Publications

No abstract is provided for this article.

Simulation studies are a powerful tool within the field of network psychometrics, providing insight into sample size determination and best practices for achieving accurate results. Regrettably, the advantages simulation studies provide have thus far been predominately harnessed by psychological researchers with a statistical or methodological background. While user-friendly methods for simulating psychological networks have been developed, there remains a lack of comprehensive guidelines for their effective utilization. In response, this tutorial paper serves as a guide on how to set up simulation studies for three widely used network modeling techniques: the Ising model, the Gaussian Graphical Model (GGM), and the Graphical Vector Autoregressive (GVAR) model. We explain the rationale behind simulation studies and present a step-by-step roadmap to initiate one's own simulation studies. This roadmap covers all essential aspects using a practical example to guide the reader. In addition, we provide an overview of open-source software packages available for simulating network models in the statistical programming environment R, and demonstrate their practical application. In doing so, we aim to enable more researchers to explore and resolve statistical questions within network psychometrics.

Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms.

Lee (2012) proposes that personality research should utilise recent theories of causality. Although we agree that such theories are important, we also note that their empirical application has not been very successful to date. The reason may be that psychological systems are frequently characterised by feedback, nonlinearity and individual differences in causal structure. Such features do not preclude the application of causal modelling but do limit the usefulness of the approach for the analysis of typical personality data. To adequately investigate personality, intensive time series of repeated measurements are needed.

In psychology, regulation of measurement practices relies on the use of psychometric models that relate the targeted psychological construct to a set of observations in empirical research. Recent work has argued for a new conceptualization, in which psychological constructs are viewed as networks. However, in contrast to existing models, network psychometrics has not articulated normative guidelines for measurement. To develop normative criteria for measurement in the network framework, a clearer picture is needed about possible measurement targets in a network. Using the example of depression, we explore five such potential measurement targets in the network framework: the structure of the network, the current state of the network, and three properties of the dynamical landscape, the expected state, vulnerability, and stable state. We discuss strategies for measuring these targets as well as challenges that arise in doing so.

As the majority of the global population resides in cities, it is imperative to understand urban well-being. While cities offer concentrated social and economic opportunities, the question arises whether these benefits translate to equitable levels of satisfaction in these domains. Using a robust and objective measure of urbanicity on a sample of 156,000 U.K. residents aged 40 and up, we find that urban living is associated with lower scores across seven dimensions of well-being, social satisfaction, and economic satisfaction. In addition, these scores exhibit greater variability within urban areas, revealing increased inequality. Last, we identify optimal distances in the hinterlands of cities with the highest satisfaction and the least variation. Our findings raise concern for the psychological well-being of urban residents and show the importance of nonlinear methods in urban research.

This paper comments on an article by Michell (2000), who argues that psychometrics should be qualified as pathological science for two reasons: (a) psychometrics assumes psychological attributes to be quantitative without testing this hypothesis; and (b) the fact that this hypothesis is not tested is disguised. Michell further argues that the hypothesis should be tested using additive conjoint measurement theory. Although relevant to classical test theory, Michell’s arguments do not apply to psychometrics in general. In particular, they are largely irrelevant to item response theory models. We show that these models result from introducing probabilistic relations, which are needed to deal with measurement error, and not from a breakdown in critical inquiry, as Michell suggests. Moreover, at least one class of these models can be formulated in terms of additive conjoint measurement theory, which renders Michell’s call for the additive conjoint model in need of qualification. Finally, item response theory models are routinely tested against empirical data, and although the assumption that an attribute is quantitative cannot be tested directly, such tests do address the conjunction of this assumption and other model assumptions. We conclude that, although Michell’s arguments are important to psychological measurement, they are largely irrelevant to item response theory. In fact, we argue that they can be phrased in terms of this theory in a natural way.

This article aims to improve theory formation in psychology by developing a practical methodology for constructing explanatory theories: theory construction methodology (TCM). TCM is a sequence of five steps. First, the theorist identifies a domain of empirical phenomena that becomes the target of explanation. Second, the theorist constructs a prototheory, a set of theoretical principles that putatively explain these phenomena. Third, the prototheory is used to construct a formal model, a set of model equations that encode explanatory principles. Fourth, the theorist investigates the explanatory adequacy of the model by formalizing its empirical phenomena and assessing whether it indeed reproduces these phenomena. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether the identified phenomena are indeed reproduced faithfully and whether the explanatory principles are sufficiently parsimonious and substantively plausible. We explain TCM with an example taken from research on intelligence (the mutualism model of intelligence), in which key elements of the method have been successfully implemented. We discuss the place of TCM in the larger scheme of scientific research and propose an outline for a university curriculum that can systematically educate psychologists in the process of theory formation.

Causal reasoning plays an important role in thinking about measurement models in psychology and the social sciences. In this chapter, we discuss measurement

Click to increase image sizeClick to decrease image size Notes1 This definition raises the intriguing possibility of other types of links. For example, we might conceive of "enjoyment of reading" as a correlational indicator of extraversion: More extraverted people tend to spend less time reading, but reading is neither a cause nor an effect of extraverted personality.2 The only way that causal indicators can affect the variance of the latent variable or its covariance with other variables in the model is if there is some misspecification in the relations between causal and effect indicators; in particular, if the variance shared among effect indicators (i.e., the latent variable) does not fully mediate the relations between each causal indicator and each effect indicator. Such misspecification can cause the reflective factor loading estimates to shift to account for the misspecification. When the model is correct, however, causal indicators can be removed from the model without affecting the latent variable.

Causal theories of measurement view test items as effects of a common cause. Behavior domain theories view test item responses as behaviors sampled from a common domain. A domain score is a composite score over this domain. The question arises whether latent variables can simultaneously constitute domain scores and common causes of item scores. One argument to the contrary holds that behavior domain theory offers more effective guidance for item construction than a causal theory of measurement. A second argument appeals to the apparent circularity of taking a domain score, which is defined in terms of a domain of behaviors, as a cause of those behaviors. Both arguments require qualification and behavior domain theory seems to rely on implicit causal relationships in two respects. Three strategies permit reconciliation of the two theories: One can take a causal structure as providing the basis for a homogeneous domain. One can construct a homogeneous domain and then investigate whether a causal structure explains the homogeneity. Or, one can take the domain score as linked to an existing attribute constrained by indirect measurement.