407 publications from this institution
This is the matrix containing the empirical threshold parameters that were used as a starting point for the simulation study. These parameters were retrieved from a network analysis with IsingFit in R, the data come from the VATSPUD study (courtesy of Ken Kendler, see the paper for a brief description of the data) and consist of the 14 disaggregated symptoms of major depression according to DSM-III-R.
R. L. Brennan (Ed.). Westport, CT: Praeger, 2006, 779 pages, $125.00. Not counting its editor, R. L. Brennan, who has earned my eternal admiration for successfully managing what must have been a mo...
In this longitudinal research we adopt a complexity approach to examine temporal dynamics of variables related to compliance with behavioural measures during the COVID-19 pandemic. Dutch participants (N = 2,399) completed surveys with COVID-19-related variables five times over a period of 10 weeks (April 23th – June 30th 2020). With this 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.
The author identifies four conceptualizations of the relation between symptoms and disorders as utilized in diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition ( DSM‐IV ; American Psychiatric Association, 1994): A constructivist perspective, which holds that disorders are conveniently grouped sets of symptoms; a diagnostic perspective, which holds that disorders are latent classes underlying the symptoms; a dimensional perspective, which holds that symptoms measure latent continua; and a causal systems perspective, which holds that disorders are causal networks consisting of symptoms and direct causal relations between them. Advantages and disadvantages of these conceptualizations are discussed. The author concludes that the psychometric analysis of diagnostic systems is not settled, and that these systems require deeper psychometric analysis than they currently receive. © 2008 Wiley Periodicals, Inc. J Clin Psychol 64: 1089–1108, 2008.
Paranormal beliefs encompass a wide variety of phenomena, including the existence of supernatural entities such as ghosts and witches, as well as extraordinary human abilities such as telepathy and clairvoyance. In the current study, we used a nationally representative sample ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mstyle displaystyle="true" scriptlevel="0"> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2534</mml:mn> </mml:mstyle> </mml:math> ) to investigate the presence and correlates of paranormal beliefs among the secular Dutch population. The results indicated that most single paranormal phenomena (e.g. belief in clairvoyance) are endorsed by 10–20% of Dutch respondents; however, 55.6% of respondents qualify as paranormal believers based on the preregistered criterion that they believe in at least one phenomenon with considerable certainty. In addition, we invited four analysis teams with different methodological expertise to assess the structure of paranormal beliefs using traditional factor analysis, network analysis, Bayesian network analysis and latent class analysis (LCA). The teams’ analyses indicated adequate fit of a four-factor structure reported in a 1985 study, but also emphasized different conclusions across techniques; network analyses showed evidence against strong connectedness within most clusters, and suggested a five-cluster structure. The application of various analytic techniques painted a nuanced picture of paranormal beliefs and believers in The Netherlands and suggests that despite increased secularization, subgroups of the general population still believe in paranormal phenomena.
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.
Does psi exist? In a widely publicized article featuring nine experiments with over one thousand participants, Bem (2011) claimed that future events retroactively affect people's responses. In a response, we pointed out that Bem's analyses were partly exploratory. Moreover, we reanalyzed Bem's data using a default Bayesian t-test and showed that Bem's evidence for psi is weak to nonexistent. A robustness analysis confirmed our skeptical conclusions. Recently, Bem, Utts, and Johnson (2011) question several aspects of our analysis. In this brief reply we clarify our analysis procedure and demonstrate that our arguments still hold.
When it originated in the late 19th century, psychometrics was a field with both a scientific and a social mission: Psychometrics provided new methods for research into individual differences and at the same time considered these methods a means of creating a new social order. In contrast, contemporary psychometrics—because of its highly technical nature and its limited involvement in substantive psychological research—has created the impression of being a value-free discipline. In this article, we develop a contrasting characterization of contemporary psychometrics as a value-laden discipline. We expose four such values: that individual differences are quantitative (rather than qualitative), that measurement should be objective in a specific sense, that test items should be fair, and that the utility of a model is more important than its truth. Our goal is not to criticize psychometrics for supporting these values but rather to bring them into the open and to show that they are not inevitable and are in need of systematic evaluation.
No abstract is provided for this article.
This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.
No abstract is provided for this article.
Generalized Network Psychometrics: Combining Network and Latent Variable Models - Volume 82 Issue 4
Preface 1. Introduction 2. True scores 3. Latent variables 4. Scales 5. Relations between the models 6. The concept of validity References Index.
No abstract is provided for this article.
Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.
[S]cience can stand on its own feet and does not need any help from rationalists, secular humanists, Marxists and similar religious movements; and … non-scientific cultures, procedures and assumpti...
