Search

In psychology, many studies measure the same variables in different groups. In the case of a large number of variables when a strong a priori idea about the underlying latent construct is lacking, researchers often start by reducing the variables to a few principal components in an exploratory way. Herewith, one often wants to evaluate whether the components represent the same construct in the different groups. To this end, it makes sense to remove outlying variables that have significantly different loadings on the extracted components across the groups, hampering equivalent interpretations of the components. Moreover, identifying such outlying variables is important when testing theories about which variables behave similarly or differently across groups. In this article, we first scrutinize the lower bound congruence method (LBCM; De Roover, Timmerman, & Ceulemans in Behavior Research Methods, 49, 216–229, 2017), which was recently proposed for solving the outlying-variable detection problem. LBCM investigates how Tucker's congruence between the loadings of the obtained cluster-loading matrices improves when specific variables are discarded. We show that LBCM has the tendency to output outlying variables that either are false positives or concern very small, and thus practically insignificant, loading differences. To address this issue, we present a new heuristic: the lower and resampled upper bound congruence method (LRUBCM). This method uses a resampling technique to obtain a sampling distribution for the congruence coefficient, under the hypothesis that no outlying variable is present. In a simulation study, we show that LRUBCM outperforms LBCM. Finally, we illustrate the use of the method by means of empirical data. [ABSTRACT FROM AUTHOR]

Background: Relating to the macro-level changes and the increasing complexity of the academic system, a growing number of studies began to investigate the perceived working context impact on well-being and job satisfaction of academics. A unique duality characterizes this context: academics cannot be longer defined as stress-free, but at the same time they are still satisfied and engaged in their work. There is a need to evaluate the academic environment not only in terms of stressor and strain, but also in terms of which experiences are sources of fulfillment. The study aimed to explore psychometric properties of a new instrument (AQoLW) for assessing context-specific features of the academic work and environment that characterized academics' quality of life at work.Method: A 24 item scale was deployed to academics (full, associate, and assistant professors) in a public university in the north of Italy. Items were defined to represent the main academic activities in order to measure if respondents perceived each of it as a challenging or a hindrance demand. The scale was administered online to 1,012 academics, 443 females (48.7%), mean aged 51.1 years (SD = 8.2). In order to test three theoretical models underling AQoLW, a training sample was randomly extracted (242 participants) and analyzed using confirmatory factor analysis (CFA). A validation sample with the remaining 668 participants was used to test the measurement invariance by role of the best model emerging from the training sample.Results: Model fit demonstrate the goodness of a latent structure composed by five intercorrelated factors (CFI = 0.91, RMSEA = 0.08, SRMR = 0.07). Cronbach α of the five subscales was good, ranging from 0.76 to 0.88. The scale overtakes configural invariance, but not strong invariance by role.Conclusions: The scale is able to intercept the mainly dimensions of the academic work that contribute to the quality of life of academics' staff, namely: research and public engagement, didactic work and relationships with students, career development and competition, ordinary obligations, and fund raising. AQoLW is the first tool to evaluate the academic work and its environment, identifying which activities are stressful demands and which are engaging, and promote scholars' satisfaction.

We describe and evaluate a random permutation test of measurement invariance with ordered-categorical data. To calculate a p-value for the observed (∆)χ2, an empirical reference distribution is built by repeatedly shuffling the grouping variable, then saving the χ2 from a configural model, or the ∆χ2 between configural and scalar-invariance models, fitted to each permuted dataset. The current gold standard in this context is a robust mean- and variance-adjusted ∆χ2 test proposed by Satorra (2000), which yields inflated Type I errors, particularly when thresholds are asymmetric, unless samples sizes are quite large (Bandalos, 2014; Sass et al., 2014). In a Monte Carlo simulation, we compare permutation to three implementations of Satorra’s robust χ2 across a variety of conditions evaluating configural and scalar invariance. Results suggest permutation can better control Type I error rates while providing comparable power under conditions that the standard robust test yields inflated errors. [ABSTRACT FROM AUTHOR]