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library(lavaan)
library(semPlot)
library(haven)
library(dplyr)library(caTools) # For Linear regression
library(car) # To check multicollinearity
library(quantmod)
library(MASS)
library(haven)data <- read_sav("/Users/mj/Library/CloudStorage/Dropbox/Chat GPT and User Adaptation/ChatGPT_cleaned.sav")
datamodel 2 or model 3 - KU (Know and Understand) items are: know wha AI and recall the definitions, how to use AI applications, can compare differences between AI concepts. - Test Chronbach’s alpha of the three items and check if it makes sense to delete the second item.
#Testing chronbach's alpha of Ng_K# Specify mediation model with MH expert, objective, accurate only
model_3<-
'
# Direct effects of IVs on DV
factcheck_A ~ c1*Lit_Pin_TK + c2*Lit_Pin_SK + c3*Lit_Ng_SE + c4*Lit_Ng_KU + c5*Lit_Ng_EC
# Direct effects of IVs on first-level mediators
MH_expert ~ a3_1*Lit_Pin_TK + a3_2*Lit_Pin_SK + a3_3*Lit_Ng_SE + a3_4*Lit_Ng_KU + a3_5*Lit_Ng_EC
MH_objective ~ a4_1*Lit_Pin_TK + a4_2*Lit_Pin_SK + a4_3*Lit_Ng_SE + a4_4*Lit_Ng_KU + a4_5*Lit_Ng_EC
MH_accurate ~ a5_1*Lit_Pin_TK + a5_2*Lit_Pin_SK + a5_3*Lit_Ng_SE + a5_4*Lit_Ng_KU + a5_5*Lit_Ng_EC
# Direct effects of first-level mediators on second-level mediators
credible ~ b1_3*MH_expert + b1_4*MH_objective + b1_5*MH_accurate
trust ~ b2_3*MH_expert + b2_4*MH_objective + b2_5*MH_accurate
# Direct effects of second-level mediators on DV
factcheck_A ~ d1*credible + d2*trust
# Indirect effects via first and second-level mediators
SE_expert_credible := a3_3*b1_3*d1
SE_obj_credible := a4_3*b1_4*d1
SE_acc_credible := a5_3*b1_5*d1
EC_expert_credible := a3_5*b1_3*d1
EC_obj_credible := a4_5*b1_3*d1
EC_acc_credible := a5_5*b1_3*d1
KU_expert_credible := a3_4*b1_3*d1
#Estimating covariance and residuals
#Estimating variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_TK
Lit_Pin_SK~~Lit_Pin_SK
Lit_Ng_SE~~Lit_Ng_SE
Lit_Ng_KU~~Lit_Ng_KU
Lit_Ng_EC~~Lit_Ng_EC
#Estimating co-variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_SK
Lit_Pin_TK~~Lit_Ng_SE
Lit_Pin_TK~~Lit_Ng_KU
Lit_Pin_TK~~Lit_Ng_EC
Lit_Pin_SK~~Lit_Ng_SE
Lit_Pin_SK~~Lit_Ng_KU
Lit_Pin_SK~~Lit_Ng_EC
Lit_Ng_SE~~Lit_Ng_KU
Lit_Ng_SE~~Lit_Ng_EC
Lit_Ng_KU~~Lit_Ng_EC
#Estimating the residual variances (e1,…) for endogenous variables (Ms and Ys)
MH_expert~~MH_expert
MH_objective~~MH_objective
MH_accurate~~MH_accurate
credible~~credible
trust~~trust
factcheck_A~~factcheck_A
#Estimating the covariances of residuals for Ms
MH_expert~~MH_objective
MH_expert~~MH_accurate
MH_objective~~MH_accurate
credible~~trust
'# Add bootstrapping to the lavaan function call
result.model_3 <- lavaan(model_3, data = data, se = "boot", bootstrap = 5000)
# Display the results with the specified options
summary(result.model_3, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)lavaan 0.6-18 ended normally after 43 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 53
Used Total
Number of observations 223 225
Model Test User Model:
Test statistic 25.538
Degrees of freedom 13
P-value (Chi-square) 0.020
Model Test Baseline Model:
Test statistic 1437.268
Degrees of freedom 55
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.991
Tucker-Lewis Index (TLI) 0.962
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3342.687
Loglikelihood unrestricted model (H1) -3329.918
Akaike (AIC) 6791.374
Bayesian (BIC) 6971.954
Sample-size adjusted Bayesian (SABIC) 6803.990
Root Mean Square Error of Approximation:
RMSEA 0.066
90 Percent confidence interval - lower 0.026
90 Percent confidence interval - upper 0.103
P-value H_0: RMSEA <= 0.050 0.220
P-value H_0: RMSEA >= 0.080 0.294
Standardized Root Mean Square Residual:
SRMR 0.036
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 5000
Number of successful bootstrap draws 5000
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factcheck_A ~
Lt_P_TK (c1) -0.125 0.107 -1.168 0.243 -0.125 -0.131
Lt_P_SK (c2) 0.257 0.096 2.669 0.008 0.257 0.265
Lt_N_SE (c3) 0.111 0.099 1.126 0.260 0.111 0.100
Lt_N_KU (c4) 0.030 0.117 0.254 0.800 0.030 0.021
Lt_N_EC (c5) 0.042 0.106 0.399 0.690 0.042 0.040
MH_expert ~
Lt_P_TK (a3_1) -0.088 0.117 -0.754 0.451 -0.088 -0.085
Lt_P_SK (a3_2) 0.016 0.113 0.140 0.888 0.016 0.015
Lt_N_SE (a3_3) 0.250 0.102 2.447 0.014 0.250 0.209
Lt_N_KU (a3_4) -0.373 0.132 -2.822 0.005 -0.373 -0.246
Lt_N_EC (a3_5) 0.438 0.106 4.112 0.000 0.438 0.390
MH_objective ~
Lt_P_TK (a4_1) -0.072 0.145 -0.496 0.620 -0.072 -0.061
Lt_P_SK (a4_2) -0.084 0.146 -0.579 0.563 -0.084 -0.070
Lt_N_SE (a4_3) 0.272 0.117 2.318 0.020 0.272 0.197
Lt_N_KU (a4_4) -0.209 0.157 -1.335 0.182 -0.209 -0.120
Lt_N_EC (a4_5) 0.429 0.127 3.381 0.001 0.429 0.331
MH_accurate ~
Lt_P_TK (a5_1) -0.162 0.126 -1.289 0.197 -0.162 -0.153
Lt_P_SK (a5_2) -0.048 0.126 -0.379 0.705 -0.048 -0.045
Lt_N_SE (a5_3) 0.265 0.108 2.465 0.014 0.265 0.216
Lt_N_KU (a5_4) -0.241 0.135 -1.789 0.074 -0.241 -0.155
Lt_N_EC (a5_5) 0.482 0.111 4.362 0.000 0.482 0.418
credible ~
MH_xprt (b1_3) 0.364 0.050 7.250 0.000 0.364 0.418
MH_bjct (b1_4) 0.066 0.042 1.559 0.119 0.066 0.087
MH_ccrt (b1_5) 0.345 0.051 6.797 0.000 0.345 0.407
trust ~
MH_xprt (b2_3) 0.304 0.056 5.452 0.000 0.304 0.386
MH_bjct (b2_4) 0.033 0.040 0.813 0.416 0.033 0.048
MH_ccrt (b2_5) 0.293 0.061 4.817 0.000 0.293 0.382
factcheck_A ~
credibl (d1) -0.403 0.138 -2.920 0.004 -0.403 -0.378
trust (d2) 0.041 0.149 0.276 0.782 0.041 0.035
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Lit_Pin_TK ~~
Lit_Pin_SK 1.468 0.140 10.498 0.000 1.468 0.820
Lit_Ng_SE 0.719 0.123 5.846 0.000 0.719 0.458
Lit_Ng_KU 0.721 0.095 7.556 0.000 0.721 0.583
Lit_Ng_EC 0.912 0.134 6.812 0.000 0.912 0.547
Lit_Pin_SK ~~
Lit_Ng_SE 0.625 0.113 5.513 0.000 0.625 0.405
Lit_Ng_KU 0.653 0.083 7.920 0.000 0.653 0.536
Lit_Ng_EC 0.782 0.121 6.468 0.000 0.782 0.477
Lit_Ng_SE ~~
Lit_Ng_KU 0.565 0.075 7.530 0.000 0.565 0.529
Lit_Ng_EC 0.816 0.132 6.181 0.000 0.816 0.567
Lit_Ng_KU ~~
Lit_Ng_EC 0.745 0.082 9.092 0.000 0.745 0.656
.MH_expert ~~
.MH_objective 0.961 0.156 6.147 0.000 0.961 0.501
.MH_accurate 0.978 0.155 6.317 0.000 0.978 0.588
.MH_objective ~~
.MH_accurate 1.089 0.138 7.911 0.000 1.089 0.561
.credible ~~
.trust 0.281 0.048 5.839 0.000 0.281 0.527
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Lit_Pin_TK 1.818 0.157 11.594 0.000 1.818 1.000
Lit_Pin_SK 1.761 0.139 12.645 0.000 1.761 1.000
Lit_Ng_SE 1.354 0.151 8.991 0.000 1.354 1.000
Lit_Ng_KU 0.843 0.071 11.918 0.000 0.843 1.000
Lit_Ng_EC 1.529 0.135 11.291 0.000 1.529 1.000
.MH_expert 1.644 0.190 8.669 0.000 1.644 0.851
.MH_objective 2.242 0.179 12.498 0.000 2.242 0.873
.MH_accurate 1.683 0.159 10.596 0.000 1.683 0.827
.credible 0.506 0.054 9.296 0.000 0.506 0.345
.trust 0.560 0.058 9.737 0.000 0.560 0.468
.factcheck_A 1.396 0.124 11.255 0.000 1.396 0.841
R-Square:
Estimate
MH_expert 0.149
MH_objective 0.127
MH_accurate 0.173
credible 0.655
trust 0.532
factcheck_A 0.159
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
SE_exprt_crdbl -0.037 0.022 -1.639 0.101 -0.037 -0.033
SE_obj_credibl -0.007 0.007 -1.023 0.306 -0.007 -0.007
SE_acc_credibl -0.037 0.022 -1.711 0.087 -0.037 -0.033
EC_exprt_crdbl -0.064 0.029 -2.205 0.027 -0.064 -0.062
EC_obj_credibl -0.063 0.029 -2.203 0.028 -0.063 -0.052
EC_acc_credibl -0.071 0.030 -2.382 0.017 -0.071 -0.066
KU_exprt_crdbl 0.055 0.026 2.075 0.038 0.055 0.039# Specify mediation model with all five MH (efficient, useful, expert, objective, and accurate)
model_2<-
'
# Direct effects of IVs on DV
factcheck_A ~ c1*Lit_Pin_TK + c2*Lit_Pin_SK + c3*Lit_Ng_SE + c4*Lit_Ng_KU + c5*Lit_Ng_EC
# Direct effects of IVs on first-level mediators
MH_efficient ~ a1_1*Lit_Pin_TK + a1_2*Lit_Pin_SK + a1_3*Lit_Ng_SE + a1_4*Lit_Ng_KU + a1_5*Lit_Ng_EC
MH_useful ~ a2_1*Lit_Pin_TK + a2_2*Lit_Pin_SK + a2_3*Lit_Ng_SE + a2_4*Lit_Ng_KU + a2_5*Lit_Ng_EC
MH_expert ~ a3_1*Lit_Pin_TK + a3_2*Lit_Pin_SK + a3_3*Lit_Ng_SE + a3_4*Lit_Ng_KU + a3_5*Lit_Ng_EC
MH_objective ~ a4_1*Lit_Pin_TK + a4_2*Lit_Pin_SK + a4_3*Lit_Ng_SE + a4_4*Lit_Ng_KU + a4_5*Lit_Ng_EC
MH_accurate ~ a5_1*Lit_Pin_TK + a5_2*Lit_Pin_SK + a5_3*Lit_Ng_SE + a5_4*Lit_Ng_KU + a5_5*Lit_Ng_EC
# Direct effects of first-level mediators on second-level mediators
credible ~ b1_1*MH_efficient + b1_2*MH_useful + b1_3*MH_expert + b1_4*MH_objective + b1_5*MH_accurate
trust ~ b2_1*MH_efficient + b2_2*MH_useful + b2_3*MH_expert + b2_4*MH_objective + b2_5*MH_accurate
# Direct effects of second-level mediators on DV
factcheck_A ~ d1*credible + d2*trust
# Indirect effects via first and second-level mediators
SE_expert_credible := a3_3*b1_3*d1
SE_obj_credible := a4_3*b1_4*d1
SE_acc_credible := a5_3*b1_5*d1
EC_expert_credible := a3_5*b1_3*d1
EC_obj_credible := a4_5*b1_3*d1
EC_acc_credible := a5_5*b1_3*d1
KU_expert_credible := a3_4*b1_3*d1
#Estimating covariance and residuals
#Estimating variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_TK
Lit_Pin_SK~~Lit_Pin_SK
Lit_Ng_SE~~Lit_Ng_SE
Lit_Ng_KU~~Lit_Ng_KU
Lit_Ng_EC~~Lit_Ng_EC
#Estimating co-variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_SK
Lit_Pin_TK~~Lit_Ng_SE
Lit_Pin_TK~~Lit_Ng_KU
Lit_Pin_TK~~Lit_Ng_EC
Lit_Pin_SK~~Lit_Ng_SE
Lit_Pin_SK~~Lit_Ng_KU
Lit_Pin_SK~~Lit_Ng_EC
Lit_Ng_SE~~Lit_Ng_KU
Lit_Ng_SE~~Lit_Ng_EC
Lit_Ng_KU~~Lit_Ng_EC
#Estimating the residual variances (e1,…) for endogenous variables (Ms and Ys)
MH_efficient~~MH_efficient
MH_useful~~MH_useful
MH_expert~~MH_expert
MH_objective~~MH_objective
MH_accurate~~MH_accurate
credible~~credible
trust~~trust
factcheck_A~~factcheck_A
#Estimating the covariances of residuals for Ms
MH_efficient~~MH_useful
MH_efficient~~MH_expert
MH_efficient~~MH_objective
MH_efficient~~MH_accurate
MH_useful~~MH_expert
MH_useful~~MH_objective
MH_useful~~MH_accurate
MH_expert~~MH_objective
MH_expert~~MH_accurate
MH_objective~~MH_accurate
credible~~trust
'# Add bootstrapping to the lavaan function call
result.model_2 <- lavaan(model_2, data = data, se = "boot", bootstrap = 5000)
# Display the results with the specified options
summary(result.model_2, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)lavaan 0.6-18 ended normally after 58 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 76
Used Total
Number of observations 223 225
Model Test User Model:
Test statistic 20.784
Degrees of freedom 15
P-value (Chi-square) 0.144
Model Test Baseline Model:
Test statistic 1802.248
Degrees of freedom 78
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3729.368
Loglikelihood unrestricted model (H1) -3718.976
Akaike (AIC) 7610.736
Bayesian (BIC) 7869.681
Sample-size adjusted Bayesian (SABIC) 7628.827
Root Mean Square Error of Approximation:
RMSEA 0.042
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.081
P-value H_0: RMSEA <= 0.050 0.590
P-value H_0: RMSEA >= 0.080 0.055
Standardized Root Mean Square Residual:
SRMR 0.023
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 5000
Number of successful bootstrap draws 5000
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factcheck_A ~
Lt_P_TK (c1) -0.125 0.109 -1.153 0.249 -0.125 -0.131
Lt_P_SK (c2) 0.257 0.098 2.634 0.008 0.257 0.265
Lt_N_SE (c3) 0.111 0.097 1.150 0.250 0.111 0.100
Lt_N_KU (c4) 0.030 0.116 0.256 0.798 0.030 0.021
Lt_N_EC (c5) 0.042 0.109 0.387 0.699 0.042 0.041
MH_efficient ~
Lt_P_TK (a1_1) -0.065 0.079 -0.819 0.413 -0.065 -0.109
Lt_P_SK (a1_2) 0.025 0.083 0.303 0.762 0.025 0.042
Lt_N_SE (a1_3) 0.131 0.052 2.535 0.011 0.131 0.190
Lt_N_KU (a1_4) -0.013 0.106 -0.123 0.902 -0.013 -0.015
Lt_N_EC (a1_5) 0.099 0.074 1.335 0.182 0.099 0.152
MH_useful ~
Lt_P_TK (a2_1) -0.017 0.085 -0.203 0.839 -0.017 -0.024
Lt_P_SK (a2_2) -0.070 0.090 -0.778 0.437 -0.070 -0.098
Lt_N_SE (a2_3) 0.240 0.087 2.763 0.006 0.240 0.295
Lt_N_KU (a2_4) -0.100 0.105 -0.946 0.344 -0.100 -0.097
Lt_N_EC (a2_5) 0.241 0.088 2.718 0.007 0.241 0.314
MH_expert ~
Lt_P_TK (a3_1) -0.088 0.121 -0.730 0.466 -0.088 -0.085
Lt_P_SK (a3_2) 0.016 0.115 0.138 0.890 0.016 0.015
Lt_N_SE (a3_3) 0.250 0.102 2.446 0.014 0.250 0.209
Lt_N_KU (a3_4) -0.373 0.135 -2.760 0.006 -0.373 -0.246
Lt_N_EC (a3_5) 0.438 0.109 4.017 0.000 0.438 0.390
MH_objective ~
Lt_P_TK (a4_1) -0.072 0.144 -0.500 0.617 -0.072 -0.061
Lt_P_SK (a4_2) -0.084 0.143 -0.589 0.556 -0.084 -0.070
Lt_N_SE (a4_3) 0.272 0.118 2.310 0.021 0.272 0.197
Lt_N_KU (a4_4) -0.209 0.157 -1.334 0.182 -0.209 -0.120
Lt_N_EC (a4_5) 0.429 0.126 3.406 0.001 0.429 0.331
MH_accurate ~
Lt_P_TK (a5_1) -0.162 0.128 -1.260 0.208 -0.162 -0.153
Lt_P_SK (a5_2) -0.048 0.127 -0.376 0.707 -0.048 -0.045
Lt_N_SE (a5_3) 0.265 0.108 2.452 0.014 0.265 0.216
Lt_N_KU (a5_4) -0.241 0.135 -1.780 0.075 -0.241 -0.155
Lt_N_EC (a5_5) 0.482 0.109 4.419 0.000 0.482 0.418
credible ~
MH_ffcn (b1_1) 0.003 0.082 0.033 0.974 0.003 0.002
MH_usfl (b1_2) 0.177 0.084 2.106 0.035 0.177 0.139
MH_xprt (b1_3) 0.291 0.060 4.815 0.000 0.291 0.334
MH_bjct (b1_4) 0.078 0.042 1.847 0.065 0.078 0.103
MH_ccrt (b1_5) 0.311 0.052 6.043 0.000 0.311 0.367
trust ~
MH_ffcn (b2_1) 0.065 0.090 0.714 0.475 0.065 0.047
MH_usfl (b2_2) 0.347 0.078 4.461 0.000 0.347 0.300
MH_xprt (b2_3) 0.141 0.063 2.248 0.025 0.141 0.179
MH_bjct (b2_4) 0.058 0.039 1.513 0.130 0.058 0.085
MH_ccrt (b2_5) 0.223 0.058 3.832 0.000 0.223 0.290
factcheck_A ~
credibl (d1) -0.403 0.139 -2.905 0.004 -0.403 -0.379
trust (d2) 0.041 0.148 0.277 0.781 0.041 0.035
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Lit_Pin_TK ~~
Lit_Pin_SK 1.468 0.140 10.495 0.000 1.468 0.820
Lit_Ng_SE 0.719 0.124 5.787 0.000 0.719 0.458
Lit_Ng_KU 0.721 0.097 7.436 0.000 0.721 0.583
Lit_Ng_EC 0.912 0.136 6.693 0.000 0.912 0.547
Lit_Pin_SK ~~
Lit_Ng_SE 0.625 0.114 5.489 0.000 0.625 0.405
Lit_Ng_KU 0.653 0.084 7.767 0.000 0.653 0.536
Lit_Ng_EC 0.782 0.123 6.369 0.000 0.782 0.477
Lit_Ng_SE ~~
Lit_Ng_KU 0.565 0.074 7.640 0.000 0.565 0.529
Lit_Ng_EC 0.816 0.134 6.084 0.000 0.816 0.567
Lit_Ng_KU ~~
Lit_Ng_EC 0.745 0.081 9.187 0.000 0.745 0.656
.MH_efficient ~~
.MH_useful 0.421 0.062 6.831 0.000 0.421 0.639
.MH_expert 0.547 0.080 6.836 0.000 0.547 0.551
.MH_objective 0.275 0.085 3.253 0.001 0.275 0.237
.MH_accurate 0.378 0.069 5.514 0.000 0.378 0.376
.MH_useful ~~
.MH_expert 0.745 0.103 7.217 0.000 0.745 0.682
.MH_objective 0.385 0.086 4.494 0.000 0.385 0.302
.MH_accurate 0.586 0.097 6.050 0.000 0.586 0.530
.MH_expert ~~
.MH_objective 0.961 0.158 6.077 0.000 0.961 0.501
.MH_accurate 0.978 0.153 6.397 0.000 0.978 0.588
.MH_objective ~~
.MH_accurate 1.089 0.140 7.803 0.000 1.089 0.561
.credible ~~
.trust 0.254 0.045 5.638 0.000 0.254 0.510
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Lit_Pin_TK 1.818 0.159 11.410 0.000 1.818 1.000
Lit_Pin_SK 1.761 0.141 12.514 0.000 1.761 1.000
Lit_Ng_SE 1.354 0.155 8.730 0.000 1.354 1.000
Lit_Ng_KU 0.843 0.069 12.209 0.000 0.843 1.000
Lit_Ng_EC 1.529 0.139 10.991 0.000 1.529 1.000
.MH_efficient 0.599 0.088 6.793 0.000 0.599 0.931
.MH_useful 0.727 0.091 7.962 0.000 0.727 0.811
.MH_expert 1.644 0.186 8.828 0.000 1.644 0.851
.MH_objective 2.242 0.183 12.260 0.000 2.242 0.873
.MH_accurate 1.683 0.158 10.622 0.000 1.683 0.827
.credible 0.493 0.055 8.951 0.000 0.493 0.337
.trust 0.503 0.052 9.745 0.000 0.503 0.420
.factcheck_A 1.396 0.124 11.301 0.000 1.396 0.842
R-Square:
Estimate
MH_efficient 0.069
MH_useful 0.189
MH_expert 0.149
MH_objective 0.127
MH_accurate 0.173
credible 0.663
trust 0.580
factcheck_A 0.158
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
SE_exprt_crdbl -0.029 0.018 -1.646 0.100 -0.029 -0.026
SE_obj_credibl -0.009 0.008 -1.110 0.267 -0.009 -0.008
SE_acc_credibl -0.033 0.020 -1.686 0.092 -0.033 -0.030
EC_exprt_crdbl -0.051 0.025 -2.063 0.039 -0.051 -0.049
EC_obj_credibl -0.050 0.024 -2.133 0.033 -0.050 -0.042
EC_acc_credibl -0.056 0.024 -2.308 0.021 -0.056 -0.053
KU_exprt_crdbl 0.044 0.022 1.942 0.052 0.044 0.031# Specify mediation model
model <-
'
#direct effect
factcheck_A ~ Lit_Pin_TK + Lit_Pin_SK + Lit_Ng_SE + Lit_Ng_KU + Lit_Ng_EC
#indirect effects a-> b
MH_efficient ~ a1*Lit_Pin_TK + a2*Lit_Pin_SK + a3*Lit_Ng_SE + a4*Lit_Ng_KU + a5*Lit_Ng_EC
MH_useful ~ a6*Lit_Pin_TK + a7*Lit_Pin_SK + a8*Lit_Ng_SE + a9*Lit_Ng_KU + a10*Lit_Ng_EC
MH_expert ~ a11*Lit_Pin_TK + a12*Lit_Pin_SK + a13*Lit_Ng_SE + a14*Lit_Ng_KU + a15*Lit_Ng_EC
MH_objective ~ a16*Lit_Pin_TK + Lit_Pin_SK + Lit_Ng_SE + Lit_Ng_KU + Lit_Ng_EC
MH_accurate ~ a21*Lit_Pin_TK + Lit_Pin_SK + Lit_Ng_SE + Lit_Ng_KU + Lit_Ng_EC
# indirect effects a -> c
credible ~ Lit_Pin_TK + Lit_Pin_SK + Lit_Ng_SE + Lit_Ng_KU + Lit_Ng_EC
trust ~ Lit_Pin_TK + Lit_Pin_SK + Lit_Ng_SE + Lit_Ng_KU + Lit_Ng_EC
# indirect effects b -> c
credible ~ MH_efficient + MH_useful + MH_expert + MH_objective + MH_accurate
trust ~ MH_efficient + MH_useful + MH_expert + MH_objective + MH_accurate
# indirect effects c -> d
factcheck_A ~ credible + trust
#Estimating covariance and residuals
#Estimating variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_TK
Lit_Pin_SK~~Lit_Pin_SK
Lit_Ng_SE~~Lit_Ng_SE
Lit_Ng_KU~~Lit_Ng_KU
Lit_Ng_EC~~Lit_Ng_EC
#Estimating co-variances of exogenous variables (Xs)
Lit_Pin_TK~~Lit_Pin_SK
Lit_Pin_TK~~Lit_Ng_SE
Lit_Pin_TK~~Lit_Ng_KU
Lit_Pin_TK~~Lit_Ng_EC
Lit_Pin_SK~~Lit_Ng_SE
Lit_Pin_SK~~Lit_Ng_KU
Lit_Pin_SK~~Lit_Ng_EC
Lit_Ng_SE~~Lit_Ng_KU
Lit_Ng_SE~~Lit_Ng_EC
Lit_Ng_KU~~Lit_Ng_EC
#Estimating the residual variances (e1,…) for endogenous variables (Ms and Ys)
MH_efficient~~MH_efficient
MH_useful~~MH_useful
MH_expert~~MH_expert
MH_objective~~MH_objective
MH_accurate~~MH_accurate
credible~~credible
trust~~trust
factcheck_A~~factcheck_A
#Estimating the covariances of residuals for Ms
MH_efficient~~MH_useful
MH_efficient~~MH_expert
MH_efficient~~MH_objective
MH_efficient~~MH_accurate
MH_useful~~MH_expert
MH_useful~~MH_objective
MH_useful~~MH_accurate
MH_expert~~MH_objective
MH_expert~~MH_accurate
MH_objective~~MH_accurate
credible~~trust'
result.model <- lavaan(model, data=data)
result.model
summary (result.model, fit.measure=TRUE, standardized = TRUE, rsquare = TRUE, modindices = TRUE)colnames(data)library(psych)tbl_df(data)data %>%
dplyr::select(Expectancyviolation_1:Expectancyviolation_4) %>%
psych::alpha(,title = "EV")