
χ^{2}

df

p

CFI

ΔCFI

TLI

RMSEA

Δ RMSEA

SRMR

BIC

AIC


Configural: loadings + intercepts free

399.266

154

0.233

0.924
 
0.910

0.091
 
0.049

13,076

13,076

Metric: loadings fixed + intercepts free

415.117

167

0.001*

0.923

0.001

0.916

0.088

0.003

0.061

13,014

12,734

Scalar: loadings + intercept fixed

476.726

180

0.064

0.908

0.015

0.907

0.092

0.004

0.067

12,999

12,769

Partial invariance

438.214

179

0.001*

0.915

0.008

0.907

0.092

0.000

0.065

13,043

12,758

 * = Statistically significant p < 0.05. Partial invariance achieved by freeing intercept 5. The model is regarded as acceptable if the chisquare is not significant. However, this is disregarded when the sample size exceeds 200. The Comparative Fit Index (CFI) compares the examined model of interest with the null model. The Tucker Lewis Index (TLI) is computed by the division of the chi square for the target model and the null model by their corresponding df vales (relative chi squares), which are then subtracted from each other, and their difference is finally divided by the relative chi square for the null model minus 1. The Root Mean Square Error of Approximation (RMSEA) represents the square root of the average or mean of the covariance residuals. The Bayesian Information Criterion (BIC) expresses the log of a Bayes factor of the target model compared to the saturated model. Finally, the Akaike information criterion (AIC) is regarded as an information theory goodness of fit measure applicable when maximum likelihood estimation is used [5]. After freeing the intercept for one item (Item 5; “I’ve had energy to spare”), partial scalar invariance was supported