SI Invariance, October 2020 Version
Overview and questions
Variables
- All scaled using min-max [0,1] scaling
BSS Variables
- BSS 1 (desire to live)
- BSS 9 (urge)
- BSS 15 (intent)
Predictors
- Agitation - BDI11
- Fatigue - BDI16
- Hopeless - BDI2
- Sad - BDI1
- Self Hate - BDI7
Questions
CORRELATION MATRICES
Thomas
| BSS1 | BSS9 | AGITATION | SAD | HOPELESS | FATIGUE | SELF_HATE | |
|---|---|---|---|---|---|---|---|
| BSS1 | |||||||
| BSS9 | 0.419*** | ||||||
| AGITATION | 0.285** | 0.126 | |||||
| SAD | 0.329*** | 0.309** | 0.323*** | ||||
| HOPELESS | 0.271** | 0.219* | 0.396*** | 0.418*** | |||
| FATIGUE | 0.032 | 0.089 | 0.152 | 0.255** | 0.263** | ||
| SELF_HATE | 0.285** | 0.246* | 0.397*** | 0.387*** | 0.553*** | 0.167 | |
| Computed correlation used pearson-method with listwise-deletion. | |||||||
Evan
| BSS1 | BSS9 | AGITATION | SAD | HOPELESS | FATIGUE | |
|---|---|---|---|---|---|---|
| BSS1 | ||||||
| BSS9 | 0.303** | |||||
| AGITATION | 0.219* | 0.433*** | ||||
| SAD | 0.103 | 0.744*** | 0.490*** | |||
| HOPELESS | 0.195 | 0.715*** | 0.571*** | 0.854*** | ||
| FATIGUE | 0.009 | 0.545*** | 0.251* | 0.531*** | 0.439*** | |
| Computed correlation used pearson-method with listwise-deletion. | ||||||
Abby
| BSS1 | BSS9 | AGITATION | SAD | HOPELESS | FATIGUE | SELF_HATE | |
|---|---|---|---|---|---|---|---|
| BSS1 | |||||||
| BSS9 | 0.256** | ||||||
| AGITATION | 0.160 | 0.184 | |||||
| SAD | 0.233* | 0.254** | 0.256** | ||||
| HOPELESS | 0.313*** | 0.184* | 0.215* | 0.504*** | |||
| FATIGUE | 0.190* | 0.159 | 0.182 | 0.415*** | 0.388*** | ||
| SELF_HATE | 0.228* | 0.194* | 0.134 | 0.348*** | 0.594*** | 0.362*** | |
| Computed correlation used pearson-method with listwise-deletion. | |||||||
Autocorrelation in Evan’s data
| LAG | ACF_AGI | ACF_FAT | ACF_NEG | ACF_HOPELESS | ACF_URGE | ACF_RESIST | ACF_INTENT | |
|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 1 | 0.322 | 0.388 | 0.426 | 0.364 | 0.433 | 0.716 | 0.462 |
| 3 | 2 | 0.257 | 0.26 | 0.301 | 0.283 | 0.307 | 0.647 | 0.361 |
| 4 | 3 | 0.174 | 0.22 | 0.282 | 0.233 | 0.244 | 0.673 | 0.332 |
| 5 | 4 | 0.148 | 0.177 | 0.232 | 0.217 | 0.224 | 0.683 | 0.279 |
| 6 | 5 | 0.135 | 0.185 | 0.242 | 0.226 | 0.226 | 0.66 | 0.277 |
| 7 | 6 | 0.138 | 0.17 | 0.19 | 0.182 | 0.176 | 0.631 | 0.251 |
| 8 | 7 | 0.117 | 0.107 | 0.194 | 0.157 | 0.14 | 0.61 | 0.224 |
| 9 | 8 | 0.11 | 0.057 | 0.165 | 0.191 | 0.163 | 0.598 | 0.205 |
| 10 | 9 | 0.098 | 0.074 | 0.19 | 0.185 | 0.14 | 0.598 | 0.214 |
| 11 | 10 | 0.077 | 0.051 | 0.138 | 0.164 | 0.109 | 0.579 | 0.185 |
##Fatigue 
##Agitation 
CFA output and comparisons
CFAs
Summaries
summary(Thomas_CFA.fit, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-7 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 6
##
## Number of observations 102
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 74.864
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -51.735
## Loglikelihood unrestricted model (H1) -51.735
##
## Akaike (AIC) 115.470
## Bayesian (BIC) 131.220
## Sample-size adjusted Bayesian (BIC) 112.268
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.192 0.551
## BSS9_rescale 1.247 0.267 4.677 0.000 0.239 0.761
## BSS15_rescale 1.302 0.285 4.562 0.000 0.250 0.805
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.084 0.014 6.230 0.000 0.084 0.697
## .BSS9_rescale 0.042 0.012 3.514 0.000 0.042 0.421
## .BSS15_rescale 0.034 0.012 2.777 0.005 0.034 0.352
## BSS 0.037 0.014 2.653 0.008 1.000 1.000summary(Evan_CFA.fit, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-7 ended normally after 54 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 6
##
## Number of observations 100
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 65.276
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 61.851
## Loglikelihood unrestricted model (H1) 61.851
##
## Akaike (AIC) -111.701
## Bayesian (BIC) -96.070
## Sample-size adjusted Bayesian (BIC) -115.020
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.091 0.305
## BSS9_rescale 2.117 1.009 2.099 0.036 0.192 0.995
## BSS15_rescale 1.334 0.446 2.990 0.003 0.121 0.656
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.080 0.012 6.841 0.000 0.080 0.907
## .BSS9_rescale 0.000 0.013 0.026 0.980 0.000 0.009
## .BSS15_rescale 0.019 0.006 3.255 0.001 0.019 0.569
## BSS 0.008 0.006 1.360 0.174 1.000 1.000summary(Abby_CFA.fit, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 6
##
## Number of observations 114
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 16.207
## Degrees of freedom 3
## P-value 0.001
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -139.406
## Loglikelihood unrestricted model (H1) -139.406
##
## Akaike (AIC) 290.811
## Bayesian (BIC) 307.229
## Sample-size adjusted Bayesian (BIC) 288.265
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.294 0.879
## BSS9_rescale 0.420 0.432 0.973 0.330 0.124 0.291
## BSS15_rescale 0.376 0.385 0.976 0.329 0.111 0.304
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.025 0.086 0.295 0.768 0.025 0.227
## .BSS9_rescale 0.165 0.027 6.201 0.000 0.165 0.915
## .BSS15_rescale 0.120 0.020 6.008 0.000 0.120 0.908
## BSS 0.086 0.087 0.993 0.321 1.000 1.000Thomas’ Data
lavaanPlot(model = Thomas_CFA.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "orange"), coefs = T, covs = TRUE, stars = c("latent","regress"))Evan’s Data
lavaanPlot(model = Evan_CFA.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "blue"), coefs = T, covs = TRUE, stars = c("latent","regress"))Abby’s Data
lavaanPlot(model = Abby_CFA.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "red"), coefs = T, covs = TRUE, stars = c("latent","regress"))Comparisions
Vuong test
##
## Model 1
## Class: lavaan
## Call: lavaan::lavaan(model = CFA_MODEL, data = DATA_COMBINED, model.type = "cfa", ...
##
## Model 2
## Class: lavaan
## Call: lavaan::lavaan(model = CFA_MODEL, data = DATA_COMBINED, group = "DATA", ...
##
## Variance test
## H0: Model 1 and Model 2 are indistinguishable
## H1: Model 1 and Model 2 are distinguishable
## w2 = 1.816, p = 0.000138
##
## Non-nested likelihood ratio test
## H0: Model fits are equal for the focal population
## H1A: Model 1 fits better than Model 2
## z = -8.213, p = 1
## H1B: Model 2 fits better than Model 1
## z = -8.213, p = < 2.2e-16Measurement Invariance
Weak + Strong, but not Strict invariance – structure, intercepts, and factor loadings are the same across groups. Means and residuals are not. This may be d/t the samples being somewhat different? Or the measurement? Either way, it’s pretty cool!
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.residuals
## Model 5 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 0 312.58 413.99 0.0000
## fit.loadings 4 310.36 396.75 5.7834 5.783 4 0.2159
## fit.intercepts 8 308.78 380.14 12.1995 6.416 4 0.1702
## fit.residuals 14 440.54 489.36 155.9564 143.757 6 <2e-16 ***
## fit.means 16 650.47 691.78 369.8862 213.930 2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1.000 0.000 NA NA
## fit.loadings 0.988 0.065 0.012 0.065
## fit.intercepts 0.971 0.071 0.016 0.006
## fit.residuals 0.037 0.310 0.935 0.240
## fit.means 0.000 0.458 0.037 0.148SEM output and comparisons
SEM
Summaries
## lavaan 0.6-7 ended normally after 67 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 20
##
## Number of observations 102
##
## Model Test User Model:
##
## Test statistic 8.434
## Degrees of freedom 8
## P-value (Chi-square) 0.392
##
## Model Test Baseline Model:
##
## Test statistic 164.040
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997
## Tucker-Lewis Index (TLI) 0.992
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -140.138
## Loglikelihood unrestricted model (H1) -135.921
##
## Akaike (AIC) 320.276
## Bayesian (BIC) 372.776
## Sample-size adjusted Bayesian (BIC) 309.603
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.023
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.120
## P-value RMSEA <= 0.05 0.572
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.191 0.548
## BSS9_rescale 1.147 0.237 4.843 0.000 0.219 0.696
## BSS15_rescale 1.410 0.285 4.952 0.000 0.269 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.062 0.062 1.001 0.317 0.324 0.107
## SAD_rescale 0.209 0.071 2.931 0.003 1.094 0.364
## HOPELESS_rescl 0.131 0.066 1.977 0.048 0.688 0.233
## FATIGUE_rescal 0.008 0.058 0.141 0.888 0.043 0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.035 0.011 3.108 0.002 0.035 0.323
## FATIGUE_rescal 0.016 0.011 1.514 0.130 0.016 0.152
## HOPELESS_rescl 0.044 0.012 3.720 0.000 0.044 0.396
## SAD_rescale ~~
## FATIGUE_rescal 0.028 0.011 2.495 0.013 0.028 0.255
## HOPELESS_rescl 0.047 0.012 3.898 0.000 0.047 0.418
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.029 0.011 2.572 0.010 0.029 0.263
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.085 0.013 6.420 0.000 0.085 0.700
## .BSS9_rescale 0.051 0.010 5.282 0.000 0.051 0.516
## .BSS15_rescale 0.024 0.010 2.403 0.016 0.024 0.249
## AGITATION_rscl 0.108 0.015 7.141 0.000 0.108 1.000
## SAD_rescale 0.111 0.015 7.141 0.000 0.111 1.000
## HOPELESS_rescl 0.114 0.016 7.141 0.000 0.114 1.000
## FATIGUE_rescal 0.107 0.015 7.141 0.000 0.107 1.000
## .BSS 0.025 0.009 2.679 0.007 0.682 0.682## lavaan 0.6-7 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 20
##
## Number of observations 100
##
## Model Test User Model:
##
## Test statistic 20.042
## Degrees of freedom 8
## P-value (Chi-square) 0.010
##
## Model Test Baseline Model:
##
## Test statistic 383.709
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967
## Tucker-Lewis Index (TLI) 0.913
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 171.129
## Loglikelihood unrestricted model (H1) 181.150
##
## Akaike (AIC) -302.258
## Bayesian (BIC) -250.154
## Sample-size adjusted Bayesian (BIC) -313.319
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.123
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.191
## P-value RMSEA <= 0.05 0.039
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.045
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.088 0.296
## BSS9_rescale 2.112 0.718 2.944 0.003 0.186 0.966
## BSS15_rescale 1.424 0.498 2.857 0.004 0.126 0.681
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.012 0.027 0.420 0.674 0.130 0.033
## SAD_rescale 0.111 0.056 1.966 0.049 1.259 0.344
## HOPELESS_rescl 0.110 0.057 1.934 0.053 1.246 0.335
## FATIGUE_rescal 0.073 0.035 2.100 0.036 0.824 0.225
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.034 0.008 4.398 0.000 0.034 0.490
## FATIGUE_rescal 0.017 0.007 2.434 0.015 0.017 0.251
## HOPELESS_rescl 0.039 0.008 4.957 0.000 0.039 0.571
## SAD_rescale ~~
## FATIGUE_rescal 0.040 0.008 4.687 0.000 0.040 0.531
## HOPELESS_rescl 0.063 0.010 6.496 0.000 0.063 0.854
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.032 0.008 4.021 0.000 0.032 0.439
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.081 0.011 7.025 0.000 0.081 0.912
## .BSS9_rescale 0.003 0.003 0.898 0.369 0.003 0.068
## .BSS15_rescale 0.018 0.003 6.324 0.000 0.018 0.536
## AGITATION_rscl 0.065 0.009 7.071 0.000 0.065 1.000
## SAD_rescale 0.075 0.011 7.071 0.000 0.075 1.000
## HOPELESS_rescl 0.072 0.010 7.071 0.000 0.072 1.000
## FATIGUE_rescal 0.075 0.011 7.071 0.000 0.075 1.000
## .BSS 0.003 0.002 1.455 0.146 0.345 0.345## lavaan 0.6-7 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 20
##
## Number of observations 114
##
## Model Test User Model:
##
## Test statistic 5.538
## Degrees of freedom 8
## P-value (Chi-square) 0.699
##
## Model Test Baseline Model:
##
## Test statistic 112.288
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.071
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -197.771
## Loglikelihood unrestricted model (H1) -195.002
##
## Akaike (AIC) 435.541
## Bayesian (BIC) 490.265
## Sample-size adjusted Bayesian (BIC) 427.052
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.084
## P-value RMSEA <= 0.05 0.835
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.032
##
## 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
## BSS =~
## BSS1_rescale 1.000 0.199 0.595
## BSS9_rescale 0.874 0.321 2.718 0.007 0.174 0.410
## BSS15_rescale 0.733 0.273 2.681 0.007 0.146 0.401
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.078 0.089 0.885 0.376 0.394 0.114
## SAD_rescale 0.169 0.102 1.652 0.098 0.850 0.252
## HOPELESS_rescl 0.190 0.091 2.100 0.036 0.955 0.319
## FATIGUE_rescal 0.068 0.093 0.725 0.469 0.339 0.101
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.022 0.008 2.648 0.008 0.022 0.256
## FATIGUE_rescal 0.016 0.008 1.913 0.056 0.016 0.182
## HOPELESS_rescl 0.021 0.009 2.240 0.025 0.021 0.215
## SAD_rescale ~~
## FATIGUE_rescal 0.037 0.009 4.093 0.000 0.037 0.415
## HOPELESS_rescl 0.050 0.010 4.802 0.000 0.050 0.504
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.039 0.010 3.864 0.000 0.039 0.388
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.072 0.016 4.453 0.000 0.072 0.645
## .BSS9_rescale 0.150 0.023 6.478 0.000 0.150 0.832
## .BSS15_rescale 0.111 0.017 6.538 0.000 0.111 0.839
## AGITATION_rscl 0.083 0.011 7.550 0.000 0.083 1.000
## SAD_rescale 0.088 0.012 7.550 0.000 0.088 1.000
## HOPELESS_rescl 0.111 0.015 7.550 0.000 0.111 1.000
## FATIGUE_rescal 0.088 0.012 7.550 0.000 0.088 1.000
## .BSS 0.026 0.014 1.886 0.059 0.650 0.650Thomas’ Data
Evan’s Data
Abby’s Data
Multigroup SEM
## lavaan 0.6-7 ended normally after 155 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
##
## Number of observations per group:
## THOMAS 102
## EVAN 100
## ABBY 114
##
## Model Test User Model:
##
## Test statistic 34.014
## Degrees of freedom 24
## P-value (Chi-square) 0.084
## Test statistic for each group:
## THOMAS 8.434
## EVAN 20.042
## ABBY 5.538
##
## Model Test Baseline Model:
##
## Test statistic 660.037
## Degrees of freedom 63
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.983
## Tucker-Lewis Index (TLI) 0.956
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -166.780
## Loglikelihood unrestricted model (H1) -149.773
##
## Akaike (AIC) 495.560
## Bayesian (BIC) 799.775
## Sample-size adjusted Bayesian (BIC) 542.864
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.108
## P-value RMSEA <= 0.05 0.306
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.035
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [THOMAS]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.191 0.548
## BSS9_rescale 1.147 0.237 4.843 0.000 0.219 0.696
## BSS15_rescale 1.410 0.285 4.952 0.000 0.269 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.062 0.062 1.001 0.317 0.324 0.107
## SAD_rescale 0.209 0.071 2.931 0.003 1.094 0.364
## HOPELESS_rescl 0.131 0.066 1.977 0.048 0.688 0.233
## FATIGUE_rescal 0.008 0.058 0.141 0.888 0.043 0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.035 0.011 3.108 0.002 0.035 0.323
## FATIGUE_rescal 0.016 0.011 1.514 0.130 0.016 0.152
## HOPELESS_rescl 0.044 0.012 3.720 0.000 0.044 0.396
## SAD_rescale ~~
## FATIGUE_rescal 0.028 0.011 2.495 0.013 0.028 0.255
## HOPELESS_rescl 0.047 0.012 3.898 0.000 0.047 0.418
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.029 0.011 2.572 0.010 0.029 0.263
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.208 0.065 3.174 0.002 0.208 0.597
## .BSS9_rescale 0.059 0.065 0.906 0.365 0.059 0.187
## .BSS15_rescale 0.091 0.068 1.336 0.182 0.091 0.293
## AGITATION_rscl 0.500 0.033 15.338 0.000 0.500 1.519
## SAD_rescale 0.507 0.033 15.388 0.000 0.507 1.524
## HOPELESS_rescl 0.552 0.033 16.493 0.000 0.552 1.633
## FATIGUE_rescal 0.588 0.032 18.150 0.000 0.588 1.797
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.085 0.013 6.420 0.000 0.085 0.700
## .BSS9_rescale 0.051 0.010 5.282 0.000 0.051 0.516
## .BSS15_rescale 0.024 0.010 2.403 0.016 0.024 0.249
## AGITATION_rscl 0.108 0.015 7.141 0.000 0.108 1.000
## SAD_rescale 0.111 0.015 7.141 0.000 0.111 1.000
## HOPELESS_rescl 0.114 0.016 7.141 0.000 0.114 1.000
## FATIGUE_rescal 0.107 0.015 7.141 0.000 0.107 1.000
## .BSS 0.025 0.009 2.679 0.007 0.682 0.682
##
##
## Group 2 [EVAN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.088 0.296
## BSS9_rescale 2.112 0.718 2.944 0.003 0.186 0.966
## BSS15_rescale 1.424 0.498 2.857 0.004 0.126 0.681
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.012 0.027 0.420 0.674 0.130 0.033
## SAD_rescale 0.111 0.056 1.966 0.049 1.259 0.344
## HOPELESS_rescl 0.110 0.057 1.934 0.053 1.246 0.335
## FATIGUE_rescal 0.073 0.035 2.100 0.036 0.824 0.225
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.034 0.008 4.398 0.000 0.034 0.490
## FATIGUE_rescal 0.017 0.007 2.434 0.015 0.017 0.251
## HOPELESS_rescl 0.039 0.008 4.957 0.000 0.039 0.571
## SAD_rescale ~~
## FATIGUE_rescal 0.040 0.008 4.687 0.000 0.040 0.531
## HOPELESS_rescl 0.063 0.010 6.496 0.000 0.063 0.854
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.032 0.008 4.021 0.000 0.032 0.439
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.133 0.046 2.925 0.003 0.133 0.448
## .BSS9_rescale -0.101 0.022 -4.657 0.000 -0.101 -0.522
## .BSS15_rescale -0.047 0.027 -1.749 0.080 -0.047 -0.254
## AGITATION_rscl 0.246 0.025 9.658 0.000 0.246 0.966
## SAD_rescale 0.336 0.027 12.310 0.000 0.336 1.231
## HOPELESS_rescl 0.340 0.027 12.653 0.000 0.340 1.265
## FATIGUE_rescal 0.333 0.027 12.175 0.000 0.333 1.217
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.081 0.011 7.025 0.000 0.081 0.912
## .BSS9_rescale 0.003 0.003 0.898 0.369 0.003 0.068
## .BSS15_rescale 0.018 0.003 6.324 0.000 0.018 0.536
## AGITATION_rscl 0.065 0.009 7.071 0.000 0.065 1.000
## SAD_rescale 0.075 0.011 7.071 0.000 0.075 1.000
## HOPELESS_rescl 0.072 0.010 7.071 0.000 0.072 1.000
## FATIGUE_rescal 0.075 0.011 7.071 0.000 0.075 1.000
## .BSS 0.003 0.002 1.455 0.146 0.345 0.345
##
##
## Group 3 [ABBY]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.199 0.595
## BSS9_rescale 0.874 0.321 2.718 0.007 0.174 0.410
## BSS15_rescale 0.733 0.273 2.681 0.007 0.146 0.401
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATION_rscl 0.078 0.089 0.885 0.376 0.394 0.114
## SAD_rescale 0.169 0.102 1.652 0.098 0.850 0.252
## HOPELESS_rescl 0.190 0.091 2.100 0.036 0.955 0.319
## FATIGUE_rescal 0.068 0.093 0.725 0.469 0.339 0.101
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.022 0.008 2.648 0.008 0.022 0.256
## FATIGUE_rescal 0.016 0.008 1.913 0.056 0.016 0.182
## HOPELESS_rescl 0.021 0.009 2.240 0.025 0.021 0.215
## SAD_rescale ~~
## FATIGUE_rescal 0.037 0.009 4.093 0.000 0.037 0.415
## HOPELESS_rescl 0.050 0.010 4.802 0.000 0.050 0.504
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.039 0.010 3.864 0.000 0.039 0.388
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.406 0.075 5.406 0.000 0.406 1.215
## .BSS9_rescale 0.390 0.089 4.376 0.000 0.390 0.920
## .BSS15_rescale 0.558 0.076 7.325 0.000 0.558 1.532
## AGITATION_rscl 0.418 0.027 15.454 0.000 0.418 1.447
## SAD_rescale 0.488 0.028 17.568 0.000 0.488 1.645
## HOPELESS_rescl 0.515 0.031 16.464 0.000 0.515 1.542
## FATIGUE_rescal 0.632 0.028 22.676 0.000 0.632 2.124
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.072 0.016 4.453 0.000 0.072 0.645
## .BSS9_rescale 0.150 0.023 6.478 0.000 0.150 0.832
## .BSS15_rescale 0.111 0.017 6.538 0.000 0.111 0.839
## AGITATION_rscl 0.083 0.011 7.550 0.000 0.083 1.000
## SAD_rescale 0.088 0.012 7.550 0.000 0.088 1.000
## HOPELESS_rescl 0.111 0.015 7.550 0.000 0.111 1.000
## FATIGUE_rescal 0.088 0.012 7.550 0.000 0.088 1.000
## .BSS 0.026 0.014 1.886 0.059 0.650 0.650Multigroup SEM w/equality constraints
## lavaan 0.6-7 ended normally after 151 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 8
##
## Number of observations per group:
## THOMAS 102
## EVAN 100
## ABBY 114
##
## Model Test User Model:
##
## Test statistic 38.255
## Degrees of freedom 32
## P-value (Chi-square) 0.207
## Test statistic for each group:
## THOMAS 10.165
## EVAN 21.591
## ABBY 6.500
##
## Model Test Baseline Model:
##
## Test statistic 660.037
## Degrees of freedom 63
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.990
## Tucker-Lewis Index (TLI) 0.979
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -168.900
## Loglikelihood unrestricted model (H1) -149.773
##
## Akaike (AIC) 483.801
## Bayesian (BIC) 757.970
## Sample-size adjusted Bayesian (BIC) 526.433
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.043
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.088
## P-value RMSEA <= 0.05 0.557
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [THOMAS]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.182 0.525
## BSS9_rescale 1.191 0.228 5.217 0.000 0.216 0.690
## BSS15_rescale 1.495 0.263 5.686 0.000 0.272 0.879
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI (.p4.) 0.030 0.028 1.050 0.294 0.164 0.054
## SAD_rsc (.p5.) 0.167 0.042 3.922 0.000 0.916 0.305
## HOPELES (.p6.) 0.130 0.039 3.314 0.001 0.717 0.243
## FATIGUE (.p7.) 0.071 0.028 2.542 0.011 0.389 0.127
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.035 0.011 3.108 0.002 0.035 0.323
## FATIGUE_rescal 0.016 0.011 1.514 0.130 0.016 0.152
## HOPELESS_rescl 0.044 0.012 3.720 0.000 0.044 0.396
## SAD_rescale ~~
## FATIGUE_rescal 0.028 0.011 2.495 0.013 0.028 0.255
## HOPELESS_rescl 0.047 0.012 3.898 0.000 0.047 0.418
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.029 0.011 2.572 0.010 0.029 0.263
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.209 0.048 4.373 0.000 0.209 0.603
## .BSS9_rescale 0.051 0.058 0.872 0.383 0.051 0.161
## .BSS15_rescale 0.074 0.060 1.244 0.214 0.074 0.240
## AGITATION_rscl 0.500 0.033 15.338 0.000 0.500 1.519
## SAD_rescale 0.507 0.033 15.388 0.000 0.507 1.524
## HOPELESS_rescl 0.552 0.033 16.493 0.000 0.552 1.633
## FATIGUE_rescal 0.588 0.032 18.150 0.000 0.588 1.797
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.087 0.013 6.556 0.000 0.087 0.724
## .BSS9_rescale 0.052 0.010 5.284 0.000 0.052 0.524
## .BSS15_rescale 0.022 0.010 2.131 0.033 0.022 0.228
## AGITATION_rscl 0.108 0.015 7.141 0.000 0.108 1.000
## SAD_rescale 0.111 0.015 7.141 0.000 0.111 1.000
## HOPELESS_rescl 0.114 0.016 7.141 0.000 0.114 1.000
## FATIGUE_rescal 0.107 0.015 7.141 0.000 0.107 1.000
## .BSS 0.023 0.008 2.934 0.003 0.708 0.708
##
##
## Group 2 [EVAN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.115 0.375
## BSS9_rescale 1.643 0.271 6.057 0.000 0.189 0.969
## BSS15_rescale 1.100 0.218 5.038 0.000 0.126 0.682
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI (.p4.) 0.030 0.028 1.050 0.294 0.258 0.066
## SAD_rsc (.p5.) 0.167 0.042 3.922 0.000 1.448 0.395
## HOPELES (.p6.) 0.130 0.039 3.314 0.001 1.133 0.305
## FATIGUE (.p7.) 0.071 0.028 2.542 0.011 0.615 0.168
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.034 0.008 4.398 0.000 0.034 0.490
## FATIGUE_rescal 0.017 0.007 2.434 0.015 0.017 0.251
## HOPELESS_rescl 0.039 0.008 4.957 0.000 0.039 0.571
## SAD_rescale ~~
## FATIGUE_rescal 0.040 0.008 4.687 0.000 0.040 0.531
## HOPELESS_rescl 0.063 0.010 6.496 0.000 0.063 0.854
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.032 0.008 4.021 0.000 0.032 0.439
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.104 0.036 2.889 0.004 0.104 0.338
## .BSS9_rescale -0.101 0.021 -4.804 0.000 -0.101 -0.520
## .BSS15_rescale -0.046 0.026 -1.770 0.077 -0.046 -0.249
## AGITATION_rscl 0.246 0.025 9.658 0.000 0.246 0.966
## SAD_rescale 0.336 0.027 12.310 0.000 0.336 1.231
## HOPELESS_rescl 0.340 0.027 12.653 0.000 0.340 1.265
## FATIGUE_rescal 0.333 0.027 12.175 0.000 0.333 1.217
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.081 0.012 6.999 0.000 0.081 0.860
## .BSS9_rescale 0.002 0.003 0.869 0.385 0.002 0.061
## .BSS15_rescale 0.018 0.003 6.382 0.000 0.018 0.534
## AGITATION_rscl 0.065 0.009 7.071 0.000 0.065 1.000
## SAD_rescale 0.075 0.011 7.071 0.000 0.075 1.000
## HOPELESS_rescl 0.072 0.010 7.071 0.000 0.072 1.000
## FATIGUE_rescal 0.075 0.011 7.071 0.000 0.075 1.000
## .BSS 0.005 0.002 2.565 0.010 0.343 0.343
##
##
## Group 3 [ABBY]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.174 0.530
## BSS9_rescale 1.000 0.370 2.706 0.007 0.174 0.411
## BSS15_rescale 0.865 0.318 2.718 0.007 0.150 0.413
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI (.p4.) 0.030 0.028 1.050 0.294 0.171 0.049
## SAD_rsc (.p5.) 0.167 0.042 3.922 0.000 0.959 0.285
## HOPELES (.p6.) 0.130 0.039 3.314 0.001 0.751 0.251
## FATIGUE (.p7.) 0.071 0.028 2.542 0.011 0.407 0.121
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.022 0.008 2.648 0.008 0.022 0.256
## FATIGUE_rescal 0.016 0.008 1.913 0.056 0.016 0.182
## HOPELESS_rescl 0.021 0.009 2.240 0.025 0.021 0.215
## SAD_rescale ~~
## FATIGUE_rescal 0.037 0.009 4.093 0.000 0.037 0.415
## HOPELESS_rescl 0.050 0.010 4.802 0.000 0.050 0.504
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.039 0.010 3.864 0.000 0.039 0.388
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.457 0.045 10.252 0.000 0.457 1.395
## .BSS9_rescale 0.408 0.083 4.898 0.000 0.408 0.965
## .BSS15_rescale 0.568 0.072 7.920 0.000 0.568 1.564
## AGITATION_rscl 0.418 0.027 15.454 0.000 0.418 1.447
## SAD_rescale 0.488 0.028 17.568 0.000 0.488 1.645
## HOPELESS_rescl 0.515 0.031 16.464 0.000 0.515 1.542
## FATIGUE_rescal 0.632 0.028 22.676 0.000 0.632 2.124
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.077 0.015 5.124 0.000 0.077 0.719
## .BSS9_rescale 0.149 0.024 6.288 0.000 0.149 0.831
## .BSS15_rescale 0.109 0.017 6.267 0.000 0.109 0.829
## AGITATION_rscl 0.083 0.011 7.550 0.000 0.083 1.000
## SAD_rescale 0.088 0.012 7.550 0.000 0.088 1.000
## HOPELESS_rescl 0.111 0.015 7.550 0.000 0.111 1.000
## FATIGUE_rescal 0.088 0.012 7.550 0.000 0.088 1.000
## .BSS 0.021 0.011 1.840 0.066 0.700 0.700Multigroup SEM w/equality constraints - fatigue only
## lavaan 0.6-7 ended normally after 163 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 81
## Number of equality constraints 2
##
## Number of observations per group:
## THOMAS 102
## EVAN 100
## ABBY 114
##
## Model Test User Model:
##
## Test statistic 34.954
## Degrees of freedom 26
## P-value (Chi-square) 0.113
## Test statistic for each group:
## THOMAS 9.190
## EVAN 20.218
## ABBY 5.546
##
## Model Test Baseline Model:
##
## Test statistic 660.037
## Degrees of freedom 63
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.985
## Tucker-Lewis Index (TLI) 0.964
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -167.250
## Loglikelihood unrestricted model (H1) -149.773
##
## Akaike (AIC) 492.500
## Bayesian (BIC) 789.203
## Sample-size adjusted Bayesian (BIC) 538.635
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.057
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.102
## P-value RMSEA <= 0.05 0.375
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.036
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [THOMAS]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.195 0.556
## BSS9_rescale 1.125 0.227 4.964 0.000 0.219 0.696
## BSS15_rescale 1.390 0.272 5.112 0.000 0.271 0.870
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI 0.062 0.063 0.985 0.325 0.316 0.104
## SAD_rsc 0.203 0.071 2.852 0.004 1.040 0.346
## HOPELES 0.124 0.067 1.868 0.062 0.637 0.215
## FATIGUE (.p7.) 0.059 0.026 2.227 0.026 0.303 0.099
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.035 0.011 3.108 0.002 0.035 0.323
## FATIGUE_rescal 0.016 0.011 1.514 0.130 0.016 0.152
## HOPELESS_rescl 0.044 0.012 3.720 0.000 0.044 0.396
## SAD_rescale ~~
## FATIGUE_rescal 0.028 0.011 2.495 0.013 0.028 0.255
## HOPELESS_rescl 0.047 0.012 3.898 0.000 0.047 0.418
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.029 0.011 2.572 0.010 0.029 0.263
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.185 0.062 2.957 0.003 0.185 0.526
## .BSS9_rescale 0.037 0.062 0.604 0.546 0.037 0.118
## .BSS15_rescale 0.063 0.063 1.001 0.317 0.063 0.202
## AGITATION_rscl 0.500 0.033 15.338 0.000 0.500 1.519
## SAD_rescale 0.507 0.033 15.388 0.000 0.507 1.524
## HOPELESS_rescl 0.552 0.033 16.493 0.000 0.552 1.633
## FATIGUE_rescal 0.588 0.032 18.150 0.000 0.588 1.797
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.085 0.013 6.408 0.000 0.085 0.691
## .BSS9_rescale 0.051 0.010 5.323 0.000 0.051 0.515
## .BSS15_rescale 0.024 0.010 2.384 0.017 0.024 0.243
## AGITATION_rscl 0.108 0.015 7.141 0.000 0.108 1.000
## SAD_rescale 0.111 0.015 7.141 0.000 0.111 1.000
## HOPELESS_rescl 0.114 0.016 7.141 0.000 0.114 1.000
## FATIGUE_rescal 0.107 0.015 7.141 0.000 0.107 1.000
## .BSS 0.026 0.009 2.751 0.006 0.678 0.678
##
##
## Group 2 [EVAN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.079 0.269
## BSS9_rescale 2.339 0.816 2.867 0.004 0.186 0.966
## BSS15_rescale 1.575 0.568 2.774 0.006 0.125 0.679
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI 0.010 0.025 0.417 0.677 0.130 0.033
## SAD_rsc 0.105 0.054 1.945 0.052 1.316 0.359
## HOPELES 0.099 0.052 1.908 0.056 1.241 0.333
## FATIGUE (.p7.) 0.059 0.026 2.227 0.026 0.743 0.203
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.034 0.008 4.398 0.000 0.034 0.490
## FATIGUE_rescal 0.017 0.007 2.434 0.015 0.017 0.251
## HOPELESS_rescl 0.039 0.008 4.957 0.000 0.039 0.571
## SAD_rescale ~~
## FATIGUE_rescal 0.040 0.008 4.687 0.000 0.040 0.531
## HOPELESS_rescl 0.063 0.010 6.496 0.000 0.063 0.854
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.032 0.008 4.021 0.000 0.032 0.439
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.144 0.043 3.357 0.001 0.144 0.488
## .BSS9_rescale -0.098 0.021 -4.590 0.000 -0.098 -0.512
## .BSS15_rescale -0.045 0.027 -1.692 0.091 -0.045 -0.245
## AGITATION_rscl 0.246 0.025 9.658 0.000 0.246 0.966
## SAD_rescale 0.336 0.027 12.310 0.000 0.336 1.231
## HOPELESS_rescl 0.340 0.027 12.653 0.000 0.340 1.265
## FATIGUE_rescal 0.333 0.027 12.175 0.000 0.333 1.217
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.081 0.011 7.035 0.000 0.081 0.928
## .BSS9_rescale 0.002 0.003 0.871 0.384 0.002 0.067
## .BSS15_rescale 0.018 0.003 6.317 0.000 0.018 0.539
## AGITATION_rscl 0.065 0.009 7.071 0.000 0.065 1.000
## SAD_rescale 0.075 0.011 7.071 0.000 0.075 1.000
## HOPELESS_rescl 0.072 0.010 7.071 0.000 0.072 1.000
## FATIGUE_rescal 0.075 0.011 7.071 0.000 0.075 1.000
## .BSS 0.002 0.002 1.407 0.159 0.348 0.348
##
##
## Group 3 [ABBY]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS =~
## BSS1_rescale 1.000 0.199 0.595
## BSS9_rescale 0.875 0.322 2.719 0.007 0.174 0.410
## BSS15_rescale 0.734 0.274 2.681 0.007 0.146 0.401
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BSS ~
## AGITATI 0.079 0.088 0.892 0.372 0.397 0.115
## SAD_rsc 0.172 0.100 1.721 0.085 0.864 0.256
## HOPELES 0.192 0.089 2.152 0.031 0.964 0.322
## FATIGUE (.p7.) 0.059 0.026 2.227 0.026 0.297 0.088
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AGITATION_rescale ~~
## SAD_rescale 0.022 0.008 2.648 0.008 0.022 0.256
## FATIGUE_rescal 0.016 0.008 1.913 0.056 0.016 0.182
## HOPELESS_rescl 0.021 0.009 2.240 0.025 0.021 0.215
## SAD_rescale ~~
## FATIGUE_rescal 0.037 0.009 4.093 0.000 0.037 0.415
## HOPELESS_rescl 0.050 0.010 4.802 0.000 0.050 0.504
## HOPELESS_rescale ~~
## FATIGUE_rescal 0.039 0.010 3.864 0.000 0.039 0.388
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.410 0.067 6.150 0.000 0.410 1.225
## .BSS9_rescale 0.393 0.085 4.605 0.000 0.393 0.926
## .BSS15_rescale 0.560 0.073 7.683 0.000 0.560 1.538
## AGITATION_rscl 0.418 0.027 15.454 0.000 0.418 1.447
## SAD_rescale 0.488 0.028 17.568 0.000 0.488 1.645
## HOPELESS_rescl 0.515 0.031 16.464 0.000 0.515 1.542
## FATIGUE_rescal 0.632 0.028 22.676 0.000 0.632 2.124
## .BSS 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BSS1_rescale 0.072 0.016 4.461 0.000 0.072 0.646
## .BSS9_rescale 0.150 0.023 6.475 0.000 0.150 0.832
## .BSS15_rescale 0.111 0.017 6.536 0.000 0.111 0.840
## AGITATION_rscl 0.083 0.011 7.550 0.000 0.083 1.000
## SAD_rescale 0.088 0.012 7.550 0.000 0.088 1.000
## HOPELESS_rescl 0.111 0.015 7.550 0.000 0.111 1.000
## FATIGUE_rescal 0.088 0.012 7.550 0.000 0.088 1.000
## .BSS 0.026 0.014 1.886 0.059 0.651 0.651Comparision between constrained and free models
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff
## All_SEM.fit.groups 24 495.56 799.77 34.014
## All_SEM.fit.groups.equality 32 483.80 757.97 38.255 4.241 8
## Pr(>Chisq)
## All_SEM.fit.groups
## All_SEM.fit.groups.equality 0.8348## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff
## All_SEM.fit.groups 24 495.56 799.77 34.014
## All_SEM.fit.groups.equality2 26 492.50 789.20 34.954 0.93974 2
## Pr(>Chisq)
## All_SEM.fit.groups
## All_SEM.fit.groups.equality2 0.6251SEM output with self-hate
Self-hate SEM
Thomas’ Data (Self hate)
Abby’s Data (Self hate)
Comparision between constrained and free models
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff
## All_SEM.fit.sh.groups 20 799.81 1029.3 15.913
## All_SEM.fit.sh.groups.equality 25 790.62 1003.3 16.732 0.81895 5
## Pr(>Chisq)
## All_SEM.fit.sh.groups
## All_SEM.fit.sh.groups.equality 0.9758