	partner_na	csi_overall	self_withd	self_compa	self_indus	self_enthu	self_intel	self_volat	self_polit	self_order	self_assert	self_opena	partner_withd	partner_compa	partner_indus	partner_enthu	partner_intel	partner_volat	partner_polit	partner_order	partner_assert	partner_opena
1002	1002001	3	4	3	3	2	4	3	1	2	5	5	3	2	3	1	4	3	5	2	5	3	5	2	5	2	4	5	4	1	5	4	4	2	5	4	5	4	3	3	5	4	5	2	4	5	5	4	3	1	4	5	5	3	2	4	4	3	5	3	5	4	3	2	4	4	5	5	4	3	5	4	5	1	4	3	3	5	5	2	5	5	5	1	4	2	3	4	4	1	5	4	3	1	5	2	5	4	5	2	5	3	5	5	3	5	1	2	3	4	5	2	4	5	4	2	2	4	3	3	5	3	5	4	4	4	1	4	4	4	3	1	5	5	4	5	1	5	2	3	2	3	5	4	3	1	2	4	3	3	2	3	5	5	1	4	1	5	2	3	4	2	5	4	4	4	2	5	3	4	4	3	4	4	2	5	1	5	4	4	3	5	5	4	4	3	1	4	2	4	4	1	5	4	4	5	2	5	3	3	5	3	2	3	2	1	3	3	2	4	2	2	3	2	5	4	3	1	2	3	2	4	1	3	2	4	2	1	1	4	3	1	5	1	3	2	4	5	1	4	2	6	5	3	4	5	5	5	5	5	2	5	5	4	5	5	5	5	4	5	5	5	5	5	5	5	5	5	5	5	5	5	5	22	0	NA	5		4		51	White	2	4	1	1	1	1	4.15	4.10	2.90	2.95	3.90	2.00	4.55	3.65	3.40	3.75	2.8	2.4	3.9	1.4	153	4.1	4.2	1.9	3.9	2.9	4.2	4.0	3.9	2.0	4.9	2.6	4.8	4.4	3.3	3.8	1.4	4.3	2.9	3.5	3.7
1002	1002002	4	4	2	1	5	1	2	1	4	5	4	2	1	2	4	2	4	3	2	4	2	4	2	2	5	1	4	2	1	2	1	5	4	2	5	1	5	2	1	4	4	4	2	1	2	1	4	1	4	2	4	4	2	2	4	1	4	2	2	2	2	4	2	1	4	1	4	2	1	4	4	4	1	2	4	1	5	4	4	5	4	4	2	3	4	1	5	1	4	4	2	5	2	3	4	1	4	4	2	4	4	5	2	3	4	1	2	4	2	4	5	2	2	5	4	4	5	4	4	5	4	4	2	4	4	4	4	5	2	5	1	5	4	5	4	4	5	2	4	5	2	4	2	4	5	2	4	2	1	5	5	5	2	4	4	2	5	2	2	4	4	5	2	4	4	2	5	4	3	5	2	3	2	5	2	2	5	4	4	5	5	5	2	3	4	4	5	1	1	5	4	4	1	4	4	4	5	2	1	4	4	3	2	2	1	1	1	1	1	1	1	2	1	2	2	1	3	1	1	1	4	2	3	2	1	2	2	1	3	2	2	1	1	2	3	2	3	1	1	2	6	5	4	5	5	5	5	4	5	4	5	5	4	5	5	4	4	5	5	4	5	5	5	5	5	3	5	5	5	5	4	5	22	1	NA	3		2		51	White	4	3	1	1	1	2	2.10	4.05	2.10	2.20	3.85	3.25	4.35	2.55	3.25	4.30	1.8	1.4	2.2	1.8	151	3.1	4.0	2.0	1.9	4.1	1.1	4.1	2.2	2.5	3.6	3.6	4.2	2.1	4.1	3.9	2.9	4.5	3.0	2.4	4.7
1003	1003001	4	5	4	2	4	2	4	2	5	5	5	5	4	4	2	4	3	4	4	4	5	4	4	4	4	2	5	4	2	2	4	5	2	4	4	4	2	4	2	2	4	5	4	4	4	4	4	4	2	4	5	5	4	4	2	2	2	4	4	2	4	5	2	4	5	4	4	4	2	2	5	5	4	4	4	4	5	4	2	2	2	4	2	4	5	2	5	4	2	2	4	5	4	4	4	4	4	4	2	5	4	4	2	2	2	4	4	2	4	5	4	4	2	4	5	4	5	1	5	4	5	4	1	4	2	4	2	2	4	4	2	4	2	4	2	2	4	2	4	5	4	5	2	4	4	2	2	2	4	4	4	5	5	4	2	1	2	4	1	5	4	4	2	4	5	2	5	4	4	4	4	4	2	5	2	2	4	2	4	4	4	4	4	4	2	3	4	4	4	4	2	4	1	4	4	2	5	2	4	4	4	2	3	2	3	4	4	1	2	2	2	3	3	1	5	4	3	1	2	3	2	3	2	2	2	2	1	1	3	2	2	2	2	3	3	2	4	2	1	2	5	5	5	3	4	5	5	5	5	2	5	5	5	5	5	3	5	5	5	4	5	4	5	5	5	4	5	5	5	4	5	5	23	1	NA	5		4		35	White	4	1	1	1	1	1	3.70	4.30	3.60	3.25	3.40	3.15	3.95	2.40	3.85	3.65	2.7	2.7	2.3	2.0	148	4.2	4.8	3.4	3.8	3.8	3.2	3.8	3.8	2.7	3.0	3.7	4.2	2.3	3.9	3.0	2.6	3.7	2.5	3.8	4.3
1003	1003002	4	4	2	3	4	3	2	2	3	5	5	4	1	4	5	3	3	4	4	4	4	5	1	4	5	3	3	2	3	5	3	4	2	4	4	3	2	4	3	5	5	4	2	5	5	3	2	2	3	4	5	4	2	3	4	3	3	3	3	4	5	4	1	4	4	3	3	3	4	5	4	4	2	5	4	3	4	3	4	5	4	5	1	4	2	4	3	3	3	5	4	5	1	5	3	3	4	2	3	4	5	5	4	4	4	3	3	2	3	4	5	5	4	3	4	3	3	4	3	3	5	5	3	2	4	3	5	4	2	3	4	5	3	3	4	4	5	5	3	3	5	4	3	3	4	5	5	3	2	3	5	5	4	4	5	4	3	2	3	3	4	4	3	4	5	5	3	4	4	3	5	4	3	5	4	3	5	4	1	4	5	5	3	4	4	3	3	3	3	3	5	5	2	4	3	4	4	3	2	3	4	4	3	3	2	4	3	2	3	1	3	3	4	4	4	3	4	2	3	4	4	4	3	2	3	5	5	1	4	2	2	5	4	3	4	4	3	3	4	4	4	4	4	4	4	5	5	4	4	3	4	5	5	4	5	4	5	4	4	4	5	4	5	5	5	4	4	4	5	4	4	4	22	0	NA	2		4		35	White	2	1	1	1	1	2	3.70	3.60	2.15	3.70	4.30	4.25	4.30	3.30	3.10	3.65	3.0	3.3	3.5	3.4	138	4.3	4.3	1.5	4.1	4.0	3.1	2.9	2.8	3.3	4.6	4.8	4.7	3.2	3.6	4.1	3.7	3.9	3.4	2.6	3.2
1004	1004001	2	5	4	4	4	2	4	4	4	5	2	5	4	5	4	3	4	4	5	5	2	4	3	3	5	4	5	4	5	5	4	4	4	3	4	3	4	4	4	4	2	5	4	4	5	4	2	5	5	5	4	4	4	4	4	2	4	3	4	2	2	4	3	3	3	3	4	2	3	4	2	4	4	5	5	4	4	4	5	2	2	5	2	5	3	4	5	4	4	2	2	5	1	5	4	4	4	4	5	5	4	4	5	3	4	1	5	5	2	5	4	5	4	5	5	1	5	5	5	4	1	4	4	5	2	1	5	5	4	5	3	5	2	5	5	1	5	4	1	5	2	4	4	5	5	2	5	5	2	3	4	4	4	4	4	2	5	2	2	1	4	4	2	4	4	2	5	2	1	2	2	4	2	4	4	2	5	5	2	2	5	5	4	4	5	2	5	4	4	2	2	4	4	4	5	1	2	3	5	5	3	2	4	4	4	1	1	1	3	5	2	3	1	5	2	4	4	4	4	1	4	4	3	1	4	2	2	1	3	5	1	3	1	5	4	5	5	2	4	1	4	5	5	5	4	5	5	5	4	5	5	5	4	5	4	5	5	5	4	4	5	5	5	4	3	4	5	5	5	3	5	5	18	1	NA	1		1		15	Asian/Hmong	4	3	4	3	3	1	2.85	4.25	3.55	4.25	4.00	2.30	4.50	3.75	3.55	3.85	3.9	1.9	4.1	1.9	147	2.4	4.5	3.3	4.1	4.1	3.3	4.0	3.8	4.4	3.9	3.1	4.3	3.5	4.3	4.3	1.5	4.7	4.0	2.8	3.4
1004	1004002	3	3	4	2	3	3	4	5	3	5	3	3	3	2	2	3	5	4	4	3	2	4	4	3	3	3	4	3	2	3	3	3	2	2	3	1	5	4	3	4	2	4	4	3	3	1	4	3	3	3	4	3	3	2	4	2	3	3	3	4	2	4	3	3	4	1	4	3	2	3	2	3	3	4	3	3	5	4	2	2	4	5	2	4	3	2	5	3	3	3	2	4	2	3	3	3	3	2	2	4	3	4	4	4	3	4	3	2	4	4	4	3	4	3	4	3	5	4	4	5	2	4	2	3	3	3	4	4	3	3	2	3	3	4	2	2	4	4	3	3	3	3	3	3	3	4	3	3	3	5	3	3	3	4	4	2	3	3	3	2	3	5	4	4	4	4	4	4	3	3	3	3	3	5	3	3	5	4	3	3	4	5	3	4	3	3	3	3	3	3	3	5	2	4	3	3	5	3	3	5	4	3	3	1	3	3	2	1	3	4	2	2	1	2	3	3	3	4	2	2	3	2	3	2	3	3	2	1	3	4	1	2	1	3	2	3	3	2	3	2	5	3	3	5	3	5	5	5	5	5	5	5	5	5	5	5	4	5	5	5	5	5	5	5	3	4	5	4	5	5	5	5	23	0	NA	2		1		15	asian american/hmong	4	2	6	2	3	2	2.45	3.90	3.20	2.75	3.25	3.05	3.85	3.25	3.50	3.40	2.9	2.2	3.0	1.8	149	2.7	3.6	3.0	2.8	3.1	2.2	4.2	3.4	2.7	3.4	3.0	3.8	3.1	3.8	3.2	3.1	3.9	3.4	3.2	3.6

head(esm %>% filter(Participant_ID == "1002001") %>% arrange(time_idx)) %>%
  knitr::kable(caption = "Example first 6 rows of the esm dataframe") %>%
  kable_styling() %>%
  column_spec(4, width_min = "1in") %>%
  scroll_box(width = "900px")

Example first 6 rows of the esm dataframe

Couple_ID	Participant_ID	time	day	time_idx	day_idx	mpa	mna	tipi_1	tipi_2	tipi_3	tipi_4	tipi_5	tipi_6	tipi_7	tipi_8	tipi_9	tipi_10	csi_happiness	csi_general_6	csi_satisfaction_1	csi_satisfaction_4	partner_presence	P_num	tipi_extra	tipi_agree	tipi_consci	tipi_neuro	tipi_open	csi_short
1002	1002001	0	2023-10-07	0	0	60	25	2	6	2	4	3	2	5	4	3	4	5	5	5	5	1	1	2.0	5.5	3.0	3.5	3.5	20
1002	1002001	1	2023-10-07	1	0	50	20	1	7	1	5	4	2	6	4	6	5	5	5	5	5	1	1	1.5	6.5	2.5	5.5	4.5	20
1002	1002001	2	2023-10-07	2	0	45	30	1	7	1	6	3	1	6	4	5	6	5	5	5	5	1	1	1.0	6.5	2.5	5.5	4.5	20
1002	1002001	3	2023-10-07	3	0	71	17	5	6	3	5	5	1	4	5	4	5	5	5	5	5	1	1	3.0	5.0	4.0	4.5	5.0	20
1002	1002001	4	2023-10-07	4	0	40	20	5	7	3	6	5	2	6	2	5	6	5	5	5	5	1	1	3.5	6.5	2.5	5.5	5.5	20
1002	1002001	0	2023-10-08	5	1	80	20	5	5	4	5	4	3	6	4	4	6	5	5	5	5	0	1	4.0	5.5	4.0	4.5	5.0	20

# retain only analytic variables
baseline <- baseline %>%
  select(Couple_ID, P_num, duration,
         age, sex, gender, sexuality, edu, ses, politics, religion, race_cat,
         panas_self_1:panas_self_20,
         self_neuro:partner_opena)
esm <- esm %>%
  select(Couple_ID, P_num, time_idx, day_idx, mpa, mna, partner_presence,
         tipi_extra:csi_short)

Research Question 1: Evidence of Assortative Mating

Hypothesis 1: Baseline Assortative Mating

At baseline, romantic partners are similar in their personality traits and general affects such that their scale scores are significantly and positively correlated.

# run function
h1_results <- h1_function(
  var_list = c(paste0("self_", 
                      c("pa", "na",
                        "agree", "compa", "polit", 
                        "consci", "indus", "order", 
                        "extra", "assert", "enthu", 
                        "neuro", "volat", "withd", 
                        "open", "intel", "opena"))),
  prof_list = data.frame(
    pa      = dict %>% filter(scale == "Self Positive Affect") %>% pull(var),
    na      = dict %>% filter(scale == "Self Negative Affect") %>% pull(var),
    panas   = paste0("panas_self_", 1:20),
    domains = paste0("self_", c("agree", "consci", "extra", "neuro", "open")),
    aspects = paste0("self_", c("compa", "polit", "indus", "order", "assert",
                                "enthu", "volat", "withd", "intel", "opena"))),
  .data = baseline)

# merge profile correlations back to dataframe
baseline <- merge(baseline, h1_results$profile_df, all = TRUE)

# print results
h1_results$bivariate %>% 
  knitr::kable(caption = "Bivariate between-partner correlation") %>% 
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Bivariate between-partner correlation

variable	correlation	p_value	LL	UL
self_pa	0.100	0.242	-0.068	0.263
self_na	0.179	0.036	0.012	0.336
self_agree	0.174	0.041	0.007	0.331
self_compa	0.111	0.195	-0.057	0.273
self_polit	0.154	0.071	-0.014	0.313
self_consci	-0.005	0.953	-0.172	0.162
self_indus	-0.005	0.949	-0.172	0.162
self_order	0.032	0.710	-0.136	0.198
self_extra	0.129	0.131	-0.039	0.290
self_assert	0.151	0.078	-0.017	0.310
self_enthu	-0.008	0.923	-0.175	0.159
self_neuro	-0.084	0.326	-0.248	0.084
self_volat	-0.216	0.011	-0.370	-0.051
self_withd	0.087	0.308	-0.081	0.251
self_open	0.238	0.005	0.074	0.389
self_intel	-0.076	0.374	-0.240	0.092
self_opena	0.300	0.000	0.140	0.445

h1_results$profile %>% 
  knitr::kable(
    caption = "Proportion of signficant between-partner profile correlations") %>% 
  kable_styling("striped")

Proportion of signficant between-partner profile correlations

profile	raw	centered	standardized
pa	0.075	0.08824	0.05797
na	0.20202	0.10667	0.0942
panas	0.54098	0.16667	0.16667
domains	0.10084	0.02439	0.02899
aspects	0.23932	0.12162	0.12319

Hypothesis 2: Perceived vs. Actual

At baseline, perceived similarity is stronger than actual similarity

# run function
h2_results <- h2_function(
  perception_list = c("pa", "na",
                      "agree", "compa", "polit", 
                      "consci", "indus", "order", 
                      "extra", "assert", "enthu", 
                      "neuro", "volat", "withd", 
                      "open", "intel", "opena"),
  .data = baseline
)

# view results
h2_results$similarity_df %>%
  knitr::kable(
    caption = "Actual and perceived similarities as bivariate correlations"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Actual and perceived similarities as bivariate correlations

similarity	personality	correlation	p-value
actual	pa	0.1 [-0.068 - 0.263]	0.242
P1-perceived	pa	0.508 [0.372 - 0.622]	0
P2-perceived	pa	0.448 [0.304 - 0.573]	0
actual	na	0.179 [0.012 - 0.336]	0.036
P1-perceived	na	0.426 [0.279 - 0.554]	0
P2-perceived	na	0.439 [0.293 - 0.565]	0
actual	agree	0.174 [0.007 - 0.331]	0.041
P1-perceived	agree	0.343 [0.187 - 0.483]	0
P2-perceived	agree	0.315 [0.156 - 0.458]	0
actual	compa	0.111 [-0.057 - 0.273]	0.195
P1-perceived	compa	0.321 [0.162 - 0.463]	0
P2-perceived	compa	0.315 [0.157 - 0.458]	0
actual	polit	0.154 [-0.014 - 0.313]	0.071
P1-perceived	polit	0.216 [0.051 - 0.37]	0.011
P2-perceived	polit	0.204 [0.038 - 0.359]	0.016
actual	consci	-0.005 [-0.172 - 0.162]	0.953
P1-perceived	consci	-0.07 [-0.234 - 0.099]	0.417
P2-perceived	consci	-0.068 [-0.233 - 0.1]	0.428
actual	indus	-0.005 [-0.172 - 0.162]	0.949
P1-perceived	indus	-0.093 [-0.256 - 0.075]	0.278
P2-perceived	indus	-0.039 [-0.205 - 0.129]	0.646
actual	order	0.032 [-0.136 - 0.198]	0.71
P1-perceived	order	0.041 [-0.127 - 0.207]	0.631
P2-perceived	order	-0.063 [-0.228 - 0.105]	0.46
actual	extra	0.129 [-0.039 - 0.29]	0.131
P1-perceived	extra	0.153 [-0.014 - 0.312]	0.073
P2-perceived	extra	0.21 [0.044 - 0.364]	0.013
actual	assert	0.151 [-0.017 - 0.31]	0.078
P1-perceived	assert	0.034 [-0.134 - 0.2]	0.692
P2-perceived	assert	0.123 [-0.045 - 0.285]	0.149
actual	enthu	-0.008 [-0.175 - 0.159]	0.923
P1-perceived	enthu	0.147 [-0.021 - 0.306]	0.086
P2-perceived	enthu	0.203 [0.037 - 0.358]	0.017
actual	neuro	-0.084 [-0.248 - 0.084]	0.326
P1-perceived	neuro	-0.186 [-0.343 - -0.02]	0.029
P2-perceived	neuro	-0.029 [-0.195 - 0.139]	0.737
actual	volat	-0.216 [-0.37 - -0.051]	0.011
P1-perceived	volat	-0.247 [-0.398 - -0.083]	0.003
P2-perceived	volat	-0.122 [-0.283 - 0.046]	0.154
actual	withd	0.087 [-0.081 - 0.251]	0.308
P1-perceived	withd	-0.067 [-0.232 - 0.101]	0.434
P2-perceived	withd	0.051 [-0.117 - 0.216]	0.556
actual	open	0.238 [0.074 - 0.389]	0.005
P1-perceived	open	0.327 [0.169 - 0.468]	0
P2-perceived	open	0.362 [0.208 - 0.499]	0
actual	intel	-0.076 [-0.24 - 0.092]	0.374
P1-perceived	intel	0.091 [-0.077 - 0.254]	0.287
P2-perceived	intel	0.087 [-0.081 - 0.25]	0.311
actual	opena	0.3 [0.14 - 0.445]	0
P1-perceived	opena	0.303 [0.143 - 0.448]	0
P2-perceived	opena	0.424 [0.277 - 0.552]	0

h2_results$compare_df %>%
  knitr::kable(
    caption = "Comparison between actual and perceived similarities"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Comparison between actual and perceived similarities

V1	V2	personality	z_stat	sig
actual	P1-perceived	pa	-3.774	TRUE
actual	P2-perceived	pa	-3.140	TRUE
P1-perceived	P2-perceived	pa	0.635	FALSE
actual	P1-perceived	na	-2.254	TRUE
actual	P2-perceived	na	-2.384	TRUE
P1-perceived	P2-perceived	na	-0.130	FALSE
actual	P1-perceived	agree	-1.495	FALSE
actual	P2-perceived	agree	-1.238	FALSE
P1-perceived	P2-perceived	agree	0.257	FALSE
actual	P1-perceived	compa	-1.816	FALSE
actual	P2-perceived	compa	-1.767	FALSE
P1-perceived	P2-perceived	compa	0.049	FALSE
actual	P1-perceived	polit	-0.527	FALSE
actual	P2-perceived	polit	-0.426	FALSE
P1-perceived	P2-perceived	polit	0.101	FALSE
actual	P1-perceived	consci	0.531	FALSE
actual	P2-perceived	consci	0.518	FALSE
P1-perceived	P2-perceived	consci	-0.012	FALSE
actual	P1-perceived	indus	0.721	FALSE
actual	P2-perceived	indus	0.279	FALSE
P1-perceived	P2-perceived	indus	-0.442	FALSE
actual	P1-perceived	order	-0.077	FALSE
actual	P2-perceived	order	0.784	FALSE
P1-perceived	P2-perceived	order	0.861	FALSE
actual	P1-perceived	extra	-0.202	FALSE
actual	P2-perceived	extra	-0.683	FALSE
P1-perceived	P2-perceived	extra	-0.481	FALSE
actual	P1-perceived	assert	0.966	FALSE
actual	P2-perceived	assert	0.227	FALSE
P1-perceived	P2-perceived	assert	-0.740	FALSE
actual	P1-perceived	enthu	-1.282	FALSE
actual	P2-perceived	enthu	-1.761	FALSE
P1-perceived	P2-perceived	enthu	-0.478	FALSE
actual	P1-perceived	neuro	0.857	FALSE
actual	P2-perceived	neuro	-0.456	FALSE
P1-perceived	P2-perceived	neuro	-1.313	FALSE
actual	P1-perceived	volat	0.270	FALSE
actual	P2-perceived	volat	-0.797	FALSE
P1-perceived	P2-perceived	volat	-1.067	FALSE
actual	P1-perceived	withd	1.273	FALSE
actual	P2-perceived	withd	0.303	FALSE
P1-perceived	P2-perceived	withd	-0.969	FALSE
actual	P1-perceived	open	-0.794	FALSE
actual	P2-perceived	open	-1.127	FALSE
P1-perceived	P2-perceived	open	-0.333	FALSE
actual	P1-perceived	intel	-1.378	FALSE
actual	P2-perceived	intel	-1.343	FALSE
P1-perceived	P2-perceived	intel	0.036	FALSE
actual	P1-perceived	opena	-0.029	FALSE
actual	P2-perceived	opena	-1.177	FALSE
P1-perceived	P2-perceived	opena	-1.148	FALSE

# plot
similarity_df <- h2_results$similarity_df
similarity_df$personality <- rep(
  c(
    "Positive Affect", "Negative Affect",
    "Agreeableness", "Compassion", "Politeness",
    "Conscientiousness", "Industriousness", "Orderliness",
    "Extraversion", "Assertiveness", "Enthusiasm",
    "Neuroticism", "Volatility", "Withdrawal",
    "Openness", "Intellect", "Openness Aspect"),
  each = 3)
plot_forest(
  perception_list = c("pa", "na",
                      "agree", "compa", "polit", 
                      "consci", "indus", "order", 
                      "extra", "assert", "enthu", 
                      "neuro", "volat", "withd", 
                      "open", "intel", "opena"),
  similarity_df = similarity_df)

Hypothesis 3: Dynamic Assortative Mating

Dynamically, romantic partners are similar in their momentary variability on personality and affective states.

# run function
h3_results <- h3_function(
  var_list = c("mpa", "mna", 
               paste0("tipi_", c("agree", "consci", "extra", "neuro", "open"))),
  .data = esm,
  .dict = dict %>% filter(data == "esm")
)

# merge within-person indices back to baseline dataframe
baseline <- merge(baseline,
                  h3_results$within_df)

# show results
h3_results$cor_tab %>% 
  knitr::kable(
    caption = "Dynamic Between-Partner Correlations") %>% 
  kable_styling("striped") %>%
  scroll_box(width = "900px")

Dynamic Between-Partner Correlations

variable	r SD	p SD	LL SD	UL SD	r RVI	p RVI	LL RVI	UL RVI	r MSSD	p MSSD	LL MSSD	UL MSSD	r RMSSD	p RMSSD	LL RMSSD	UL RMSSD	r avg	proportion sig	n avg
mpa	0.195	0.022	0.029	0.351	0.19	0.027	0.022	0.348	0.18	0.035	0.013	0.337	0.299	0	0.137	0.446	0.211	0.203	26.13
mna	0.227	0.007	0.062	0.38	0.113	0.191	-0.057	0.277	0.133	0.119	-0.035	0.294	0.18	0.037	0.011	0.338	0.133	0.116	26.13
tipi_agree	0.264	0.002	0.101	0.413	0.2	0.019	0.033	0.355	0.196	0.021	0.03	0.352	0.127	0.14	-0.042	0.288	0.134	0.119	26.087
tipi_consci	0.079	0.355	-0.089	0.243	0.077	0.367	-0.091	0.241	0.192	0.024	0.026	0.348	0.097	0.258	-0.071	0.26	0.029	0.051	26.087
tipi_extra	0.206	0.015	0.04	0.361	0.268	0.001	0.105	0.416	0.186	0.029	0.02	0.343	0.366	0	0.212	0.503	0.132	0.117	26.08
tipi_neuro	0.239	0.005	0.075	0.391	0.149	0.08	-0.018	0.309	0.239	0.005	0.075	0.391	0.083	0.334	-0.085	0.247	0.124	0.104	26.072
tipi_open	0.203	0.017	0.037	0.358	0.166	0.052	-0.001	0.325	0.148	0.082	-0.019	0.308	0.114	0.184	-0.054	0.277	0.074	0.095	26.058

Research Question 2: Benefits of Assortative Mating

Hypothesis 4: Baseline Benefits

At baseline, partner similarity in self-reported personality traits and general affect is associated with enhanced relationship quality

# run function
h4_results <- h4_function(
  var_list = c(paste0("self_", 
                      c("pa", "na",
                        "agree", "compa", "polit", 
                        "consci", "indus", "order", 
                        "extra", "assert", "enthu", 
                        "neuro", "volat", "withd", 
                        "open", "intel", "opena"))),
  prof_list = c("pa", "na", "panas", "domains", "aspects"),
  quality_var = "csi_overall",
  .data = baseline
)

# results
h4_results$interaction_tab %>%
  knitr::kable(
    caption = "Multiple Regression Models with Interaction Effects"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Multiple Regression Models with Interaction Effects

personality	participant	actor_est	actor_tval	actor_pval	partner_est	partner_tval	partner_pval	int_est	int_tval	int_pval
self_pa	P1	16.74	0.806	0.422	9.556	0.469	0.64	-3.152	-0.538	0.592
self_pa	P2	6.417	0.296	0.768	-3.258	-0.148	0.883	0.35	0.056	0.955
self_na	P1	3.401	0.481	0.631	5.98	0.816	0.416	-3.883	-1.361	0.176
self_na	P2	7.845	1.021	0.309	10.694	1.443	0.151	-6.428	-2.148	0.033
self_agree	P1	10.377	0.333	0.74	2.13	0.065	0.949	1.288	0.159	0.874
self_agree	P2	4.926	0.135	0.892	-3.804	-0.11	0.912	2.376	0.265	0.791
self_compa	P1	-0.584	-0.032	0.975	-14.426	-0.73	0.467	3.737	0.806	0.421
self_compa	P2	3.537	0.165	0.869	1.945	0.098	0.922	1.353	0.269	0.788
self_polit	P1	52.686	2.104	0.037	53.333	2.167	0.032	-11.457	-1.79	0.076
self_polit	P2	9.649	0.342	0.733	4.645	0.162	0.872	-0.694	-0.095	0.925
self_consci	P1	11.358	0.5	0.618	3.95	0.174	0.862	-1.823	-0.271	0.787
self_consci	P2	15.523	0.627	0.532	13.572	0.549	0.584	-4.459	-0.608	0.544
self_indus	P1	13.292	1.062	0.29	8.475	0.638	0.525	-3.098	-0.764	0.446
self_indus	P2	14.517	1.008	0.315	11.135	0.821	0.413	-3.778	-0.859	0.392
self_order	P1	-6.21	-0.35	0.727	-11.471	-0.654	0.514	2.737	0.556	0.579
self_order	P2	13.007	0.686	0.494	13.718	0.714	0.476	-4.293	-0.806	0.421
self_extra	P1	12.937	0.646	0.519	5.26	0.257	0.798	-1.581	-0.269	0.789
self_extra	P2	11.494	0.541	0.589	5.904	0.285	0.776	-0.275	-0.045	0.964
self_assert	P1	23.805	1.918	0.057	20.072	1.649	0.102	-6.912	-1.852	0.066
self_assert	P2	26.689	2.07	0.04	25.607	1.948	0.054	-6.811	-1.722	0.087
self_enthu	P1	-1.856	-0.117	0.907	-8.725	-0.508	0.612	3.118	0.699	0.486
self_enthu	P2	-4.276	-0.239	0.811	-10.246	-0.623	0.535	4.165	0.896	0.372
self_neuro	P1	11.447	1.066	0.288	11.749	1.095	0.275	-5.411	-1.545	0.125
self_neuro	P2	11.183	0.991	0.323	9.773	0.865	0.388	-5.81	-1.577	0.117
self_volat	P1	-1.86	-0.279	0.78	-2.727	-0.379	0.705	-0.822	-0.355	0.723
self_volat	P2	0.697	0.091	0.928	-0.409	-0.058	0.954	-2.092	-0.848	0.398
self_withd	P1	16.106	1.446	0.151	18.029	1.659	0.1	-6.232	-1.827	0.07
self_withd	P2	15.772	1.374	0.172	14.286	1.214	0.227	-6.325	-1.755	0.081
self_open	P1	-1.249	-0.047	0.962	-6.007	-0.223	0.824	1.729	0.252	0.802
self_open	P2	9.917	0.352	0.725	5.378	0.195	0.846	-0.309	-0.043	0.966
self_intel	P1	4.974	0.266	0.791	-2.294	-0.118	0.906	0.758	0.155	0.877
self_intel	P2	43.051	2.101	0.038	39.947	2.028	0.045	-9.13	-1.777	0.078
self_opena	P1	-11.229	-0.702	0.484	-8.725	-0.523	0.602	2.827	0.669	0.505
self_opena	P2	-9.168	-0.521	0.604	-12.55	-0.743	0.459	3.838	0.86	0.392

h4_results$difference_tab %>%
  knitr::kable(
    caption = "Simple Regression Model with Difference Score"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Simple Regression Model with Difference Score

personality	participant	diff_est	diff_tval	diff_pval
self_pa	P1	0.86	0.236	0.814
self_pa	P2	1.847	0.471	0.639
self_na	P1	-3.254	-1.126	0.262
self_na	P2	-1.011	-0.323	0.747
self_agree	P1	-5.408	-1.126	0.262
self_agree	P2	-8.118	-1.576	0.117
self_compa	P1	-2.705	-0.811	0.419
self_compa	P2	-7.995	-2.26	0.025
self_polit	P1	-3.094	-0.761	0.448
self_polit	P2	-2.072	-0.472	0.638
self_consci	P1	0.021	0.005	0.996
self_consci	P2	2.548	0.618	0.538
self_indus	P1	0.03	0.01	0.992
self_indus	P2	-1.286	-0.406	0.686
self_order	P1	-0.724	-0.208	0.835
self_order	P2	4.23	1.134	0.259
self_extra	P1	-4.466	-1.128	0.261
self_extra	P2	-10.51	-2.509	0.013
self_assert	P1	0.661	0.202	0.841
self_assert	P2	-1.008	-0.285	0.776
self_enthu	P1	-2.271	-0.771	0.442
self_enthu	P2	-7.397	-2.373	0.019
self_neuro	P1	4.165	1.528	0.129
self_neuro	P2	4.264	1.45	0.149
self_volat	P1	0.227	0.106	0.916
self_volat	P2	2.095	0.91	0.364
self_withd	P1	4.545	1.577	0.117
self_withd	P2	4.838	1.558	0.122
self_open	P1	-0.883	-0.208	0.835
self_open	P2	-4.888	-1.075	0.284
self_intel	P1	-0.888	-0.296	0.767
self_intel	P2	0.579	0.179	0.858
self_opena	P1	4.251	1.289	0.2
self_opena	P2	-1.853	-0.519	0.605

h4_results$profile_tab %>%
  knitr::kable(
    caption = "Simple Regression Model with Profile Similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Simple Regression Model with Profile Similarity

profile	participant	raw_est	raw_tval	raw_pval	cen_est	cen_tval	cen_pval	std_est	std_tval	std_pval
pa	P1	-4.544	-1.017	0.311	-7.999	-1.915	0.058	-8.348	-2.03	0.044
pa	P2	-7.173	-1.478	0.142	-5.646	-1.245	0.215	-3.97	-0.885	0.378
na	P1	4.314	1.006	0.316	1.451	0.376	0.708	0.82	0.212	0.832
na	P2	0.816	0.174	0.863	-1.783	-0.428	0.669	-2.17	-0.521	0.603
panas	P1	5.393	1.325	0.187	-7.325	-1.593	0.114	-8.679	-1.849	0.067
panas	P2	4.126	0.924	0.357	-12.276	-2.511	0.013	-11.931	-2.377	0.019
domains	P1	2.087	0.795	0.428	-2.629	-1.089	0.278	-2.475	-0.975	0.331
domains	P2	5.051	1.804	0.073	-3.77	-1.455	0.148	-3.477	-1.274	0.205
aspects	P1	5.814	1.595	0.113	-2.288	-0.714	0.476	-2.609	-0.78	0.437
aspects	P2	8.328	2.136	0.035	-4.48	-1.304	0.194	-4.597	-1.28	0.203

Simple slope analysis for significant interaction effects

# Negative Affect - P2 Satisfaction
ss_wide_df <- baseline %>%
  pivot_wider(id_cols = "Couple_ID",
              names_from = "P_num",
              values_from = c("self_na", "csi_overall"))
nice_slopes(
  data = ss_wide_df,
  response = "csi_overall_2",
  predictor = "self_na_2",
  moderator = "self_na_1") %>%
  knitr::kable(
    caption = "Simple slope analysis"
  ) %>% 
  kableExtra::kable_styling()

Simple slope analysis

Dependent Variable	Predictor (+/-1 SD)	df	B	t	p	sr2	CI_lower	CI_upper
csi_overall_2	self_na_2 (LOW-self_na_1)	134	-0.068	-0.57	0.569	0.002	0.000	0.016
csi_overall_2	self_na_2 (MEAN-self_na_1)	134	-0.226	-2.73	0.007	0.048	0.000	0.115
csi_overall_2	self_na_2 (HIGH-self_na_1)	134	-0.384	-3.80	0.000	0.093	0.003	0.183

Hypothesis 5: APIM Actual Similarities

# run function
h5_results <- h5_function(
  var_list = c("pa", "na",
               "agree", "compa", "polit", 
               "consci", "indus", "order", 
               "extra", "assert", "enthu", 
               "neuro", "volat", "withd", 
               "open", "intel", "opena"),
  quality_var = "csi_overall",
  .data = baseline
)

# view results
h5_results$actual %>%
  knitr::kable(
    caption = "Standardized solutions for APIM models with actual similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Standardized solutions for APIM models with actual similarity

lhs	op	rhs	label	est.std	se	z	pvalue	ci.lower	ci.upper
csi_overall_1	~	self_pa_1	a1	0.150	0.084	1.786	0.074	-0.015	0.315
csi_overall_2	~	self_pa_2	a2	0.222	0.081	2.733	0.006	0.063	0.382
csi_overall_2	~	self_pa_1	p12	-0.045	0.084	-0.537	0.591	-0.209	0.119
csi_overall_1	~	self_pa_2	p21	-0.041	0.084	-0.486	0.627	-0.207	0.125
csi_overall_1	~	pa_diff	d1	0.034	0.084	0.397	0.691	-0.132	0.199
csi_overall_2	~	pa_diff	d2	0.039	0.083	0.465	0.642	-0.124	0.202
csi_overall_1	~	self_na_1	a1	-0.220	0.085	-2.598	0.009	-0.386	-0.054
csi_overall_2	~	self_na_2	a2	-0.263	0.079	-3.306	0.001	-0.418	-0.107
csi_overall_2	~	self_na_1	p12	-0.179	0.084	-2.138	0.033	-0.343	-0.015
csi_overall_1	~	self_na_2	p21	-0.121	0.083	-1.458	0.145	-0.284	0.042
csi_overall_1	~	na_diff	d1	-0.012	0.086	-0.139	0.889	-0.181	0.157
csi_overall_2	~	na_diff	d2	0.066	0.084	0.783	0.434	-0.099	0.232
csi_overall_1	~	self_agree_1	a1	0.356	0.077	4.608	0.000	0.205	0.508
csi_overall_2	~	self_agree_2	a2	0.266	0.086	3.098	0.002	0.098	0.434
csi_overall_2	~	self_agree_1	p12	0.120	0.084	1.432	0.152	-0.044	0.285
csi_overall_1	~	self_agree_2	p21	0.111	0.085	1.304	0.192	-0.056	0.278
csi_overall_1	~	agree_diff	d1	-0.123	0.085	-1.443	0.149	-0.290	0.044
csi_overall_2	~	agree_diff	d2	-0.066	0.088	-0.745	0.456	-0.239	0.107
csi_overall_1	~	self_compa_1	a1	0.369	0.077	4.787	0.000	0.218	0.520
csi_overall_2	~	self_compa_2	a2	0.212	0.090	2.344	0.019	0.035	0.388
csi_overall_2	~	self_compa_1	p12	0.190	0.082	2.326	0.020	0.030	0.350
csi_overall_1	~	self_compa_2	p21	-0.026	0.091	-0.281	0.778	-0.204	0.153
csi_overall_1	~	compa_diff	d1	-0.144	0.091	-1.580	0.114	-0.323	0.035
csi_overall_2	~	compa_diff	d2	-0.127	0.092	-1.375	0.169	-0.307	0.054
csi_overall_1	~	self_polit_1	a1	0.228	0.081	2.813	0.005	0.069	0.387
csi_overall_2	~	self_polit_2	a2	0.161	0.085	1.894	0.058	-0.006	0.328
csi_overall_2	~	self_polit_1	p12	0.049	0.087	0.565	0.572	-0.122	0.220
csi_overall_1	~	self_polit_2	p21	0.246	0.080	3.084	0.002	0.090	0.402
csi_overall_1	~	polit_diff	d1	0.043	0.084	0.512	0.608	-0.121	0.207
csi_overall_2	~	polit_diff	d2	0.005	0.088	0.053	0.957	-0.168	0.177
csi_overall_1	~	self_consci_1	a1	0.137	0.083	1.647	0.100	-0.026	0.301
csi_overall_2	~	self_consci_2	a2	0.020	0.085	0.231	0.817	-0.147	0.186
csi_overall_2	~	self_consci_1	p12	-0.034	0.085	-0.404	0.686	-0.201	0.132
csi_overall_1	~	self_consci_2	p21	-0.064	0.084	-0.755	0.450	-0.228	0.101
csi_overall_1	~	consci_diff	d1	-0.011	0.084	-0.127	0.899	-0.176	0.155
csi_overall_2	~	consci_diff	d2	0.056	0.085	0.657	0.511	-0.111	0.223
csi_overall_1	~	self_indus_1	a1	0.132	0.085	1.548	0.122	-0.035	0.299
csi_overall_2	~	self_indus_2	a2	0.075	0.087	0.867	0.386	-0.095	0.245
csi_overall_2	~	self_indus_1	p12	-0.003	0.087	-0.039	0.969	-0.173	0.166
csi_overall_1	~	self_indus_2	p21	-0.063	0.086	-0.733	0.463	-0.232	0.106
csi_overall_1	~	indus_diff	d1	-0.039	0.088	-0.444	0.657	-0.212	0.133
csi_overall_2	~	indus_diff	d2	-0.018	0.089	-0.202	0.840	-0.192	0.156
csi_overall_1	~	self_order_1	a1	0.106	0.084	1.260	0.207	-0.059	0.272
csi_overall_2	~	self_order_2	a2	-0.053	0.085	-0.629	0.529	-0.219	0.113
csi_overall_2	~	self_order_1	p12	-0.036	0.085	-0.427	0.669	-0.203	0.130
csi_overall_1	~	self_order_2	p21	-0.059	0.085	-0.692	0.489	-0.225	0.107
csi_overall_1	~	order_diff	d1	-0.013	0.085	-0.149	0.881	-0.180	0.154
csi_overall_2	~	order_diff	d2	0.089	0.085	1.048	0.295	-0.077	0.255
csi_overall_1	~	self_extra_1	a1	0.190	0.083	2.300	0.021	0.028	0.352
csi_overall_2	~	self_extra_2	a2	0.270	0.078	3.450	0.001	0.117	0.424
csi_overall_2	~	self_extra_1	p12	0.110	0.080	1.382	0.167	-0.046	0.266
csi_overall_1	~	self_extra_2	p21	-0.021	0.085	-0.244	0.807	-0.188	0.146
csi_overall_1	~	extra_diff	d1	-0.087	0.084	-1.026	0.305	-0.252	0.079
csi_overall_2	~	extra_diff	d2	-0.155	0.080	-1.938	0.053	-0.311	0.002
csi_overall_1	~	self_assert_1	a1	0.047	0.087	0.543	0.587	-0.123	0.217
csi_overall_2	~	self_assert_2	a2	0.164	0.083	1.977	0.048	0.001	0.327
csi_overall_2	~	self_assert_1	p12	0.104	0.085	1.229	0.219	-0.062	0.270
csi_overall_1	~	self_assert_2	p21	-0.073	0.086	-0.851	0.395	-0.241	0.095
csi_overall_1	~	assert_diff	d1	0.021	0.086	0.244	0.807	-0.147	0.189
csi_overall_2	~	assert_diff	d2	-0.001	0.084	-0.014	0.989	-0.166	0.164
csi_overall_1	~	self_enthu_1	a1	0.288	0.078	3.684	0.000	0.135	0.440
csi_overall_2	~	self_enthu_2	a2	0.332	0.079	4.204	0.000	0.177	0.487
csi_overall_2	~	self_enthu_1	p12	0.133	0.078	1.710	0.087	-0.019	0.286
csi_overall_1	~	self_enthu_2	p21	0.086	0.086	1.000	0.317	-0.082	0.254
csi_overall_1	~	enthu_diff	d1	-0.063	0.086	-0.734	0.463	-0.233	0.106
csi_overall_2	~	enthu_diff	d2	-0.099	0.083	-1.193	0.233	-0.262	0.064
csi_overall_1	~	self_neuro_1	a1	-0.159	0.082	-1.949	0.051	-0.320	0.001
csi_overall_2	~	self_neuro_2	a2	-0.194	0.080	-2.424	0.015	-0.350	-0.037
csi_overall_2	~	self_neuro_1	p12	-0.239	0.079	-3.033	0.002	-0.393	-0.085
csi_overall_1	~	self_neuro_2	p21	-0.146	0.082	-1.775	0.076	-0.307	0.015
csi_overall_1	~	neuro_diff	d1	0.116	0.082	1.412	0.158	-0.045	0.278
csi_overall_2	~	neuro_diff	d2	0.105	0.081	1.306	0.191	-0.053	0.263
csi_overall_1	~	self_volat_1	a1	-0.171	0.084	-2.030	0.042	-0.336	-0.006
csi_overall_2	~	self_volat_2	a2	-0.203	0.083	-2.447	0.014	-0.365	-0.040
csi_overall_2	~	self_volat_1	p12	-0.248	0.082	-3.039	0.002	-0.409	-0.088
csi_overall_1	~	self_volat_2	p21	-0.219	0.084	-2.610	0.009	-0.383	-0.054
csi_overall_1	~	volat_diff	d1	-0.003	0.085	-0.041	0.968	-0.170	0.163
csi_overall_2	~	volat_diff	d2	0.082	0.083	0.979	0.328	-0.082	0.245
csi_overall_1	~	self_withd_1	a1	-0.121	0.084	-1.448	0.148	-0.286	0.043
csi_overall_2	~	self_withd_2	a2	-0.128	0.083	-1.546	0.122	-0.290	0.034
csi_overall_2	~	self_withd_1	p12	-0.184	0.082	-2.256	0.024	-0.344	-0.024
csi_overall_1	~	self_withd_2	p21	-0.038	0.085	-0.449	0.653	-0.204	0.128
csi_overall_1	~	withd_diff	d1	0.113	0.085	1.338	0.181	-0.053	0.279
csi_overall_2	~	withd_diff	d2	0.090	0.083	1.085	0.278	-0.073	0.254
csi_overall_1	~	self_open_1	a1	0.120	0.087	1.385	0.166	-0.050	0.290
csi_overall_2	~	self_open_2	a2	0.228	0.086	2.658	0.008	0.060	0.395
csi_overall_2	~	self_open_1	p12	0.090	0.084	1.066	0.287	-0.075	0.255
csi_overall_1	~	self_open_2	p21	0.017	0.090	0.186	0.852	-0.160	0.194
csi_overall_1	~	open_diff	d1	-0.015	0.088	-0.174	0.862	-0.187	0.157
csi_overall_2	~	open_diff	d2	-0.034	0.085	-0.398	0.691	-0.201	0.133
csi_overall_1	~	self_intel_1	a1	0.208	0.082	2.551	0.011	0.048	0.368
csi_overall_2	~	self_intel_2	a2	0.264	0.084	3.141	0.002	0.099	0.428
csi_overall_2	~	self_intel_1	p12	0.134	0.081	1.642	0.101	-0.026	0.293
csi_overall_1	~	self_intel_2	p21	0.017	0.088	0.194	0.846	-0.156	0.190
csi_overall_1	~	intel_diff	d1	-0.027	0.088	-0.309	0.757	-0.199	0.145
csi_overall_2	~	intel_diff	d2	0.095	0.086	1.108	0.268	-0.073	0.264
csi_overall_1	~	self_opena_1	a1	-0.029	0.088	-0.327	0.743	-0.201	0.144
csi_overall_2	~	self_opena_2	a2	0.200	0.093	2.157	0.031	0.018	0.381
csi_overall_2	~	self_opena_1	p12	0.049	0.087	0.557	0.578	-0.122	0.220
csi_overall_1	~	self_opena_2	p21	0.142	0.094	1.512	0.131	-0.042	0.326
csi_overall_1	~	opena_diff	d1	0.161	0.090	1.798	0.072	-0.015	0.337
csi_overall_2	~	opena_diff	d2	0.034	0.090	0.381	0.703	-0.142	0.210

h5_results$perceived_p1 %>%
  knitr::kable(
    caption = "Standardized solutions for APIM models with P1-perceived similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Standardized solutions for APIM models with P1-perceived similarity

perception	lhs	op	rhs	label	est.std	se	z	pvalue	ci.lower	ci.upper
P1	csi_overall_1	~	self_pa_1	a1	0.042	0.098	0.429	0.668	-0.149	0.233
P1	csi_overall_2	~	partner_pa_1	ap1	0.261	0.092	2.835	0.005	0.080	0.441
P1	csi_overall_2	~	self_pa_1	p12	-0.203	0.094	-2.150	0.032	-0.388	-0.018
P1	csi_overall_1	~	partner_pa_1	p21	0.141	0.096	1.473	0.141	-0.047	0.328
P1	csi_overall_1	~	pa_diff_p1	d1	-0.134	0.084	-1.596	0.110	-0.300	0.031
P1	csi_overall_2	~	pa_diff_p1	d2	-0.202	0.082	-2.469	0.014	-0.362	-0.042
P1	csi_overall_1	~	self_na_1	a1	-0.106	0.110	-0.971	0.331	-0.321	0.108
P1	csi_overall_2	~	partner_na_1	ap1	0.053	0.098	0.546	0.585	-0.138	0.244
P1	csi_overall_2	~	self_na_1	p12	-0.203	0.112	-1.815	0.069	-0.422	0.016
P1	csi_overall_1	~	partner_na_1	p21	-0.233	0.093	-2.499	0.012	-0.415	-0.050
P1	csi_overall_1	~	na_diff_p1	d1	-0.079	0.099	-0.793	0.428	-0.274	0.116
P1	csi_overall_2	~	na_diff_p1	d2	-0.052	0.102	-0.513	0.608	-0.253	0.148
P1	csi_overall_1	~	self_agree_1	a1	0.246	0.077	3.183	0.001	0.095	0.398
P1	csi_overall_2	~	partner_agree_1	ap1	0.097	0.094	1.035	0.301	-0.087	0.281
P1	csi_overall_2	~	self_agree_1	p12	0.115	0.088	1.305	0.192	-0.058	0.288
P1	csi_overall_1	~	partner_agree_1	p21	0.287	0.082	3.513	0.000	0.127	0.447
P1	csi_overall_1	~	agree_diff_p1	d1	-0.131	0.079	-1.669	0.095	-0.286	0.023
P1	csi_overall_2	~	agree_diff_p1	d2	-0.095	0.088	-1.078	0.281	-0.268	0.078
P1	csi_overall_1	~	self_compa_1	a1	0.315	0.081	3.873	0.000	0.156	0.474
P1	csi_overall_2	~	partner_compa_1	ap1	-0.004	0.106	-0.038	0.969	-0.212	0.204
P1	csi_overall_2	~	self_compa_1	p12	0.199	0.088	2.257	0.024	0.026	0.371
P1	csi_overall_1	~	partner_compa_1	p21	0.097	0.100	0.968	0.333	-0.099	0.293
P1	csi_overall_1	~	compa_diff_p1	d1	-0.161	0.095	-1.706	0.088	-0.347	0.024
P1	csi_overall_2	~	compa_diff_p1	d2	-0.193	0.099	-1.941	0.052	-0.387	0.002
P1	csi_overall_1	~	self_polit_1	a1	0.148	0.082	1.818	0.069	-0.012	0.308
P1	csi_overall_2	~	partner_polit_1	ap1	0.120	0.086	1.398	0.162	-0.048	0.288
P1	csi_overall_2	~	self_polit_1	p12	0.047	0.093	0.506	0.613	-0.135	0.229
P1	csi_overall_1	~	partner_polit_1	p21	0.414	0.070	5.905	0.000	0.277	0.552
P1	csi_overall_1	~	polit_diff_p1	d1	-0.045	0.081	-0.559	0.576	-0.203	0.113
P1	csi_overall_2	~	polit_diff_p1	d2	0.000	0.091	0.001	1.000	-0.179	0.179
P1	csi_overall_1	~	self_consci_1	a1	0.142	0.082	1.730	0.084	-0.019	0.303
P1	csi_overall_2	~	partner_consci_1	ap1	-0.001	0.085	-0.012	0.990	-0.167	0.165
P1	csi_overall_2	~	self_consci_1	p12	-0.035	0.084	-0.413	0.680	-0.200	0.130
P1	csi_overall_1	~	partner_consci_1	p21	0.111	0.083	1.334	0.182	-0.052	0.274
P1	csi_overall_1	~	consci_diff_p1	d1	-0.138	0.083	-1.671	0.095	-0.300	0.024
P1	csi_overall_2	~	consci_diff_p1	d2	-0.159	0.084	-1.899	0.058	-0.323	0.005
P1	csi_overall_1	~	self_indus_1	a1	0.106	0.084	1.265	0.206	-0.058	0.271
P1	csi_overall_2	~	partner_indus_1	ap1	0.054	0.084	0.647	0.517	-0.110	0.219
P1	csi_overall_2	~	self_indus_1	p12	-0.042	0.086	-0.488	0.626	-0.211	0.127
P1	csi_overall_1	~	partner_indus_1	p21	0.189	0.081	2.341	0.019	0.031	0.348
P1	csi_overall_1	~	indus_diff_p1	d1	-0.161	0.083	-1.941	0.052	-0.324	0.002
P1	csi_overall_2	~	indus_diff_p1	d2	-0.177	0.085	-2.096	0.036	-0.343	-0.012
P1	csi_overall_1	~	self_order_1	a1	0.120	0.085	1.421	0.155	-0.046	0.286
P1	csi_overall_2	~	partner_order_1	ap1	-0.024	0.086	-0.284	0.777	-0.194	0.145
P1	csi_overall_2	~	self_order_1	p12	-0.036	0.086	-0.419	0.675	-0.204	0.132
P1	csi_overall_1	~	partner_order_1	p21	0.013	0.086	0.152	0.879	-0.155	0.181
P1	csi_overall_1	~	order_diff_p1	d1	-0.106	0.086	-1.231	0.218	-0.274	0.063
P1	csi_overall_2	~	order_diff_p1	d2	-0.066	0.087	-0.756	0.449	-0.236	0.105
P1	csi_overall_1	~	self_extra_1	a1	0.184	0.085	2.173	0.030	0.018	0.350
P1	csi_overall_2	~	partner_extra_1	ap1	0.177	0.081	2.182	0.029	0.018	0.337
P1	csi_overall_2	~	self_extra_1	p12	0.092	0.084	1.093	0.275	-0.073	0.257
P1	csi_overall_1	~	partner_extra_1	p21	0.101	0.084	1.203	0.229	-0.063	0.265
P1	csi_overall_1	~	extra_diff_p1	d1	0.029	0.085	0.336	0.737	-0.138	0.195
P1	csi_overall_2	~	extra_diff_p1	d2	-0.169	0.082	-2.048	0.041	-0.330	-0.007
P1	csi_overall_1	~	self_assert_1	a1	0.039	0.089	0.435	0.663	-0.136	0.213
P1	csi_overall_2	~	partner_assert_1	ap1	0.102	0.083	1.230	0.219	-0.061	0.266
P1	csi_overall_2	~	self_assert_1	p12	0.098	0.087	1.124	0.261	-0.073	0.269
P1	csi_overall_1	~	partner_assert_1	p21	-0.033	0.085	-0.384	0.701	-0.199	0.134
P1	csi_overall_1	~	assert_diff_p1	d1	0.016	0.089	0.179	0.858	-0.158	0.190
P1	csi_overall_2	~	assert_diff_p1	d2	-0.094	0.087	-1.084	0.279	-0.265	0.076
P1	csi_overall_1	~	self_enthu_1	a1	0.246	0.078	3.135	0.002	0.092	0.399
P1	csi_overall_2	~	partner_enthu_1	ap1	0.190	0.082	2.324	0.020	0.030	0.350
P1	csi_overall_2	~	self_enthu_1	p12	0.074	0.082	0.906	0.365	-0.086	0.234
P1	csi_overall_1	~	partner_enthu_1	p21	0.214	0.080	2.671	0.008	0.057	0.370
P1	csi_overall_1	~	enthu_diff_p1	d1	-0.039	0.081	-0.480	0.631	-0.198	0.120
P1	csi_overall_2	~	enthu_diff_p1	d2	-0.189	0.081	-2.331	0.020	-0.348	-0.030
P1	csi_overall_1	~	self_neuro_1	a1	-0.242	0.078	-3.080	0.002	-0.396	-0.088
P1	csi_overall_2	~	partner_neuro_1	ap1	-0.096	0.087	-1.104	0.270	-0.267	0.074
P1	csi_overall_2	~	self_neuro_1	p12	-0.260	0.083	-3.117	0.002	-0.424	-0.097
P1	csi_overall_1	~	partner_neuro_1	p21	-0.389	0.076	-5.107	0.000	-0.538	-0.240
P1	csi_overall_1	~	neuro_diff_p1	d1	0.078	0.083	0.939	0.348	-0.085	0.241
P1	csi_overall_2	~	neuro_diff_p1	d2	0.069	0.089	0.773	0.440	-0.106	0.244
P1	csi_overall_1	~	self_volat_1	a1	-0.245	0.083	-2.950	0.003	-0.408	-0.082
P1	csi_overall_2	~	partner_volat_1	ap1	-0.115	0.086	-1.336	0.182	-0.284	0.054
P1	csi_overall_2	~	self_volat_1	p12	-0.253	0.090	-2.816	0.005	-0.429	-0.077
P1	csi_overall_1	~	partner_volat_1	p21	-0.446	0.072	-6.154	0.000	-0.588	-0.304
P1	csi_overall_1	~	volat_diff_p1	d1	0.025	0.085	0.299	0.765	-0.141	0.192
P1	csi_overall_2	~	volat_diff_p1	d2	0.081	0.092	0.883	0.377	-0.099	0.262
P1	csi_overall_1	~	self_withd_1	a1	-0.157	0.080	-1.952	0.051	-0.315	0.001
P1	csi_overall_2	~	partner_withd_1	ap1	-0.041	0.088	-0.465	0.642	-0.214	0.132
P1	csi_overall_2	~	self_withd_1	p12	-0.211	0.081	-2.597	0.009	-0.371	-0.052
P1	csi_overall_1	~	partner_withd_1	p21	-0.221	0.084	-2.619	0.009	-0.386	-0.056
P1	csi_overall_1	~	withd_diff_p1	d1	0.095	0.086	1.107	0.268	-0.073	0.263
P1	csi_overall_2	~	withd_diff_p1	d2	0.060	0.088	0.688	0.491	-0.112	0.233
P1	csi_overall_1	~	self_open_1	a1	0.032	0.086	0.374	0.708	-0.136	0.200
P1	csi_overall_2	~	partner_open_1	ap1	0.220	0.087	2.530	0.011	0.049	0.390
P1	csi_overall_2	~	self_open_1	p12	0.066	0.086	0.762	0.446	-0.103	0.235
P1	csi_overall_1	~	partner_open_1	p21	0.261	0.085	3.061	0.002	0.094	0.428
P1	csi_overall_1	~	open_diff_p1	d1	-0.089	0.083	-1.071	0.284	-0.251	0.074
P1	csi_overall_2	~	open_diff_p1	d2	-0.083	0.084	-0.989	0.323	-0.247	0.081
P1	csi_overall_1	~	self_intel_1	a1	0.184	0.082	2.242	0.025	0.023	0.344
P1	csi_overall_2	~	partner_intel_1	ap1	0.159	0.083	1.926	0.054	-0.003	0.321
P1	csi_overall_2	~	self_intel_1	p12	0.120	0.085	1.421	0.155	-0.046	0.286
P1	csi_overall_1	~	partner_intel_1	p21	0.235	0.079	2.966	0.003	0.080	0.391
P1	csi_overall_1	~	intel_diff_p1	d1	-0.005	0.083	-0.063	0.950	-0.168	0.157
P1	csi_overall_2	~	intel_diff_p1	d2	0.088	0.085	1.043	0.297	-0.078	0.255
P1	csi_overall_1	~	self_opena_1	a1	-0.066	0.088	-0.749	0.454	-0.239	0.107
P1	csi_overall_2	~	partner_opena_1	ap1	0.234	0.097	2.425	0.015	0.045	0.424
P1	csi_overall_2	~	self_opena_1	p12	0.035	0.088	0.393	0.695	-0.138	0.208
P1	csi_overall_1	~	partner_opena_1	p21	0.227	0.097	2.344	0.019	0.037	0.417
P1	csi_overall_1	~	opena_diff_p1	d1	-0.034	0.094	-0.358	0.720	-0.218	0.151
P1	csi_overall_2	~	opena_diff_p1	d2	0.008	0.094	0.080	0.936	-0.177	0.192

h5_results$perceived_p2 %>%
  knitr::kable(
    caption = "Standardized solutions for APIM models with P2-perceived similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Standardized solutions for APIM models with P2-perceived similarity

perception	lhs	op	rhs	label	est.std	se	z	pvalue	ci.lower	ci.upper
P2	csi_overall_2	~	self_pa_2	a2	0.215	0.068	3.175	0.002	0.082	0.348
P2	csi_overall_1	~	partner_pa_2	ap2	-0.023	0.071	-0.321	0.748	-0.162	0.116
P2	csi_overall_1	~	self_pa_2	p21	0.145	0.059	2.441	0.015	0.029	0.261
P2	csi_overall_2	~	partner_pa_2	p21	0.134	0.056	2.412	0.016	0.025	0.243
P2	csi_overall_1	~	pa_diff_p2	d1	0.127	0.085	1.488	0.137	-0.040	0.294
P2	csi_overall_2	~	pa_diff_p2	d2	-0.056	0.083	-0.667	0.504	-0.219	0.108
P2	csi_overall_2	~	self_na_2	a2	-0.242	0.067	-3.609	0.000	-0.374	-0.111
P2	csi_overall_1	~	partner_na_2	ap2	-0.028	0.073	-0.381	0.703	-0.170	0.115
P2	csi_overall_1	~	self_na_2	p21	-0.246	0.054	-4.552	0.000	-0.353	-0.140
P2	csi_overall_2	~	partner_na_2	p21	-0.264	0.057	-4.627	0.000	-0.376	-0.152
P2	csi_overall_1	~	na_diff_p2	d1	0.102	0.086	1.187	0.235	-0.066	0.270
P2	csi_overall_2	~	na_diff_p2	d2	0.113	0.082	1.379	0.168	-0.047	0.272
P2	csi_overall_2	~	self_agree_2	a2	0.205	0.063	3.247	0.001	0.081	0.329
P2	csi_overall_1	~	partner_agree_2	ap2	0.098	0.071	1.379	0.168	-0.041	0.236
P2	csi_overall_1	~	self_agree_2	p21	0.294	0.047	6.201	0.000	0.201	0.387
P2	csi_overall_2	~	partner_agree_2	p21	0.355	0.057	6.216	0.000	0.243	0.466
P2	csi_overall_1	~	agree_diff_p2	d1	0.019	0.081	0.229	0.819	-0.141	0.178
P2	csi_overall_2	~	agree_diff_p2	d2	-0.178	0.076	-2.342	0.019	-0.326	-0.029
P2	csi_overall_2	~	self_compa_2	a2	0.231	0.069	3.352	0.001	0.096	0.366
P2	csi_overall_1	~	partner_compa_2	ap2	0.079	0.073	1.093	0.275	-0.063	0.222
P2	csi_overall_1	~	self_compa_2	p21	0.259	0.056	4.617	0.000	0.149	0.368
P2	csi_overall_2	~	partner_compa_2	p21	0.254	0.057	4.420	0.000	0.141	0.366
P2	csi_overall_1	~	compa_diff_p2	d1	0.070	0.088	0.793	0.428	-0.102	0.242
P2	csi_overall_2	~	compa_diff_p2	d2	-0.162	0.083	-1.962	0.050	-0.324	0.000
P2	csi_overall_2	~	self_polit_2	a2	0.076	0.071	1.073	0.283	-0.063	0.216
P2	csi_overall_1	~	partner_polit_2	ap2	0.106	0.072	1.478	0.139	-0.035	0.247
P2	csi_overall_1	~	self_polit_2	p21	0.241	0.051	4.767	0.000	0.142	0.340
P2	csi_overall_2	~	partner_polit_2	p21	0.283	0.059	4.838	0.000	0.168	0.398
P2	csi_overall_1	~	polit_diff_p2	d1	-0.078	0.082	-0.948	0.343	-0.238	0.083
P2	csi_overall_2	~	polit_diff_p2	d2	-0.151	0.081	-1.861	0.063	-0.311	0.008
P2	csi_overall_2	~	self_consci_2	a2	0.108	0.082	1.311	0.190	-0.053	0.268
P2	csi_overall_1	~	partner_consci_2	ap2	0.157	0.078	2.004	0.045	0.003	0.311
P2	csi_overall_1	~	self_consci_2	p21	0.139	0.061	2.295	0.022	0.020	0.258
P2	csi_overall_2	~	partner_consci_2	p21	0.143	0.064	2.220	0.026	0.017	0.269
P2	csi_overall_1	~	consci_diff_p2	d1	0.077	0.085	0.900	0.368	-0.090	0.244
P2	csi_overall_2	~	consci_diff_p2	d2	-0.035	0.088	-0.399	0.690	-0.207	0.137
P2	csi_overall_2	~	self_indus_2	a2	0.194	0.083	2.324	0.020	0.030	0.357
P2	csi_overall_1	~	partner_indus_2	ap2	0.098	0.078	1.255	0.210	-0.055	0.251
P2	csi_overall_1	~	self_indus_2	p21	0.176	0.063	2.799	0.005	0.053	0.299
P2	csi_overall_2	~	partner_indus_2	p21	0.173	0.064	2.703	0.007	0.048	0.299
P2	csi_overall_1	~	indus_diff_p2	d1	0.087	0.088	0.988	0.323	-0.086	0.260
P2	csi_overall_2	~	indus_diff_p2	d2	0.009	0.092	0.093	0.926	-0.171	0.188
P2	csi_overall_2	~	self_order_2	a2	-0.012	0.079	-0.150	0.880	-0.167	0.144
P2	csi_overall_1	~	partner_order_2	ap2	0.173	0.077	2.236	0.025	0.021	0.325
P2	csi_overall_1	~	self_order_2	p21	0.063	0.060	1.051	0.293	-0.054	0.179
P2	csi_overall_2	~	partner_order_2	p21	0.065	0.063	1.037	0.300	-0.058	0.188
P2	csi_overall_1	~	order_diff_p2	d1	0.118	0.083	1.411	0.158	-0.046	0.281
P2	csi_overall_2	~	order_diff_p2	d2	-0.094	0.085	-1.104	0.270	-0.260	0.073
P2	csi_overall_2	~	self_extra_2	a2	0.369	0.061	6.065	0.000	0.250	0.488
P2	csi_overall_1	~	partner_extra_2	ap2	0.029	0.072	0.408	0.683	-0.112	0.170
P2	csi_overall_1	~	self_extra_2	p21	0.247	0.058	4.251	0.000	0.133	0.361
P2	csi_overall_2	~	partner_extra_2	p21	0.217	0.053	4.067	0.000	0.112	0.321
P2	csi_overall_1	~	extra_diff_p2	d1	0.065	0.085	0.767	0.443	-0.101	0.231
P2	csi_overall_2	~	extra_diff_p2	d2	-0.042	0.078	-0.543	0.587	-0.195	0.110
P2	csi_overall_2	~	self_assert_2	a2	0.270	0.068	3.995	0.000	0.137	0.402
P2	csi_overall_1	~	partner_assert_2	ap2	-0.041	0.073	-0.553	0.580	-0.185	0.103
P2	csi_overall_1	~	self_assert_2	p21	0.133	0.061	2.184	0.029	0.014	0.253
P2	csi_overall_2	~	partner_assert_2	p21	0.117	0.054	2.154	0.031	0.011	0.224
P2	csi_overall_1	~	assert_diff_p2	d1	0.004	0.085	0.045	0.964	-0.162	0.169
P2	csi_overall_2	~	assert_diff_p2	d2	-0.087	0.080	-1.085	0.278	-0.245	0.070
P2	csi_overall_2	~	self_enthu_2	a2	0.393	0.062	6.323	0.000	0.271	0.515
P2	csi_overall_1	~	partner_enthu_2	ap2	0.108	0.073	1.488	0.137	-0.034	0.251
P2	csi_overall_1	~	self_enthu_2	p21	0.267	0.057	4.717	0.000	0.156	0.378
P2	csi_overall_2	~	partner_enthu_2	p21	0.247	0.054	4.567	0.000	0.141	0.353
P2	csi_overall_1	~	enthu_diff_p2	d1	0.109	0.085	1.280	0.200	-0.058	0.276
P2	csi_overall_2	~	enthu_diff_p2	d2	0.013	0.079	0.168	0.866	-0.142	0.168
P2	csi_overall_2	~	self_neuro_2	a2	-0.274	0.067	-4.082	0.000	-0.405	-0.142
P2	csi_overall_1	~	partner_neuro_2	ap2	-0.139	0.075	-1.851	0.064	-0.287	0.008
P2	csi_overall_1	~	self_neuro_2	p21	-0.300	0.052	-5.770	0.000	-0.403	-0.198
P2	csi_overall_2	~	partner_neuro_2	p21	-0.327	0.059	-5.588	0.000	-0.442	-0.212
P2	csi_overall_1	~	neuro_diff_p2	d1	-0.003	0.081	-0.038	0.970	-0.161	0.155
P2	csi_overall_2	~	neuro_diff_p2	d2	0.061	0.077	0.789	0.430	-0.091	0.213
P2	csi_overall_2	~	self_volat_2	a2	-0.276	0.069	-3.977	0.000	-0.412	-0.140
P2	csi_overall_1	~	partner_volat_2	ap2	-0.191	0.076	-2.519	0.012	-0.340	-0.042
P2	csi_overall_1	~	self_volat_2	p21	-0.339	0.053	-6.384	0.000	-0.443	-0.235
P2	csi_overall_2	~	partner_volat_2	p21	-0.365	0.059	-6.153	0.000	-0.481	-0.249
P2	csi_overall_1	~	volat_diff_p2	d1	-0.059	0.080	-0.745	0.457	-0.216	0.097
P2	csi_overall_2	~	volat_diff_p2	d2	0.101	0.077	1.307	0.191	-0.050	0.253
P2	csi_overall_2	~	self_withd_2	a2	-0.237	0.069	-3.417	0.001	-0.373	-0.101
P2	csi_overall_1	~	partner_withd_2	ap2	-0.052	0.075	-0.692	0.489	-0.200	0.095
P2	csi_overall_1	~	self_withd_2	p21	-0.207	0.056	-3.724	0.000	-0.316	-0.098
P2	csi_overall_2	~	partner_withd_2	p21	-0.214	0.059	-3.653	0.000	-0.328	-0.099
P2	csi_overall_1	~	withd_diff_p2	d1	0.060	0.083	0.722	0.471	-0.103	0.222
P2	csi_overall_2	~	withd_diff_p2	d2	-0.032	0.081	-0.398	0.690	-0.191	0.126
P2	csi_overall_2	~	self_open_2	a2	0.223	0.069	3.224	0.001	0.087	0.358
P2	csi_overall_1	~	partner_open_2	ap2	-0.022	0.070	-0.307	0.759	-0.159	0.116
P2	csi_overall_1	~	self_open_2	p21	0.161	0.062	2.607	0.009	0.040	0.282
P2	csi_overall_2	~	partner_open_2	p21	0.132	0.050	2.619	0.009	0.033	0.231
P2	csi_overall_1	~	open_diff_p2	d1	0.184	0.085	2.170	0.030	0.018	0.351
P2	csi_overall_2	~	open_diff_p2	d2	-0.061	0.084	-0.722	0.470	-0.225	0.104
P2	csi_overall_2	~	self_intel_2	a2	0.315	0.077	4.100	0.000	0.164	0.466
P2	csi_overall_1	~	partner_intel_2	ap2	0.021	0.076	0.273	0.785	-0.128	0.169
P2	csi_overall_1	~	self_intel_2	p21	0.174	0.065	2.665	0.008	0.046	0.303
P2	csi_overall_2	~	partner_intel_2	p21	0.149	0.056	2.660	0.008	0.039	0.258
P2	csi_overall_1	~	intel_diff_p2	d1	0.147	0.090	1.643	0.100	-0.028	0.323
P2	csi_overall_2	~	intel_diff_p2	d2	0.109	0.089	1.222	0.222	-0.066	0.284
P2	csi_overall_2	~	self_opena_2	a2	0.142	0.071	1.982	0.047	0.002	0.282
P2	csi_overall_1	~	partner_opena_2	ap2	-0.031	0.075	-0.420	0.674	-0.178	0.115
P2	csi_overall_1	~	self_opena_2	p21	0.089	0.062	1.426	0.154	-0.033	0.211
P2	csi_overall_2	~	partner_opena_2	p21	0.078	0.054	1.437	0.151	-0.028	0.183
P2	csi_overall_1	~	opena_diff_p2	d1	0.050	0.089	0.562	0.574	-0.124	0.224
P2	csi_overall_2	~	opena_diff_p2	d2	-0.109	0.086	-1.270	0.204	-0.277	0.059

Hypothesis 6: APIM Perceived Similarities

# output included in h5_function
h5_results$perceived_p1 %>%
  knitr::kable(
    caption = "Standardized solutions for APIM models with P1-perceived similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Standardized solutions for APIM models with P1-perceived similarity

perception	lhs	op	rhs	label	est.std	se	z	pvalue	ci.lower	ci.upper
P1	csi_overall_1	~	self_pa_1	a1	0.042	0.098	0.429	0.668	-0.149	0.233
P1	csi_overall_2	~	partner_pa_1	ap1	0.261	0.092	2.835	0.005	0.080	0.441
P1	csi_overall_2	~	self_pa_1	p12	-0.203	0.094	-2.150	0.032	-0.388	-0.018
P1	csi_overall_1	~	partner_pa_1	p21	0.141	0.096	1.473	0.141	-0.047	0.328
P1	csi_overall_1	~	pa_diff_p1	d1	-0.134	0.084	-1.596	0.110	-0.300	0.031
P1	csi_overall_2	~	pa_diff_p1	d2	-0.202	0.082	-2.469	0.014	-0.362	-0.042
P1	csi_overall_1	~	self_na_1	a1	-0.106	0.110	-0.971	0.331	-0.321	0.108
P1	csi_overall_2	~	partner_na_1	ap1	0.053	0.098	0.546	0.585	-0.138	0.244
P1	csi_overall_2	~	self_na_1	p12	-0.203	0.112	-1.815	0.069	-0.422	0.016
P1	csi_overall_1	~	partner_na_1	p21	-0.233	0.093	-2.499	0.012	-0.415	-0.050
P1	csi_overall_1	~	na_diff_p1	d1	-0.079	0.099	-0.793	0.428	-0.274	0.116
P1	csi_overall_2	~	na_diff_p1	d2	-0.052	0.102	-0.513	0.608	-0.253	0.148
P1	csi_overall_1	~	self_agree_1	a1	0.246	0.077	3.183	0.001	0.095	0.398
P1	csi_overall_2	~	partner_agree_1	ap1	0.097	0.094	1.035	0.301	-0.087	0.281
P1	csi_overall_2	~	self_agree_1	p12	0.115	0.088	1.305	0.192	-0.058	0.288
P1	csi_overall_1	~	partner_agree_1	p21	0.287	0.082	3.513	0.000	0.127	0.447
P1	csi_overall_1	~	agree_diff_p1	d1	-0.131	0.079	-1.669	0.095	-0.286	0.023
P1	csi_overall_2	~	agree_diff_p1	d2	-0.095	0.088	-1.078	0.281	-0.268	0.078
P1	csi_overall_1	~	self_compa_1	a1	0.315	0.081	3.873	0.000	0.156	0.474
P1	csi_overall_2	~	partner_compa_1	ap1	-0.004	0.106	-0.038	0.969	-0.212	0.204
P1	csi_overall_2	~	self_compa_1	p12	0.199	0.088	2.257	0.024	0.026	0.371
P1	csi_overall_1	~	partner_compa_1	p21	0.097	0.100	0.968	0.333	-0.099	0.293
P1	csi_overall_1	~	compa_diff_p1	d1	-0.161	0.095	-1.706	0.088	-0.347	0.024
P1	csi_overall_2	~	compa_diff_p1	d2	-0.193	0.099	-1.941	0.052	-0.387	0.002
P1	csi_overall_1	~	self_polit_1	a1	0.148	0.082	1.818	0.069	-0.012	0.308
P1	csi_overall_2	~	partner_polit_1	ap1	0.120	0.086	1.398	0.162	-0.048	0.288
P1	csi_overall_2	~	self_polit_1	p12	0.047	0.093	0.506	0.613	-0.135	0.229
P1	csi_overall_1	~	partner_polit_1	p21	0.414	0.070	5.905	0.000	0.277	0.552
P1	csi_overall_1	~	polit_diff_p1	d1	-0.045	0.081	-0.559	0.576	-0.203	0.113
P1	csi_overall_2	~	polit_diff_p1	d2	0.000	0.091	0.001	1.000	-0.179	0.179
P1	csi_overall_1	~	self_consci_1	a1	0.142	0.082	1.730	0.084	-0.019	0.303
P1	csi_overall_2	~	partner_consci_1	ap1	-0.001	0.085	-0.012	0.990	-0.167	0.165
P1	csi_overall_2	~	self_consci_1	p12	-0.035	0.084	-0.413	0.680	-0.200	0.130
P1	csi_overall_1	~	partner_consci_1	p21	0.111	0.083	1.334	0.182	-0.052	0.274
P1	csi_overall_1	~	consci_diff_p1	d1	-0.138	0.083	-1.671	0.095	-0.300	0.024
P1	csi_overall_2	~	consci_diff_p1	d2	-0.159	0.084	-1.899	0.058	-0.323	0.005
P1	csi_overall_1	~	self_indus_1	a1	0.106	0.084	1.265	0.206	-0.058	0.271
P1	csi_overall_2	~	partner_indus_1	ap1	0.054	0.084	0.647	0.517	-0.110	0.219
P1	csi_overall_2	~	self_indus_1	p12	-0.042	0.086	-0.488	0.626	-0.211	0.127
P1	csi_overall_1	~	partner_indus_1	p21	0.189	0.081	2.341	0.019	0.031	0.348
P1	csi_overall_1	~	indus_diff_p1	d1	-0.161	0.083	-1.941	0.052	-0.324	0.002
P1	csi_overall_2	~	indus_diff_p1	d2	-0.177	0.085	-2.096	0.036	-0.343	-0.012
P1	csi_overall_1	~	self_order_1	a1	0.120	0.085	1.421	0.155	-0.046	0.286
P1	csi_overall_2	~	partner_order_1	ap1	-0.024	0.086	-0.284	0.777	-0.194	0.145
P1	csi_overall_2	~	self_order_1	p12	-0.036	0.086	-0.419	0.675	-0.204	0.132
P1	csi_overall_1	~	partner_order_1	p21	0.013	0.086	0.152	0.879	-0.155	0.181
P1	csi_overall_1	~	order_diff_p1	d1	-0.106	0.086	-1.231	0.218	-0.274	0.063
P1	csi_overall_2	~	order_diff_p1	d2	-0.066	0.087	-0.756	0.449	-0.236	0.105
P1	csi_overall_1	~	self_extra_1	a1	0.184	0.085	2.173	0.030	0.018	0.350
P1	csi_overall_2	~	partner_extra_1	ap1	0.177	0.081	2.182	0.029	0.018	0.337
P1	csi_overall_2	~	self_extra_1	p12	0.092	0.084	1.093	0.275	-0.073	0.257
P1	csi_overall_1	~	partner_extra_1	p21	0.101	0.084	1.203	0.229	-0.063	0.265
P1	csi_overall_1	~	extra_diff_p1	d1	0.029	0.085	0.336	0.737	-0.138	0.195
P1	csi_overall_2	~	extra_diff_p1	d2	-0.169	0.082	-2.048	0.041	-0.330	-0.007
P1	csi_overall_1	~	self_assert_1	a1	0.039	0.089	0.435	0.663	-0.136	0.213
P1	csi_overall_2	~	partner_assert_1	ap1	0.102	0.083	1.230	0.219	-0.061	0.266
P1	csi_overall_2	~	self_assert_1	p12	0.098	0.087	1.124	0.261	-0.073	0.269
P1	csi_overall_1	~	partner_assert_1	p21	-0.033	0.085	-0.384	0.701	-0.199	0.134
P1	csi_overall_1	~	assert_diff_p1	d1	0.016	0.089	0.179	0.858	-0.158	0.190
P1	csi_overall_2	~	assert_diff_p1	d2	-0.094	0.087	-1.084	0.279	-0.265	0.076
P1	csi_overall_1	~	self_enthu_1	a1	0.246	0.078	3.135	0.002	0.092	0.399
P1	csi_overall_2	~	partner_enthu_1	ap1	0.190	0.082	2.324	0.020	0.030	0.350
P1	csi_overall_2	~	self_enthu_1	p12	0.074	0.082	0.906	0.365	-0.086	0.234
P1	csi_overall_1	~	partner_enthu_1	p21	0.214	0.080	2.671	0.008	0.057	0.370
P1	csi_overall_1	~	enthu_diff_p1	d1	-0.039	0.081	-0.480	0.631	-0.198	0.120
P1	csi_overall_2	~	enthu_diff_p1	d2	-0.189	0.081	-2.331	0.020	-0.348	-0.030
P1	csi_overall_1	~	self_neuro_1	a1	-0.242	0.078	-3.080	0.002	-0.396	-0.088
P1	csi_overall_2	~	partner_neuro_1	ap1	-0.096	0.087	-1.104	0.270	-0.267	0.074
P1	csi_overall_2	~	self_neuro_1	p12	-0.260	0.083	-3.117	0.002	-0.424	-0.097
P1	csi_overall_1	~	partner_neuro_1	p21	-0.389	0.076	-5.107	0.000	-0.538	-0.240
P1	csi_overall_1	~	neuro_diff_p1	d1	0.078	0.083	0.939	0.348	-0.085	0.241
P1	csi_overall_2	~	neuro_diff_p1	d2	0.069	0.089	0.773	0.440	-0.106	0.244
P1	csi_overall_1	~	self_volat_1	a1	-0.245	0.083	-2.950	0.003	-0.408	-0.082
P1	csi_overall_2	~	partner_volat_1	ap1	-0.115	0.086	-1.336	0.182	-0.284	0.054
P1	csi_overall_2	~	self_volat_1	p12	-0.253	0.090	-2.816	0.005	-0.429	-0.077
P1	csi_overall_1	~	partner_volat_1	p21	-0.446	0.072	-6.154	0.000	-0.588	-0.304
P1	csi_overall_1	~	volat_diff_p1	d1	0.025	0.085	0.299	0.765	-0.141	0.192
P1	csi_overall_2	~	volat_diff_p1	d2	0.081	0.092	0.883	0.377	-0.099	0.262
P1	csi_overall_1	~	self_withd_1	a1	-0.157	0.080	-1.952	0.051	-0.315	0.001
P1	csi_overall_2	~	partner_withd_1	ap1	-0.041	0.088	-0.465	0.642	-0.214	0.132
P1	csi_overall_2	~	self_withd_1	p12	-0.211	0.081	-2.597	0.009	-0.371	-0.052
P1	csi_overall_1	~	partner_withd_1	p21	-0.221	0.084	-2.619	0.009	-0.386	-0.056
P1	csi_overall_1	~	withd_diff_p1	d1	0.095	0.086	1.107	0.268	-0.073	0.263
P1	csi_overall_2	~	withd_diff_p1	d2	0.060	0.088	0.688	0.491	-0.112	0.233
P1	csi_overall_1	~	self_open_1	a1	0.032	0.086	0.374	0.708	-0.136	0.200
P1	csi_overall_2	~	partner_open_1	ap1	0.220	0.087	2.530	0.011	0.049	0.390
P1	csi_overall_2	~	self_open_1	p12	0.066	0.086	0.762	0.446	-0.103	0.235
P1	csi_overall_1	~	partner_open_1	p21	0.261	0.085	3.061	0.002	0.094	0.428
P1	csi_overall_1	~	open_diff_p1	d1	-0.089	0.083	-1.071	0.284	-0.251	0.074
P1	csi_overall_2	~	open_diff_p1	d2	-0.083	0.084	-0.989	0.323	-0.247	0.081
P1	csi_overall_1	~	self_intel_1	a1	0.184	0.082	2.242	0.025	0.023	0.344
P1	csi_overall_2	~	partner_intel_1	ap1	0.159	0.083	1.926	0.054	-0.003	0.321
P1	csi_overall_2	~	self_intel_1	p12	0.120	0.085	1.421	0.155	-0.046	0.286
P1	csi_overall_1	~	partner_intel_1	p21	0.235	0.079	2.966	0.003	0.080	0.391
P1	csi_overall_1	~	intel_diff_p1	d1	-0.005	0.083	-0.063	0.950	-0.168	0.157
P1	csi_overall_2	~	intel_diff_p1	d2	0.088	0.085	1.043	0.297	-0.078	0.255
P1	csi_overall_1	~	self_opena_1	a1	-0.066	0.088	-0.749	0.454	-0.239	0.107
P1	csi_overall_2	~	partner_opena_1	ap1	0.234	0.097	2.425	0.015	0.045	0.424
P1	csi_overall_2	~	self_opena_1	p12	0.035	0.088	0.393	0.695	-0.138	0.208
P1	csi_overall_1	~	partner_opena_1	p21	0.227	0.097	2.344	0.019	0.037	0.417
P1	csi_overall_1	~	opena_diff_p1	d1	-0.034	0.094	-0.358	0.720	-0.218	0.151
P1	csi_overall_2	~	opena_diff_p1	d2	0.008	0.094	0.080	0.936	-0.177	0.192

h5_results$perceived_p2 %>%
  knitr::kable(
    caption = "Standardized solutions for APIM models with P2-perceived similarity"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Standardized solutions for APIM models with P2-perceived similarity

perception	lhs	op	rhs	label	est.std	se	z	pvalue	ci.lower	ci.upper
P2	csi_overall_2	~	self_pa_2	a2	0.215	0.068	3.175	0.002	0.082	0.348
P2	csi_overall_1	~	partner_pa_2	ap2	-0.023	0.071	-0.321	0.748	-0.162	0.116
P2	csi_overall_1	~	self_pa_2	p21	0.145	0.059	2.441	0.015	0.029	0.261
P2	csi_overall_2	~	partner_pa_2	p21	0.134	0.056	2.412	0.016	0.025	0.243
P2	csi_overall_1	~	pa_diff_p2	d1	0.127	0.085	1.488	0.137	-0.040	0.294
P2	csi_overall_2	~	pa_diff_p2	d2	-0.056	0.083	-0.667	0.504	-0.219	0.108
P2	csi_overall_2	~	self_na_2	a2	-0.242	0.067	-3.609	0.000	-0.374	-0.111
P2	csi_overall_1	~	partner_na_2	ap2	-0.028	0.073	-0.381	0.703	-0.170	0.115
P2	csi_overall_1	~	self_na_2	p21	-0.246	0.054	-4.552	0.000	-0.353	-0.140
P2	csi_overall_2	~	partner_na_2	p21	-0.264	0.057	-4.627	0.000	-0.376	-0.152
P2	csi_overall_1	~	na_diff_p2	d1	0.102	0.086	1.187	0.235	-0.066	0.270
P2	csi_overall_2	~	na_diff_p2	d2	0.113	0.082	1.379	0.168	-0.047	0.272
P2	csi_overall_2	~	self_agree_2	a2	0.205	0.063	3.247	0.001	0.081	0.329
P2	csi_overall_1	~	partner_agree_2	ap2	0.098	0.071	1.379	0.168	-0.041	0.236
P2	csi_overall_1	~	self_agree_2	p21	0.294	0.047	6.201	0.000	0.201	0.387
P2	csi_overall_2	~	partner_agree_2	p21	0.355	0.057	6.216	0.000	0.243	0.466
P2	csi_overall_1	~	agree_diff_p2	d1	0.019	0.081	0.229	0.819	-0.141	0.178
P2	csi_overall_2	~	agree_diff_p2	d2	-0.178	0.076	-2.342	0.019	-0.326	-0.029
P2	csi_overall_2	~	self_compa_2	a2	0.231	0.069	3.352	0.001	0.096	0.366
P2	csi_overall_1	~	partner_compa_2	ap2	0.079	0.073	1.093	0.275	-0.063	0.222
P2	csi_overall_1	~	self_compa_2	p21	0.259	0.056	4.617	0.000	0.149	0.368
P2	csi_overall_2	~	partner_compa_2	p21	0.254	0.057	4.420	0.000	0.141	0.366
P2	csi_overall_1	~	compa_diff_p2	d1	0.070	0.088	0.793	0.428	-0.102	0.242
P2	csi_overall_2	~	compa_diff_p2	d2	-0.162	0.083	-1.962	0.050	-0.324	0.000
P2	csi_overall_2	~	self_polit_2	a2	0.076	0.071	1.073	0.283	-0.063	0.216
P2	csi_overall_1	~	partner_polit_2	ap2	0.106	0.072	1.478	0.139	-0.035	0.247
P2	csi_overall_1	~	self_polit_2	p21	0.241	0.051	4.767	0.000	0.142	0.340
P2	csi_overall_2	~	partner_polit_2	p21	0.283	0.059	4.838	0.000	0.168	0.398
P2	csi_overall_1	~	polit_diff_p2	d1	-0.078	0.082	-0.948	0.343	-0.238	0.083
P2	csi_overall_2	~	polit_diff_p2	d2	-0.151	0.081	-1.861	0.063	-0.311	0.008
P2	csi_overall_2	~	self_consci_2	a2	0.108	0.082	1.311	0.190	-0.053	0.268
P2	csi_overall_1	~	partner_consci_2	ap2	0.157	0.078	2.004	0.045	0.003	0.311
P2	csi_overall_1	~	self_consci_2	p21	0.139	0.061	2.295	0.022	0.020	0.258
P2	csi_overall_2	~	partner_consci_2	p21	0.143	0.064	2.220	0.026	0.017	0.269
P2	csi_overall_1	~	consci_diff_p2	d1	0.077	0.085	0.900	0.368	-0.090	0.244
P2	csi_overall_2	~	consci_diff_p2	d2	-0.035	0.088	-0.399	0.690	-0.207	0.137
P2	csi_overall_2	~	self_indus_2	a2	0.194	0.083	2.324	0.020	0.030	0.357
P2	csi_overall_1	~	partner_indus_2	ap2	0.098	0.078	1.255	0.210	-0.055	0.251
P2	csi_overall_1	~	self_indus_2	p21	0.176	0.063	2.799	0.005	0.053	0.299
P2	csi_overall_2	~	partner_indus_2	p21	0.173	0.064	2.703	0.007	0.048	0.299
P2	csi_overall_1	~	indus_diff_p2	d1	0.087	0.088	0.988	0.323	-0.086	0.260
P2	csi_overall_2	~	indus_diff_p2	d2	0.009	0.092	0.093	0.926	-0.171	0.188
P2	csi_overall_2	~	self_order_2	a2	-0.012	0.079	-0.150	0.880	-0.167	0.144
P2	csi_overall_1	~	partner_order_2	ap2	0.173	0.077	2.236	0.025	0.021	0.325
P2	csi_overall_1	~	self_order_2	p21	0.063	0.060	1.051	0.293	-0.054	0.179
P2	csi_overall_2	~	partner_order_2	p21	0.065	0.063	1.037	0.300	-0.058	0.188
P2	csi_overall_1	~	order_diff_p2	d1	0.118	0.083	1.411	0.158	-0.046	0.281
P2	csi_overall_2	~	order_diff_p2	d2	-0.094	0.085	-1.104	0.270	-0.260	0.073
P2	csi_overall_2	~	self_extra_2	a2	0.369	0.061	6.065	0.000	0.250	0.488
P2	csi_overall_1	~	partner_extra_2	ap2	0.029	0.072	0.408	0.683	-0.112	0.170
P2	csi_overall_1	~	self_extra_2	p21	0.247	0.058	4.251	0.000	0.133	0.361
P2	csi_overall_2	~	partner_extra_2	p21	0.217	0.053	4.067	0.000	0.112	0.321
P2	csi_overall_1	~	extra_diff_p2	d1	0.065	0.085	0.767	0.443	-0.101	0.231
P2	csi_overall_2	~	extra_diff_p2	d2	-0.042	0.078	-0.543	0.587	-0.195	0.110
P2	csi_overall_2	~	self_assert_2	a2	0.270	0.068	3.995	0.000	0.137	0.402
P2	csi_overall_1	~	partner_assert_2	ap2	-0.041	0.073	-0.553	0.580	-0.185	0.103
P2	csi_overall_1	~	self_assert_2	p21	0.133	0.061	2.184	0.029	0.014	0.253
P2	csi_overall_2	~	partner_assert_2	p21	0.117	0.054	2.154	0.031	0.011	0.224
P2	csi_overall_1	~	assert_diff_p2	d1	0.004	0.085	0.045	0.964	-0.162	0.169
P2	csi_overall_2	~	assert_diff_p2	d2	-0.087	0.080	-1.085	0.278	-0.245	0.070
P2	csi_overall_2	~	self_enthu_2	a2	0.393	0.062	6.323	0.000	0.271	0.515
P2	csi_overall_1	~	partner_enthu_2	ap2	0.108	0.073	1.488	0.137	-0.034	0.251
P2	csi_overall_1	~	self_enthu_2	p21	0.267	0.057	4.717	0.000	0.156	0.378
P2	csi_overall_2	~	partner_enthu_2	p21	0.247	0.054	4.567	0.000	0.141	0.353
P2	csi_overall_1	~	enthu_diff_p2	d1	0.109	0.085	1.280	0.200	-0.058	0.276
P2	csi_overall_2	~	enthu_diff_p2	d2	0.013	0.079	0.168	0.866	-0.142	0.168
P2	csi_overall_2	~	self_neuro_2	a2	-0.274	0.067	-4.082	0.000	-0.405	-0.142
P2	csi_overall_1	~	partner_neuro_2	ap2	-0.139	0.075	-1.851	0.064	-0.287	0.008
P2	csi_overall_1	~	self_neuro_2	p21	-0.300	0.052	-5.770	0.000	-0.403	-0.198
P2	csi_overall_2	~	partner_neuro_2	p21	-0.327	0.059	-5.588	0.000	-0.442	-0.212
P2	csi_overall_1	~	neuro_diff_p2	d1	-0.003	0.081	-0.038	0.970	-0.161	0.155
P2	csi_overall_2	~	neuro_diff_p2	d2	0.061	0.077	0.789	0.430	-0.091	0.213
P2	csi_overall_2	~	self_volat_2	a2	-0.276	0.069	-3.977	0.000	-0.412	-0.140
P2	csi_overall_1	~	partner_volat_2	ap2	-0.191	0.076	-2.519	0.012	-0.340	-0.042
P2	csi_overall_1	~	self_volat_2	p21	-0.339	0.053	-6.384	0.000	-0.443	-0.235
P2	csi_overall_2	~	partner_volat_2	p21	-0.365	0.059	-6.153	0.000	-0.481	-0.249
P2	csi_overall_1	~	volat_diff_p2	d1	-0.059	0.080	-0.745	0.457	-0.216	0.097
P2	csi_overall_2	~	volat_diff_p2	d2	0.101	0.077	1.307	0.191	-0.050	0.253
P2	csi_overall_2	~	self_withd_2	a2	-0.237	0.069	-3.417	0.001	-0.373	-0.101
P2	csi_overall_1	~	partner_withd_2	ap2	-0.052	0.075	-0.692	0.489	-0.200	0.095
P2	csi_overall_1	~	self_withd_2	p21	-0.207	0.056	-3.724	0.000	-0.316	-0.098
P2	csi_overall_2	~	partner_withd_2	p21	-0.214	0.059	-3.653	0.000	-0.328	-0.099
P2	csi_overall_1	~	withd_diff_p2	d1	0.060	0.083	0.722	0.471	-0.103	0.222
P2	csi_overall_2	~	withd_diff_p2	d2	-0.032	0.081	-0.398	0.690	-0.191	0.126
P2	csi_overall_2	~	self_open_2	a2	0.223	0.069	3.224	0.001	0.087	0.358
P2	csi_overall_1	~	partner_open_2	ap2	-0.022	0.070	-0.307	0.759	-0.159	0.116
P2	csi_overall_1	~	self_open_2	p21	0.161	0.062	2.607	0.009	0.040	0.282
P2	csi_overall_2	~	partner_open_2	p21	0.132	0.050	2.619	0.009	0.033	0.231
P2	csi_overall_1	~	open_diff_p2	d1	0.184	0.085	2.170	0.030	0.018	0.351
P2	csi_overall_2	~	open_diff_p2	d2	-0.061	0.084	-0.722	0.470	-0.225	0.104
P2	csi_overall_2	~	self_intel_2	a2	0.315	0.077	4.100	0.000	0.164	0.466
P2	csi_overall_1	~	partner_intel_2	ap2	0.021	0.076	0.273	0.785	-0.128	0.169
P2	csi_overall_1	~	self_intel_2	p21	0.174	0.065	2.665	0.008	0.046	0.303
P2	csi_overall_2	~	partner_intel_2	p21	0.149	0.056	2.660	0.008	0.039	0.258
P2	csi_overall_1	~	intel_diff_p2	d1	0.147	0.090	1.643	0.100	-0.028	0.323
P2	csi_overall_2	~	intel_diff_p2	d2	0.109	0.089	1.222	0.222	-0.066	0.284
P2	csi_overall_2	~	self_opena_2	a2	0.142	0.071	1.982	0.047	0.002	0.282
P2	csi_overall_1	~	partner_opena_2	ap2	-0.031	0.075	-0.420	0.674	-0.178	0.115
P2	csi_overall_1	~	self_opena_2	p21	0.089	0.062	1.426	0.154	-0.033	0.211
P2	csi_overall_2	~	partner_opena_2	p21	0.078	0.054	1.437	0.151	-0.028	0.183
P2	csi_overall_1	~	opena_diff_p2	d1	0.050	0.089	0.562	0.574	-0.124	0.224
P2	csi_overall_2	~	opena_diff_p2	d2	-0.109	0.086	-1.270	0.204	-0.277	0.059

Hypothesis 7: Dynamic Benefits

Dynamically, partner similarity in momentary variability on personality and affective states is associated with enhanced relationship quality.

# run function 
# same as h4 but without the profile argument
h7_results <- h4_function(
  var_list = apply(expand.grid(
    c("mpa", "mna", 
      "tipi_agree", "tipi_consci", "tipi_extra", "tipi_neuro", "tipi_open"),
    c("_sd", "_rvi", "_mssd", "_rmssd")), 1, paste, collapse = ""),
  quality_var = "csi_overall",
  .data = baseline
)

### z-correlation as predictors ###
fluctuation_tab <- data.frame()

# sort data
fluctuation_dat <- baseline %>% arrange(Couple_ID)

#   extract vector of each partner score on relationship quality variables
p1_qual <- fluctuation_dat[fluctuation_dat$P_num == 1, "csi_overall", drop = T]
p2_qual <- fluctuation_dat[fluctuation_dat$P_num == 2, "csi_overall", drop = T]

for(ivar in paste0(
  c("mpa", "mna", 
    "tipi_agree", "tipi_consci", "tipi_extra", "tipi_neuro", "tipi_open"),
  "_z")) {
  
  # extract vector of fisher's z-transformed within-couple correlation
  #   same for both participants, use P_num = 1 to avoid duplication
  z_var <- fluctuation_dat[fluctuation_dat$P_num == 1, ivar, drop = T]
  
  # fit simple regression models
  mod_1 <- summary(lm(p1_qual ~ z_var))$coefficients
  mod_2 <- summary(lm(p2_qual ~ z_var))$coefficients
  
  # extract coefficients
  z_1   <- round(mod_1["z_var", 
                       c("Estimate", "t value", "Pr(>|t|)")], 3)
  z_2   <- round(mod_2["z_var", 
                       c("Estimate", "t value", "Pr(>|t|)")], 3)
  
  # store values in difference_tab
  fluctuation_tab <- rbind(
    fluctuation_tab,
    
    # first partner
    c(ivar, "P1", z_1),
    
    # second partner
    c(ivar, "P2", z_2)
    )
} # END for ivar LOOP

#   rename results table
names(fluctuation_tab) <- c("personality", "participant",
                            "z_est", "z_tval", "z_pval")

# view results
h7_results$interaction_tab %>%
  knitr::kable(
    caption = "Multiple Regression Models with Interaction Effects"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Multiple Regression Models with Interaction Effects

personality	participant	actor_est	actor_tval	actor_pval	partner_est	partner_tval	partner_pval	int_est	int_tval	int_pval
mpa_sd	P1	-0.416	-0.72	0.473	0.275	0.468	0.641	0.012	0.353	0.725
mpa_sd	P2	0.002	0.003	0.998	-0.216	-0.343	0.732	-0.001	-0.02	0.984
mna_sd	P1	-1.122	-2.315	0.022	-0.449	-0.843	0.401	0.056	1.729	0.086
mna_sd	P2	-0.397	-0.682	0.497	-0.436	-0.822	0.412	0.01	0.282	0.778
tipi_agree_sd	P1	-15.679	-1.037	0.302	-9.888	-0.655	0.513	9.914	0.684	0.495
tipi_agree_sd	P2	2.982	0.184	0.854	9.638	0.593	0.554	-11.451	-0.736	0.463
tipi_consci_sd	P1	2.093	0.143	0.886	5.011	0.323	0.747	-1.188	-0.084	0.933
tipi_consci_sd	P2	21.394	1.298	0.196	25.455	1.639	0.104	-26.932	-1.8	0.074
tipi_extra_sd	P1	9.276	0.614	0.541	9.418	0.517	0.606	-1.356	-0.1	0.92
tipi_extra_sd	P2	3.718	0.188	0.851	6.527	0.399	0.691	1.946	0.133	0.895
tipi_neuro_sd	P1	-11.311	-0.779	0.437	-8.162	-0.54	0.59	10.826	0.847	0.399
tipi_neuro_sd	P2	1.274	0.078	0.938	-0.125	-0.008	0.994	-2.768	-0.2	0.842
tipi_open_sd	P1	0.314	0.022	0.982	4.13	0.238	0.812	-2.23	-0.132	0.895
tipi_open_sd	P2	11.323	0.606	0.545	11.007	0.721	0.472	-11.938	-0.657	0.513
mpa_rvi	P1	-9.109	-0.34	0.734	-9.246	-0.354	0.724	40.456	0.673	0.502
mpa_rvi	P2	45.757	1.626	0.106	19.318	0.67	0.504	-85.134	-1.316	0.191
mna_rvi	P1	-29.682	-1.114	0.267	-27.34	-1.005	0.317	81.98	1.291	0.199
mna_rvi	P2	19.755	0.678	0.499	5.534	0.194	0.847	-44.485	-0.654	0.514
tipi_agree_rvi	P1	23.157	0.644	0.521	12.69	0.336	0.737	-29.891	-0.343	0.732
tipi_agree_rvi	P2	61.391	1.523	0.13	50.125	1.305	0.194	-129.817	-1.395	0.165
tipi_consci_rvi	P1	4.529	0.1	0.921	-21.646	-0.546	0.586	50.565	0.449	0.654
tipi_consci_rvi	P2	75.018	1.758	0.081	74.87	1.53	0.128	-206.932	-1.707	0.09
tipi_extra_rvi	P1	38.836	0.978	0.33	42.798	0.9	0.37	-43.43	-0.438	0.662
tipi_extra_rvi	P2	70.818	1.372	0.172	54.486	1.264	0.208	-104.066	-0.967	0.335
tipi_neuro_rvi	P1	3.96	0.101	0.92	-18.53	-0.486	0.627	29.634	0.335	0.738
tipi_neuro_rvi	P2	22.776	0.552	0.582	14.646	0.345	0.73	-49.939	-0.522	0.603
tipi_open_rvi	P1	34.341	1.073	0.285	36.677	0.914	0.363	-76.28	-0.735	0.464
tipi_open_rvi	P2	91.264	2.142	0.034	74.749	2.2	0.029	-235.358	-2.137	0.034
mpa_mssd	P1	-0.002	-0.359	0.721	0.006	0.893	0.374	0	0.011	0.991
mpa_mssd	P2	-0.003	-0.356	0.722	-0.006	-0.915	0.362	0	0.562	0.575
mna_mssd	P1	-0.016	-2.617	0.01	-0.002	-0.368	0.714	0	1.798	0.074
mna_mssd	P2	-0.008	-1.131	0.26	-0.011	-1.74	0.084	0	0.9	0.37
tipi_agree_mssd	P1	-1.444	-0.785	0.434	-0.5	-0.229	0.819	0.191	0.257	0.797
tipi_agree_mssd	P2	-0.169	-0.073	0.942	0.033	0.017	0.987	-0.663	-0.838	0.403
tipi_consci_mssd	P1	1.588	0.934	0.352	1.615	0.878	0.382	-0.363	-0.813	0.418
tipi_consci_mssd	P2	0.112	0.057	0.955	1.542	0.848	0.398	-0.629	-1.315	0.191
tipi_extra_mssd	P1	1.853	1.248	0.214	3.306	1.471	0.144	-0.381	-0.738	0.462
tipi_extra_mssd	P2	1.004	0.411	0.682	1.277	0.791	0.43	0.013	0.024	0.981
tipi_neuro_mssd	P1	-0.402	-0.27	0.787	2.021	1	0.319	-0.07	-0.152	0.879
tipi_neuro_mssd	P2	0.634	0.292	0.771	-0.234	-0.146	0.884	-0.412	-0.829	0.408
tipi_open_mssd	P1	0.853	0.523	0.602	2.959	1.191	0.236	-1.033	-1.058	0.292
tipi_open_mssd	P2	1.339	0.498	0.619	1.237	0.701	0.484	-0.697	-0.661	0.51
mpa_rmssd	P1	14.176	0.432	0.666	-13.973	-0.742	0.459	32.487	0.354	0.724
mpa_rmssd	P2	29.763	1.469	0.144	4.228	0.12	0.905	-142.415	-1.441	0.152
mna_rmssd	P1	-3.996	-0.146	0.884	-14.137	-0.776	0.439	49.535	0.572	0.568
mna_rmssd	P2	22.06	1.14	0.256	8.956	0.309	0.758	-114.795	-1.249	0.214
tipi_agree_rmssd	P1	48.164	1.889	0.061	31.537	1.231	0.22	-143.325	-1.235	0.219
tipi_agree_rmssd	P2	62.325	2.282	0.024	26.215	0.964	0.337	-249.89	-2.019	0.045
tipi_consci_rmssd	P1	72.061	1.735	0.085	-11.112	-0.623	0.534	-122.752	-0.606	0.546
tipi_consci_rmssd	P2	23.008	1.172	0.243	32.4	0.709	0.48	-216.216	-0.97	0.334
tipi_extra_rmssd	P1	61.328	2.081	0.039	89.557	2.143	0.034	-267.526	-1.636	0.104
tipi_extra_rmssd	P2	79.026	1.726	0.087	44.848	1.388	0.167	-272.476	-1.52	0.131
tipi_neuro_rmssd	P1	26.148	0.923	0.358	-15.002	-0.687	0.493	-12.801	-0.106	0.916
tipi_neuro_rmssd	P2	23.662	1.006	0.316	14.248	0.466	0.642	-182.339	-1.398	0.164
tipi_open_rmssd	P1	35.257	1.882	0.062	76.258	1.704	0.091	-302.887	-1.463	0.146
tipi_open_rmssd	P2	72.909	1.52	0.131	37.683	1.877	0.063	-469.749	-2.118	0.036

h7_results$difference_tab %>%
  knitr::kable(
    caption = "Simple Regression Model with Difference Score"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Simple Regression Model with Difference Score

personality	participant	diff_est	diff_tval	diff_pval
mpa_sd	P1	-0.109	-0.328	0.744
mpa_sd	P2	-0.22	-0.616	0.539
mna_sd	P1	-0.692	-2.245	0.026
mna_sd	P2	-0.329	-0.977	0.33
tipi_agree_sd	P1	-3.063	-0.462	0.645
tipi_agree_sd	P2	1.413	0.197	0.844
tipi_consci_sd	P1	-1.022	-0.165	0.869
tipi_consci_sd	P2	4.23	0.634	0.527
tipi_extra_sd	P1	-5.942	-1.066	0.288
tipi_extra_sd	P2	-4.863	-0.808	0.42
tipi_neuro_sd	P1	-8.564	-1.374	0.172
tipi_neuro_sd	P2	-4.115	-0.609	0.543
tipi_open_sd	P1	1.616	0.268	0.789
tipi_open_sd	P2	2.924	0.45	0.653
mpa_rvi	P1	-12.663	-0.87	0.386
mpa_rvi	P2	-11.977	-0.758	0.45
mna_rvi	P1	-17.814	-1.375	0.172
mna_rvi	P2	-0.821	-0.059	0.953
tipi_agree_rvi	P1	12.188	0.745	0.458
tipi_agree_rvi	P2	14.185	0.808	0.42
tipi_consci_rvi	P1	-9.539	-0.637	0.525
tipi_consci_rvi	P2	21.487	1.339	0.183
tipi_extra_rvi	P1	-12.478	-0.782	0.435
tipi_extra_rvi	P2	-6.156	-0.357	0.721
tipi_neuro_rvi	P1	-13.599	-0.878	0.382
tipi_neuro_rvi	P2	-5.885	-0.352	0.726
tipi_open_rvi	P1	13.576	0.928	0.355
tipi_open_rvi	P2	11.063	0.704	0.483
mpa_mssd	P1	0.001	0.301	0.764
mpa_mssd	P2	-0.004	-0.858	0.393
mna_mssd	P1	-0.008	-1.663	0.099
mna_mssd	P2	-0.008	-1.479	0.141
tipi_agree_mssd	P1	-0.365	-0.298	0.766
tipi_agree_mssd	P2	-1.071	-0.811	0.419
tipi_consci_mssd	P1	0.873	0.647	0.518
tipi_consci_mssd	P2	-0.261	-0.18	0.858
tipi_extra_mssd	P1	0.47	0.463	0.644
tipi_extra_mssd	P2	0.108	0.099	0.922
tipi_neuro_mssd	P1	-0.499	-0.418	0.677
tipi_neuro_mssd	P2	-1.432	-1.117	0.266
tipi_open_mssd	P1	0.355	0.301	0.764
tipi_open_mssd	P2	0.609	0.479	0.632
mpa_rmssd	P1	-23.421	-1.394	0.166
mpa_rmssd	P2	-0.335	-0.018	0.985
mna_rmssd	P1	-17.441	-1.081	0.282
mna_rmssd	P2	7.949	0.46	0.647
tipi_agree_rmssd	P1	34.64	1.746	0.083
tipi_agree_rmssd	P2	28.988	1.356	0.177
tipi_consci_rmssd	P1	-10.283	-0.756	0.451
tipi_consci_rmssd	P2	11.632	0.794	0.429
tipi_extra_rmssd	P1	24.446	1.019	0.31
tipi_extra_rmssd	P2	3.679	0.142	0.887
tipi_neuro_rmssd	P1	-7.464	-0.46	0.646
tipi_neuro_rmssd	P2	-0.783	-0.045	0.964
tipi_open_rmssd	P1	27.474	1.721	0.088
tipi_open_rmssd	P2	13.081	0.757	0.45

fluctuation_tab %>%
  knitr::kable(
    caption = "Simple Regression Model with Fluctuations"
  ) %>%
  kable_styling("striped") %>%
  scroll_box(height = "300px")

Simple Regression Model with Fluctuations

personality	participant	z_est	z_tval	z_pval
mpa_z	P1	13.925	2.61	0.01
mpa_z	P2	13.909	2.393	0.018
mna_z	P1	0.686	0.137	0.892
mna_z	P2	5.49	1.028	0.306
tipi_agree_z	P1	-7.518	-1.395	0.165
tipi_agree_z	P2	0.389	0.067	0.947
tipi_consci_z	P1	4.025	0.574	0.567
tipi_consci_z	P2	7.746	1.033	0.304
tipi_extra_z	P1	20.604	2.87	0.005
tipi_extra_z	P2	14.564	1.851	0.066
tipi_neuro_z	P1	6.869	1.067	0.288
tipi_neuro_z	P2	13.643	1.987	0.049
tipi_open_z	P1	3.657	0.599	0.55
tipi_open_z	P2	1.968	0.3	0.764

Simple slope analysis for significant interaction effects

# openness rvi - P2 Satisfaction
ss_wide_df <- baseline %>%
  pivot_wider(id_cols = "Couple_ID",
              names_from = "P_num",
              values_from = c("tipi_open_rvi", "csi_overall"))
nice_slopes(
  data = ss_wide_df,
  response = "csi_overall_2",
  predictor = "tipi_open_rvi_2",
  moderator = "tipi_open_rvi_1") %>%
  knitr::kable(
    caption = "Simple slope analysis"
  ) %>% 
  kableExtra::kable_styling()

Simple slope analysis

Dependent Variable	Predictor (+/-1 SD)	df	B	t	p	sr2	CI_lower	CI_upper
csi_overall_2	tipi_open_rvi_2 (LOW-tipi_open_rvi_1)	133	0.249	1.887	0.061	0.026	0	0.078
csi_overall_2	tipi_open_rvi_2 (MEAN-tipi_open_rvi_1)	133	0.075	0.846	0.399	0.005	0	0.029
csi_overall_2	tipi_open_rvi_2 (HIGH-tipi_open_rvi_1)	133	-0.100	-0.931	0.354	0.006	0	0.032

Exploratory Model: Coupled Damped Oscillator

# make sure all time points are present
template <- expand.grid(Couple_ID = unique(esm$Couple_ID),
                        P_num = 1:2,
                        time_idx = 0:34)

# left join the template with esm so all missing rows are present
rties_dat <- left_join(template, esm, by = c("Couple_ID", "P_num", "time_idx"))

# drop duplicates if any
rties_dat <- rties_dat[!duplicated(rties_dat), ]

# prep data according to rties instructions
rties_dat$couple <- rties_dat$Couple_ID - 1000
rties_dat$person <- ifelse(rties_dat$P_num == 1, 
                           rties_dat$couple, rties_dat$couple + 500)

# remove duplicated data at same time_idx
rties_dat <- rties_dat[!duplicated(rties_dat[c("Couple_ID", "P_num", "time_idx")])| 
                        duplicated(rties_dat[c("Couple_ID", "P_num", "time_idx")], 
                                   fromLast = TRUE), ]

# fix time_idx to start at 1
rties_dat$time_idx <- rties_dat$time_idx + 1

## use rties::dataPrep()
#prepped_dat <- dataPrep(basedata = rties_dat, 
#                        dyadId = "couple", personId = "person",
#                        obs_name = "tipi_neuro", dist_name = "P_num",
#                        time_name = "time_idx")
#
#
### test taus, embeds, and delta
## not successful
#taus <- c(4,5)
#embeds <- c(3,4,5)
#delta <- 1
#
#derivs <- estDerivs(prepData=prepped_dat, taus=taus, embeds=embeds, delta=delta, 
#                    idConvention=500)

Dynamic Dyadic Plots

Here are dynamic dyadic plots of both partners from a random selection of couples who provided at least 28 shared datapoints (80% completion rate).

# random selection of couples with at least 28 shared datapoints
set.seed(202403)
id_list <- baseline %>% filter(tipi_open_n >= 28) %>%
  pull(Couple_ID) %>%
  unique()
id_random <- sample(id_list, size = 9)

# update time to hour unit
old_time <- 1:35
new_time <- c(seq(from = 0, to = 12, by = 3),
              seq(from = 0, to = 12, by = 3)+24*1,
              seq(from = 0, to = 12, by = 3)+24*2,
              seq(from = 0, to = 12, by = 3)+24*3,
              seq(from = 0, to = 12, by = 3)+24*4,
              seq(from = 0, to = 12, by = 3)+24*5,
              seq(from = 0, to = 12, by = 3)+24*6)
rties_dat$new_time <- NA
for (i in seq_along(old_time)) {
  # Update the corresponding value in time with the value from new_time
  rties_dat$new_time[rties_dat$time_idx == old_time[i]] <- new_time[i]
}
rties_dat$time_idx <- rties_dat$new_time

# plot random couples
plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="mna", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Negative Affect", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="mpa", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Positive Affect", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="tipi_agree", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Agreeableness", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="tipi_consci", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Conscientiousness", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="tipi_extra", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Extraversion", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="tipi_neuro", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Neuroticism", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="tipi_open", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Openness", printPlots = F)

plot_timeseries(basedata=rties_dat %>% filter(Couple_ID %in% id_random), 
                dyad="couple", 
                obs_name="csi_short", 
                dist_name="P_num", 
                time_name="time_idx", 
                dist0name="P2", dist1name= "P1", 
                plot_obs_name="Relationship Satisfaction", printPlots = F)

To further highlight the effects of co-fluctuations in positive affect, here are dynamic dyadic plots of both partners from a random selection of couples who provided at least 28 shared datapoints (80% completion rate), separated by high and low (top and bottom 20%) relationship satisfaction.

set.seed(202403)
baseline_wide <- baseline %>%
  pivot_wider(id_cols = "Couple_ID",
              names_from = "P_num",
              values_from = c("tipi_open_n", "csi_overall"))
thresholds <- quantile(baseline$csi_overall, probs = c(0.20, 0.80))
high_list <- baseline_wide %>% 
  filter(
    tipi_open_n_1 >= 28,
    csi_overall_1 >= thresholds[2],
    csi_overall_2 >= thresholds[2]) %>%
  pull(Couple_ID) %>%
  unique()
high_random <- sample(high_list, size = 9)

low_list <- baseline_wide %>% 
  filter(
    tipi_open_n_1 >= 28,
    csi_overall_1 <= thresholds[1],
    csi_overall_2 <= thresholds[1]) %>%
  pull(Couple_ID) %>%
  unique()
low_random <- sample(low_list, size = 9)

plot_timeseries(
  basedata=rties_dat %>% filter(Couple_ID %in% high_random), 
  dyad="couple", 
  obs_name="mpa", 
  dist_name="P_num", 
  time_name="time_idx", 
  dist0name="P2", dist1name= "P1", 
  plot_obs_name="Positive Affect", printPlots = F,
  .title = "Co-Fluctuations on Positive Affect for High Satisfaction Couples")

plot_timeseries(
  basedata=rties_dat %>% filter(Couple_ID %in% low_random), 
  dyad="couple", 
  obs_name="mpa", 
  dist_name="P_num", 
  time_name="time_idx", 
  dist0name="P2", dist1name= "P1", 
  plot_obs_name="Positive Affect", printPlots = F,
  .title = "Co-Fluctuations on Positive Affect for Low Satisfaction Couples")

To further highlight the effects of co-fluctuations in positive affect, here are dynamic dyadic plots of both partners from a random selection of couples who provided at least 28 shared datapoints (80% completion rate), separated by high and low (top and bottom 20%) relationship duration

set.seed(202403)
baseline_wide <- baseline %>%
  pivot_wider(id_cols = "Couple_ID",
              names_from = "P_num",
              values_from = c("tipi_open_n", "duration"))
thresholds <- quantile(baseline$duration, probs = c(0.20, 0.80))
high_list <- baseline_wide %>% 
  filter(
    tipi_open_n_1 >= 28,
    duration_1 >= thresholds[2],
    duration_2 >= thresholds[2]) %>%
  pull(Couple_ID) %>%
  unique()
high_random <- sample(high_list, size = 9)

low_list <- baseline_wide %>% 
  filter(
    tipi_open_n_1 >= 28,
    duration_1 <= thresholds[1],
    duration_2 <= thresholds[1]) %>%
  pull(Couple_ID) %>%
  unique()
low_random <- sample(low_list, size = 9)

plot_timeseries(
  basedata=rties_dat %>% filter(Couple_ID %in% high_random), 
  dyad="couple", 
  obs_name="mpa", 
  dist_name="P_num", 
  time_name="time_idx", 
  dist0name="P2", dist1name= "P1", 
  plot_obs_name="Positive Affect", printPlots = F,
  .title = "Co-Fluctuations on Positive Affect for Long-Term Couples")

plot_timeseries(
  basedata=rties_dat %>% filter(Couple_ID %in% low_random), 
  dyad="couple", 
  obs_name="mpa", 
  dist_name="P_num", 
  time_name="time_idx", 
  dist0name="P2", dist1name= "P1", 
  plot_obs_name="Positive Affect", printPlots = F,
  .title = "Co-Fluctuations on Positive Affect for Short-Term Couples")