Regarding an “Almost Anything Goes” Attitude Toward Methods in Psychology

Zitzmann, Steffen; Loreth, Lukas

doi:10.3389/fpsyg.2021.612570

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…Moreover, we found high convergence for the higher-level scores computed by different methods. Therefore, our results demonstrate that methodological pluralism, as it has been advocated by Zitzmann and Loreth (2021), does not preclude convergence of results. Although the results reveal that the IIP circumplex can be modeled by means of MCFA, BCFA, TEFA, and SPMC, there were nevertheless relevant differences.…”

Section: Interpretation and Relevance Of Resultssupporting

confidence: 56%

Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure

2021

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Difficulties in interpersonal behavior are often measured by the circumplex-based Inventory of Interpersonal Problems. Its eight scales can be represented by a three-factor structure with two circumplex factors, Dominance and Love, and a general problem factor, Distress. Bayesian confirmatory factor analysis is well-suited to evaluate the higher-level structure of interpersonal problems because circumplex loading priors allow for data-driven adjustments and a more flexible investigation of the ideal circumplex pattern than conventional maximum likelihood confirmatory factor analysis. Using a non-clinical sample from an online questionnaire study (N = 822), we replicated the three-factor structure of the IIP by maximum likelihood and Bayesian confirmatory factor analysis and found great proximity of the Bayesian loadings to perfect circumplexity. We found additional support for the validity of the three-factor model of the IIP by including external criteria-Agreeableness, Extraversion, and Neuroticism from the Big Five and subclinical grandiose narcissism-in the analysis. We also investigated higher-level scores for Dominance, Love, and Distress using traditional regression factor scores and weighted sum scores. We found excellent reliability (with Rtt ≥ 0.90) for Dominance, Love, and Distress for the two scoring methods. We found high congruence of the higher-level scores with the underlying factors and good circumplex properties of the scoring models. The correlational pattern with the external measures was in line with theoretical expectations and similar to the results from the factor analysis. We encourage the use of Bayesian modeling when dealing with circumplex structure and recommend the use of higher-level scores for interpersonal problems as parsimonious, reliable, and valid measures.

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Section: Interpretation and Relevance Of Resultssupporting

confidence: 56%

Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure

2021

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“…Thus, multiple univariate models need to be estimated (one per dependent variable), which highlights once more that our approach is a stepwise and thus only a limited information method [9]. However, the fact that it does not estimate the model at once but divides it into simpler submodels can also be seen as a feature ( [36,37]; see also [38]). Studying the potential of this type of estimation for estimating complex models is an interesting subject for future research.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian EAP-Based Nonlinear Extension of Croon and Van Veldhoven’s Model for Analyzing Data from Micro–Macro Multilevel Designs

et al. 2022

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Croon and van Veldhoven discussed a model for analyzing micro–macro multilevel designs in which a variable measured at the upper level is predicted by an explanatory variable that is measured at the lower level. Additionally, the authors proposed an approach for estimating this model. In their approach, estimation is carried out by running a regression analysis on Bayesian Expected a Posterior (EAP) estimates. In this article, we present an extension of this approach to interaction and quadratic effects of explanatory variables. Specifically, we define the Bayesian EAPs, discuss a way for estimating them, and we show how their estimates can be used to obtain the interaction and the quadratic effects. We present the results of a “proof of concept” via Monte Carlo simulation, which we conducted to validate our approach and to compare two resampling procedures for obtaining standard errors. Finally, we discuss limitations of our proposed extended Bayesian EAP-based approach.

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“…There are competing plausible methods for handling noninvariance, and it is usually up to the individual researcher to select one among the methods (Robitzsch, 2022;Zitzmann & Loreth, 2021). In particular, we are not convinced by the practice of preferring partial invariance over alternative approaches because this results in comparing apples with oranges for more than two groups.…”

Section: Discussionmentioning

confidence: 99%

Why Measurement Invariance is Not Necessary for Valid Group Comparisons

Robitzsch¹,

Lüdtke²

2022

Preprint

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Measurement invariance is an important topic in the psychometric literature of group comparison in confirmatory factor analysis. It is frequently stated in the literature that measurement invariance is a prerequisite for the comparison of group means or standard deviations of the latent variable in a factor model. In this article, we argue we think that measurement invariance is not a necessary condition for a valid comparison across groups. Moreover, we argue that there is unavoidable arbitrariness in how researchers can define group comparisons in the violation of invariance. In particular, there is no support for the belief that the partial invariance approach should be preferred over competing approaches such as invariance alignment, robust linking, or Bayesian approximate invariance. Furthermore, we also argue why an intentionally misspecified multiple-group factor model with diagonally weighted least squares estimation with invariant item parameters can be justified even if the measurement invariance assumption is violated.

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Regarding an “Almost Anything Goes” Attitude Toward Methods in Psychology

Cited by 9 publications

References 18 publications

Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure

Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure

A Bayesian EAP-Based Nonlinear Extension of Croon and Van Veldhoven’s Model for Analyzing Data from Micro–Macro Multilevel Designs

Why Measurement Invariance is Not Necessary for Valid Group Comparisons

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