Percentage of variance accounted for in structural equation models: The rediscovery of the goodness of fit index.

This article delves into the often-overlooked metric of percentage of variance accounted for in structural equation models (SEM). The goodness of fit index (GFI) provides the percentage of variance of the sum of squared covariances explained by the model. Despite being introduced over four decades ago, the GFI has been overshadowed in favor of fit indices that prioritize distinctions between close and nonclose fitting models.

Overfactoring in rating scale data: A comparison between factor analysis and item response theory.

Educational and psychological measurement is typically based on dichotomous variables or rating scales comprising a few ordered categories. When the mean of the observed responses approaches the upper or the lower bound of the scale, the distribution of the data becomes skewed and, if a categorical factor model holds in the population, the Pearson correlation between variables is attenuated. The consequence of this correlation attenuation is that the traditional linear factor model renders an excessive number of factors.

What are the consequences of ignoring cross-loadings in bifactor models? A simulation study assessing parameter recovery and sensitivity of goodness-of-fit indices.

Bifactor latent models have gained popularity and are widely used to model construct multidimensionality. When adopting a confirmatory approach, a common practice is to assume that all cross-loadings take zero values. This article presents the results of a simulation study exploring the impact of ignoring non-zero cross-loadings on the performance of confirmatory bifactor analysis.

Nominal Factor Analysis of Situational Judgment Tests: Evaluation of Latent Dimensionality and Factorial Invariance.

Situational judgment tests have gained popularity in educational and psychological measurement and are widely used in personnel assessment. A situational judgment item presents a hypothetical scenario and a list of actions, and the individuals are asked to select their most likely action for that scenario. Because actions have no explicit order, the item generates nominal responses consisting of the actions selected by the individuals.

Validation of a Questionnaire for Personality Profiling using Cognitive Diagnostic Modeling.

This article describes the development of the ENCUIST (Extroversion, Neuroticism, Callous-Unemotional, Instability, Short-Test) questionnaire, which has been created to provide a personality profiling method based on a cognitive diagnostic modeling framework. The ENCUIST measures the attributes of extroversion, neuroticism, callous unemotionality and overt expressions of anger that are relevant in a forensic context. The scores provided by the ENCUIST are binary classifications of the individuals (high/low) in these attributes.

Bayesian Dimensionality Assessment for the Multidimensional Nominal Response Model.

This article introduces Bayesian estimation and evaluation procedures for the multidimensional nominal response model. The utility of this model is to perform a nominal factor analysis of items that consist of a finite number of unordered response categories. The key aspect of the model, in comparison with traditional factorial model, is that there is a slope for each response category on the latent dimensions, instead of having slopes associated to the items.

Recovery of Weak Factor Loadings When Adding the Mean Structure in Confirmatory Factor Analysis: A Simulation Study.

This article extends previous research on the recovery of weak factor loadings in confirmatory factor analysis (CFA) by exploring the effects of adding the mean structure. This issue has not been examined in previous research. This study is based on the framework of Yung and Bentler (1999) and aims to examine the conditions that affect the recovery of weak factor loadings when the model includes the mean structure, compared to analyzing the covariance structure alone.

Turn-amplitude analysis as a diagnostic test for myofascial syndrome in patients with chronic pelvic pain.

Background: Myofascial pain syndrome of the pelvic floor (MPSPF) is a common disease in the context of chronic pelvic pain (CPP); however, there is currently no gold-standard test to diagnose it.

Objective: To validate the turns-amplitude analysis (TAA) as a diagnostic test for MPSPF in patients with CPP.

Methods: A case-control study was performed, and patients were consecutively sampled within a specified period of time.

Anal stretching device for patients with chronic prostatitis and chronic pelvic pain syndrome.

Objectives: Chronic pelvic pain syndrome (CPPS) is a poorly understood and ill-treated condition. It is accompanied by the shortening and increase in tone of the pelvic floor muscles and is closely related to myofascial pain syndrome (MPS). This study aims to evaluate the utility of an anal stretching device (ASD) for improving the pain manifestations of chronic prostatitis (CP) and CPPS.

Factorial invariance in a repeated measures design: an application to the study of person-organization fit.

An important methodological concern of any research based on a person-environment (P-E) fit approach is the operationalization of the fit, which imposes some measurement requirements that are rarely empirically tested with statistical methods. Among them, the assessment of the P and E components along commensurate dimensions is possibly the most cited one. This paper proposes to test the equivalence across the P and E measures by analyzing the measurement invariance of a multi-group confirmatory factor analysis model.

Recovery of weak factor loadings in confirmatory factor analysis under conditions of model misspecification.

This article presents the results of two Monte Carlo simulation studies of the recovery of weak factor loadings, in the context of confirmatory factor analysis, for models that do not exactly hold in the population. This issue has not been examined in previous research. Model error was introduced using a procedure that allows for specifying a covariance structure with a specified discrepancy in the population.

Extending the CLAST sequential rule to one-way ANOVA under group sampling.

Several studies have demonstrated that the fixed-sample stopping rule (FSR), in which the sample size is determined in advance, is less practical and efficient than are sequential-stopping rules. The composite limited adaptive sequential test (CLAST) is one such sequential-stopping rule. Previous research has shown that CLAST is more efficient in terms of sample size and power than are the FSR and other sequential rules and that it reflects more realistically the practice of experimental psychology researchers.

[Validation of the cognitive structure of the test of signs by structural equation modeling].

Validation of the cognitive structure of the test of signs by structural equation modeling. The present work is aimed to carry out a validation study of the cognitive operations required for the correct solution of items of a math test which includes basic arithmetic operations between integer numbers. The validation of the hypothesized cognitive structure is made by means of structural equation modeling and triangulation methods.

Optimization of sample size in controlled experiments: the CLAST rule.

Sequential rules are explored in the context of null hypothesis significance testing. Several studies have demonstrated that the fixed-sample stopping rule, in which the sample size used by researchers is determined in advance, is less practical and less efficient than sequential stopping rules. It is proposed that a sequential stopping rule called CLAST (composite limited adaptive sequential test) is a superior variant of COAST (composite open adaptive sequential test), a sequential rule proposed by Frick (1998).