In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.
In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self‐sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.
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
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.
In the social sciences, measurement instruments are constructed and evaluated using psychometric models. This chapter discusses the most important of these
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.
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.
All camera data. The full layer 2 and 3 tables contain all unfiltered and filtered detections of visitors respectively. The “id” column is only present in the layer 3 tables, as detections have not been linked to unique visitors yet in layer 2. Additionally, files contenting each experimental time and a file of the map which provides the coordinates of the 32 walls in the area of the art fair where camera data was collected.See each codebook for explanations of files. <br><br>
This chapter considers alternatives to the reflective measurement model discussed in Chapter 7. Formative measurement models reverse the direction of causation
The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.
