In this longitudinal research, we adopt a complexity approach to examine the temporal dynamics of variables related to compliance with behavioural measures during the COVID-19 pandemic. Dutch participants (N = 2399) completed surveys with COVID-19-related variables five times over a period of 10 weeks (23 April-30 June 2020). With these data, we estimated within-person COVID-19 attitude networks containing a broad set of psychological variables and their relations. These networks display variables' predictive effects over time between measurements and contemporaneous effects during measurements. Results show (1) bidirectional effects between multiple variables relevant for compliance, forming potential feedback loops, and (2) a positive reinforcing structure between compliance, support for behavioural measures, involvement in the pandemic and vaccination intention. These results can explain why levels of these variables decreased throughout the course of the study. The reinforcing structure points towards potentially amplifying effects of interventions on these variables and might inform processes of polarization. We conclude that adopting a complexity approach might contribute to understanding protective behaviour in the initial phase of pandemics by combining different theoretical models and modelling bidirectional effects between variables. Future research could build upon this research by studying causality with interventions and including additional variables in the networks.
No abstract is provided for this article.
This chapter discusses theoretical foundations of network analysis by outlining different ways in which networks can be used. In the broadest of these ways, often denoted the network approach, the researcher views a substantive phenomenon through the lens of networks. In this case, networks mainly function to organize observations and to suggest theoretical ideas. The network approach can subsequently be specified in at least two ways. First, by constructing psychometric network models, which formulate a probability distribution for a set of observations, typically by representing variables as nodes and conditional associations between these variables as edges. Second, by constructing network theories, which offer putative explanations of empirical phenomena. Thus, where network theories are tied to a particular empirical domain, network models are generic, i.e., independent of any particular domain. Finally, the chapter discusses ways in which the relation between network approaches, network theories, and network models can be understood.
This manuscript analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for two‐choice response processes entails a two‐parameter logistic Item Response Theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro/contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits, but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed and person fit in item response theory. We discuss the relation between this model and existing models, as well as implications for psychology and psychometrics.
In the social sciences, measurement instruments are constructed and evaluated using psychometric models. This chapter discusses the most important of these
There is currently an unprecedented level of doubt regarding the reliability of research findings in psychology. Many recommendations have been made to improve the current situation. In this article, we report results from PsychDisclosure.org, a novel open-science initiative that provides a platform for authors of recently published articles to disclose four methodological design specification details that are not required to be disclosed under current reporting standards but that are critical for accurate interpretation and evaluation of reported findings. Grassroots sentiment-as manifested in the positive and appreciative response to our initiative-indicates that psychologists want to see changes made at the systemic level regarding disclosure of such methodological details. Almost 50% of contacted researchers disclosed the requested design specifications for the four methodological categories (excluded subjects, nonreported conditions and measures, and sample size determination). Disclosed information provided by participating authors also revealed several instances of questionable editorial practices, which need to be thoroughly examined and redressed. On the basis of these results, we argue that the time is now for mandatory methods disclosure statements for all psychology journals, which would be an important step forward in improving the reliability of findings in psychology.
This article advances a simple conception of test validity: A test is valid for measuring an attribute if (a) the attribute exists and (b) variations in the attribute causally produce variation in the measurement outcomes. This conception is shown to diverge from current validity theory in several respects. In particular, the emphasis in the proposed conception is on ontology, reference, and causality, whereas current validity theory focuses on epistemology, meaning, and correlation. It is argued that the proposed conception is not only simpler but also theoretically superior to the position taken in the existing literature. Further, it has clear theoretical and practical implications for validation research. Most important, validation research must not be directed at the relation between the measured attribute and other attributes but at the processes that convey the effect of the measured attribute on the test scores.
Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data that are not generated from a common factor model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical reanalysis, we show that when a common factor model is misused, structural parameter estimates that indicate the relations among psychological constructs can be severely biased. Moreover, this bias can arise even when model fit is perfect. In some situations, composite models perform better than common factor models. These demonstrations point to a need for models to be justified on substantive, theoretical bases in addition to statistical ones. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.
Gideon Jan Mellenbergh (known as Don to his friends and colleagues) was born in Amsterdam on August 9, 1938, and passed away in the same city on March 27, 2021.Don was a major force in the Dutch psychometric community.He was one of the founders of the Interuniversity Graduate School of Psychometrics and Sociometrics (IOPS) that united the Ph.D. programs in the Netherlands and Flanders, and which he directed between 1987 and 2000.He was a member of the Royal Dutch Academy of Sciences (KNAW), served on the boards of the Netherlands Organization for Scientific Research and National Institute of Educational Measurement, and presided accreditation committees to evaluate Dutch and Flemish university programs.Don was a connecting figure who was able to build and maintain good relationships with all members of the Dutch psychometric community, even where there were passionate differences of opinion about psychometrics.He did not care for schools of thought; he only distinguished between good and bad research-and disliked the latter.This motivated a lifelong quest to improve research methods in psychology.Don attended Hervormd Gymnasium in Amsterdam, a six-year secondary school for gifted students with a curriculum consisting of six mandatory languages (including Greek and Latin), from which he graduated in 1957 with a specialization in the natural sciences and mathematics.After shortly considering to enroll in the college of physical education-Don was an accomplished basketball player-he attended the six-year psychology program at the University of Amsterdam.Don was not very impressed with the curriculum until he met Adriaan de Groot, a towering figure in postwar Dutch psychology and the founder of the university's Psychological Methods group.After his graduation in 1965, Don was appointed as Assistant Professor of Psychological Methods.His first job was at the Department of Exam Techniques, a precursor of the Dutch educational testing bureau Cito (which was also founded by De Groot).This department was headed by Robert van Naerssen, a psychometrician far ahead of his time (van den Brink & Mellenbergh, 1984), whose impact was, however, limited
The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.
