Psychological Methods

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling , is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here...

Relations between variables can take different forms like linearity, piecewise linearity, or nonlinearity. Segmented regression analyses (SRA) are specialized statistical methods that detect breaks in the relationship between variables. They are commonly used in the social sciences for exploratory analyses. However, many relations may not be best described by a breakpoint and a resulting piecewise linear relation, but rather by a nonlinearity. In the present simulation study, we examined the application of SRA-specifically the Davies test-in the presence of various forms of nonlinearity...

A data matrix, where rows represent persons and columns represent measured subtests, can be viewed as a stack of person profiles, as rows are actually person profiles of observed responses on column subtests. Profile analysis seeks to identify a small number of latent profiles from a large number of person response profiles to identify central response patterns, which are useful for assessing the strengths and weaknesses of individuals across multiple dimensions in domains of interest. Moreover, the latent profiles are mathematically proven to be summative profiles that linearly combine all person response profiles...

This article criticizes conclusions drawn from the standard test of correlated proportions when the dependent measure contains error. It presents a tutorial on a new method of analysis based on the true and error (TE) theory. This method allows the investigator to separate measurement of error from substantive conclusions about the effects of the independent variable, but it requires replicated measures of the dependent variable. The method is illustrated with hypothetical examples and with empirical data from tests of lexicographic semiorder (LS) models proposed as descriptive theories of risky decision making...

Despite increased attention to open science and transparency, questionable research practices (QRPs) remain common, and studies published using QRPs will remain a part of the published record for some time. A particularly common type of QRP involves multiple testing, and in some forms of this, researchers report only a selection of the tests conducted. Methodological investigations of multiple testing and QRPs have often focused on implications for a single study, as well as how these practices can increase the likelihood of false positive results...

Assessing relationships between culture and cognition is central to psychological science. To this end, free-listing is a useful methodological instrument. To facilitate its wider use, we here present the free-list method along with some of its many applications and offer a tutorial on how to prepare and statistically model free-list data as a dependent variable in Bayesian regression using openly available data and code. We further demonstrate the real-world utility of the outlined workflow by modeling within-subject agreement between a free-list task and a corollary item response scale on religious beliefs with a cross-culturally diverse sample...

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods . Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research...

Big Data can bring enormous benefits to psychology. However, many psychological researchers show skepticism in undertaking Big Data research. Psychologists often do not take Big Data into consideration while developing their research projects because they have difficulties imagining how Big Data could help in their specific field of research, imagining themselves as "Big Data scientists," or for lack of specific knowledge. This article provides an introductory guide for conducting Big Data research for psychologists who are considering using this approach and want to have a general idea of its processes...

Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved mediator potentially confounds the observed mediator, thereby biasing the estimated direct and indirect effects associated with the observed mediator and threatening corresponding inferences...

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM...

Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did...

Theories are among the most important tools of science. Lewin (1943) already noted "There is nothing as practical as a good theory." Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Thagard (1989) developed a computational model for formal theory evaluation based on the concept of explanatory coherence...

This article discusses the robustness of the multivariate analysis of covariance (MANCOVA) test for an emergent variable system and proposes a modification of this test to obtain adequate information from heterogeneous normal observations. The proposed approach for testing potential effects in heterogeneous MANCOVA models can be adopted effectively, regardless of the degree of heterogeneity and sample size imbalance. As our method was not designed to handle missing values, we also show how to derive the formulas for pooling the results of multiple-imputation-based analyses into a single final estimate...

Measurement is at the heart of scientific research. As many-perhaps most-psychological constructs cannot be directly observed, there is a steady demand for reliable self-report scales to assess latent constructs. However, scale development is a tedious process that requires researchers to produce good items in large quantities. In this tutorial, we introduce, explain, and apply the Psychometric Item Generator (PIG), an open-source, free-to-use, self-sufficient natural language processing algorithm that produces large-scale, human-like, customized text output within a few mouse clicks...

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations...

Regularized continuous time structural equation models are proposed to address two recent challenges in longitudinal research: Unequally spaced measurement occasions and high model complexity. Unequally spaced measurement occasions are part of most longitudinal studies, sometimes intentionally (e.g., in experience sampling methods) sometimes unintentionally (e.g., due to missing data). Yet, prominent dynamic models, such as the autoregressive cross-lagged model, assume equally spaced measurement occasions. If this assumption is violated parameter estimates can be biased, potentially leading to false conclusions...

Comparison of nested models is common in applications of structural equation modeling (SEM). When two models are nested, model comparison can be done via a chi-square difference test or by comparing indices of approximate fit. The advantage of fit indices is that they permit some amount of misspecification in the additional constraints imposed on the model, which is a more realistic scenario. The most popular index of approximate fit is the root mean square error of approximation (RMSEA). In this article, we argue that the dominant way of comparing RMSEA values for two nested models, which is simply taking their difference, is problematic and will often mask misfit, particularly in model comparisons with large initial degrees of freedom...

In longitudinal studies, researchers are often interested in investigating relations between variables over time. A well-known issue in such a situation is that naively regressing an outcome on a predictor results in a coefficient that is a weighted average of the between-person and within-person effect, which is difficult to interpret. This article focuses on the cross-level covariance approach to disaggregating the two effects. Unlike the traditional centering/detrending approach, the cross-level covariance approach estimates the within-person effect by correlating the within-level observed variables with the between-level latent factors; thereby, partialing out the between-person association from the within-level predictor...

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary and meta-analytic data has received little attention, particularly when IPD are drawn from complex sampling surveys...

Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently, psychologists have studied relationships between these topics and other psychological measures by using estimates of the topics as regression predictors along with other manifest variables...