Jolynn Pek & Paul De Boeck, The Ohio State University
Title: Reconsidering the Uncertain Nature of Constructs
Abstract: Constructs are fundamental to psychological research, and their highly abstract nature could be a source of uncertainty in research findings. To address the uncertain nature of constructs, researchers have either (a) moved away from constructs toward specific observables and effects or (b) moved toward constructs with sharper definitions and stricter measurement practices. We propose an alternative formulation of constructs that acknowledges their uncertain nature by recognizing that constructs (a) as composite in nature to allow for heterogenous content (cf. unitary), (b) might be organized in hierarchical systems with overlap, and (c) are variable in nature as reflected in variable measurements. Constructs can thus be visualized as areas within a space instead of a point in which measures might not fully cover their content, pointing to the need for multiscale approaches to measurement. This reformulation seems more consistent with observed variability of findings, which aligns with ongoing and newer methodological avenues that emphasize generalization efforts while preserving and facilitating the integrative and explanatory role of constructs.
Rick Hoyle, Duke University
Title: Tightening the Connection between the Delineation and Measurement of Constructs
Abstract: Rigorous quantitative research in psychology requires effective measurement of well-delineated constructs. Poorly specified constructs do not provide adequate information for developing and evaluating measures. And measures that do not fully capture constructs when they are well-specified do not allow for clear inferences that inform theory. In short, rigorous and informative psychological science requires tight connections between well-delineated constructs and high-quality measures of them. I begin with a discussion of types of constructs typical of psychological theories and conceptualizations. I then present approaches to measurement available for operationally defining psychological constructs. Finally, I present an example of a new measure of political sectarianism that features a clear and detailed delineation of the construct, a thoughtfully designed measure of it, and a tight connection between them. I conclude with a discussion of ways the construct-measure connection can be tightened to increase the rigor and informativeness of quantitative research in psychology.
Yang Liu & Jolynn Pek, University of Maryland & The Ohio State University
Title: Reliability Calculations in Nonlinear Measurement Models
Abstract: In the light of measuring constructs, reliability is recognized as an important quantification. Given a latent variable measurement model that is assumed to map onto constructs of interest, reliability coefficients reflect how closely observed scores are aligned with latent scores (about the construct). When a nonlinear measurement model is employed, reliability can be computed in different ways, having distinct implications for substantive research. To begin, we explain how different definitions of reliability can be applied to nonlinear measurement models and why they lead to different reliability coefficients. An empirical illustration is provided, which is based on Magnus and Liu’s (2022) analysis of the depressive symptom survey data from the National Comorbidity Survey Replication (NCS-R). A two-dimensional hurdle graded response model was fitted to the data, and reliability coefficients were computed for various pairs of observed and latent scores. The presentation is concluded with a discussion on the usefulness of reliability coefficients (in relation to measuring constructs), or lack thereof, from a practical standpoint.
