When Forecasting Mutually Supportive Matches Will Be Practically Impossible

Forecasting which dyads will develop mutually supportive relationships is an important applied and basic research question. Applying psychometric theory to the design of forecasting studies shows that agreement between dyad members about their relationship (relational reciprocity) sets an upper limit for forecasting accuracy by determining the reliability of measurement. To test this, we estimated relational reciprocity in Study 1. Participants in seven samples (six student and one military; N = 504; Ndyads = 766) rated each other on support-related constructs in round-robin designs. Relational reciprocity was very low, undermining reliability. Formulas from psychometric theory predicted that forecasting supportive dyads would be practically impossible. To test this, we had participants in Study 2 complete a measure for matching dyads derived from recent theory. As predicted, supportive matches could not be forecast with acceptable precision. Theoretically, this falsifies some predictions of recent social-support theory. Practically, it remains unclear how to translate basic social-support research into effective interventions.

How to Think Straight About Psychometrics: Improving Measurement by Identifying its Assumptions

The aim of the current study is to present three assumptions common to psychometric theory and psychometric practice, and to show how alternatives to traditional psychometrical approaches can be used to improve psychological measurement. These alternatives are developed by adapting each of these three assumptions. The assumption of structural validity relates to the implementation of mathematical models. The process assumption which is underlying process generates the observed data. The construct assumption implies that the observed data on its own do not constitute a measurement, but the latent variable that originates the observed data. Nonparametric item response modeling and cognitive psychometric modeling are presented as alternatives for relaxing the first two assumptions, respectively. Network psychometrics is the alternative for relaxing the third assumption. Final remarks sum up the most important conclusions of the study.

A Reflection on Student Perceptions of Teaching Quality from Three Psychometric Perspectives: CCT, IRT and GT

This chapter discusses student perceptions in terms of three psychometric perspectives that dominate contemporary research on teaching quality, namely, Classical Test Theory (CTT), Item Response Theory (IRT) and Generalizability Theory (GT). These perspectives function as being exemplars for the connection between psychometric theories and the different perspectives on “what a perception is” as well as on how and for what purposes student perceptions should be used. The main message of the chapter is that the choice of a psychometric theory is not merely a technical matter, but also has implications for how the nature of perceptions is conceptualized. After presenting and linking each psychometric theory, their strengths and weaknesses in the context of student perceptions of teaching quality and issues on practical implementations are discussed.

The Diversity of Adversities with Psychometric Properties Assessment across Youth Categories through Rasch Model

This study will be focusing on several aspects such as the pattern of items distributions of adversities across youth categories, the pattern in reliability and separation index of the items and persons in measuring adversities across youth categories, the pattern in category probability curves across youth categories, and the pattern in item unidimensionality across youth categories. The research was provided empirical evidences on psychometric assessment of newly developed adversity measurement using modern psychometric theory. This information is valuable to expanding research on youth specifically in differentiating responses based on demographic profiles.

Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct relationships among symptoms. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). Furthermore, strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting symptom centrality. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

Factorial Structure Validation of the Movement Assessment Battery for Children in School-Age Children Between 8 and 10 Years Old

The adaptation of instruments to other cultural contexts is a complex task that requires careful planning to maintain the content and psychometric properties. One of the most used motor assessment tools in the world is the Movement Assessment Battery for Children 2ed. In this study, we evaluated the factorial organization of the MABC-2 for the age group 8-10 years from the perspective of classical and modern psychometric theory. For this purpose, a group of 350 school children in the city of Manaus (AM, Brazil) was evaluated. The factorial structure of the MABC-2 and a new factorial structure with four factors were tested. For data analysis, descriptive and inferential statistics were used. Factor analysis confirmed the original three-factor model. Based on these results, good evidences of validity were produced, based on the internal structure of the MABC-2 proposed by the original authors, confirming its ability to identify disorders in the development of coordination.