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Tutorial: Assessing the impact of nonignorable missingness on regression analysis using Index of Local Sensitivity to Nonignorability. Psychological Methods (IF 10.929) Pub Date : 2023-11-16 Bocheng Jing,Yi Qian,Daniel F Heitjan,Hui Xie
Data sets with missing observations are common in psychology research. One typically analyzes such data by applying statistical methods that rely on the assumption that the missing observations are missing at random (MAR). This assumption greatly simplifies analysis but is unverifiable from the data at hand, and assuming it incorrectly may lead to bias. Thus we often wish to conduct sensitivity analyses
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A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models. Psychological Methods (IF 10.929) Pub Date : 2023-11-13 Daniel McNeish
Psychological data are often clustered within organizational units, which violates the independence assumption in standard regression models. Clustered errors, multilevel models, and fixed-effects models all address this issue, but in different ways. Disciplinary preferences for approaching clustered data are strong, which can restrict questions researchers ask because certain approaches are better
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Causal inference with binary treatments from randomization versus binary treatments from categorization. Psychological Methods (IF 10.929) Pub Date : 2023-11-13 Kenneth A Bollen
The causal inference methods of potential outcomes (POs), directed acyclic graphs (DAGs), and structural equation models (SEMs) have contributed much to our understanding of causal effects. Yet the teaching and application of these methods (especially POs and DAGs) have nearly always regarded treatment as binary even when the magnitude of treatment can differ greatly. The two most common types of binary
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A simple Monte Carlo method for estimating power in multilevel designs. Psychological Methods (IF 10.929) Pub Date : 2023-11-13 Craig K Enders,Brian T Keller,Michael P Woller
Estimating power for multilevel models is complex because there are many moving parts, several sources of variation to consider, and unique sample sizes at Level 1 and Level 2. Monte Carlo computer simulation is a flexible tool that has received considerable attention in the literature. However, much of the work to date has focused on very simple models with one predictor at each level and one cross-level
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Empirical selection of referent variables: Comparing multiple-indicator multiple-cause-interaction modeling and moderated nonlinear factor analysis. Psychological Methods (IF 10.929) Pub Date : 2023-11-13 Cheng-Hsien Li
The fulfillment of measurement invariance/equivalence is considered a prerequisite for meaningfully proceeding with substantive cross-group comparisons. In the multiple-group confirmatory factor analysis approach, one model identification issue has unfortunately received little attention: the specification of a referent variable in the test of measurement invariance. A multiple-indicator multiple-cause
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Handling missing data in partially clustered randomized controlled trials. Psychological Methods (IF 10.929) Pub Date : 2023-11-06 Manshu Yang,Darrell J Gaskin
Partially clustered designs are widely used in psychological research, especially in randomized controlled trials that examine the effectiveness of prevention or intervention strategies. In a partially clustered trial, individuals are clustered into intervention groups in one or more study arms, for the purpose of intervention delivery, whereas individuals in other arms (e.g., the waitlist control
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One-tailed tests: Let's do this (responsibly). Psychological Methods (IF 10.929) Pub Date : 2023-11-02 Andrew H Hales
When preregistered, one-tailed tests control false-positive results at the same rate as two-tailed tests. They are also more powerful, provided the researcher correctly identified the direction of the effect. So it is surprising that they are not more common in psychology. Here I make an argument in favor of one-tailed tests and address common mistaken objections that researchers may have to using
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The within-between dispute in cross-lagged panel research and how to move forward. Psychological Methods (IF 10.929) Pub Date : 2023-10-30 Ellen L Hamaker
How to model cross-lagged relations in panel data continues to be a source of disagreement in psychological research. While the cross-lagged panel model (CLPM) was the modeling approach of choice for many years, it has also been criticized repeatedly for its inability to separate within-person dynamics from stable between-person differences. Hence, various alternative models that disentangle these
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Characterizing affect dynamics with a damped linear oscillator model: Theoretical considerations and recommendations for individual-level applications. Psychological Methods (IF 10.929) Pub Date : 2023-10-16 Mar J F Ollero,Eduardo Estrada,Michael D Hunter,Pablo F Cáncer
People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics
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Applying multivariate generalizability theory to psychological assessments. Psychological Methods (IF 10.929) Pub Date : 2023-09-07 Walter P Vispoel,Hyeryung Lee,Hyeri Hong,Tingting Chen
Multivariate generalizability theory (GT) represents a comprehensive framework for quantifying score consistency, separating multiple sources contributing to measurement error, correcting correlation coefficients for such error, assessing subscale viability, and determining the best ways to change measurement procedures at different levels of score aggregation. Despite such desirable attributes, multivariate
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Bayesian evidence synthesis for informative hypotheses: An introduction. Psychological Methods (IF 10.929) Pub Date : 2023-09-07 Irene Klugkist,Thom Benjamin Volker
To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories
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Modeling categorical time-to-event data: The example of social interaction dynamics captured with event-contingent experience sampling methods. Psychological Methods (IF 10.929) Pub Date : 2023-09-07 Timon Elmer,Marijtje A J van Duijn,Nilam Ram,Laura F Bringmann
The depth of information collected in participants' daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When measuring participants' behaviors in daily life, the timing of particular events-such as social interactions-is often recorded. These data facilitate the investigation of new types of research questions
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Multilevel modeling in single-case studies with count and proportion data: A demonstration and evaluation. Psychological Methods (IF 10.929) Pub Date : 2023-08-21 Haoran Li,Wen Luo,Eunkyeng Baek,Christopher G Thompson,Kwok Hap Lam
The outcomes in single-case experimental designs (SCEDs) are often counts or proportions. In our study, we provided a colloquial illustration for a new class of generalized linear mixed models (GLMMs) to fit count and proportion data from SCEDs. We also addressed important aspects in the GLMM framework including overdispersion, estimation methods, statistical inferences, model selection methods by
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Equivalence testing to judge model fit: A Monte Carlo simulation. Psychological Methods (IF 10.929) Pub Date : 2023-08-10 James L Peugh,Kaylee Litson,David F Feldon
Decades of published methodological research have shown the chi-square test of model fit performs inconsistently and unreliably as a determinant of structural equation model (SEM) fit. Likewise, SEM indices of model fit, such as comparative fit index (CFI) and root-mean-square error of approximation (RMSEA) also perform inconsistently and unreliably. Despite rather unreliable ways to statistically
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Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology. Psychological Methods (IF 10.929) Pub Date : 2023-08-10 Philipp Sterner,David Goretzko,Florian Pargent
Psychology has seen an increase in the use of machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false positive or false negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive machine learning
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Examining individual differences in how interaction behaviors change over time: A dyadic multinomial logistic growth modeling approach. Psychological Methods (IF 10.929) Pub Date : 2023-08-10 Miriam Brinberg,Graham D Bodie,Denise H Solomon,Susanne M Jones,Nilam Ram
Several theoretical perspectives suggest that dyadic experiences are distinguished by patterns of behavioral change that emerge during interactions. Methods for examining change in behavior over time are well elaborated for the study of change along continuous dimensions. Extensions for charting increases and decreases in individuals' use of specific, categorically defined behaviors, however, are rarely
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Detecting gender as a moderator in meta-analysis: The problem of restricted between-study variance. Psychological Methods (IF 10.929) Pub Date : 2023-08-10 Lydia Craig Aulisi,Hannah M Markell-Goldstein,Jose M Cortina,Carol M Wong,Xue Lei,Cyrus K Foroughi
Meta-analyses in the psychological sciences typically examine moderators that may explain heterogeneity in effect sizes. One of the most commonly examined moderators is gender. Overall, tests of gender as a moderator are rarely significant, which may be because effects rarely differ substantially between men and women. While this may be true in some cases, we also suggest that the lack of significant
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Assessing intra- and inter-individual reliabilities in intensive longitudinal studies: A two-level random dynamic model-based approach. Psychological Methods (IF 10.929) Pub Date : 2023-08-10 Yue Xiao,Pujue Wang,Hongyun Liu
Intensive longitudinal studies are becoming increasingly popular because of their potential for studying the individual dynamics of psychological processes. However, measures used in such studies are quite susceptible to measurement error due to the short lengths and therefore their psychometric properties, such as reliability, are of great concern. Most existing approaches for assessing reliability
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Bayesian regularization in multiple-indicators multiple-causes models. Psychological Methods (IF 10.929) Pub Date : 2023-07-27 Lijin Zhang,Xinya Liang
Integrating regularization methods into structural equation modeling is gaining increasing popularity. The purpose of regularization is to improve variable selection, model estimation, and prediction accuracy. In this study, we aim to: (a) compare Bayesian regularization methods for exploring covariate effects in multiple-indicators multiple-causes models, (b) examine the sensitivity of results to
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Random item slope regression: An alternative measurement model that accounts for both similarities and differences in association with individual items. Psychological Methods (IF 10.929) Pub Date : 2023-07-27 Ed Donnellan,Satoshi Usami,Kou Murayama
In psychology, researchers often predict a dependent variable (DV) consisting of multiple measurements (e.g., scale items measuring a concept). To analyze the data, researchers typically aggregate (sum/average) scores across items and use this as a DV. Alternatively, they may define the DV as a common factor using structural equation modeling. However, both approaches neglect the possibility that an
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Correspondence measures for assessing replication success. Psychological Methods (IF 10.929) Pub Date : 2023-07-27 Peter M Steiner,Patrick Sheehan,Vivian C Wong
Given recent evidence challenging the replicability of results in the social and behavioral sciences, critical questions have been raised about appropriate measures for determining replication success in comparing effect estimates across studies. At issue is the fact that conclusions about replication success often depend on the measure used for evaluating correspondence in results. Despite the importance
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How to develop, test, and extend multinomial processing tree models: A tutorial. Psychological Methods (IF 10.929) Pub Date : 2023-07-27 Oliver Schmidt,Edgar Erdfelder,Daniel W Heck
Many psychological theories assume that observable responses are determined by multiple latent processes. Multinomial processing tree (MPT) models are a class of cognitive models for discrete responses that allow researchers to disentangle and measure such processes. Before applying MPT models to specific psychological theories, it is necessary to tailor a model to specific experimental designs. In
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Demystifying omega squared: Practical guidance for effect size in common analysis of variance designs. Psychological Methods (IF 10.929) Pub Date : 2023-07-20 Antoinette D A Kroes,Jason R Finley
Omega squared (ω^2) is a measure of effect size for analysis of variance (ANOVA) designs. It is less biased than eta squared, but reported less often. This is in part due to lack of clear guidance on how to calculate it. In this paper, we discuss the logic behind effect size measures, the problem with eta squared, the history of omega squared, and why it has been underused. We then provide a user-friendly
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Enhancing predictive power by unamalgamating multi-item scales. Psychological Methods (IF 10.929) Pub Date : 2023-07-20 David Trafimow,Michael R Hyman,Alena Kostyk
The generally small but touted as "statistically significant" correlation coefficients in the social sciences jeopardize theory testing and prediction. To investigate these small coefficients' underlying causes, traditional equations such as Spearman's (1904) classic attenuation formula, Cronbach's (1951) alpha, and Guilford and Fruchter's (1973) equation for the effect of additional items on a scale's
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On estimating the frequency of a target behavior from time-constrained yes/no survey questions: A parametric approach based on the Poisson process. Psychological Methods (IF 10.929) Pub Date : 2023-07-20 Benedikt Iberl,Rolf Ulrich
We propose a novel method to analyze time-constrained yes/no questions about a target behavior (e.g., "Did you take sleeping pills during the last 12 months?"). A drawback of these questions is that the relative frequency of answering these questions with "yes" does not allow one to draw definite conclusions about the frequency of the target behavior (i.e., how often sleeping pills were taken) nor
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A general framework for the inclusion of time-varying and time-invariant covariates in latent state-trait models. Psychological Methods (IF 10.929) Pub Date : 2023-07-20 Lara Oeltjen,Tobias Koch,Jana Holtmann,Fabian F Münch,Michael Eid,Fridtjof W Nussbeck
Latent state-trait (LST) models are increasingly applied in psychology. Although existing LST models offer many possibilities for analyzing variability and change, they do not allow researchers to relate time-varying or time-invariant covariates, or a combination of both, to loading, intercept, and factor variance parameters in LST models. We present a general framework for the inclusion of nominal
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The receiver operating characteristic area under the curve (or mean ridit) as an effect size. Psychological Methods (IF 10.929) Pub Date : 2023-07-13 Michael Smithson
Several authors have recommended adopting the receiver operator characteristic (ROC) area under the curve (AUC) or mean ridit as an effect size, arguing that it measures an important and interpretable type of effect that conventional effect-size measures do not. It is base-rate insensitive, robust to outliers, and invariant under order-preserving transformations. However, applications have been limited
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A general Monte Carlo method for sample size analysis in the context of network models. Psychological Methods (IF 10.929) Pub Date : 2023-07-10 Mihai A Constantin,Noémi K Schuurman,Jeroen K Vermunt
We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hypothesized network structure or desired characteristics
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Consequences of sampling frequency on the estimated dynamics of AR processes using continuous-time models. Psychological Methods (IF 10.929) Pub Date : 2023-07-10 Rohit Batra,Simran K Johal,Meng Chen,Emilio Ferrer
Continuous-time (CT) models are a flexible approach for modeling longitudinal data of psychological constructs. When using CT models, a researcher can assume one underlying continuous function for the phenomenon of interest. In principle, these models overcome some limitations of discrete-time (DT) models and allow researchers to compare findings across measures collected using different time intervals
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Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach. Psychological Methods (IF 10.929) Pub Date : 2023-07-06 Marcos Jiménez,Francisco J Abad,Eduardo Garcia-Garzon,Hudson Golino,Alexander P Christensen,Luis Eduardo Garrido
The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in
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A novel approach to estimate moderated treatment effects and moderated mediated effects with continuous moderators. Psychological Methods (IF 10.929) Pub Date : 2023-06-12 Matthew J Valente,Judith J M Rijnhart,Oscar Gonzalez
Moderation analysis is used to study under what conditions or for which subgroups of individuals a treatment effect is stronger or weaker. When a moderator variable is categorical, such as assigned sex, treatment effects can be estimated for each group resulting in a treatment effect for males and a treatment effect for females. If a moderator variable is a continuous variable, a strategy for investigating
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A comprehensive model framework for between-individual differences in longitudinal data. Psychological Methods (IF 10.929) Pub Date : 2023-06-12 Anja F Ernst,Casper J Albers,Marieke E Timmerman
Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual
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Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods (IF 10.929) Pub Date : 2023-06-08 Holger Brandt,Siyuan Marco Chen,Daniel J Bauer
Measurement invariance (MI) is one of the main psychometric requirements for analyses that focus on potentially heterogeneous populations. MI allows researchers to compare latent factor scores across persons from different subgroups, whereas if a measure is not invariant across all items and persons then such comparisons may be misleading. If full MI does not hold further testing may identify problematic
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Is exploratory factor analysis always to be preferred? A systematic comparison of factor analytic techniques throughout the confirmatory-exploratory continuum. Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Pablo Nájera,Francisco J Abad,Miguel A Sorrel
The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification
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A tool to simulate and visualize dyadic interaction dynamics. Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Sophie W Berkhout,Noémi K Schuurman,Ellen L Hamaker
ynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data generation as a tool to inspire and guide theory development and elaborate on
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Interpretable machine learning for psychological research: Opportunities and pitfalls. Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Mirka Henninger,Rudolf Debelak,Yannick Rothacher,Carolin Strobl
In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most
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A true score imputation method to account for psychometric measurement error. Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Maxwell Mansolf
Scores on self-report questionnaires are often used in statistical models without accounting for measurement error, leading to bias in estimates related to those variables. While measurement error corrections exist, their broad application is limited by their simplicity (e.g., Spearman's correction for attenuation), which complicates their inclusion in specialized analyses, or complexity (e.g., latent
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A factored regression model for composite scores with item-level missing data. Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Egamaria Alacam,Craig K Enders,Han Du,Brian T Keller
Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score applications. Many studies have investigated this issue, and
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What are the mathematical bounds for coefficient α? Psychological Methods (IF 10.929) Pub Date : 2023-05-25 Niels Waller,William Revelle
Coefficient α, although ubiquitous in the research literature, is frequently criticized for being a poor estimate of test reliability. In this note, we consider the range of α and prove that it has no lower bound (i.e., α ∈ ( - ∞, 1]). While outlining our proofs, we present algorithms for generating data sets that will yield any fixed value of α in its range. We also prove that for some data sets-even
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A Bayesian classifier for fractal characterization of short behavioral series. Psychological Methods (IF 10.929) Pub Date : 2023-05-01 Alessandro Solfo,Cees van Leeuwen
Serial tasks in behavioral research often lead to correlated responses, invalidating the application of generalized linear models and leaving the analysis of serial correlations as the only viable option. We present a Bayesian analysis method suitable for classifying even relatively short behavioral series according to their correlation structure. Our classifier consists of three phases. Phase 1 distinguishes
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The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers. Psychological Methods (IF 10.929) Pub Date : 2023-05-01 Oscar Kjell,Salvatore Giorgi,H Andrew Schwartz
The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language. However, these state-of-the-art methods have not yet been made easily accessible for psychology researchers
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A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models. Psychological Methods (IF 10.929) Pub Date : 2023-04-27 Nikola Sekulovski,Herbert Hoijtink
Testing null hypotheses of the form "β = 0," by the use of various Null Hypothesis Significance Tests (rendering a dichotomous reject/not reject decision), is considered standard practice when evaluating the individual parameters of statistical models. Bayes factors for testing these (and other) hypotheses allow users to quantify the evidence in the data that is in favor of a hypothesis. Unfortunately
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Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator. Psychological Methods (IF 10.929) Pub Date : 2023-04-27 Wen Wei Loh,Dongning Ren
Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset
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Pooling methods for likelihood ratio tests in multiply imputed data sets. Psychological Methods (IF 10.929) Pub Date : 2023-04-27 Simon Grund,Oliver Lüdtke,Alexander Robitzsch
Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However, missing data are also common in empirical research, and multiple imputation (MI) is often used to deal with them. In multiply imputed data, there are multiple options for conducting LRTs, and new methods are still being proposed. In this article, we compare all available methods in multiple simulations covering
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A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science. Psychological Methods (IF 10.929) Pub Date : 2023-04-13 Jillian C Strayhorn,Linda M Collins,David J Vanness
In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then
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Causal inference for treatment effects in partially nested designs. Psychological Methods (IF 10.929) Pub Date : 2023-04-13 Xiao Liu,Fang Liu,Laura Miller-Graff,Kathryn H Howell,Lijuan Wang
artially nested designs (PNDs) are common in intervention studies in psychology and other social sciences. With this design, participants are assigned to treatment and control groups on an individual basis, but clustering occurs in some but not all groups (e.g., the treatment group). In recent years, there has been substantial development of methods for analyzing data from PNDs. However, little research
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How do unobserved confounding mediators and measurement error impact estimated mediation effects and corresponding statistical inferences? Introducing the R package ConMed for sensitivity analysis. Psychological Methods (IF 10.929) Pub Date : 2023-04-01 Qinyun Lin,Amy K Nuttall,Qian Zhang,Kenneth A Frank
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
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Why the use of segmented regression analysis to explore change in relations between variables is problematic: A simulation study. Psychological Methods (IF 10.929) Pub Date : 2023-03-27 Moritz Breit,Julian Preuß,Vsevolod Scherrer,Franzis Preckel
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
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Factorization of person response profiles to identify summative profiles carrying central response patterns. Psychological Methods (IF 10.929) Pub Date : 2023-03-27 Se-Kang Kim
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
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Questionable research practices and cumulative science: The consequences of selective reporting on effect size bias and heterogeneity. Psychological Methods (IF 10.929) Pub Date : 2023-03-23 Samantha F Anderson,Xinran Liu
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
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True and error analysis instead of test of correlated proportions: Can we save lexicographic semiorder models with error theory? Psychological Methods (IF 10.929) Pub Date : 2023-03-23 Michael H Birnbaum
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
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Cognitive and cultural models in psychological science: A tutorial on modeling free-list data as a dependent variable in Bayesian regression. Psychological Methods (IF 10.929) Pub Date : 2023-03-23 Theiss Bendixen,Benjamin Grant Purzycki
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
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Missing data: An update on the state of the art. Psychological Methods (IF 10.929) Pub Date : 2023-03-16 Craig K Enders
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
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An introductory guide for conducting psychological research with big data. Psychological Methods (IF 10.929) Pub Date : 2023-03-13 Michela Vezzoli,Cristina Zogmaister
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
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Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data. Psychological Methods (IF 10.929) Pub Date : 2023-03-09 Young Ri Lee,James E Pustejovsky
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)
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Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND. Psychological Methods (IF 10.929) Pub Date : 2023-03-06 Daniel Redhead,Richard McElreath,Cody T Ross
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
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Comparing theories with the Ising model of explanatory coherence. Psychological Methods (IF 10.929) Pub Date : 2023-03-02 Maximilian Maier,Noah van Dongen,Denny Borsboom
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
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Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method. Psychological Methods (IF 10.929) Pub Date : 2023-02-13 Miriam K Forbes
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
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Regularized continuous time structural equation models: A network perspective. Psychological Methods (IF 10.929) Pub Date : 2023-01-12 Jannik H Orzek,Manuel C Voelkle
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
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Cross-level covariance approach to the disaggregation of between-person effect and within-person effect. Psychological Methods (IF 10.929) Pub Date : 2023-01-09 Kazuki Hori,Yasuo Miyazaki
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