Sentences with phrase «bifactor analysis»

Additionally, constraining non-zero cross-loadings to zero can inflate the variance attributed to the general factor in bifactor analysis (Morin et al., 2016; Joshanloo et al., 2017).

Bifactor analysis allows for an examination of the common variance shared by the two MLQ factors and the unique variance specific to each of them.

Given all above considerations, and the commonness of non-trivial secondary loadings in construct validation, bifactor analysis more often than not is expected to support unidimensionality (Joshanloo & Jovanovic, 2016).

This may not necessarily suggest a predominant general Meaning - in - life factor be present, since unidimensionality based only on bifactor analysis is unstable (Joshanloo et al., 2017; Joshanloo & Jovanovic, 2016).

More specifically, it seems that relying solely on the results of bifactor analysis to decide whether a psychological scale is unidimensional or multidimensional may be questionable (Joshanloo et al., 2017).

However, bifactor analysis has received some criticism (Reise et al., 2013; Reise, 2012; Joshanloo et al., 2017).

Not exact matches

The purpose of this study was to evaluate the following: 1) the construct validity of the Meaning in Life Questionnaire (Steger et al., 2006), Greek Version using different explorative and confirmative factorial analysis approaches like Bifactor EFA, ICM - CFA, Bifactor CFA and ESEM; 2) the measurement invariance of MLQ across gender; 3) the internal consistency reliability of the MLQ; and 4) the convergent and discriminant validity of the MLQ with measures of well - being and mental distress.

Future research could also evaluate new confirmatory factor analysis techniques like Bifactor ESEM.

More specifically, the objectives of this study are the following: 1) To validate the construct validity of the Meaning in Life Questionnaire (Steger et al., 2006), Greek Version using both exploratory and confirmatory factorial analysis techniques like Bifactor EFA, Bifactor CFA and ESEM; 2) to examine measurement invariance of MLQ across gender; 3) to study the internal consistency reliability of the MLQ; and 4) to evaluate the convergent and discriminant validity of the MLQ with the constructs of well - being, hope, anxiety, depression, stress, hope and resilience.

To examine this assumption further, a Confirmatory factor analysis (CFA) followed in a second sample to verify the models emerged from EFA and bifactor EFA.

Independent Cluster Model Confirmatory Factor Analysis (ICM - CFA), Bifactor Confirmatory Factor Analysis and Exploratory Structural Equation Modeling Analysis followed in the second sample (CFA1 Sample), testing seven alternative solutions.

First two alternative models were tested using Exploratory factor analysis (EFA): 1) a standard EFA to examine the factor structure and to have a baseline model for EFA comparisons, and 2) a bifactor EFA.

In the first sample (EFA Sample), Exploratory Factor Analysis and Bifactor Exploratory Factor Analysis were carried out.

In keeping with the most recent factorial studies, our analysis supported the superior fit of a bifactor model.

In keeping with the most recent factorial studies, our analysis supported the superior fit of a bifactor model with a general psychological distress factor and two group factors with anxiety and depression.