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For multiple groups analyses, analysis of variance and Kruskal-Wallis test were used to determine significant differences.
The two-tailed student's t-test was used to compare two groups, while multiple groups analyses were performed using two-way analysis of variance (ANOVA).
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Multiple group analyses were performed using one-way analysis of variance (ANOVA) or the Kruskal-Wallis test.
Multiple group analyses indicated invariance across six organizations, gender, job positions, and three times of measurement.
Further investigation using multiple group analyses is essential to evaluate whether various populations exhibit similar cognitive frameworks to given measures.
Namely, multiple group analyses indicated invariance across six organizations of various business sectors, across gender and job positions, and, finally, across three times of measurement.
The model was estimated in multiple group analyses of the three stages and the above-defined subgroups of sex and age.
Data are expressed as mean ± standard error and compared with one-way analysis of variance and the Student-Newman-Keuls test for multiple group analyses.
To cross validate the findings, several multiple group analyses were conducted in order to test the assumed invariance of the final model (Hypothesis 2).
To test for invariance of the models across ethnicity, we performed multiple-group analyses with the best fitting and two well-fitting models.
[ 33, 34] While some techniques have been suggested (e.g., zero-weighting[ 34] and multiple-group analyses[ 24]), the performance of these techniques in MLM with design weights needs further examination.
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