$all
[1] 126AI manipulation study
Data preparation
Import
Sample size
Data Quality
Manipulation and bot
Manipulation flag
FALSE TRUE
1 62 5
2 58 1Response bias check
Total approvals vars n mean sd median trimmed mad min max range skew kurtosis
X1 1 126 2457.03 1795.3 1994 2301.22 1759.85 23 8152 8129 0.73 -0.25
se
X1 159.94
Bot flag
FALSE TRUE
124 2 Attention
Duration
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.550 2.921 3.967 5.049 5.583 45.017 
Outliers defined as 3 std. deviations below or above the meanOutliers on completion time
FALSE TRUE
125 1 On scales
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.5149 1.0597 1.1645 1.2190 1.3092 2.2344 Flagged outliers based on scales
FALSE TRUE
123 3 Removing bad participants
Exclude participants
$all
[1] 11| cond.AI_flag | outliers_completion | bot_flag | outliers_scales | n |
|---|---|---|---|---|
| TRUE | FALSE | FALSE | FALSE | 5 |
| TRUE | FALSE | TRUE | FALSE | 1 |
| FALSE | FALSE | FALSE | TRUE | 3 |
| FALSE | FALSE | TRUE | FALSE | 1 |
| FALSE | TRUE | FALSE | FALSE | 1 |
Descriptive on good participants
Conditions
Group statistics
# A tibble: 2 × 6
cond.AI n mean_EEF sd_EEF mean_EEC sd_EEC
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 AI 56 5.64 0.989 4.88 1.23
2 control 59 5.82 0.641 4.93 1.11
Ease and feedback
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 115 4.36 0.62 4.5 4.42 0.74 2 5 3 -0.94 0.86 0.06Scales
Descriptive stats on scales
all good
126 115 Variable vars n mean sd median trimmed mad min max range
IM1 IM1 1 115 5.443478 1.1934860 5 5.569892 1.4826 1 7 6
IM2 IM2 2 115 5.852174 1.2157120 6 6.043011 1.4826 1 7 6
IM3 IM3 3 115 5.643478 1.2296862 6 5.827957 1.4826 1 7 6
EEF1 EEF1 4 115 5.591304 0.9070451 6 5.612903 1.4826 2 7 5
EEF2 EEF2 5 115 5.773913 1.0350089 6 5.870968 1.4826 2 7 5
EEF3 EEF3 6 115 5.843478 0.9513730 6 5.924731 1.4826 3 7 4
EEC1 EEC1 7 115 4.686957 1.2591087 5 4.731183 1.4826 1 7 6
EEC2 EEC2 8 115 5.000000 1.3311385 5 5.075269 1.4826 2 7 5
EEC3 EEC3 9 115 5.034783 1.2837065 5 5.150538 1.4826 1 7 6
ADT1 ADT1 10 115 5.495652 1.5007245 6 5.720430 1.4826 1 7 6
ADT2 ADT2 11 115 5.452174 1.5231747 6 5.666667 1.4826 1 7 6
ADT3 ADT3 12 115 5.391304 1.5766701 6 5.602151 1.4826 1 7 6
skew kurtosis se
IM1 -1.0489429 1.751720098 0.11129314
IM2 -1.6643802 3.423150713 0.11336573
IM3 -1.3534400 2.160446783 0.11466883
EEF1 -0.6543198 1.573326278 0.08458239
EEF2 -0.7202855 0.600751369 0.09651507
EEF3 -0.5968928 -0.009295716 0.08871599
EEC1 -0.3430226 -0.308180058 0.11741249
EEC2 -0.5972380 -0.280302443 0.12412931
EEC3 -0.8287281 0.261966603 0.11970625
ADT1 -1.2111939 0.937801485 0.13994328
ADT2 -1.1058594 0.611552905 0.14203677
ADT3 -1.0129413 0.102156242 0.14702525
Assumptions
Non-normality test across all scales
$IM1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.85797, p-value = 4.083e-09
$IM2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.77939, p-value = 7.426e-12
$IM3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.8243, p-value = 2.18e-10
$EEF1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.85186, p-value = 2.326e-09
$EEF2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.86864, p-value = 1.133e-08
$EEF3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.86777, p-value = 1.04e-08
$EEC1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.93477, p-value = 2.842e-05
$EEC2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.90513, p-value = 5.784e-07
$EEC3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.89004, p-value = 1.035e-07
$ADT1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.83474, p-value = 5.185e-10
$ADT2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.84743, p-value = 1.561e-09
$ADT3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.84495, p-value = 1.254e-09Non-normality test on EEF composite
Shapiro-Wilk normality test
data: data_filtered$EEF_composite
W = 0.94903, p-value = 0.0002562Non-normality test on EEC composite
Shapiro-Wilk normality test
data: data_filtered$EEC_composite
W = 0.94529, p-value = 0.0001403Non-normality test on DV per condition
EEF composite
# A tibble: 2 × 3
cond.AI n shapiro_p
<chr> <int> <dbl>
1 AI 56 0.00525
2 control 59 0.00482EEC composite
# A tibble: 2 × 3
cond.AI n shapiro_p
<chr> <int> <dbl>
1 AI 56 0.0000132
2 control 59 0.321 Non-normality test on IM composite
Shapiro-Wilk normality test
data: data_filtered$IM_composite
W = 0.85765, p-value = 3.962e-09Non-normality test on IM per condition
# A tibble: 2 × 3
cond.AI n shapiro_p
<chr> <int> <dbl>
1 AI 56 0.000000366
2 control 59 0.0000415 Factor analyses
KMO
Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = efa_data_good)
Overall MSA = 0.84
MSA for each item =
IM1 IM2 IM3 EEF1 EEF2 EEF3 EEC1 EEC2 EEC3 ADT1 ADT2 ADT3
0.88 0.84 0.89 0.84 0.84 0.90 0.86 0.83 0.85 0.77 0.81 0.83 
# A tibble: 8 × 6
cond.AI Measure Mean Median SD N
<chr> <chr> <dbl> <dbl> <dbl> <int>
1 AI ADT_composite 5.32 6 1.63 56
2 AI EEC_composite 4.88 5.33 1.23 56
3 AI EEF_composite 5.64 5.83 0.989 56
4 AI IM_composite 5.58 5.67 1.17 56
5 control ADT_composite 5.57 6 1.33 59
6 control EEC_composite 4.93 5 1.11 59
7 control EEF_composite 5.82 5.67 0.641 59
8 control IM_composite 5.71 6 1.13 59EFA
Parallel analysis suggests that the number of factors = 4 and the number of components = NA Threshold=0.40
Loadings:
MR2 MR1 MR4 MR3
IM1 NA 0.831 NA NA
IM2 NA 0.867 NA NA
IM3 NA 0.831 NA NA
EEF1 NA NA NA 0.840
EEF2 NA NA NA 0.720
EEF3 NA NA NA 0.595
EEC1 NA NA 0.763 NA
EEC2 NA NA 0.745 NA
EEC3 NA NA 0.787 NA
ADT1 0.956 NA NA NA
ADT2 0.922 NA NA NA
ADT3 0.890 NA NA NA
MR2 MR1 MR4 MR3
SS loadings NA NA NA NA
Proportion Var NA NA NA NA
Cumulative Var NA NA NA NAFactor Analysis using method = minres
Call: fa(r = cor(efa_data_good, use = "pairwise.complete.obs"), nfactors = 4,
rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
MR2 MR1 MR4 MR3 h2 u2 com
IM1 0.16 0.83 0.30 0.21 0.85 0.149 1.5
IM2 0.11 0.87 0.23 0.19 0.85 0.147 1.3
IM3 0.13 0.83 0.27 0.22 0.83 0.170 1.4
EEF1 0.15 0.19 0.14 0.84 0.78 0.220 1.2
EEF2 0.17 0.14 0.18 0.72 0.60 0.402 1.3
EEF3 0.14 0.29 0.30 0.59 0.55 0.455 2.1
EEC1 0.12 0.30 0.76 0.07 0.69 0.310 1.4
EEC2 0.17 0.29 0.74 0.29 0.75 0.248 1.7
EEC3 0.11 0.21 0.79 0.32 0.78 0.222 1.5
ADT1 0.96 0.09 0.12 0.11 0.95 0.051 1.1
ADT2 0.92 0.15 0.10 0.17 0.91 0.088 1.1
ADT3 0.89 0.14 0.15 0.18 0.87 0.133 1.2
MR2 MR1 MR4 MR3
SS loadings 2.74 2.53 2.16 1.98
Proportion Var 0.23 0.21 0.18 0.16
Cumulative Var 0.23 0.44 0.62 0.78
Proportion Explained 0.29 0.27 0.23 0.21
Cumulative Proportion 0.29 0.56 0.79 1.00
Mean item complexity = 1.4
Test of the hypothesis that 4 factors are sufficient.
df null model = 66 with the objective function = 10.79
df of the model are 24 and the objective function was 0.3
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR2 MR1 MR4 MR3
Correlation of (regression) scores with factors 0.98 0.95 0.91 0.90
Multiple R square of scores with factors 0.97 0.90 0.83 0.81
Minimum correlation of possible factor scores 0.93 0.80 0.66 0.63
CFA
lavaan 0.6-19 ended normally after 44 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 68.574 60.496
Degrees of freedom 48 48
P-value (Chi-square) 0.027 0.106
Scaling correction factor 1.134
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1240.604 1042.507
Degrees of freedom 66 66
P-value 0.000 0.000
Scaling correction factor 1.190
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.982 0.987
Tucker-Lewis Index (TLI) 0.976 0.982
Robust Comparative Fit Index (CFI) 0.988
Robust Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1655.229 -1655.229
Scaling correction factor 1.399
for the MLR correction
Loglikelihood unrestricted model (H1) -1620.942 -1620.942
Scaling correction factor 1.235
for the MLR correction
Akaike (AIC) 3370.457 3370.457
Bayesian (BIC) 3452.805 3452.805
Sample-size adjusted Bayesian (SABIC) 3357.981 3357.981
Root Mean Square Error of Approximation:
RMSEA 0.061 0.048
90 Percent confidence interval - lower 0.021 0.000
90 Percent confidence interval - upper 0.092 0.079
P-value H_0: RMSEA <= 0.050 0.276 0.521
P-value H_0: RMSEA >= 0.080 0.169 0.047
Robust RMSEA 0.051
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.087
P-value H_0: Robust RMSEA <= 0.050 0.464
P-value H_0: Robust RMSEA >= 0.080 0.096
Standardized Root Mean Square Residual:
SRMR 0.046 0.046
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
EEC =~
EEC1 1.000 1.000 1.000
EEC2 1.196 0.107 11.193 0.000 0.987 1.406
EEC3 1.098 0.099 11.031 0.000 0.903 1.293
EEF =~
EEF1 1.000 1.000 1.000
EEF2 1.059 0.153 6.908 0.000 0.759 1.360
EEF3 0.927 0.156 5.959 0.000 0.622 1.231
ADT =~
ADT1 1.000 1.000 1.000
ADT2 1.001 0.041 24.537 0.000 0.921 1.081
ADT3 1.011 0.049 20.766 0.000 0.915 1.106
IM =~
IM1 1.000 1.000 1.000
IM2 1.005 0.061 16.507 0.000 0.886 1.124
IM3 1.011 0.060 16.914 0.000 0.894 1.129
Std.lv Std.all
0.991 0.791
1.186 0.895
1.088 0.851
0.757 0.839
0.802 0.779
0.702 0.741
1.447 0.968
1.448 0.955
1.463 0.932
1.102 0.927
1.107 0.915
1.114 0.910
Covariances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
EEC ~~
EEF 0.448 0.094 4.784 0.000 0.264 0.631
ADT 0.510 0.169 3.014 0.003 0.178 0.842
IM 0.692 0.185 3.745 0.000 0.330 1.054
EEF ~~
ADT 0.426 0.121 3.531 0.000 0.189 0.662
IM 0.449 0.099 4.521 0.000 0.255 0.644
ADT ~~
IM 0.523 0.185 2.826 0.005 0.160 0.886
Std.lv Std.all
0.596 0.596
0.356 0.356
0.633 0.633
0.388 0.388
0.538 0.538
0.328 0.328
Variances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
.EEC1 0.589 0.100 5.905 0.000 0.393 0.784
.EEC2 0.350 0.101 3.462 0.001 0.152 0.548
.EEC3 0.450 0.109 4.107 0.000 0.235 0.664
.EEF1 0.242 0.082 2.959 0.003 0.082 0.402
.EEF2 0.418 0.092 4.544 0.000 0.238 0.598
.EEF3 0.405 0.104 3.880 0.000 0.200 0.609
.ADT1 0.139 0.060 2.304 0.021 0.021 0.256
.ADT2 0.203 0.055 3.690 0.000 0.095 0.311
.ADT3 0.324 0.084 3.866 0.000 0.160 0.489
.IM1 0.198 0.045 4.417 0.000 0.110 0.285
.IM2 0.239 0.059 4.082 0.000 0.124 0.354
.IM3 0.257 0.075 3.431 0.001 0.110 0.404
EEC 0.983 0.215 4.576 0.000 0.562 1.404
EEF 0.574 0.155 3.712 0.000 0.271 0.877
ADT 2.094 0.364 5.745 0.000 1.380 2.808
IM 1.214 0.262 4.644 0.000 0.702 1.727
Std.lv Std.all
0.589 0.375
0.350 0.199
0.450 0.275
0.242 0.297
0.418 0.394
0.405 0.451
0.139 0.062
0.203 0.088
0.324 0.132
0.198 0.140
0.239 0.163
0.257 0.171
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
R-Square:
Estimate
EEC1 0.625
EEC2 0.801
EEC3 0.725
EEF1 0.703
EEF2 0.606
EEF3 0.549
ADT1 0.938
ADT2 0.912
ADT3 0.868
IM1 0.860
IM2 0.837
IM3 0.829Cronbach Alpha EEC EEF ADT IM
0.884 0.823 0.966 0.941 Omega EEC EEF ADT IM
0.883 0.832 0.967 0.941 AVE EEC EEF ADT IM
0.720 0.616 0.905 0.842 $type
[1] "cor.bentler"
$cov
EEC1 EEC2 EEC3 EEF1 EEF2 EEF3 ADT1 ADT2 ADT3 IM1
EEC1 0.000
EEC2 -0.001 0.000
EEC3 0.017 -0.007 0.000
EEF1 -0.163 0.003 -0.007 0.000
EEF2 -0.099 -0.008 0.007 0.032 0.000
EEF3 -0.003 0.076 0.174 -0.015 -0.034 0.000
ADT1 -0.036 0.012 -0.034 -0.043 -0.034 -0.009 0.000
ADT2 -0.034 0.042 -0.041 0.014 0.027 0.023 0.001 0.000
ADT3 0.039 0.021 0.036 0.012 0.074 0.042 0.001 -0.003 0.000
IM1 0.037 0.049 -0.018 -0.015 -0.044 0.101 -0.012 0.042 0.052 0.000
IM2 0.016 -0.041 -0.057 -0.039 -0.062 0.077 -0.063 0.006 0.016 0.001
IM3 0.021 0.015 -0.022 -0.024 -0.046 0.136 -0.022 0.017 0.029 -0.006
IM2 IM3
EEC1
EEC2
EEC3
EEF1
EEF2
EEF3
ADT1
ADT2
ADT3
IM1
IM2 0.000
IM3 0.006 0.000
$cov.z
EEC1 EEC2 EEC3 EEF1 EEF2 EEF3 ADT1 ADT2 ADT3 IM1
EEC1 0.000
EEC2 -0.030 0.000
EEC3 0.320 -0.153 0.000
EEF1 -2.639 0.069 -0.126 0.000
EEF2 -1.841 -0.173 0.119 0.716 0.000
EEF3 -0.030 0.824 2.321 -0.405 -0.639 0.000
ADT1 -0.652 0.333 -0.647 -1.001 -0.607 -0.120 0.000
ADT2 -0.569 0.948 -0.700 0.362 0.480 0.290 0.030 0.000
ADT3 0.766 0.445 0.742 0.216 1.541 0.584 0.053 -0.176 0.000
IM1 0.628 1.005 -0.292 -0.231 -0.840 1.115 -0.269 1.041 0.971 0.000
IM2 0.286 -0.722 -0.909 -0.581 -0.977 0.810 -1.157 0.095 0.320 0.020
IM3 0.343 0.278 -0.330 -0.379 -0.778 1.758 -0.499 0.321 0.517 -0.112
IM2 IM3
EEC1
EEC2
EEC3
EEF1
EEF2
EEF3
ADT1
ADT2
ADT3
IM1
IM2 0.000
IM3 0.099 0.000
$summary
cov
srmr 0.046
srmr.se 0.015
srmr.exactfit.z 0.000
srmr.exactfit.pvalue 0.500
usrmr 0.000
usrmr.se 0.028
usrmr.ci.lower -0.045
usrmr.ci.upper 0.045
usrmr.closefit.h0.value 0.050
usrmr.closefit.z -1.812
usrmr.closefit.pvalue 0.965CFA with correlation matrix
lavaan 0.6-19 ended normally after 44 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 68.574 60.496
Degrees of freedom 48 48
P-value (Chi-square) 0.027 0.106
Scaling correction factor 1.134
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1240.604 1042.507
Degrees of freedom 66 66
P-value 0.000 0.000
Scaling correction factor 1.190
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.982 0.987
Tucker-Lewis Index (TLI) 0.976 0.982
Robust Comparative Fit Index (CFI) 0.988
Robust Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1655.229 -1655.229
Scaling correction factor 1.399
for the MLR correction
Loglikelihood unrestricted model (H1) -1620.942 -1620.942
Scaling correction factor 1.235
for the MLR correction
Akaike (AIC) 3370.457 3370.457
Bayesian (BIC) 3452.805 3452.805
Sample-size adjusted Bayesian (SABIC) 3357.981 3357.981
Root Mean Square Error of Approximation:
RMSEA 0.061 0.048
90 Percent confidence interval - lower 0.021 0.000
90 Percent confidence interval - upper 0.092 0.079
P-value H_0: RMSEA <= 0.050 0.276 0.521
P-value H_0: RMSEA >= 0.080 0.169 0.047
Robust RMSEA 0.051
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.087
P-value H_0: Robust RMSEA <= 0.050 0.464
P-value H_0: Robust RMSEA >= 0.080 0.096
Standardized Root Mean Square Residual:
SRMR 0.046 0.046
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.196 0.107 11.193 0.000 1.186 0.895
EEC3 1.098 0.099 11.031 0.000 1.088 0.851
EEF =~
EEF1 1.000 0.757 0.839
EEF2 1.059 0.153 6.908 0.000 0.802 0.779
EEF3 0.927 0.156 5.959 0.000 0.702 0.741
ADT =~
ADT1 1.000 1.447 0.968
ADT2 1.001 0.041 24.537 0.000 1.448 0.955
ADT3 1.011 0.049 20.766 0.000 1.463 0.932
IM =~
IM1 1.000 1.102 0.927
IM2 1.005 0.061 16.507 0.000 1.107 0.915
IM3 1.011 0.060 16.914 0.000 1.114 0.910
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC ~~
EEF 0.448 0.094 4.784 0.000 0.596 0.596
ADT 0.510 0.169 3.014 0.003 0.356 0.356
IM 0.692 0.185 3.745 0.000 0.633 0.633
EEF ~~
ADT 0.426 0.121 3.531 0.000 0.388 0.388
IM 0.449 0.099 4.521 0.000 0.538 0.538
ADT ~~
IM 0.523 0.185 2.826 0.005 0.328 0.328
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.100 5.905 0.000 0.589 0.375
.EEC2 0.350 0.101 3.462 0.001 0.350 0.199
.EEC3 0.450 0.109 4.107 0.000 0.450 0.275
.EEF1 0.242 0.082 2.959 0.003 0.242 0.297
.EEF2 0.418 0.092 4.544 0.000 0.418 0.394
.EEF3 0.405 0.104 3.880 0.000 0.405 0.451
.ADT1 0.139 0.060 2.304 0.021 0.139 0.062
.ADT2 0.203 0.055 3.690 0.000 0.203 0.088
.ADT3 0.324 0.084 3.866 0.000 0.324 0.132
.IM1 0.198 0.045 4.417 0.000 0.198 0.140
.IM2 0.239 0.059 4.082 0.000 0.239 0.163
.IM3 0.257 0.075 3.431 0.001 0.257 0.171
EEC 0.983 0.215 4.576 0.000 1.000 1.000
EEF 0.574 0.155 3.712 0.000 1.000 1.000
ADT 2.094 0.364 5.745 0.000 1.000 1.000
IM 1.214 0.262 4.644 0.000 1.000 1.000Latent factor correlation matrix with p-values: IM ADT EEF EEC
IM "1" "0.33 (0.001)" "0.54 (0)" "0.63 (0)"
ADT "0.33 (0.001)" "1" "0.39 (0)" "0.36 (0)"
EEF "0.54 (0)" "0.39 (0)" "1" "0.6 (0)"
EEC "0.63 (0)" "0.36 (0)" "0.6 (0)" "1" Common method bias
Harman’s test
CFA with one factor
SEM
SEM with two non-connected DVs
No mediation
lavaan 0.6-19 ended normally after 29 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 93.818 85.693
Degrees of freedom 33 33
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.095
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 762.973 680.484
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.121
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.915 0.917
Tucker-Lewis Index (TLI) 0.884 0.887
Robust Comparative Fit Index (CFI) 0.919
Robust Tucker-Lewis Index (TLI) 0.890
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1271.202 -1271.202
Scaling correction factor 1.344
for the MLR correction
Loglikelihood unrestricted model (H1) -1224.293 -1224.293
Scaling correction factor 1.192
for the MLR correction
Akaike (AIC) 2584.404 2584.404
Bayesian (BIC) 2642.048 2642.048
Sample-size adjusted Bayesian (SABIC) 2575.671 2575.671
Root Mean Square Error of Approximation:
RMSEA 0.127 0.118
90 Percent confidence interval - lower 0.097 0.089
90 Percent confidence interval - upper 0.157 0.147
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.994 0.983
Robust RMSEA 0.123
90 Percent confidence interval - lower 0.092
90 Percent confidence interval - upper 0.156
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.986
Standardized Root Mean Square Residual:
SRMR 0.261 0.261
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 0.762 0.844
EEF2 1.064 0.146 7.294 0.000 0.811 0.787
EEF3 0.903 0.143 6.333 0.000 0.688 0.727
EEC =~
EEC1 1.000 0.982 0.784
EEC2 1.195 0.107 11.125 0.000 1.174 0.886
EEC3 1.128 0.105 10.765 0.000 1.108 0.867
IM =~
IM1 1.000 1.095 0.921
IM2 1.020 0.061 16.670 0.000 1.116 0.922
IM3 1.017 0.060 17.062 0.000 1.113 0.909
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
AI_2 -0.196 0.158 -1.242 0.214 -0.257 -0.129
EEC ~
AI_2 0.112 0.167 0.671 0.502 0.114 0.057
EEF 0.775 0.191 4.063 0.000 0.601 0.601
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.234 0.077 3.059 0.002 0.234 0.287
.EEF2 0.403 0.090 4.471 0.000 0.403 0.380
.EEF3 0.423 0.109 3.893 0.000 0.423 0.472
.EEC1 0.607 0.099 6.128 0.000 0.607 0.386
.EEC2 0.379 0.121 3.138 0.002 0.379 0.216
.EEC3 0.406 0.107 3.798 0.000 0.406 0.249
.IM1 0.214 0.046 4.674 0.000 0.214 0.151
.IM2 0.219 0.061 3.582 0.000 0.219 0.149
.IM3 0.259 0.075 3.469 0.001 0.259 0.173
.EEF 0.572 0.143 3.992 0.000 0.983 0.983
.EEC 0.621 0.161 3.867 0.000 0.644 0.644
IM 1.199 0.262 4.579 0.000 1.000 1.000
R-Square:
Estimate
EEF1 0.713
EEF2 0.620
EEF3 0.528
EEC1 0.614
EEC2 0.784
EEC3 0.751
IM1 0.849
IM2 0.851
IM3 0.827
EEF 0.017
EEC 0.356With mediation
Plain - both DVs
lavaan 0.6-19 ended normally after 31 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 24
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 39.268 36.279
Degrees of freedom 30 30
P-value (Chi-square) 0.120 0.199
Scaling correction factor 1.082
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 762.973 680.484
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.121
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.987 0.990
Tucker-Lewis Index (TLI) 0.981 0.985
Robust Comparative Fit Index (CFI) 0.990
Robust Tucker-Lewis Index (TLI) 0.986
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1243.927 -1243.927
Scaling correction factor 1.329
for the MLR correction
Loglikelihood unrestricted model (H1) -1224.293 -1224.293
Scaling correction factor 1.192
for the MLR correction
Akaike (AIC) 2535.854 2535.854
Bayesian (BIC) 2601.732 2601.732
Sample-size adjusted Bayesian (SABIC) 2525.873 2525.873
Root Mean Square Error of Approximation:
RMSEA 0.052 0.043
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.093 0.085
P-value H_0: RMSEA <= 0.050 0.442 0.570
P-value H_0: RMSEA >= 0.080 0.143 0.077
Robust RMSEA 0.044
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.090
P-value H_0: Robust RMSEA <= 0.050 0.538
P-value H_0: Robust RMSEA >= 0.080 0.109
Standardized Root Mean Square Residual:
SRMR 0.051 0.051
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 0.759 0.841
EEF2 1.058 0.157 6.750 0.000 0.803 0.780
EEF3 0.921 0.155 5.953 0.000 0.699 0.738
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.194 0.107 11.143 0.000 1.184 0.893
EEC3 1.101 0.099 11.071 0.000 1.091 0.854
IM =~
IM1 1.000 1.102 0.927
IM2 1.006 0.061 16.564 0.000 1.108 0.915
IM3 1.012 0.060 16.976 0.000 1.115 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.136 0.212 -0.643 0.521 -0.124 -0.062
EEF ~
IM 0.366 0.061 6.010 0.000 0.531 0.531
AI_2 -0.143 0.143 -1.000 0.317 -0.188 -0.094
EEC ~
IM 0.396 0.100 3.956 0.000 0.440 0.440
AI_2 0.105 0.149 0.707 0.480 0.106 0.053
EEF 0.478 0.151 3.153 0.002 0.366 0.366
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.239 0.081 2.933 0.003 0.239 0.293
.EEF2 0.417 0.093 4.476 0.000 0.417 0.392
.EEF3 0.408 0.108 3.777 0.000 0.408 0.455
.EEC1 0.589 0.100 5.909 0.000 0.589 0.375
.EEC2 0.355 0.101 3.525 0.000 0.355 0.202
.EEC3 0.443 0.108 4.110 0.000 0.443 0.271
.IM1 0.199 0.044 4.475 0.000 0.199 0.141
.IM2 0.238 0.057 4.138 0.000 0.238 0.162
.IM3 0.257 0.074 3.453 0.001 0.257 0.171
.EEF 0.405 0.133 3.044 0.002 0.703 0.703
.EEC 0.496 0.141 3.523 0.000 0.505 0.505
.IM 1.209 0.258 4.689 0.000 0.996 0.996
R-Square:
Estimate
EEF1 0.707
EEF2 0.608
EEF3 0.545
EEC1 0.625
EEC2 0.798
EEC3 0.729
IM1 0.859
IM2 0.838
IM3 0.829
EEF 0.297
EEC 0.495
IM 0.004Plain - only EEF
lavaan 0.6-19 ended normally after 27 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 15
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 11.353 9.792
Degrees of freedom 12 12
P-value (Chi-square) 0.499 0.634
Scaling correction factor 1.159
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 484.153 421.834
Degrees of freedom 21 21
P-value 0.000 0.000
Scaling correction factor 1.148
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.002 1.010
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.010
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -793.240 -793.240
Scaling correction factor 1.412
for the MLR correction
Loglikelihood unrestricted model (H1) -787.564 -787.564
Scaling correction factor 1.300
for the MLR correction
Akaike (AIC) 1616.480 1616.480
Bayesian (BIC) 1657.654 1657.654
Sample-size adjusted Bayesian (SABIC) 1610.242 1610.242
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.091 0.075
P-value H_0: RMSEA <= 0.050 0.726 0.845
P-value H_0: RMSEA >= 0.080 0.090 0.036
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.086
P-value H_0: Robust RMSEA <= 0.050 0.803
P-value H_0: Robust RMSEA >= 0.080 0.067
Standardized Root Mean Square Residual:
SRMR 0.047 0.047
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 0.772 0.855
EEF2 1.040 0.167 6.236 0.000 0.804 0.780
EEF3 0.886 0.151 5.884 0.000 0.684 0.722
IM =~
IM1 1.000 1.096 0.923
IM2 1.015 0.059 17.090 0.000 1.113 0.920
IM3 1.017 0.059 17.215 0.000 1.115 0.911
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.135 0.211 -0.638 0.523 -0.123 -0.062
EEF ~
IM 0.369 0.063 5.871 0.000 0.524 0.524
AI_2 -0.149 0.144 -1.032 0.302 -0.192 -0.096
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.219 0.083 2.653 0.008 0.219 0.268
.EEF2 0.416 0.090 4.644 0.000 0.416 0.392
.EEF3 0.429 0.109 3.933 0.000 0.429 0.478
.IM1 0.210 0.044 4.819 0.000 0.210 0.149
.IM2 0.226 0.057 3.947 0.000 0.226 0.154
.IM3 0.256 0.073 3.519 0.000 0.256 0.171
.EEF 0.423 0.137 3.088 0.002 0.710 0.710
.IM 1.198 0.258 4.636 0.000 0.996 0.996
R-Square:
Estimate
EEF1 0.732
EEF2 0.608
EEF3 0.522
IM1 0.851
IM2 0.846
IM3 0.829
EEF 0.290
IM 0.004SEM - only EEC
lavaan 0.6-19 ended normally after 28 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 15
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 8.181 8.038
Degrees of freedom 12 12
P-value (Chi-square) 0.771 0.782
Scaling correction factor 1.018
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 557.292 530.997
Degrees of freedom 21 21
P-value 0.000 0.000
Scaling correction factor 1.050
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.012 1.014
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.013
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -856.192 -856.192
Scaling correction factor 1.303
for the MLR correction
Loglikelihood unrestricted model (H1) -852.102 -852.102
Scaling correction factor 1.176
for the MLR correction
Akaike (AIC) 1742.385 1742.385
Bayesian (BIC) 1783.559 1783.559
Sample-size adjusted Bayesian (SABIC) 1736.146 1736.146
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.066 0.064
P-value H_0: RMSEA <= 0.050 0.903 0.911
P-value H_0: RMSEA >= 0.080 0.023 0.020
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.065
P-value H_0: Robust RMSEA <= 0.050 0.907
P-value H_0: Robust RMSEA >= 0.080 0.022
Standardized Root Mean Square Residual:
SRMR 0.019 0.019
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.011 0.806
EEC2 1.164 0.100 11.649 0.000 1.177 0.888
EEC3 1.070 0.098 10.966 0.000 1.082 0.846
IM =~
IM1 1.000 1.102 0.927
IM2 1.006 0.062 16.240 0.000 1.108 0.916
IM3 1.011 0.060 16.848 0.000 1.114 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.137 0.212 -0.643 0.520 -0.124 -0.062
EEC ~
IM 0.583 0.083 6.989 0.000 0.635 0.635
AI_2 0.037 0.161 0.227 0.820 0.036 0.018
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.550 0.098 5.600 0.000 0.550 0.350
.EEC2 0.371 0.103 3.605 0.000 0.371 0.211
.EEC3 0.463 0.119 3.889 0.000 0.463 0.284
.IM1 0.198 0.046 4.328 0.000 0.198 0.140
.IM2 0.237 0.059 4.005 0.000 0.237 0.162
.IM3 0.259 0.076 3.424 0.001 0.259 0.173
.EEC 0.610 0.145 4.219 0.000 0.597 0.597
.IM 1.210 0.258 4.696 0.000 0.996 0.996
R-Square:
Estimate
EEC1 0.650
EEC2 0.789
EEC3 0.716
IM1 0.860
IM2 0.838
IM3 0.827
EEC 0.403
IM 0.004Partial mediation EEF -> EEC
lavaan 0.6-19 ended normally after 31 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 24
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 39.268 36.279
Degrees of freedom 30 30
P-value (Chi-square) 0.120 0.199
Scaling correction factor 1.082
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 762.973 680.484
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.121
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.987 0.990
Tucker-Lewis Index (TLI) 0.981 0.985
Robust Comparative Fit Index (CFI) 0.990
Robust Tucker-Lewis Index (TLI) 0.986
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1243.927 -1243.927
Scaling correction factor 1.329
for the MLR correction
Loglikelihood unrestricted model (H1) -1224.293 -1224.293
Scaling correction factor 1.192
for the MLR correction
Akaike (AIC) 2535.854 2535.854
Bayesian (BIC) 2601.732 2601.732
Sample-size adjusted Bayesian (SABIC) 2525.873 2525.873
Root Mean Square Error of Approximation:
RMSEA 0.052 0.043
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.093 0.085
P-value H_0: RMSEA <= 0.050 0.442 0.570
P-value H_0: RMSEA >= 0.080 0.143 0.077
Robust RMSEA 0.044
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.090
P-value H_0: Robust RMSEA <= 0.050 0.538
P-value H_0: Robust RMSEA >= 0.080 0.109
Standardized Root Mean Square Residual:
SRMR 0.051 0.051
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.194 0.107 11.143 0.000 1.184 0.893
EEC3 1.101 0.099 11.071 0.000 1.091 0.854
EEF =~
EEF1 1.000 0.759 0.841
EEF2 1.058 0.157 6.750 0.000 0.803 0.780
EEF3 0.921 0.155 5.953 0.000 0.699 0.738
IM =~
IM1 1.000 1.102 0.927
IM2 1.006 0.061 16.564 0.000 1.108 0.915
IM3 1.012 0.060 16.976 0.000 1.115 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.136 0.212 -0.643 0.521 -0.124 -0.062
EEF ~
IM 0.366 0.061 6.010 0.000 0.531 0.531
AI_2 -0.143 0.143 -1.000 0.317 -0.188 -0.094
EEC ~
IM 0.396 0.100 3.956 0.000 0.440 0.440
EEF 0.478 0.151 3.153 0.002 0.366 0.366
AI_2 0.105 0.149 0.707 0.480 0.106 0.053
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.100 5.909 0.000 0.589 0.375
.EEC2 0.355 0.101 3.525 0.000 0.355 0.202
.EEC3 0.443 0.108 4.110 0.000 0.443 0.271
.EEF1 0.239 0.081 2.933 0.003 0.239 0.293
.EEF2 0.417 0.093 4.476 0.000 0.417 0.392
.EEF3 0.408 0.108 3.777 0.000 0.408 0.455
.IM1 0.199 0.044 4.475 0.000 0.199 0.141
.IM2 0.238 0.057 4.138 0.000 0.238 0.162
.IM3 0.257 0.074 3.453 0.001 0.257 0.171
.EEC 0.496 0.141 3.523 0.000 0.505 0.505
.EEF 0.405 0.133 3.044 0.002 0.703 0.703
.IM 1.209 0.258 4.689 0.000 0.996 0.996
R-Square:
Estimate
EEC1 0.625
EEC2 0.798
EEC3 0.729
EEF1 0.707
EEF2 0.608
EEF3 0.545
IM1 0.859
IM2 0.838
IM3 0.829
EEC 0.495
EEF 0.297
IM 0.004Partial mediation EEC -> EEF
lavaan 0.6-19 ended normally after 31 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 24
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 39.268 36.279
Degrees of freedom 30 30
P-value (Chi-square) 0.120 0.199
Scaling correction factor 1.082
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 762.973 680.484
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.121
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.987 0.990
Tucker-Lewis Index (TLI) 0.981 0.985
Robust Comparative Fit Index (CFI) 0.990
Robust Tucker-Lewis Index (TLI) 0.986
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1243.927 -1243.927
Scaling correction factor 1.329
for the MLR correction
Loglikelihood unrestricted model (H1) -1224.293 -1224.293
Scaling correction factor 1.192
for the MLR correction
Akaike (AIC) 2535.854 2535.854
Bayesian (BIC) 2601.732 2601.732
Sample-size adjusted Bayesian (SABIC) 2525.873 2525.873
Root Mean Square Error of Approximation:
RMSEA 0.052 0.043
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.093 0.085
P-value H_0: RMSEA <= 0.050 0.442 0.570
P-value H_0: RMSEA >= 0.080 0.143 0.077
Robust RMSEA 0.044
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.090
P-value H_0: Robust RMSEA <= 0.050 0.538
P-value H_0: Robust RMSEA >= 0.080 0.109
Standardized Root Mean Square Residual:
SRMR 0.051 0.051
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.194 0.107 11.143 0.000 1.184 0.893
EEC3 1.101 0.099 11.071 0.000 1.091 0.854
EEF =~
EEF1 1.000 0.759 0.841
EEF2 1.058 0.157 6.750 0.000 0.803 0.780
EEF3 0.921 0.155 5.953 0.000 0.699 0.738
IM =~
IM1 1.000 1.102 0.927
IM2 1.006 0.061 16.564 0.000 1.108 0.915
IM3 1.012 0.060 16.976 0.000 1.115 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.136 0.212 -0.643 0.521 -0.124 -0.062
EEF ~
IM 0.179 0.090 1.978 0.048 0.260 0.260
AI_2 -0.155 0.136 -1.136 0.256 -0.204 -0.102
EEC 0.329 0.122 2.686 0.007 0.429 0.429
EEC ~
IM 0.570 0.082 6.995 0.000 0.634 0.634
AI_2 0.037 0.158 0.234 0.815 0.037 0.019
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.100 5.909 0.000 0.589 0.375
.EEC2 0.355 0.101 3.525 0.000 0.355 0.202
.EEC3 0.443 0.108 4.110 0.000 0.443 0.271
.EEF1 0.239 0.081 2.933 0.003 0.239 0.293
.EEF2 0.417 0.093 4.476 0.000 0.417 0.392
.EEF3 0.408 0.108 3.777 0.000 0.408 0.455
.IM1 0.199 0.044 4.475 0.000 0.199 0.141
.IM2 0.238 0.057 4.138 0.000 0.238 0.162
.IM3 0.257 0.074 3.453 0.001 0.257 0.171
.EEC 0.589 0.146 4.038 0.000 0.599 0.599
.EEF 0.342 0.115 2.974 0.003 0.592 0.592
.IM 1.209 0.258 4.689 0.000 0.996 0.996
R-Square:
Estimate
EEC1 0.625
EEC2 0.798
EEC3 0.729
EEF1 0.707
EEF2 0.608
EEF3 0.545
IM1 0.859
IM2 0.838
IM3 0.829
EEC 0.401
EEF 0.408
IM 0.004With moderation
With latent interactions
Based on ADT as a latent variablelavaan 0.6-19 ended normally after 48 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 41
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 133.998 122.878
Degrees of freedom 94 94
P-value (Chi-square) 0.004 0.024
Scaling correction factor 1.090
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1552.498 1293.066
Degrees of freedom 120 120
P-value 0.000 0.000
Scaling correction factor 1.201
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.972 0.975
Tucker-Lewis Index (TLI) 0.964 0.969
Robust Comparative Fit Index (CFI) 0.978
Robust Tucker-Lewis Index (TLI) 0.971
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2296.788 -2296.788
Scaling correction factor 2.153
for the MLR correction
Loglikelihood unrestricted model (H1) -2229.789 -2229.789
Scaling correction factor 1.413
for the MLR correction
Akaike (AIC) 4675.576 4675.576
Bayesian (BIC) 4788.118 4788.118
Sample-size adjusted Bayesian (SABIC) 4658.525 4658.525
Root Mean Square Error of Approximation:
RMSEA 0.061 0.052
90 Percent confidence interval - lower 0.035 0.022
90 Percent confidence interval - upper 0.083 0.075
P-value H_0: RMSEA <= 0.050 0.219 0.438
P-value H_0: RMSEA >= 0.080 0.082 0.019
Robust RMSEA 0.054
90 Percent confidence interval - lower 0.021
90 Percent confidence interval - upper 0.079
P-value H_0: Robust RMSEA <= 0.050 0.388
P-value H_0: Robust RMSEA >= 0.080 0.042
Standardized Root Mean Square Residual:
SRMR 0.055 0.055
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.992 0.791
EEC2 1.196 0.107 11.189 0.000 1.186 0.895
EEC3 1.098 0.100 11.011 0.000 1.089 0.852
EEF =~
EEF1 1.000 0.756 0.839
EEF2 1.063 0.158 6.731 0.000 0.803 0.781
EEF3 0.922 0.154 5.989 0.000 0.696 0.736
ADT =~
ADT1 1.000 1.447 0.968
ADT2 1.001 0.041 24.365 0.000 1.448 0.955
ADT3 1.011 0.049 20.682 0.000 1.463 0.932
IM =~
IM1 1.000 1.100 0.926
IM2 1.009 0.059 17.240 0.000 1.110 0.918
IM3 1.010 0.057 17.608 0.000 1.111 0.908
IM_ADT =~
IM1.ADT1 1.000 1.644 0.851
IM2.ADT2 1.364 0.339 4.029 0.000 2.244 0.895
IM3.ADT3 1.196 0.371 3.220 0.001 1.966 0.869
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
AI_2 -0.058 0.185 -0.316 0.752 -0.053 -0.027
ADT 0.183 0.054 3.387 0.001 0.241 0.241
IM_ADT -0.263 0.117 -2.237 0.025 -0.393 -0.393
EEF ~
IM 0.312 0.058 5.402 0.000 0.455 0.455
AI_2 -0.121 0.140 -0.864 0.388 -0.160 -0.080
ADT 0.122 0.056 2.187 0.029 0.234 0.234
EEC ~
IM 0.522 0.082 6.388 0.000 0.578 0.578
AI_2 0.058 0.152 0.383 0.702 0.059 0.029
ADT 0.115 0.060 1.925 0.054 0.168 0.168
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.168 0.056 3.003 0.003 0.364 0.364
ADT ~~
IM_ADT -0.513 0.416 -1.233 0.218 -0.216 -0.216
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.100 5.910 0.000 0.589 0.374
.EEC2 0.351 0.100 3.500 0.000 0.351 0.199
.EEC3 0.449 0.110 4.094 0.000 0.449 0.274
.EEF1 0.241 0.081 2.968 0.003 0.241 0.297
.EEF2 0.413 0.092 4.513 0.000 0.413 0.390
.EEF3 0.409 0.106 3.880 0.000 0.409 0.458
.ADT1 0.139 0.061 2.296 0.022 0.139 0.062
.ADT2 0.203 0.055 3.697 0.000 0.203 0.088
.ADT3 0.324 0.084 3.861 0.000 0.324 0.131
.IM1 0.200 0.043 4.618 0.000 0.200 0.142
.IM2 0.230 0.057 4.052 0.000 0.230 0.158
.IM3 0.262 0.075 3.500 0.000 0.262 0.175
.IM1.ADT1 1.030 0.408 2.526 0.012 1.030 0.276
.IM2.ADT2 1.244 0.712 1.747 0.081 1.244 0.198
.IM3.ADT3 1.253 0.541 2.319 0.020 1.253 0.245
.EEC 0.565 0.145 3.890 0.000 0.574 0.574
.EEF 0.378 0.126 3.005 0.003 0.661 0.661
ADT 2.093 0.365 5.737 0.000 1.000 1.000
.IM 0.902 0.203 4.448 0.000 0.746 0.746
IM_ADT 2.704 1.257 2.151 0.032 1.000 1.000
R-Square:
Estimate
EEC1 0.626
EEC2 0.801
EEC3 0.726
EEF1 0.703
EEF2 0.610
EEF3 0.542
ADT1 0.938
ADT2 0.912
ADT3 0.869
IM1 0.858
IM2 0.842
IM3 0.825
IM1.ADT1 0.724
IM2.ADT2 0.802
IM3.ADT3 0.755
EEC 0.426
EEF 0.339
IM 0.254R square EEC1 EEC2 EEC3 EEF1 EEF2 EEF3 ADT1 ADT2
0.626 0.801 0.726 0.703 0.610 0.542 0.938 0.912
ADT3 IM1 IM2 IM3 IM1.ADT1 IM2.ADT2 IM3.ADT3 EEC
0.869 0.858 0.842 0.825 0.724 0.802 0.755 0.426
EEF IM
0.339 0.254 high >5Full model based on the moderator as a dummy
Based on ADT as a dummy variablelavaan 0.6-19 ended normally after 45 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 44.120 42.295
Degrees of freedom 42 42
P-value (Chi-square) 0.382 0.458
Scaling correction factor 1.043
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 786.673 729.650
Degrees of freedom 63 63
P-value 0.000 0.000
Scaling correction factor 1.078
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997 1.000
Tucker-Lewis Index (TLI) 0.996 0.999
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 0.999
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1234.503 -1234.503
Scaling correction factor 1.267
for the MLR correction
Loglikelihood unrestricted model (H1) -1212.443 -1212.443
Scaling correction factor 1.137
for the MLR correction
Akaike (AIC) 2529.006 2529.006
Bayesian (BIC) 2611.354 2611.354
Sample-size adjusted Bayesian (SABIC) 2516.530 2516.530
Root Mean Square Error of Approximation:
RMSEA 0.021 0.008
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.068 0.063
P-value H_0: RMSEA <= 0.050 0.803 0.856
P-value H_0: RMSEA >= 0.080 0.012 0.007
Robust RMSEA 0.008
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.065
P-value H_0: Robust RMSEA <= 0.050 0.840
P-value H_0: Robust RMSEA >= 0.080 0.010
Standardized Root Mean Square Residual:
SRMR 0.043 0.043
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.196 0.106 11.273 0.000 1.185 0.894
EEC3 1.099 0.100 11.045 0.000 1.089 0.852
EEF =~
EEF1 1.000 0.757 0.839
EEF2 1.065 0.158 6.746 0.000 0.807 0.783
EEF3 0.921 0.155 5.946 0.000 0.698 0.737
IM =~
IM1 1.000 1.102 0.927
IM2 1.005 0.061 16.476 0.000 1.107 0.915
IM3 1.011 0.060 16.824 0.000 1.115 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.320 0.054 5.940 0.000 0.466 0.466
ADT_high 0.416 0.135 3.093 0.002 0.550 0.259
AI_2 -0.138 0.257 -0.537 0.592 -0.182 -0.091
AI2_ADT -0.006 0.295 -0.021 0.984 -0.008 -0.004
EEC ~
IM 0.534 0.078 6.805 0.000 0.594 0.594
ADT_high 0.511 0.197 2.593 0.010 0.515 0.242
AI_2 0.238 0.245 0.974 0.330 0.240 0.120
AI2_ADT -0.298 0.317 -0.941 0.347 -0.301 -0.141
IM ~
AI_2 -0.281 0.425 -0.661 0.509 -0.255 -0.127
AI2_ADT 0.231 0.478 0.483 0.629 0.210 0.098
ADT_high 0.484 0.325 1.488 0.137 0.439 0.207
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.162 0.059 2.738 0.006 0.357 0.357
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.098 5.998 0.000 0.589 0.375
.EEC2 0.352 0.100 3.527 0.000 0.352 0.200
.EEC3 0.447 0.111 4.030 0.000 0.447 0.274
.EEF1 0.242 0.079 3.046 0.002 0.242 0.296
.EEF2 0.411 0.092 4.479 0.000 0.411 0.387
.EEF3 0.410 0.108 3.794 0.000 0.410 0.457
.IM1 0.198 0.045 4.384 0.000 0.198 0.140
.IM2 0.239 0.058 4.119 0.000 0.239 0.163
.IM3 0.257 0.075 3.426 0.001 0.257 0.171
.EEC 0.556 0.145 3.844 0.000 0.566 0.566
.EEF 0.368 0.122 3.012 0.003 0.641 0.641
.IM 1.128 0.232 4.854 0.000 0.929 0.929
R-Square:
Estimate
EEC1 0.625
EEC2 0.800
EEC3 0.726
EEF1 0.704
EEF2 0.613
EEF3 0.543
IM1 0.860
IM2 0.837
IM3 0.829
EEC 0.434
EEF 0.359
IM 0.071ADT as an IV
Based on ADT as a dummy variablelavaan 0.6-19 ended normally after 32 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 26
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 52.532 50.200
Degrees of freedom 46 46
P-value (Chi-square) 0.236 0.311
Scaling correction factor 1.046
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 786.673 729.650
Degrees of freedom 63 63
P-value 0.000 0.000
Scaling correction factor 1.078
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.991 0.994
Tucker-Lewis Index (TLI) 0.988 0.991
Robust Comparative Fit Index (CFI) 0.994
Robust Tucker-Lewis Index (TLI) 0.992
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1238.709 -1238.709
Scaling correction factor 1.296
for the MLR correction
Loglikelihood unrestricted model (H1) -1212.443 -1212.443
Scaling correction factor 1.137
for the MLR correction
Akaike (AIC) 2529.419 2529.419
Bayesian (BIC) 2600.787 2600.787
Sample-size adjusted Bayesian (SABIC) 2518.606 2518.606
Root Mean Square Error of Approximation:
RMSEA 0.035 0.028
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.074 0.069
P-value H_0: RMSEA <= 0.050 0.695 0.771
P-value H_0: RMSEA >= 0.080 0.023 0.012
Robust RMSEA 0.029
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.071
P-value H_0: Robust RMSEA <= 0.050 0.750
P-value H_0: Robust RMSEA >= 0.080 0.018
Standardized Root Mean Square Residual:
SRMR 0.081 0.081
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.965 0.783
EEC2 1.196 0.107 11.224 0.000 1.154 0.890
EEC3 1.098 0.100 11.001 0.000 1.061 0.846
EEF =~
EEF1 1.000 0.734 0.830
EEF2 1.065 0.157 6.790 0.000 0.782 0.773
EEF3 0.924 0.153 6.019 0.000 0.678 0.728
IM =~
IM1 1.000 1.100 0.926
IM2 1.008 0.061 16.603 0.000 1.109 0.916
IM3 1.014 0.060 16.921 0.000 1.115 0.911
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.319 0.053 5.987 0.000 0.478 0.478
ADT_high 0.425 0.150 2.831 0.005 0.579 0.273
AI_2 -0.142 0.139 -1.020 0.308 -0.193 -0.096
AI2_ADT 0.000 0.000 0.000
EEC ~
IM 0.529 0.081 6.557 0.000 0.603 0.603
ADT_high 0.386 0.160 2.421 0.015 0.400 0.188
AI_2 0.038 0.154 0.245 0.807 0.039 0.020
AI2_ADT 0.000 0.000 0.000
IM ~
AI_2 -0.136 0.212 -0.641 0.521 -0.124 -0.062
AI2_ADT 0.000 0.000 0.000
ADT_high 0.000 0.000 0.000
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.162 0.058 2.789 0.005 0.357 0.357
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.099 5.949 0.000 0.589 0.387
.EEC2 0.351 0.099 3.554 0.000 0.351 0.209
.EEC3 0.448 0.111 4.029 0.000 0.448 0.285
.EEF1 0.243 0.079 3.054 0.002 0.243 0.310
.EEF2 0.412 0.092 4.464 0.000 0.412 0.402
.EEF3 0.408 0.107 3.809 0.000 0.408 0.470
.IM1 0.202 0.045 4.522 0.000 0.202 0.143
.IM2 0.236 0.057 4.153 0.000 0.236 0.161
.IM3 0.255 0.073 3.477 0.001 0.255 0.170
.EEC 0.561 0.146 3.845 0.000 0.602 0.602
.EEF 0.367 0.121 3.034 0.002 0.681 0.681
.IM 1.206 0.258 4.673 0.000 0.996 0.996
R-Square:
Estimate
EEC1 0.613
EEC2 0.791
EEC3 0.715
EEF1 0.690
EEF2 0.598
EEF3 0.530
IM1 0.857
IM2 0.839
IM3 0.830
EEC 0.398
EEF 0.319
IM 0.004Derived model
Based on ADT as a dummy variablelavaan 0.6-19 ended normally after 31 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 23
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 54.475 52.266
Degrees of freedom 49 49
P-value (Chi-square) 0.274 0.348
Scaling correction factor 1.042
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 786.673 729.650
Degrees of freedom 63 63
P-value 0.000 0.000
Scaling correction factor 1.078
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.992 0.995
Tucker-Lewis Index (TLI) 0.990 0.994
Robust Comparative Fit Index (CFI) 0.995
Robust Tucker-Lewis Index (TLI) 0.994
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1239.681 -1239.681
Scaling correction factor 1.337
for the MLR correction
Loglikelihood unrestricted model (H1) -1212.443 -1212.443
Scaling correction factor 1.137
for the MLR correction
Akaike (AIC) 2525.361 2525.361
Bayesian (BIC) 2588.495 2588.495
Sample-size adjusted Bayesian (SABIC) 2515.796 2515.796
Root Mean Square Error of Approximation:
RMSEA 0.031 0.024
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.070 0.066
P-value H_0: RMSEA <= 0.050 0.746 0.811
P-value H_0: RMSEA >= 0.080 0.015 0.008
Robust RMSEA 0.025
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.068
P-value H_0: Robust RMSEA <= 0.050 0.792
P-value H_0: Robust RMSEA >= 0.080 0.011
Standardized Root Mean Square Residual:
SRMR 0.080 0.080
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.966 0.783
EEC2 1.194 0.106 11.225 0.000 1.154 0.889
EEC3 1.098 0.100 11.004 0.000 1.061 0.846
EEF =~
EEF1 1.000 0.732 0.829
EEF2 1.063 0.153 6.947 0.000 0.779 0.770
EEF3 0.931 0.156 5.958 0.000 0.682 0.732
IM =~
IM1 1.000 1.100 0.926
IM2 1.008 0.061 16.599 0.000 1.109 0.916
IM3 1.014 0.060 16.879 0.000 1.115 0.911
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.324 0.054 6.046 0.000 0.486 0.486
ADT_high 0.425 0.150 2.838 0.005 0.580 0.273
AI_2 0.000 0.000 0.000
AI2_ADT 0.000 0.000 0.000
EEC ~
IM 0.528 0.082 6.482 0.000 0.602 0.602
ADT_high 0.386 0.160 2.416 0.016 0.400 0.188
AI_2 0.000 0.000 0.000
AI2_ADT 0.000 0.000 0.000
IM ~
AI_2 0.000 0.000 0.000
AI2_ADT 0.000 0.000 0.000
ADT_high 0.000 0.000 0.000
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.161 0.059 2.733 0.006 0.352 0.352
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.588 0.099 5.934 0.000 0.588 0.386
.EEC2 0.353 0.100 3.525 0.000 0.353 0.209
.EEC3 0.448 0.111 4.036 0.000 0.448 0.285
.EEF1 0.245 0.081 3.039 0.002 0.245 0.313
.EEF2 0.417 0.093 4.486 0.000 0.417 0.408
.EEF3 0.403 0.106 3.791 0.000 0.403 0.464
.IM1 0.202 0.045 4.536 0.000 0.202 0.143
.IM2 0.236 0.057 4.153 0.000 0.236 0.161
.IM3 0.255 0.073 3.465 0.001 0.255 0.170
.EEC 0.563 0.145 3.876 0.000 0.603 0.603
.EEF 0.370 0.126 2.945 0.003 0.689 0.689
.IM 1.210 0.262 4.626 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.614
EEC2 0.791
EEC3 0.715
EEF1 0.687
EEF2 0.592
EEF3 0.536
IM1 0.857
IM2 0.839
IM3 0.830
EEC 0.397
EEF 0.311
IM 0.000IM
ADT and full mediation by IM
lavaan 0.6-19 ended normally after 39 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 115
Model Test User Model:
Standard Scaled
Test Statistic 68.574 60.496
Degrees of freedom 48 48
P-value (Chi-square) 0.027 0.106
Scaling correction factor 1.134
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1240.604 1042.507
Degrees of freedom 66 66
P-value 0.000 0.000
Scaling correction factor 1.190
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.982 0.987
Tucker-Lewis Index (TLI) 0.976 0.982
Robust Comparative Fit Index (CFI) 0.988
Robust Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1655.229 -1655.229
Scaling correction factor 1.399
for the MLR correction
Loglikelihood unrestricted model (H1) -1620.942 -1620.942
Scaling correction factor 1.235
for the MLR correction
Akaike (AIC) 3370.457 3370.457
Bayesian (BIC) 3452.805 3452.805
Sample-size adjusted Bayesian (SABIC) 3357.981 3357.981
Root Mean Square Error of Approximation:
RMSEA 0.061 0.048
90 Percent confidence interval - lower 0.021 0.000
90 Percent confidence interval - upper 0.092 0.079
P-value H_0: RMSEA <= 0.050 0.276 0.521
P-value H_0: RMSEA >= 0.080 0.169 0.047
Robust RMSEA 0.051
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.087
P-value H_0: Robust RMSEA <= 0.050 0.464
P-value H_0: Robust RMSEA >= 0.080 0.096
Standardized Root Mean Square Residual:
SRMR 0.046 0.046
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.991 0.791
EEC2 1.196 0.107 11.193 0.000 1.186 0.895
EEC3 1.098 0.099 11.032 0.000 1.088 0.851
EEF =~
EEF1 1.000 0.757 0.839
EEF2 1.059 0.153 6.908 0.000 0.802 0.779
EEF3 0.927 0.156 5.959 0.000 0.702 0.741
IM =~
IM1 1.000 1.102 0.927
IM2 1.005 0.061 16.507 0.000 1.107 0.915
IM3 1.011 0.060 16.914 0.000 1.114 0.910
ADT =~
ADT1 1.000 1.447 0.968
ADT2 1.001 0.041 24.537 0.000 1.448 0.955
ADT3 1.011 0.049 20.766 0.000 1.463 0.932
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.164 0.084 1.957 0.050 0.239 0.239
EEC 0.293 0.115 2.546 0.011 0.383 0.383
ADT 0.091 0.050 1.816 0.069 0.174 0.174
EEC ~
IM 0.521 0.082 6.364 0.000 0.579 0.579
ADT 0.113 0.061 1.858 0.063 0.166 0.166
IM ~
ADT 0.250 0.091 2.751 0.006 0.328 0.328
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.589 0.100 5.905 0.000 0.589 0.375
.EEC2 0.350 0.101 3.462 0.001 0.350 0.199
.EEC3 0.450 0.109 4.107 0.000 0.450 0.275
.EEF1 0.242 0.082 2.959 0.003 0.242 0.297
.EEF2 0.418 0.092 4.544 0.000 0.418 0.394
.EEF3 0.405 0.104 3.880 0.000 0.405 0.451
.IM1 0.198 0.045 4.417 0.000 0.198 0.140
.IM2 0.239 0.059 4.082 0.000 0.239 0.163
.IM3 0.257 0.075 3.431 0.001 0.257 0.171
.ADT1 0.139 0.060 2.304 0.021 0.139 0.062
.ADT2 0.203 0.055 3.690 0.000 0.203 0.088
.ADT3 0.324 0.084 3.866 0.000 0.324 0.132
.EEC 0.565 0.144 3.923 0.000 0.575 0.575
.EEF 0.330 0.115 2.867 0.004 0.575 0.575
.IM 1.084 0.215 5.041 0.000 0.892 0.892
ADT 2.094 0.364 5.745 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.625
EEC2 0.801
EEC3 0.725
EEF1 0.703
EEF2 0.606
EEF3 0.549
IM1 0.860
IM2 0.837
IM3 0.829
ADT1 0.938
ADT2 0.912
ADT3 0.868
EEC 0.425
EEF 0.425
IM 0.108Multigroup analysis on ADT
Interaction effect between ADT and AI groups
continous variablelavaan 0.6-19 ended normally after 54 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 84
Number of observations per group:
AI 56
control 59
Model Test User Model:
Standard Scaled
Test Statistic 141.060 150.565
Degrees of freedom 96 96
P-value (Chi-square) 0.002 0.000
Scaling correction factor 0.937
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
AI 86.003 86.003
control 64.562 64.562
Model Test Baseline Model:
Test statistic 1321.078 1231.518
Degrees of freedom 132 132
P-value 0.000 0.000
Scaling correction factor 1.073
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.962 0.950
Tucker-Lewis Index (TLI) 0.948 0.932
Robust Comparative Fit Index (CFI) 0.957
Robust Tucker-Lewis Index (TLI) 0.940
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1635.048 -1635.048
Scaling correction factor 1.283
for the MLR correction
Loglikelihood unrestricted model (H1) -1564.518 -1564.518
Scaling correction factor 1.099
for the MLR correction
Akaike (AIC) 3438.096 3438.096
Bayesian (BIC) 3668.671 3668.671
Sample-size adjusted Bayesian (SABIC) 3403.162 3403.162
Root Mean Square Error of Approximation:
RMSEA 0.090 0.099
90 Percent confidence interval - lower 0.056 0.066
90 Percent confidence interval - upper 0.121 0.130
P-value H_0: RMSEA <= 0.050 0.030 0.011
P-value H_0: RMSEA >= 0.080 0.714 0.846
Robust RMSEA 0.096
90 Percent confidence interval - lower 0.065
90 Percent confidence interval - upper 0.125
P-value H_0: Robust RMSEA <= 0.050 0.010
P-value H_0: Robust RMSEA >= 0.080 0.821
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [AI]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.053 0.827
EEC2 1.153 0.150 7.679 0.000 1.214 0.913
EEC3 1.141 0.156 7.314 0.000 1.202 0.870
EEF =~
EEF1 1.000 0.919 0.860
EEF2 1.115 0.201 5.533 0.000 1.024 0.873
EEF3 0.905 0.148 6.109 0.000 0.832 0.830
IM =~
IM1 1.000 1.129 0.940
IM2 0.976 0.091 10.764 0.000 1.102 0.892
IM3 1.011 0.062 16.280 0.000 1.142 0.913
ADT =~
ADT1 1.000 1.598 0.979
ADT2 0.997 0.057 17.519 0.000 1.594 0.969
ADT3 0.998 0.066 15.125 0.000 1.595 0.947
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.060 0.152 0.394 0.694 0.073 0.073
EEC 0.478 0.201 2.383 0.017 0.548 0.548
ADT 0.087 0.068 1.282 0.200 0.151 0.151
EEC ~
IM 0.581 0.138 4.219 0.000 0.623 0.623
ADT 0.082 0.079 1.039 0.299 0.125 0.125
IM ~
ADT 0.235 0.129 1.827 0.068 0.333 0.333
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.643 0.170 27.277 0.000 4.643 3.645
.EEC2 4.982 0.178 28.043 0.000 4.982 3.747
.EEC3 5.018 0.185 27.168 0.000 5.018 3.630
.EEF1 5.482 0.143 38.380 0.000 5.482 5.129
.EEF2 5.625 0.157 35.868 0.000 5.625 4.793
.EEF3 5.821 0.134 43.480 0.000 5.821 5.810
.IM1 5.357 0.161 33.363 0.000 5.357 4.458
.IM2 5.786 0.165 35.054 0.000 5.786 4.684
.IM3 5.589 0.167 33.451 0.000 5.589 4.470
.ADT1 5.393 0.218 24.711 0.000 5.393 3.302
.ADT2 5.286 0.220 24.054 0.000 5.286 3.214
.ADT3 5.268 0.225 23.396 0.000 5.268 3.126
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.513 0.150 3.416 0.001 0.513 0.316
.EEC2 0.293 0.142 2.057 0.040 0.293 0.166
.EEC3 0.465 0.184 2.533 0.011 0.465 0.244
.EEF1 0.298 0.128 2.320 0.020 0.298 0.261
.EEF2 0.328 0.141 2.326 0.020 0.328 0.238
.EEF3 0.312 0.130 2.407 0.016 0.312 0.311
.IM1 0.169 0.070 2.408 0.016 0.169 0.117
.IM2 0.311 0.111 2.814 0.005 0.311 0.204
.IM3 0.260 0.125 2.070 0.038 0.260 0.166
.ADT1 0.112 0.087 1.288 0.198 0.112 0.042
.ADT2 0.165 0.068 2.420 0.016 0.165 0.061
.ADT3 0.294 0.106 2.784 0.005 0.294 0.104
.EEC 0.604 0.214 2.820 0.005 0.545 0.545
.EEF 0.469 0.176 2.664 0.008 0.555 0.555
.IM 1.134 0.360 3.151 0.002 0.889 0.889
ADT 2.555 0.566 4.515 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.684
EEC2 0.834
EEC3 0.756
EEF1 0.739
EEF2 0.762
EEF3 0.689
IM1 0.883
IM2 0.796
IM3 0.834
ADT1 0.958
ADT2 0.939
ADT3 0.896
EEC 0.455
EEF 0.445
IM 0.111
Group 2 [control]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.930 0.755
EEC2 1.245 0.155 8.014 0.000 1.158 0.877
EEC3 1.046 0.130 8.019 0.000 0.973 0.831
EEF =~
EEF1 1.000 0.452 0.651
EEF2 0.907 0.166 5.450 0.000 0.410 0.483
EEF3 1.492 0.735 2.029 0.043 0.675 0.756
IM =~
IM1 1.000 1.076 0.920
IM2 1.029 0.088 11.658 0.000 1.107 0.936
IM3 1.008 0.108 9.375 0.000 1.084 0.906
ADT =~
ADT1 1.000 1.283 0.956
ADT2 0.993 0.053 18.649 0.000 1.274 0.933
ADT3 1.024 0.065 15.727 0.000 1.314 0.911
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.236 0.098 2.418 0.016 0.561 0.561
EEC 0.101 0.096 1.058 0.290 0.208 0.208
ADT 0.071 0.063 1.128 0.260 0.201 0.201
EEC ~
IM 0.445 0.090 4.965 0.000 0.515 0.515
ADT 0.160 0.084 1.905 0.057 0.221 0.221
IM ~
ADT 0.272 0.120 2.274 0.023 0.325 0.325
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.729 0.160 29.465 0.000 4.729 3.836
.EEC2 5.017 0.172 29.168 0.000 5.017 3.797
.EEC3 5.051 0.152 33.142 0.000 5.051 4.315
.EEF1 5.695 0.090 62.929 0.000 5.695 8.193
.EEF2 5.915 0.111 53.486 0.000 5.915 6.963
.EEF3 5.864 0.116 50.515 0.000 5.864 6.576
.IM1 5.525 0.152 36.287 0.000 5.525 4.724
.IM2 5.915 0.154 38.406 0.000 5.915 5.000
.IM3 5.695 0.156 36.550 0.000 5.695 4.758
.ADT1 5.593 0.175 32.017 0.000 5.593 4.168
.ADT2 5.610 0.178 31.555 0.000 5.610 4.108
.ADT3 5.508 0.188 29.335 0.000 5.508 3.819
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.655 0.134 4.888 0.000 0.655 0.431
.EEC2 0.404 0.154 2.633 0.008 0.404 0.232
.EEC3 0.424 0.141 3.010 0.003 0.424 0.309
.EEF1 0.279 0.115 2.428 0.015 0.279 0.577
.EEF2 0.553 0.131 4.226 0.000 0.553 0.767
.EEF3 0.340 0.116 2.937 0.003 0.340 0.428
.IM1 0.211 0.052 4.045 0.000 0.211 0.154
.IM2 0.174 0.043 4.016 0.000 0.174 0.124
.IM3 0.256 0.085 3.008 0.003 0.256 0.179
.ADT1 0.155 0.079 1.970 0.049 0.155 0.086
.ADT2 0.241 0.086 2.807 0.005 0.241 0.129
.ADT3 0.354 0.131 2.713 0.007 0.354 0.170
.EEC 0.530 0.196 2.710 0.007 0.612 0.612
.EEF 0.073 0.072 1.012 0.311 0.359 0.359
.IM 1.035 0.232 4.453 0.000 0.894 0.894
ADT 1.645 0.437 3.764 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.569
EEC2 0.768
EEC3 0.691
EEF1 0.423
EEF2 0.233
EEF3 0.572
IM1 0.846
IM2 0.876
IM3 0.821
ADT1 0.914
ADT2 0.871
ADT3 0.830
EEC 0.388
EEF 0.641
IM 0.106`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
- as categorical variables of ADT
dummies where high as above 5lavaan 0.6-19 ended normally after 87 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations per group:
AI 56
control 59
Model Test User Model:
Standard Scaled
Test Statistic 81.559 86.041
Degrees of freedom 60 60
P-value (Chi-square) 0.034 0.015
Scaling correction factor 0.948
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
AI 45.433 45.433
control 40.608 40.608
Model Test Baseline Model:
Test statistic 835.666 784.979
Degrees of freedom 90 90
P-value 0.000 0.000
Scaling correction factor 1.065
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.971 0.963
Tucker-Lewis Index (TLI) 0.957 0.944
Robust Comparative Fit Index (CFI) 0.967
Robust Tucker-Lewis Index (TLI) 0.950
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1216.586 -1216.586
Scaling correction factor 1.232
for the MLR correction
Loglikelihood unrestricted model (H1) -1175.806 -1175.806
Scaling correction factor 1.097
for the MLR correction
Akaike (AIC) 2565.171 2565.171
Bayesian (BIC) 2746.337 2746.337
Sample-size adjusted Bayesian (SABIC) 2537.723 2537.723
Root Mean Square Error of Approximation:
RMSEA 0.079 0.087
90 Percent confidence interval - lower 0.023 0.038
90 Percent confidence interval - upper 0.120 0.127
P-value H_0: RMSEA <= 0.050 0.149 0.093
P-value H_0: RMSEA >= 0.080 0.508 0.626
Robust RMSEA 0.085
90 Percent confidence interval - lower 0.038
90 Percent confidence interval - upper 0.123
P-value H_0: Robust RMSEA <= 0.050 0.094
P-value H_0: Robust RMSEA >= 0.080 0.596
Standardized Root Mean Square Residual:
SRMR 0.049 0.049
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [AI]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.054 0.827
EEC2 1.152 0.150 7.673 0.000 1.214 0.913
EEC3 1.141 0.156 7.293 0.000 1.203 0.870
EEF =~
EEF1 1.000 0.918 0.859
EEF2 1.118 0.203 5.521 0.000 1.027 0.875
EEF3 0.904 0.148 6.098 0.000 0.830 0.829
IM =~
IM1 1.000 1.129 0.940
IM2 0.976 0.091 10.782 0.000 1.103 0.893
IM3 1.011 0.062 16.283 0.000 1.141 0.913
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.056 0.155 0.363 0.717 0.069 0.069
EEC 0.488 0.199 2.454 0.014 0.560 0.560
ADT_high 0.295 0.243 1.215 0.224 0.322 0.152
EEC ~
IM 0.600 0.135 4.455 0.000 0.642 0.642
ADT_high 0.163 0.240 0.678 0.498 0.154 0.073
IM ~
ADT_high 0.731 0.352 2.078 0.038 0.648 0.307
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.246 0.300 14.174 0.000 4.246 3.333
.EEC2 4.525 0.305 14.837 0.000 4.525 3.403
.EEC3 4.565 0.320 14.253 0.000 4.565 3.302
.EEF1 5.066 0.256 19.783 0.000 5.066 4.739
.EEF2 5.160 0.286 18.028 0.000 5.160 4.397
.EEF3 5.445 0.219 24.873 0.000 5.445 5.435
.IM1 4.874 0.319 15.301 0.000 4.874 4.056
.IM2 5.314 0.344 15.470 0.000 5.314 4.302
.IM3 5.101 0.332 15.351 0.000 5.101 4.079
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.512 0.150 3.404 0.001 0.512 0.316
.EEC2 0.295 0.143 2.059 0.040 0.295 0.167
.EEC3 0.464 0.184 2.521 0.012 0.464 0.243
.EEF1 0.299 0.127 2.358 0.018 0.299 0.262
.EEF2 0.323 0.140 2.305 0.021 0.323 0.234
.EEF3 0.315 0.130 2.427 0.015 0.315 0.313
.IM1 0.169 0.069 2.435 0.015 0.169 0.117
.IM2 0.310 0.110 2.819 0.005 0.310 0.203
.IM3 0.261 0.126 2.070 0.038 0.261 0.167
.EEC 0.614 0.217 2.825 0.005 0.553 0.553
.EEF 0.468 0.169 2.772 0.006 0.555 0.555
.IM 1.155 0.385 3.004 0.003 0.906 0.906
R-Square:
Estimate
EEC1 0.684
EEC2 0.833
EEC3 0.757
EEF1 0.738
EEF2 0.766
EEF3 0.687
IM1 0.883
IM2 0.797
IM3 0.833
EEC 0.447
EEF 0.445
IM 0.094
Group 2 [control]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.934 0.758
EEC2 1.231 0.153 8.067 0.000 1.150 0.871
EEC3 1.048 0.130 8.054 0.000 0.979 0.836
EEF =~
EEF1 1.000 0.449 0.645
EEF2 0.915 0.170 5.391 0.000 0.411 0.483
EEF3 1.510 0.622 2.428 0.015 0.677 0.760
IM =~
IM1 1.000 1.075 0.919
IM2 1.028 0.089 11.501 0.000 1.105 0.934
IM3 1.010 0.110 9.189 0.000 1.087 0.908
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.252 0.094 2.686 0.007 0.603 0.603
EEC 0.061 0.095 0.638 0.524 0.127 0.127
ADT_high 0.334 0.147 2.267 0.023 0.746 0.348
EEC ~
IM 0.460 0.092 4.979 0.000 0.529 0.529
ADT_high 0.551 0.202 2.734 0.006 0.590 0.276
IM ~
ADT_high 0.479 0.328 1.459 0.144 0.445 0.208
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.206 0.219 19.235 0.000 4.206 3.412
.EEC2 4.373 0.231 18.967 0.000 4.373 3.310
.EEC3 4.503 0.199 22.590 0.000 4.503 3.847
.EEF1 5.355 0.110 48.583 0.000 5.355 7.703
.EEF2 5.604 0.123 45.415 0.000 5.604 6.597
.EEF3 5.351 0.201 26.625 0.000 5.351 6.000
.IM1 5.201 0.280 18.542 0.000 5.201 4.446
.IM2 5.582 0.296 18.826 0.000 5.582 4.718
.IM3 5.367 0.276 19.458 0.000 5.367 4.484
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.647 0.130 4.964 0.000 0.647 0.426
.EEC2 0.423 0.155 2.727 0.006 0.423 0.242
.EEC3 0.412 0.143 2.874 0.004 0.412 0.301
.EEF1 0.282 0.095 2.957 0.003 0.282 0.584
.EEF2 0.553 0.119 4.653 0.000 0.553 0.766
.EEF3 0.336 0.100 3.354 0.001 0.336 0.423
.IM1 0.211 0.054 3.884 0.000 0.211 0.155
.IM2 0.178 0.041 4.306 0.000 0.178 0.128
.IM3 0.251 0.085 2.972 0.003 0.251 0.176
.EEC 0.509 0.188 2.705 0.007 0.583 0.583
.EEF 0.058 0.055 1.056 0.291 0.288 0.288
.IM 1.107 0.269 4.117 0.000 0.957 0.957
R-Square:
Estimate
EEC1 0.574
EEC2 0.758
EEC3 0.699
EEF1 0.416
EEF2 0.234
EEF3 0.577
IM1 0.845
IM2 0.872
IM3 0.824
EEC 0.417
EEF 0.712
IM 0.043GG plot
EEF
EEC
IM
Differential effect of ADT on DVs
lavaan 0.6-19 ended normally after 28 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 15
Number of observations 115
Model Test User Model:
Test statistic 21.938
Degrees of freedom 12
P-value (Chi-square) 0.038
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
EEC =~
EEC1 1.000 0.983 0.784
EEC2 1.196 0.120 10.004 0.000 1.176 0.888
EEC3 1.123 0.114 9.823 0.000 1.105 0.864
EEF =~
EEF1 1.000 0.758 0.840
EEF2 1.070 0.127 8.415 0.000 0.812 0.788
EEF3 0.913 0.116 7.878 0.000 0.693 0.731
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
ADT_high 0.609 0.158 3.856 0.000 0.802 0.377
EEC ~
ADT_high 0.676 0.203 3.337 0.001 0.688 0.323
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.353 0.085 4.133 0.000 0.541 0.541
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.605 0.099 6.098 0.000 0.605 0.385
.EEC2 0.373 0.093 3.995 0.000 0.373 0.212
.EEC3 0.414 0.089 4.640 0.000 0.414 0.253
.EEF1 0.240 0.057 4.181 0.000 0.240 0.295
.EEF2 0.403 0.078 5.197 0.000 0.403 0.380
.EEF3 0.417 0.070 5.960 0.000 0.417 0.465
.EEC 0.866 0.180 4.800 0.000 0.895 0.895
.EEF 0.493 0.099 4.963 0.000 0.858 0.858[1] "Standardized coefficient for EEF: 0.377429934835339"[1] "Standardized coefficient for EEC: 0.32347352853529"Statical differential effect from ADT of the DVs
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_unconstrained 12 1758.8 1799.9 21.938
fit_constrained 13 1756.9 1795.3 22.060 0.12188 0 1 0.727Control (company size)
lavaan 0.6-19 ended normally after 32 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 24
Number of observations 115
Model Test User Model:
Test statistic 42.058
Degrees of freedom 30
P-value (Chi-square) 0.071
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
EEC =~
EEC1 1.000
EEC2 1.186 0.115 10.320 0.000
EEC3 1.101 0.110 9.971 0.000
EEF =~
EEF1 1.000
EEF2 1.057 0.126 8.373 0.000
EEF3 0.920 0.115 7.968 0.000
IM =~
IM1 1.000
IM2 1.007 0.061 16.498 0.000
IM3 1.013 0.062 16.278 0.000
Regressions:
Estimate Std.Err z-value P(>|z|)
EEF ~
IM 0.373 0.069 5.426 0.000
Cmpny_sz_ctgry 0.018 0.061 0.296 0.767
EEC ~
IM 0.556 0.087 6.407 0.000
Cmpny_sz_ctgry -0.099 0.071 -1.385 0.166
IM ~
Cmpny_sz_ctgry -0.144 0.093 -1.557 0.119
Covariances:
Estimate Std.Err z-value P(>|z|)
.EEC ~~
.EEF 0.195 0.064 3.065 0.002
Variances:
Estimate Std.Err z-value P(>|z|)
.EEC1 0.584 0.096 6.087 0.000
.EEC2 0.366 0.088 4.169 0.000
.EEC3 0.437 0.087 5.046 0.000
.EEF1 0.238 0.057 4.171 0.000
.EEF2 0.417 0.078 5.345 0.000
.EEF3 0.409 0.069 5.895 0.000
.IM1 0.200 0.043 4.595 0.000
.IM2 0.237 0.047 5.028 0.000
.IM3 0.256 0.049 5.199 0.000
.EEC 0.580 0.125 4.640 0.000
.EEF 0.410 0.086 4.761 0.000
.IM 1.186 0.184 6.456 0.000Nested SEM - doesnt work for Quarto
Moderation
ANOVA
EEF
Robost one-way ANOVA
Call: rlm(formula = EEF_composite ~ Condition, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-2.74938 -0.48067 -0.08272 0.51933 1.25062
Coefficients:
Value Std. Error t value
(Intercept) 5.8140 0.1082 53.7381
Condition2 -0.0646 0.1550 -0.4168
Residual standard error: 0.7699 on 113 degrees of freedomChecks assumptions
One-way ANOVA using trimmed means
Call:
t1way(formula = EEF_composite ~ Condition, data = data_with_dummies,
tr = 0.2)
Test statistic: F = 0.0668
Degrees of freedom 1: 1
Degrees of freedom 2: 62.91
p-value: 0.79692
Explanatory measure of effect size: 0.12
Bootstrap CI: [0.01; 0.37]##EEC ### One-way ANOVA using trimmed means
Call: rlm(formula = EEC_composite ~ Condition, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-3.40617 -0.61602 0.05065 0.59383 2.05065
Coefficients:
Value Std. Error t value
(Intercept) 4.9494 0.1383 35.7832
Condition2 0.1235 0.1982 0.6230
Residual standard error: 0.9133 on 113 degrees of freedomCall:
t1way(formula = EEC_composite ~ Condition, data = data_with_dummies,
tr = 0.2)
Test statistic: F = 0.9456
Degrees of freedom 1: 1
Degrees of freedom 2: 68.99
p-value: 0.33424
Explanatory measure of effect size: 0.14
Bootstrap CI: [0.01; 0.44]##IM ### Robost one-way ANOVA
Call: rlm(formula = IM_composite ~ Condition, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-4.7117 -0.6319 0.1145 0.4478 1.2883
Coefficients:
Value Std. Error t value
(Intercept) 5.8855 0.1303 45.1635
Condition2 -0.1738 0.1867 -0.9308
Residual standard error: 0.8185 on 113 degrees of freedomCall:
t1way(formula = IM_composite ~ Condition, data = data_with_dummies,
tr = 0.2)
Test statistic: F = 0.9048
Degrees of freedom 1: 1
Degrees of freedom 2: 68.54
p-value: 0.34483
Explanatory measure of effect size: 0.11
Bootstrap CI: [0.01; 0.32]AI interaction with ADT
Call: rlm(formula = EEF_composite ~ Condition * ADT_high, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-2.37744 -0.58570 0.08097 0.62256 1.08097
Coefficients:
Value Std. Error t value
(Intercept) 5.4211 0.1701 31.8786
Condition2 -0.0436 0.2405 -0.1814
ADT_high 0.5956 0.2065 2.8839
Condition2:ADT_high -0.0540 0.2940 -0.1838
Residual standard error: 0.8684 on 111 degrees of freedom
Call: rlm(formula = EEC_composite ~ Condition * ADT_high, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-3.2203 -0.8082 0.1130 0.7716 1.7797
Coefficients:
Value Std. Error t value
(Intercept) 4.3960 0.2439 18.0230
Condition2 0.2113 0.3449 0.6127
ADT_high 0.8324 0.2962 2.8101
Condition2:ADT_high -0.2194 0.4217 -0.5204
Residual standard error: 1.144 on 111 degrees of freedom
Call: rlm(formula = IM_composite ~ Condition * ADT_high, data = data_with_dummies)
Residuals:
Min 1Q Median 3Q Max
-4.3333 -0.6155 0.1024 0.4616 1.6667
Coefficients:
Value Std. Error t value
(Intercept) 5.5384 0.1944 28.4932
Condition2 -0.2051 0.2749 -0.7461
ADT_high 0.4722 0.2361 2.0004
Condition2:ADT_high 0.0920 0.3360 0.2739
Residual standard error: 0.8366 on 111 degrees of freedom