We aim to replicate the results of Study 3 from Czarna et al. (2022). Study 3 involves dyadic data where they tested a moderated Actor-Partner Interdependence Model (APIM).
The authors have provided access to their datasets and scripts via the Open Science Framework (OSF). You can access these materials at the following link: https://osf.io/8j4td/?view_only=dee38744aef04831b98484076616d98d.
We will use their provided codes as a basis to reconstruct and validate their core findings.
Reference: Czarna, A. Z., Śmieja, M., Wider, M., Dufner, M., & Sedikides, C. (2022). Narcissism and partner-enhancement at different relationship stages. Journal of Research in Personality, 98, 104212. https://doi.org/10.1016/j.jrp.2022.104212
## [1] "Timef" "Codef" "Agef"
## [4] "Plecf" "Sexf" "Orientationf"
## [7] "Monthsf" "Selfesteemf" "NPIf"
## [10] "Kto1f" "Kto2f" "Kto3f"
## [13] "Kto4f" "Kto5f" "Kto6f"
## [16] "Kto7f" "Kto8f" "Kto9f"
## [19] "Kto10f" "Kto11f" "Kto12f"
## [22] "Kto13f" "Kto14f" "Kto15f"
## [25] "Partner_enhancement_f" "Timem" "Codem"
## [28] "Agem" "Plecm" "Sexm"
## [31] "Orientationm" "Monthsm" "Selfesteemm"
## [34] "NPIm" "Kto1m" "Kto2m"
## [37] "Kto3m" "Kto4m" "Kto5m"
## [40] "Kto6m" "Kto7m" "Kto8m"
## [43] "Kto9m" "Kto10m" "Kto11m"
## [46] "Kto12m" "Kto13m" "Kto14m"
## [49] "Kto15m" "Partner_enhancement_m"## Rows: 84
## Columns: 50
## $ Timef <chr> "43620,91287", "43627,74419", "43638,70676", "43…
## $ Codef <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
## $ Agef <int> 23, 23, 24, 24, 24, 24, 27, 22, 22, 22, 24, 25, …
## $ Plecf <chr> "Kobieta", "Kobieta", "Kobieta", "Kobieta", "Kob…
## $ Sexf <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Orientationf <chr> "hetero", "hetero", "hetero", "hetero", "hetero"…
## $ Monthsf <int> 12, 24, 54, 72, 78, 24, 78, 54, 12, 48, 24, 84, …
## $ Selfesteemf <int> 4, 6, 1, 5, 3, 5, 3, 6, 4, 3, 6, 3, 3, 5, 6, 5, …
## $ NPIf <chr> "3", "4,153846154", "3,076923077", "5", "2,69230…
## $ Kto1f <int> 4, 5, 9, 5, 2, 7, 4, 4, 6, 5, 6, 5, 2, 6, 4, 5, …
## $ Kto2f <int> 8, 7, 9, 5, 7, 4, 9, 8, 7, 8, 7, 5, 6, 8, 5, 5, …
## $ Kto3f <int> 5, 5, 7, 6, 5, 8, 5, 8, 7, 7, 8, 5, 2, 5, 6, 2, …
## $ Kto4f <int> 5, 5, 9, 7, 3, 5, 3, 5, 7, 6, 8, 5, 2, 6, 5, 4, …
## $ Kto5f <int> 7, 5, 5, 7, 7, 8, 3, 2, 7, 5, 9, 8, 2, 6, 7, 2, …
## $ Kto6f <int> 7, 5, 1, 4, 1, 2, 5, 5, 5, 5, 3, 4, 7, 5, 5, 9, …
## $ Kto7f <int> 5, 5, 5, 4, 4, 5, 5, 7, 6, 6, 7, 2, 7, 7, 5, 5, …
## $ Kto8f <int> 5, 6, 1, 5, 6, 2, 7, 6, 8, 5, 3, 5, 5, 3, 6, 8, …
## $ Kto9f <int> 5, 6, 1, 5, 2, 3, 8, 6, 5, 5, 5, 3, 5, 5, 4, 4, …
## $ Kto10f <int> 5, 5, 1, 5, 7, 2, 8, 5, 8, 8, 5, 5, 4, 5, 5, 7, …
## $ Kto11f <int> 2, 8, 9, 5, 9, 6, 1, 5, 4, 6, 8, 5, 3, 6, 9, 6, …
## $ Kto12f <int> 2, 6, 1, 9, 5, 3, 9, 7, 5, 5, 8, 2, 4, 4, 7, 1, …
## $ Kto13f <int> 4, 6, 1, 1, 9, 6, 1, 5, 3, 6, 8, 5, 7, 7, 7, 1, …
## $ Kto14f <int> 2, 6, 5, 7, 1, 5, 1, 7, 5, 5, 8, 4, 7, 6, 5, 1, …
## $ Kto15f <int> 3, 5, 1, 7, 5, 2, 1, 5, 5, 3, 8, 3, 5, 4, 5, 4, …
## $ Partner_enhancement_f <chr> "4,6", "5,666666667", "4,333333333", "5,46666666…
## $ Timem <chr> "43620,90824", "43627,47656", "43638,72503", "43…
## $ Codem <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
## $ Agem <int> 24, 26, 27, 25, 23, 26, 27, 24, 22, 23, 26, 24, …
## $ Plecm <chr> "M\xc7\xf9czyzna", "M\xc7\xf9czyzna", "M\xc7\xf9…
## $ Sexm <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ Orientationm <chr> "hetero", "hetero", "hetero", "hetero", "hetero"…
## $ Monthsm <int> 12, 24, 54, 72, 78, 24, 78, 54, 12, 48, 24, 84, …
## $ Selfesteemm <int> 5, 4, 5, 4, 4, 6, 5, 4, 6, 1, 5, 5, 5, 5, 6, 6, …
## $ NPIm <chr> "4,384615385", "3,076923077", "2,846153846", "3,…
## $ Kto1m <int> 5, 5, 3, 5, 5, 5, 3, 1, 6, 4, 6, 5, 4, 5, 7, 5, …
## $ Kto2m <int> 3, 3, 1, 5, 3, 4, 2, 1, 8, 1, 3, 2, 2, 2, 3, 5, …
## $ Kto3m <int> 3, 2, 3, 5, 3, 5, 3, 1, 6, 4, 4, 5, 6, 4, 5, 8, …
## $ Kto4m <int> 4, 5, 1, 5, 6, 5, 2, 1, 2, 5, 5, 3, 5, 3, 5, 5, …
## $ Kto5m <int> 3, 6, 5, 5, 6, 6, 7, 5, 2, 5, 3, 5, 6, 5, 4, 8, …
## $ Kto6m <int> 4, 5, 6, 5, 7, 6, 3, 5, 1, 5, 6, 5, 8, 3, 5, 3, …
## $ Kto7m <int> 5, 7, 3, 5, 8, 5, 5, 5, 1, 5, 5, 5, 5, 5, 5, 5, …
## $ Kto8m <int> 4, 5, 6, 5, 8, 8, 5, 5, 2, 5, 6, 5, 5, 5, 4, 4, …
## $ Kto9m <int> 1, 4, 6, 5, 8, 8, 5, 5, 2, 5, 5, 5, 7, 4, 7, 4, …
## $ Kto10m <int> 5, 7, 7, 5, 7, 9, 4, 5, 1, 3, 5, 5, 7, 2, 5, 4, …
## $ Kto11m <int> 5, 1, 1, 6, 4, 4, 7, 5, 8, 4, 3, 5, 5, 5, 1, 4, …
## $ Kto12m <int> 5, 2, 4, 5, 5, 5, 5, 3, 9, 7, 3, 4, 6, 5, 2, 6, …
## $ Kto13m <int> 5, 2, 6, 5, 4, 3, 5, 5, 9, 5, 3, 5, 4, 5, 5, 8, …
## $ Kto14m <int> 5, 5, 3, 5, 6, 6, 5, 1, 6, 5, 4, 5, 4, 5, 5, 8, …
## $ Kto15m <int> 5, 5, 6, 5, 7, 4, 7, 5, 7, 8, 4, 5, 5, 5, 3, 7, …
## $ Partner_enhancement_m <chr> "4,133333333", "4,266666667", "4,066666667", "5,…Create a standardized score based on the combined sample of males and females.
Study3$Partner_enhancement_m <- as.numeric(gsub(",", ".", Study3$Partner_enhancement_m))
Study3$Partner_enhancement_f <- as.numeric(gsub(",", ".", Study3$Partner_enhancement_f))
Study3$NPIf <- as.numeric(gsub(",", ".", Study3$NPIf))
Study3$NPIm <- as.numeric(gsub(",", ".", Study3$NPIm))
all_NPI_scores <- c(Study3$NPIf, Study3$NPIm)
mean_NPI <- mean(all_NPI_scores, na.rm = TRUE)
sd_NPI <- sd(all_NPI_scores, na.rm = TRUE)
Study3$zNPIf<- (Study3$NPIf - mean_NPI) / sd_NPI
Study3$zNPIm <- (Study3$NPIm - mean_NPI) / sd_NPI
all_months <- c(Study3$Monthsf, Study3$Monthsm)
mean_months <- mean(all_months, na.rm = TRUE)
sd_months <- sd(all_months, na.rm = TRUE)
Study3$zMonthsf<- (Study3$Monthsf - mean_months) / sd_months
#creating interactive variables
Study3$RLXNPIf <- Study3$zMonthsf*Study3$zNPIf
Study3$RLXNPIm <- Study3$zMonthsf*Study3$zNPImlibrary(lavaan)
#saturated model (fully unrestricted)
Model_unrestricted<- '
Partner_enhancement_f ~ zNPIf + zNPIm + zMonthsf + RLXNPIf + RLXNPIm
Partner_enhancement_m ~ zNPIm + zNPIf + zMonthsf + RLXNPIm +RLXNPIf
'
results_Model_unrestricted<- sem(Model_unrestricted, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model_unrestricted, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 8 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf -0.039 0.088 -0.449 0.654 -0.039 -0.044
## RLXNPIf -0.177 0.102 -1.733 0.083 -0.177 -0.170
## RLXNPIm 0.195 0.078 2.494 0.013 0.195 0.246
## Partner_enhancement_m ~
## zNPIm 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf 0.342 0.078 4.392 0.000 0.342 0.394
## RLXNPIm -0.137 0.069 -1.972 0.049 -0.137 -0.177
## RLXNPIf -0.151 0.091 -1.662 0.097 -0.151 -0.148
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf ~~
## zNPIm 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.060
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.072
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.081
## RLXNPIm 0.007 0.120 0.060 0.952 0.007
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 0.066
## RLXNPIm 0.183 0.123 1.489 0.136 0.183
## RLXNPIf ~~
## RLXNPIm 0.124 0.104 1.197 0.231 0.124
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## -0.070
## -0.064
##
## -0.093
## -0.096
## 0.007
##
## 0.078
## 0.165
##
## 0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.260 0.087 60.720 0.000 5.260 5.956
## .Prtnr_nhncmnt_ 4.456 0.077 57.955 0.000 4.456 5.160
## zNPIf 0.038 0.110 0.342 0.732 0.038 0.037
## zNPIm -0.038 0.108 -0.350 0.727 -0.038 -0.038
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.136 -0.161
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf 0.717 0.111 6.481 0.000 0.717 1.000
## RLXNPIm 1.242 0.192 6.481 0.000 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356## df
## 0Narcissism as the moderator:
#Testing simple slopes (for low and high narcissism)
#Low narcissism - men
Study3$zNPIm_low <- (Study3$zNPIm + 1)
Study3$RLXNPIm_low <- Study3$zMonthsf*Study3$zNPIm_low
Model3_lowm<- '
Partner_enhancement_f ~ zNPIf+ zNPIm_low + zMonthsf + a2*RLXNPIf + RLXNPIm_low
Partner_enhancement_m ~ zNPIm_low + zNPIf + zMonthsf + RLXNPIm_low + RLXNPIf
'
results_Model3_lowm<- sem(Model3_lowm, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model3_lowm, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 14 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm_low -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf -0.234 0.126 -1.859 0.063 -0.234 -0.265
## RLXNPIf (a2) -0.177 0.102 -1.733 0.083 -0.177 -0.170
## RLXNPIm_l 0.195 0.078 2.494 0.013 0.195 0.356
## Partner_enhancement_m ~
## zNPIm_low 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf 0.479 0.112 4.277 0.000 0.479 0.552
## RLXNPIm_l -0.137 0.069 -1.972 0.049 -0.137 -0.256
## RLXNPIf -0.151 0.091 -1.662 0.097 -0.151 -0.148
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf ~~
## zNPIm_low 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.060
## RLXNPIm_low -0.208 0.179 -1.165 0.244 -0.208
## zNPIm_low ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.081
## RLXNPIm_low -0.084 0.174 -0.485 0.627 -0.084
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 0.066
## RLXNPIm_low 1.177 0.217 5.413 0.000 1.177
## RLXNPIf ~~
## RLXNPIm_low 0.190 0.150 1.262 0.207 0.190
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## -0.070
## -0.128
##
## -0.093
## -0.096
## -0.053
##
## 0.078
## 0.732
##
## 0.139
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.581 0.121 46.274 0.000 5.581 6.321
## .Prtnr_nhncmnt_ 4.078 0.107 38.090 0.000 4.078 4.722
## zNPIf 0.038 0.110 0.342 0.732 0.038 0.037
## zNPIm_low 0.962 0.108 8.949 0.000 0.962 0.976
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.136 -0.161
## RLXNPIm_low -0.092 0.176 -0.521 0.602 -0.092 -0.057
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm_low 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf 0.717 0.111 6.481 0.000 0.717 1.000
## RLXNPIm_low 2.602 0.401 6.481 0.000 2.602 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356#High narcissism - men
Study3$zNPIm_high <- (Study3$zNPIm - 1)
Study3$RLXNPIm_high <- Study3$zMonthsf*Study3$zNPIm_high
Model3_highm<- '
Partner_enhancement_f ~ zNPIf + zNPIm_high + zMonthsf + RLXNPIf + RLXNPIm_high
Partner_enhancement_m ~ zNPIm_high + zNPIf + zMonthsf + RLXNPIm_high + RLXNPIf
'
results_Model3_highm<- sem(Model3_highm, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model3_highm, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm_high -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf 0.156 0.108 1.440 0.150 0.156 0.176
## RLXNPIf -0.177 0.102 -1.733 0.083 -0.177 -0.170
## RLXNPIm_high 0.195 0.078 2.494 0.013 0.195 0.302
## Partner_enhancement_m ~
## zNPIm_high 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf 0.205 0.096 2.133 0.033 0.205 0.236
## RLXNPIm_high -0.137 0.069 -1.972 0.049 -0.137 -0.217
## RLXNPIf -0.151 0.091 -1.662 0.097 -0.151 -0.148
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf ~~
## zNPIm_high 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.060
## RLXNPIm_high 0.064 0.150 0.426 0.670 0.064
## zNPIm_high ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.081
## RLXNPIm_high 0.099 0.147 0.670 0.503 0.099
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 0.066
## RLXNPIm_high -0.811 0.173 -4.686 0.000 -0.811
## RLXNPIf ~~
## RLXNPIm_high 0.059 0.127 0.464 0.643 0.059
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## -0.070
## 0.047
##
## -0.093
## -0.096
## 0.073
##
## 0.078
## -0.595
##
## 0.051
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 4.938 0.128 38.705 0.000 4.938 5.592
## .Prtnr_nhncmnt_ 4.834 0.113 42.688 0.000 4.834 5.597
## zNPIf 0.038 0.110 0.342 0.732 0.038 0.037
## zNPIm_high -1.038 0.108 -9.649 0.000 -1.038 -1.053
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.136 -0.161
## RLXNPIm_high -0.091 0.149 -0.612 0.541 -0.091 -0.067
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm_high 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf 0.717 0.111 6.481 0.000 0.717 1.000
## RLXNPIm_high 1.870 0.289 6.481 0.000 1.870 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356#Low narcissism - women
Study3$zNPIf_low <- (Study3$zNPIf + 1)
Study3$RLXNPIf_low <- Study3$zMonthsf*Study3$zNPIf_low
Model3_loww<- '
Partner_enhancement_f ~ zNPIf_low + zNPIm + zMonthsf + RLXNPIf_low + RLXNPIm
Partner_enhancement_m ~ zNPIm + zNPIf_low + zMonthsf + RLXNPIm + RLXNPIf_low
'
results_Model3_loww<- sem(Model3_loww, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model3_loww, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 14 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf_low 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf 0.138 0.137 1.002 0.316 0.138 0.155
## RLXNPIf_low -0.177 0.102 -1.733 0.083 -0.177 -0.272
## RLXNPIm 0.195 0.078 2.494 0.013 0.195 0.246
## Partner_enhancement_m ~
## zNPIm 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf_low -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf 0.492 0.122 4.035 0.000 0.492 0.568
## RLXNPIm -0.137 0.069 -1.972 0.049 -0.137 -0.177
## RLXNPIf_low -0.151 0.091 -1.662 0.097 -0.151 -0.237
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf_low ~~
## zNPIm 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf_low -0.196 0.151 -1.301 0.193 -0.196
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.072
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf_low -0.172 0.147 -1.169 0.242 -0.172
## RLXNPIm 0.007 0.120 0.060 0.952 0.007
## zMonthsf ~~
## RLXNPIf_low 1.060 0.188 5.650 0.000 1.060
## RLXNPIm 0.183 0.123 1.489 0.136 0.183
## RLXNPIf_low ~~
## RLXNPIm 0.307 0.168 1.824 0.068 0.307
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## -0.143
## -0.064
##
## -0.093
## -0.129
## 0.007
##
## 0.783
## 0.165
##
## 0.203
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.038 0.125 40.249 0.000 5.038 5.705
## .Prtnr_nhncmnt_ 4.593 0.111 41.341 0.000 4.593 5.318
## zNPIf_low 1.038 0.110 9.445 0.000 1.038 1.030
## zNPIm -0.038 0.108 -0.350 0.727 -0.038 -0.038
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf_low -0.136 0.148 -0.920 0.357 -0.136 -0.100
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf_low 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf_low 1.843 0.284 6.481 0.000 1.843 1.000
## RLXNPIm 1.242 0.192 6.481 0.000 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356#High narcissism - women
Study3$zNPIf_high <- (Study3$zNPIf - 1)
Study3$RLXNPIf_high <- Study3$zMonthsf*Study3$zNPIf_high
Model3_highw<- '
Partner_enhancement_f ~ zNPIf_high + zNPIm + zMonthsf + a2*RLXNPIf_high + RLXNPIm
Partner_enhancement_m ~ zNPIm + zNPIf_high + zMonthsf + RLXNPIm + RLXNPIf_high
'
results_Model3_highw<- sem(Model3_highw, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model3_highw, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf_hgh 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf -0.216 0.132 -1.643 0.100 -0.216 -0.244
## RLXNPIf_h (a2) -0.177 0.102 -1.733 0.083 -0.177 -0.252
## RLXNPIm 0.195 0.078 2.494 0.013 0.195 0.246
## Partner_enhancement_m ~
## zNPIm 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf_hgh -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf 0.191 0.117 1.635 0.102 0.191 0.220
## RLXNPIm -0.137 0.069 -1.972 0.049 -0.137 -0.177
## RLXNPIf_h -0.151 0.091 -1.662 0.097 -0.151 -0.219
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf_high ~~
## zNPIm 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf_high 0.076 0.138 0.551 0.582 0.076
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.072
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf_high 0.011 0.135 0.081 0.935 0.011
## RLXNPIm 0.007 0.120 0.060 0.952 0.007
## zMonthsf ~~
## RLXNPIf_high -0.929 0.170 -5.456 0.000 -0.929
## RLXNPIm 0.183 0.123 1.489 0.136 0.183
## RLXNPIf_high ~~
## RLXNPIm -0.059 0.153 -0.383 0.701 -0.059
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## 0.060
## -0.064
##
## -0.093
## 0.009
## 0.007
##
## -0.741
## 0.165
##
## -0.042
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.482 0.121 45.396 0.000 5.482 6.208
## .Prtnr_nhncmnt_ 4.320 0.107 40.297 0.000 4.320 5.001
## zNPIf_high -0.962 0.110 -8.760 0.000 -0.962 -0.956
## zNPIm -0.038 0.108 -0.350 0.727 -0.038 -0.038
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf_high -0.136 0.137 -0.991 0.322 -0.136 -0.108
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf_high 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf_high 1.580 0.244 6.481 0.000 1.580 1.000
## RLXNPIm 1.242 0.192 6.481 0.000 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356Alternatively, we can streamline the procedure within lavaan by defining a new parameter (: operator), which allows for direct estimation of the interaction effects without manually shifting data points.
Model_unrestricted<- '
Partner_enhancement_f ~ zNPIf + zNPIm + af*zMonthsf + iaf*RLXNPIf+ ipf*RLXNPIm
Partner_enhancement_m ~ zNPIm + zNPIf + am*zMonthsf + iam*RLXNPIm + ipm*RLXNPIf
f_low := af - iaf
f_high := af + iaf
m_low := am - iam
m_high := am + iam
'
results_Model_unrestricted<- sem(Model_unrestricted, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model_unrestricted, fit.measures=TRUE, standardized=TRUE, rsquare = TRUE)## lavaan 0.6.16 ended normally after 8 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -766.438
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1602.875
## Bayesian (BIC) 1687.954
## Sample-size adjusted Bayesian (SABIC) 1577.546
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf 0.222 0.087 2.546 0.011 0.222 0.253
## zNPIm -0.322 0.089 -3.616 0.000 -0.322 -0.359
## zMonthsf (af) -0.039 0.088 -0.449 0.654 -0.039 -0.044
## RLXNPIf (iaf) -0.177 0.102 -1.733 0.083 -0.177 -0.170
## RLXNPIm (ipf) 0.195 0.078 2.494 0.013 0.195 0.246
## Partner_enhancement_m ~
## zNPIm 0.378 0.079 4.790 0.000 0.378 0.431
## zNPIf -0.137 0.077 -1.764 0.078 -0.137 -0.159
## zMonthsf (am) 0.342 0.078 4.392 0.000 0.342 0.394
## RLXNPIm (iam) -0.137 0.069 -1.972 0.049 -0.137 -0.177
## RLXNPIf (ipm) -0.151 0.091 -1.662 0.097 -0.151 -0.148
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.184 0.062 -2.950 0.003 -0.184
## zNPIf ~~
## zNPIm 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.060
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.072
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.081
## RLXNPIm 0.007 0.120 0.060 0.952 0.007
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 0.066
## RLXNPIm 0.183 0.123 1.489 0.136 0.183
## RLXNPIf ~~
## RLXNPIm 0.124 0.104 1.197 0.231 0.124
## Std.all
##
## -0.340
##
## 0.209
## -0.136
## -0.070
## -0.064
##
## -0.093
## -0.096
## 0.007
##
## 0.078
## 0.165
##
## 0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.260 0.087 60.720 0.000 5.260 5.956
## .Prtnr_nhncmnt_ 4.456 0.077 57.955 0.000 4.456 5.160
## zNPIf 0.038 0.110 0.342 0.732 0.038 0.037
## zNPIm -0.038 0.108 -0.350 0.727 -0.038 -0.038
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.136 -0.161
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.610 0.094 6.481 0.000 0.610 0.782
## .Prtnr_nhncmnt_ 0.480 0.074 6.481 0.000 0.480 0.644
## zNPIf 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf 0.717 0.111 6.481 0.000 0.717 1.000
## RLXNPIm 1.242 0.192 6.481 0.000 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.218
## Prtnr_nhncmnt_ 0.356
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f_low 0.138 0.137 1.002 0.316 0.138 0.125
## f_high -0.216 0.132 -1.643 0.100 -0.216 -0.214
## m_low 0.479 0.112 4.277 0.000 0.479 0.571
## m_high 0.205 0.096 2.133 0.033 0.205 0.218## df
## 0** Model 1: a fully restricted model in which all path coefficients were set equal across partners
** Model 2: Saturated model (results_Model_unrestricted)
** Model 3: equal actor and interaction effects
** Model 4: equal actor effect and zero interaction effect
Model1<- '
Partner_enhancement_f ~ a1*zNPIf + a2*zNPIm + a3*zMonthsf + a4*RLXNPIf + a5*RLXNPIm
Partner_enhancement_m ~ a1*zNPIm + a2*zNPIf + a3*zMonthsf + a4*RLXNPIm + a5*RLXNPIf
'
results_Model1<- sem(Model1, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
fitMeasures(results_Model1, "df")## df
## 5##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff
## results_Model_unrestricted 0 1602.9 1688.0 0.000
## results_Model1 5 1611.2 1684.1 18.293 18.293 0.1779 5
## Pr(>Chisq)
## results_Model_unrestricted
## results_Model1 0.002601 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model 1 fit the data significantly worse than Model 2, \(\chi^2(5) = 20.66\), \(p = .001\).
#candidate model
Model3 <- '
Partner_enhancement_f ~ a1*zNPIf + zNPIm + zMonthsf + a3*RLXNPIf + RLXNPIm
Partner_enhancement_m ~ a1*zNPIm + zNPIf + zMonthsf + a3*RLXNPIm + RLXNPIf
'
results_Model3<- sem(Model3, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
fitMeasures(results_Model3, "df")## df
## 2#comparison of model fit: candidate (partially restricted) model against saturated model
anova(results_Model_unrestricted, results_Model3)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff
## results_Model_unrestricted 0 1602.9 1688 0.0000
## results_Model3 2 1600.8 1681 1.9496 1.9496 0 2
## Pr(>Chisq)
## results_Model_unrestricted
## results_Model3 0.3773#model with interaction effects zeroed
Model4 <- '
Partner_enhancement_f ~ a1*zNPIf + zNPIm + zMonthsf + 0*RLXNPIf + RLXNPIm
Partner_enhancement_m ~ a1*zNPIm + zNPIf + zMonthsf + 0*RLXNPIm + RLXNPIf
'
results_Model4<- sem(Model4, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
#comparison of model fit: model with interaction effects zeroed against candidate model
anova(results_Model_unrestricted, results_Model4)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff
## results_Model_unrestricted 0 1602.9 1688.0 0.0000
## results_Model4 3 1604.8 1682.6 7.9764 7.9764 0.14053 3
## Pr(>Chisq)
## results_Model_unrestricted
## results_Model4 0.0465 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model 3 did not fit the data significantly worse than Model 2, \(\chi^2(2) = 1.95\), \(p = 0.377\).
## lavaan 0.6.16 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
## Number of equality constraints 2
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 1.950
## Degrees of freedom 2
## P-value (Chi-square) 0.377
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.005
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -767.412
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1600.825
## Bayesian (BIC) 1681.042
## Sample-size adjusted Bayesian (SABIC) 1576.943
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.214
## P-value H_0: RMSEA <= 0.050 0.450
## P-value H_0: RMSEA >= 0.080 0.451
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.025
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Partner_enhancement_f ~
## zNPIf (a1) 0.309 0.061 5.082 0.000 0.309 0.345
## zNPIm -0.310 0.089 -3.476 0.001 -0.310 -0.338
## zMonthsf -0.029 0.088 -0.333 0.739 -0.029 -0.032
## RLXNPIf (a3) -0.146 0.059 -2.483 0.013 -0.146 -0.137
## RLXNPIm 0.200 0.078 2.547 0.011 0.200 0.247
## Partner_enhancement_m ~
## zNPIm (a1) 0.309 0.061 5.082 0.000 0.309 0.360
## zNPIf -0.151 0.077 -1.953 0.051 -0.151 -0.179
## zMonthsf 0.336 0.078 4.331 0.000 0.336 0.396
## RLXNPIm (a3) -0.146 0.059 -2.483 0.013 -0.146 -0.192
## RLXNPIf -0.165 0.089 -1.856 0.063 -0.165 -0.165
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.187 0.063 -2.965 0.003 -0.187
## zNPIf ~~
## zNPIm 0.207 0.111 1.872 0.061 0.207
## zMonthsf -0.136 0.111 -1.231 0.218 -0.136
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.060
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.072
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.092
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.081
## RLXNPIm 0.007 0.120 0.060 0.952 0.007
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 0.066
## RLXNPIm 0.183 0.123 1.489 0.136 0.183
## RLXNPIf ~~
## RLXNPIm 0.124 0.104 1.197 0.231 0.124
## Std.all
##
## -0.342
##
## 0.209
## -0.136
## -0.070
## -0.064
##
## -0.093
## -0.096
## 0.007
##
## 0.078
## 0.165
##
## 0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 5.262 0.086 60.937 0.000 5.262 5.831
## .Prtnr_nhncmnt_ 4.451 0.077 57.774 0.000 4.451 5.256
## zNPIf 0.038 0.110 0.342 0.732 0.038 0.037
## zNPIm -0.038 0.108 -0.350 0.727 -0.038 -0.038
## zMonthsf -0.000 0.109 -0.002 0.998 -0.000 -0.000
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.136 -0.161
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Prtnr_nhncmnt_ 0.618 0.095 6.481 0.000 0.618 0.759
## .Prtnr_nhncmnt_ 0.486 0.075 6.481 0.000 0.486 0.677
## zNPIf 1.014 0.156 6.481 0.000 1.014 1.000
## zNPIm 0.971 0.150 6.481 0.000 0.971 1.000
## zMonthsf 0.994 0.153 6.481 0.000 0.994 1.000
## RLXNPIf 0.717 0.111 6.481 0.000 0.717 1.000
## RLXNPIm 1.242 0.192 6.481 0.000 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.241
## Prtnr_nhncmnt_ 0.323Model3 <- '
Partner_enhancement_f ~ a1*zNPIf + zNPIm + af*zMonthsf + a3*RLXNPIf + a4*RLXNPIm
Partner_enhancement_m ~ a1*zNPIm + zNPIf + am*zMonthsf + a3*RLXNPIm + a5*RLXNPIf
#actor
alowf := af - a3
ahighf := af + a3
alowm := am - a3
ahighm := am + a3
plowf := af - a5
phighf := af + a5
plowm := am - a4
phighm := am + a4
'
results_Model3<- sem(Model3, data=Study3, meanstructure = TRUE, fixed.x = FALSE)
summary(results_Model3, fit.measures = TRUE, standardize=TRUE, rsquare=TRUE, ci = TRUE)## lavaan 0.6.16 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
## Number of equality constraints 2
##
## Number of observations 84
##
## Model Test User Model:
##
## Test statistic 1.950
## Degrees of freedom 2
## P-value (Chi-square) 0.377
##
## Model Test Baseline Model:
##
## Test statistic 67.974
## Degrees of freedom 11
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.005
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -767.412
## Loglikelihood unrestricted model (H1) -766.438
##
## Akaike (AIC) 1600.825
## Bayesian (BIC) 1681.042
## Sample-size adjusted Bayesian (SABIC) 1576.943
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.214
## P-value H_0: RMSEA <= 0.050 0.450
## P-value H_0: RMSEA >= 0.080 0.451
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.025
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## Partner_enhancement_f ~
## zNPIf (a1) 0.309 0.061 5.082 0.000 0.190 0.428
## zNPIm -0.310 0.089 -3.476 0.001 -0.484 -0.135
## zMonthsf (af) -0.029 0.088 -0.333 0.739 -0.201 0.142
## RLXNPIf (a3) -0.146 0.059 -2.483 0.013 -0.261 -0.031
## RLXNPIm (a4) 0.200 0.078 2.547 0.011 0.046 0.353
## Partner_enhancement_m ~
## zNPIm (a1) 0.309 0.061 5.082 0.000 0.190 0.428
## zNPIf -0.151 0.077 -1.953 0.051 -0.302 0.001
## zMonthsf (am) 0.336 0.078 4.331 0.000 0.184 0.488
## RLXNPIm (a3) -0.146 0.059 -2.483 0.013 -0.261 -0.031
## RLXNPIf (a5) -0.165 0.089 -1.856 0.063 -0.339 0.009
## Std.lv Std.all
##
## 0.309 0.345
## -0.310 -0.338
## -0.029 -0.032
## -0.146 -0.137
## 0.200 0.247
##
## 0.309 0.360
## -0.151 -0.179
## 0.336 0.396
## -0.146 -0.192
## -0.165 -0.165
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .Partner_enhancement_f ~~
## .Prtnr_nhncmnt_ -0.187 0.063 -2.965 0.003 -0.311
## zNPIf ~~
## zNPIm 0.207 0.111 1.872 0.061 -0.010
## zMonthsf -0.136 0.111 -1.231 0.218 -0.353
## RLXNPIf -0.060 0.093 -0.642 0.521 -0.243
## RLXNPIm -0.072 0.123 -0.587 0.557 -0.312
## zNPIm ~~
## zMonthsf -0.092 0.108 -0.850 0.395 -0.303
## RLXNPIf -0.081 0.092 -0.880 0.379 -0.260
## RLXNPIm 0.007 0.120 0.060 0.952 -0.228
## zMonthsf ~~
## RLXNPIf 0.066 0.092 0.709 0.478 -0.116
## RLXNPIm 0.183 0.123 1.489 0.136 -0.058
## RLXNPIf ~~
## RLXNPIm 0.124 0.104 1.197 0.231 -0.079
## ci.upper Std.lv Std.all
##
## -0.063 -0.187 -0.342
##
## 0.424 0.207 0.209
## 0.081 -0.136 -0.136
## 0.123 -0.060 -0.070
## 0.168 -0.072 -0.064
##
## 0.120 -0.092 -0.093
## 0.099 -0.081 -0.096
## 0.242 0.007 0.007
##
## 0.247 0.066 0.078
## 0.424 0.183 0.165
##
## 0.328 0.124 0.132
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .Prtnr_nhncmnt_ 5.262 0.086 60.937 0.000 5.092 5.431
## .Prtnr_nhncmnt_ 4.451 0.077 57.774 0.000 4.300 4.602
## zNPIf 0.038 0.110 0.342 0.732 -0.178 0.253
## zNPIm -0.038 0.108 -0.350 0.727 -0.248 0.173
## zMonthsf -0.000 0.109 -0.002 0.998 -0.213 0.213
## RLXNPIf -0.136 0.092 -1.473 0.141 -0.317 0.045
## RLXNPIm -0.092 0.122 -0.753 0.452 -0.330 0.147
## Std.lv Std.all
## 5.262 5.831
## 4.451 5.256
## 0.038 0.037
## -0.038 -0.038
## -0.000 -0.000
## -0.136 -0.161
## -0.092 -0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .Prtnr_nhncmnt_ 0.618 0.095 6.481 0.000 0.431 0.805
## .Prtnr_nhncmnt_ 0.486 0.075 6.481 0.000 0.339 0.632
## zNPIf 1.014 0.156 6.481 0.000 0.707 1.320
## zNPIm 0.971 0.150 6.481 0.000 0.678 1.265
## zMonthsf 0.994 0.153 6.481 0.000 0.694 1.295
## RLXNPIf 0.717 0.111 6.481 0.000 0.500 0.934
## RLXNPIm 1.242 0.192 6.481 0.000 0.866 1.617
## Std.lv Std.all
## 0.618 0.759
## 0.486 0.677
## 1.014 1.000
## 0.971 1.000
## 0.994 1.000
## 0.717 1.000
## 1.242 1.000
##
## R-Square:
## Estimate
## Prtnr_nhncmnt_ 0.241
## Prtnr_nhncmnt_ 0.323
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## alowf 0.117 0.105 1.115 0.265 -0.088 0.322
## ahighf -0.175 0.106 -1.649 0.099 -0.383 0.033
## alowm 0.482 0.103 4.698 0.000 0.281 0.683
## ahighm 0.190 0.092 2.070 0.038 0.010 0.370
## plowf 0.136 0.124 1.091 0.275 -0.108 0.379
## phighf -0.194 0.125 -1.550 0.121 -0.439 0.051
## plowm 0.136 0.108 1.265 0.206 -0.075 0.348
## phighm 0.536 0.113 4.751 0.000 0.315 0.757
## Std.lv Std.all
## 0.117 0.105
## -0.175 -0.169
## 0.482 0.533
## 0.190 0.259
## 0.136 0.133
## -0.194 -0.197
## 0.136 0.149
## 0.536 0.642Similar results has been reported in Czarna et al (2022) pages, 104212 - 104213:
-Actor effect
“Relationship duration negatively predicted partner-enhancement among men low on narcissism (\(-1\) SD: \(b = -0.50\), \(SE = 0.11\), \(Z = -4.70\), \(p < .001\)). It also did so among men high on narcissism, but substantially less strongly (\(+1\) SD: \(b = -0.20\), \(SE = 0.10\), \(Z = -2.07\), \(p = .038\); Fig. 1). Importantly, the latter slope was significantly less steep than the former, \(p = .047\). However, the effects of relationship duration were not significant for women low (\(-1\) SD: \(b = -0.12\), \(SE = 0.13\), \(Z = -1.12\), \(p = .265\)) or high (\(+1\) SD: \(b = 0.18\), \(SE = 0.11\), \(Z = 1.65\), \(p = .100\)) on narcissism. Additionally, these two slopes were not significantly different. As indicated above, a formal test showed that the omnibus interaction (\(a_1\)) among men was not significantly different than among women, suggesting no three-way interaction (involving gender). In all, although women low on narcissism partner-enhanced at short and long relationship duration (just less so at long relationship duration), those high on narcissism self-enhanced at short and long relationship duration. Further, although men low on narcissism partner-enhanced less the longer the relationship was (hence the effect was detectable at short but not at long relationship duration), those high on narcissism self-enhanced both at short and long relationship duration.”
-Partner effect
“Partner narcissism positively and significantly predicted partner-enhancement at short relationship duration (2.7 months: \(b = 0.611\), \(SE = 0.149\), \(Z = 4.099\), \(p < .001\)), and positively but trendingly at long relationship duration (56.7 months: \(b = 0.192\), \(SE = 0.107\), \(Z = 1.791\), \(p = .073\), Fig. 2). Women who had a narcissistic partner engaged in partner-enhancement early on in their relationships. We will not interpret the interaction for men, as the relevant omnibus effect was not significant.”