In 2011, Dr. Bem published an article in the Journal of Personality and Social Psychology, the flagship journal of social psychology, in which he claimed that people can look into the future (Bem, 2011). In his first experiment, “precognitive detection of erotic stimuli”, participants were instructed as follows: “(...) on each trial of the experiment, pictures of two curtains will appear on the screen side by side. One of them has a picture behind it; the other has a blank wall behind it. Your task is to click on the curtain that you feel has the picture behind it. The curtain will then open, permitting you to see if you selected the correct curtain.” In the experiment, the location of the pictures was random and chance performance is therefore 50%. Nevertheless, Bem’s participants scored 53.1%, significantly higher than chance; however, the effect was present only for erotic pictures, and not for neutral pictures, positive pictures, negative pictures, and romantic-but-not-erotic pictures. Bem also claimed that the psi effects are more pronounced for extraverts, and that for certain erotic pictures women show psi but men do not. We set out to replicate Bem’s experiment in a purely confirmatory fashion. First we detailed our method, design, and planned analyses in a document that we posted online before a single participant was tested.1 As outlined in the online document, our replication focused on Bem’s key findings; therefore, we tested only women, used only neutral and erotic pictures, and included a standard extraversion questionnaire. We also tested each participant in two contiguous sessions. Each session featured the same pictures, but presented them in a different random order.2 The idea is that individual differences in psi –if these exist– lead to a positive correlation between performance on session 1 and session 2. Performance is quantified by the proportion of times that the participant chooses the curtain that hides the picture. Each session featured 60 trials, with 45 neutral pictures and 15 erotic pictures.
Klaas Sijtsma has suggested that psychological constructs, such as those invoked in the study of intelligence, personality, and psychopathology, should be understood as organizing principles with respect to elements of behavior, including item response behavior. In a discussion in the journal Psychometrika , Sijtsma ( Psychometrika, 71 (3), 451–455 (2006)) contrasted this position with the common cause interpretation of Item Response Theory (IRT) models and the associated theory of validity that I had articulated some years earlier (Borsboom, Psychological Review, 111 (4), 1061–1071 (2004)), arguing that this theory of validity was far too strong given the immature status of psychological constructs. In the present chapter, I present an alternative understanding of IRT models in terms of psychometric networks, which is inspired by Sijtsma’s idea of constructs as organizing principles. From the weak premise that psychological constructs organize behaviors, in the sense of identifying behavioral elements that structurally hang together, in the present chapter, I show how one can build up a psychometric approach that can motivate and guide the use of tests in psychology in the absence of strong common cause interpretations.
Attitude strength is a key characteristic of attitudes. Strong attitudes are durable and impactful, while weak attitudes are fluctuating and inconsequential. Recently, the Causal Attitude Network (CAN) model was proposed as a comprehensive measurement model of attitudes, which conceptualizes attitudes as networks of causally connected evaluative reactions (i.e., beliefs, feelings, and behavior toward an attitude object). Here, we test the central postulate of the CAN model that highly connected attitude networks correspond to strong attitudes. We use data from the American National Election Studies 1980-2012 on attitudes toward presidential candidates (total n = 18,795). We first show that political interest predicts connectivity of attitude networks toward presidential candidates. Second, we show that connectivity is strongly related to two defining features of strong attitudes - stability of the attitude and the attitude's impact on behavior. We conclude that network theory provides a promising framework to advance the understanding of attitude strength.
Available indoor environment measures.
Psychological science is divided into two distinct methodological traditions. One tradition seeks to understand how people function at the individual level, while the other seeks to understand how people differ from each other. Methodologies that have grown out of these traditions typically rely on different sources of data. While both use statistical models to understand the structure of the data, and these models are often similar, Molenaar (2004) showed that results from one type of analysis rarely transfer to the other, unless unrealistic assumptions hold. This raises the question how we may integrate these approaches. In this paper, we argue that formalized theories can be used to connect intra- and interindividual levels of analysis. This connection is indirect, in the sense that the relationship between theory and data is best understood through the intermediate level of phenomena: robust statistical patterns in empirical data. To illustrate this, we introduce a distinction between intra- and interindividual phenomena, and argue that many psychological theories will have implications for both types of phenomena. Formalization provides us with a methodological tool for investigating what kinds of intra- and interindividual phenomena we should expect to find if the theory under consideration were true.
Network analysis represents a novel theoretical approach to personality. Network approaches motivate alternative ways of analyzing data, and suggest new ways of modeling and simulating personality processes. In the present paper, we provide an overview of network analysis strategies as they apply to personality data. We discuss different ways to construct networks from typical personality data, show how to compute and interpret important measures of centrality and clustering, and illustrate how one can simulate on networks to mimic personality processes. All analyses are illustrated using a data set on the commonly used HEXACO questionnaire using elementary R-code that readers may easily adapt to apply to their own data.
Over the course of the last years, network research has gained increasing popularity in psychological sciences. Especially in clinical psychology and personality research, such psychological networks have been used to complement more traditional latent variable models. While prior publications have tackled the two topics of network psychometrics (how to estimate networks) and network inference (how to interpret networks), the topic of network stability still provides a major challenge: it is unclear how susceptible network structures and graph theoretical measures derived from network models are to sampling error and choices made in the estimation method, greatly limiting the certainty of substantive interpretation. In this tutorial paper, we aim to address these challenges. First, we introduce the current state-of-the-art of network psychometrics and network inference in psychology to allow readers to use this tutorial as a stand-alone to estimate and interpret psychopathological networks in R. Second, we describe how bootstrap routines can be used to assess the stability of network parameters. To facilitate this process, we developed the freely available R-package bootnet. We apply bootnet, accompanied by R syntax, to a dataset of 359 women with posttraumatic stress disorder available online. We show how to estimate confidence intervals around edge weights, and how to examine stability of centrality indices by subsampling nodes and persons. This tutorial is aimed at researchers with all levels of statistical experience, and can also be used by beginners.
<title>Abstract</title> Early Warning Signals (EWS) have generated much excitement for their potential to anticipate transitions in various systems, ranging from climate change in ecology to disease staging in medicine. EWS hold particular promise for bifurcations, a transition mechanism in which a smooth, gradual change in a control parameter of the system results in a rapid change in system dynamics. The predominant reason to expect EWS is because many bifurcations are preceded by Critical Slowing Down (CSD): if assuming the system is subject to continuous, small, Gaussian noise, the system is slower to recover from perturbations closer to the transition. However, this focus on stochasticity has overshadowed warning signs which may already be found in deterministic dynamics. This is especially true for higher-dimensional systems, where more complex attractors with intrinsic dynamics such as oscillations not only become possible - they are increasingly more likely. The present study focuses on univariate and multivariate EWS in deterministic dynamics to anticipate complex critical transitions, including the period-doubling cascade to chaos, chaos-chaos transitions, and the extinction of a chaotic attractor. In a four-dimensional continuous-time Lotka-Volterra model, EWS perform well for most bifurcations, even with lower data quality. The present study highlights three reasons why EWS may still work in the absence of CSD: changing attractor morphology (size, shape, and location in phase space), shifting power spectra (amplitude and frequency), and chaotic transitional characteristics (density across attractor). More complex attractors call for different warning detection methods to utilise warning signs already contained within purely deterministic dynamics.
This special double issue of Measurement addresses a number of foundational issues in psychological measurement. It assembles three focus articles, with associated peer commentary, and replies by the authors. The first article challenges the routine assumption that psychometrics is a quantitative science; it argues that there may be no such thing as psychological measurement at all. The second article lays out a systematic framework for latent variable modeling that is intended to capture the notion of psychological measurement in its terms. The third article seeks to provide a conceptual distinction between theoretical variables that allow for an extension in possible worlds, and theoretical variables that do not. These focus articles are followed by a number of associated peer commentaries and replies by the authors.
The missing heritability problem is pervasive and Johnson, Penke and Spinath (2011) present a number of compelling reasons for its existence. In this comment, we present another reason for the apparent discrepancy between heritability estimates and gene‐hunting results in psychopathological research: if syndromes are networks of causally related symptoms in which both symptoms and relations between them are driven by different sets of genetic polymorphisms, then gene hunting based on a phenotypic sumscore might be ill‐advised because it will only capture genetic variance shared among those symptoms and their relations.
This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.
