library(lavaan)
## Warning: package 'lavaan' was built under R version 4.2.3
## This is lavaan 0.6-15
## lavaan is FREE software! Please report any bugs.
library(semTools)
## Warning: package 'semTools' was built under R version 4.2.3
##
## ###############################################################################
## This is semTools 0.5-6
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
#######
# Site1
dat1 <- read.csv("data_site_1.csv")
head(dat1)
## z1 z2 z3 z4 mean_score treat
## 1 0.56 0.12 -0.92 -2.36 -0.6500 0
## 2 -0.46 -0.51 0.12 0.69 -0.0400 0
## 3 0.20 0.93 -0.29 -0.03 0.2025 0
## 4 -0.86 -2.20 -1.06 0.46 -0.9150 0
## 5 0.90 0.95 0.35 0.27 0.6175 0
## 6 -1.42 -0.64 0.07 -1.40 -0.8475 0
# linear regression syntax
summary(lm(mean_score ~ treat, data = dat1))
##
## Call:
## lm(formula = mean_score ~ treat, data = dat1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.81507 -0.49132 0.00242 0.49118 2.35492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06008 0.07123 0.843 0.4001
## treat 0.24000 0.10074 2.382 0.0181 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7123 on 198 degrees of freedom
## Multiple R-squared: 0.02787, Adjusted R-squared: 0.02296
## F-statistic: 5.676 on 1 and 198 DF, p-value: 0.01815
# mimic syntax
model1_syntax <- "
SOB =~ z1 + z2 + z3 + z4
SOB ~ treat"
summary(cfa(
model1_syntax, data = dat1, std.lv = TRUE),
rsquare = TRUE, standardize = TRUE)
## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 5
## P-value (Chi-square) 1.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB =~
## z1 0.896 0.075 11.927 0.000 0.914 0.904
## z2 0.696 0.072 9.647 0.000 0.710 0.707
## z3 0.498 0.072 6.940 0.000 0.508 0.508
## z4 0.298 0.073 4.066 0.000 0.304 0.305
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB ~
## treat 0.402 0.156 2.580 0.010 0.394 0.197
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .z1 0.188 0.097 1.942 0.052 0.188 0.184
## .z2 0.505 0.077 6.573 0.000 0.505 0.501
## .z3 0.743 0.081 9.176 0.000 0.743 0.742
## .z4 0.900 0.092 9.806 0.000 0.900 0.907
## .SOB 1.000 0.961 0.961
##
## R-Square:
## Estimate
## z1 0.816
## z2 0.499
## z3 0.258
## z4 0.093
## SOB 0.039
##########################################################
#####Site2
dat2 <- read.csv("data_site_2.csv")
head(dat2)
## z1 z2 z3 z4 mean_score treat
## 1 -1.54 1.22 -0.04 -1.02 -0.3450 0
## 2 1.63 1.12 1.72 0.17 1.1600 0
## 3 -0.11 -0.20 -0.70 1.32 0.0775 0
## 4 -0.35 -0.54 -0.27 0.24 -0.2300 0
## 5 0.60 0.64 -0.90 -1.04 -0.1750 0
## 6 -1.51 0.64 0.10 -0.08 -0.2125 0
# linear regression syntax
summary(lm(mean_score ~ treat, data = dat2))
##
## Call:
## lm(formula = mean_score ~ treat, data = dat2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4624 -0.4027 0.0000 0.4288 1.4424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03515 0.05848 0.601 0.5485
## treat 0.13970 0.08270 1.689 0.0928 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5848 on 198 degrees of freedom
## Multiple R-squared: 0.01421, Adjusted R-squared: 0.009228
## F-statistic: 2.853 on 1 and 198 DF, p-value: 0.09275
# mimic syntax
model2_syntax <- "
SOB =~ z1 + z2 + z3 + z4
SOB ~ treat"
summary(cfa(
model2_syntax, data = dat1, std.lv = TRUE),
rsquare = TRUE, standardize = TRUE)
## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 5
## P-value (Chi-square) 1.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB =~
## z1 0.896 0.075 11.927 0.000 0.914 0.904
## z2 0.696 0.072 9.647 0.000 0.710 0.707
## z3 0.498 0.072 6.940 0.000 0.508 0.508
## z4 0.298 0.073 4.066 0.000 0.304 0.305
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB ~
## treat 0.402 0.156 2.580 0.010 0.394 0.197
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .z1 0.188 0.097 1.942 0.052 0.188 0.184
## .z2 0.505 0.077 6.573 0.000 0.505 0.501
## .z3 0.743 0.081 9.176 0.000 0.743 0.742
## .z4 0.900 0.092 9.806 0.000 0.900 0.907
## .SOB 1.000 0.961 0.961
##
## R-Square:
## Estimate
## z1 0.816
## z2 0.499
## z3 0.258
## z4 0.093
## SOB 0.039
##########################################################
#####Site3
dat3 <- read.csv("data_site_3.csv")
head(dat3)
## z1 z2 z3 z4 mean_score treat
## 1 0.73 2.47 1.16 1.67 1.5075 0
## 2 0.25 -0.17 -0.45 -0.29 -0.1650 0
## 3 0.16 0.75 1.12 0.84 0.7175 0
## 4 -0.14 -0.78 -1.27 -0.57 -0.6900 0
## 5 0.26 -0.11 -0.13 -0.26 -0.0600 0
## 6 2.64 1.89 2.09 2.54 2.2900 0
# linear regression syntax
summary(lm(mean_score ~ treat, data = dat3))
##
## Call:
## lm(formula = mean_score ~ treat, data = dat3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.40762 -0.63816 -0.05745 0.71050 2.97487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08978 0.09260 0.969 0.33348
## treat 0.36035 0.13096 2.752 0.00648 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.926 on 198 degrees of freedom
## Multiple R-squared: 0.03683, Adjusted R-squared: 0.03197
## F-statistic: 7.572 on 1 and 198 DF, p-value: 0.006479
# mimic syntax
model3_syntax <- "
SOB =~ z1 + z2 + z3 + z4
SOB ~ treat"
summary(cfa(
model3_syntax, data = dat3, std.lv = TRUE),
rsquare = TRUE, standardize = TRUE)
## lavaan 0.6.15 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 5
## P-value (Chi-square) 1.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB =~
## z1 0.896 0.055 16.338 0.000 0.914 0.903
## z2 0.895 0.055 16.337 0.000 0.913 0.903
## z3 0.896 0.055 16.341 0.000 0.914 0.903
## z4 0.895 0.055 16.343 0.000 0.913 0.903
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SOB ~
## treat 0.402 0.147 2.736 0.006 0.395 0.197
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .z1 0.188 0.025 7.394 0.000 0.188 0.184
## .z2 0.188 0.025 7.396 0.000 0.188 0.184
## .z3 0.188 0.025 7.391 0.000 0.188 0.184
## .z4 0.188 0.025 7.389 0.000 0.188 0.184
## .SOB 1.000 0.961 0.961
##
## R-Square:
## Estimate
## z1 0.816
## z2 0.816
## z3 0.816
## z4 0.816
## SOB 0.039