Psychological methods最新文献

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Fit indices are insensitive to multiple minor violations of perfect simple structure in confirmatory factor analysis. 在验证性因子分析中,拟合指标对完美简单结构的多次轻微违规不敏感。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-13 DOI: 10.1037/met0000718
Victoria Savalei, Muhua Huang
{"title":"Fit indices are insensitive to multiple minor violations of perfect simple structure in confirmatory factor analysis.","authors":"Victoria Savalei, Muhua Huang","doi":"10.1037/met0000718","DOIUrl":"https://doi.org/10.1037/met0000718","url":null,"abstract":"<p><p>Classic confirmatory factor analysis (CFA) models are theoretically superior to exploratory factor analysis (EFA) models because they specify that each indicator only measures one factor. In contrast, in EFA, all loadings are permitted to be nonzero. In this article, we show that when fit to EFA structures and other models with many cross-loadings, classic CFA models often produce excellent fit. A key requirement for breaking this pattern is to have highly variable ratios of main loadings to corresponding cross-loadings in the true data-generating structure-and strongest misfit results when cross-loadings are of mixed sign. We show mathematically that EFA structures that are rotatable to a CFA representation are those where the main loadings and the cross-loadings are proportional for each group of indicators. With the help of a ShinyApp, we show that unless these proportionality constraints are violated severely in the true data structure, CFA models will fit well to most true models containing many cross-loadings by commonly accepted fit index cutoffs. We also show that fit indices are nonmonotone functions of the number of positive cross-loadings, and the relationship becomes monotone only when cross-loadings are of mixed sign. Overall, our findings indicate that good fit of a CFA model rules out that the true model is an EFA model with highly variable ratios of main and cross-loadings, but does not rule out most other plausible EFA structures. We discuss the implications of these findings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Efficient design of cluster randomized trials and individually randomized group treatment trials. 有效设计集群随机试验和单独随机组治疗试验。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-13 DOI: 10.1037/met0000727
Math J J M Candel, Gerard J P van Breukelen
{"title":"Efficient design of cluster randomized trials and individually randomized group treatment trials.","authors":"Math J J M Candel, Gerard J P van Breukelen","doi":"10.1037/met0000727","DOIUrl":"https://doi.org/10.1037/met0000727","url":null,"abstract":"<p><p>For cluster randomized trials and individually randomized group treatment trials that compare two treatments on a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget, when aiming for a desired power level. These designs optimize the treatment-to-control allocation ratio of study participants but also optimize the choice between the number of clusters/groups versus the number of persons per cluster/group. Given that optimal designs require prior knowledge of parameters from the analysis model, which are often unknown during the design stage-especially outcome variances-maximin designs are introduced. These designs ensure a prespecified power level for plausible ranges of the unknown parameters and maximize power for the worst-case values of these parameters. The present study not only reviews but also extends the existing literature by deriving optimal and maximin designs when the number of clusters/groups are fixed because of practical constraints. How to calculate sample sizes in such practical designs and how much budget may be saved are illustrated for an empirical example. To facilitate sample size calculation for each of the variants of the maximin designs considered, an easy-to-use interactive R Shiny app has been developed and made available. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How many factors to retain in exploratory factor analysis? A critical overview of factor retention methods. 探索性因素分析中需要保留多少因素?因子保留方法的关键概述。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-13 DOI: 10.1037/met0000733
David Goretzko
{"title":"How many factors to retain in exploratory factor analysis? A critical overview of factor retention methods.","authors":"David Goretzko","doi":"10.1037/met0000733","DOIUrl":"https://doi.org/10.1037/met0000733","url":null,"abstract":"<p><p>Determining the number of factors is a decisive, yet very difficult decision a researcher faces when conducting an exploratory factor analysis (EFA). Over the last decades, numerous so-called factor retention criteria have been developed to infer the latent dimensionality from empirical data. While some tutorials and review articles on EFA exist which give recommendations on how to determine the number of latent factors, there is no comprehensive overview that categorizes the existing approaches and integrates the results of existing simulation studies evaluating the various methods in different data conditions. With this article, we want to provide such an overview enabling (applied) researchers to make an informed decision when choosing a factor retention criterion. Summarizing the most important results from recent simulation studies, we provide guidance when to rely on which method and call for a more thoughtful handling of overly simple heuristics. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian inference for evidence accumulation models with regressors. 具有回归量的证据积累模型的贝叶斯推理。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-13 DOI: 10.1037/met0000669
Viet Hung Dao, David Gunawan, Robert Kohn, Minh-Ngoc Tran, Guy E Hawkins, Scott D Brown
{"title":"Bayesian inference for evidence accumulation models with regressors.","authors":"Viet Hung Dao, David Gunawan, Robert Kohn, Minh-Ngoc Tran, Guy E Hawkins, Scott D Brown","doi":"10.1037/met0000669","DOIUrl":"10.1037/met0000669","url":null,"abstract":"<p><p>Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become important both in basic scientific applications and solution-focused applied work. Hierarchical Bayesian estimation frameworks for the linear ballistic accumulator (LBA) model and the diffusion decision model (DDM) have been widely used, but still suffer from some key limitations, particularly for large sample sizes, for models with many parameters, and when linking decision-relevant covariates to model parameters. We extend upon previous work with methods for estimating the LBA and DDM in hierarchical Bayesian frameworks that include random effects that are correlated between people and include regression-model links between decision-relevant covariates and model parameters. Our methods work equally well in cases where the covariates are measured once per person (e.g., personality traits or psychological tests) or once per decision (e.g., neural or physiological data). We provide methods for exact Bayesian inference, using particle-based Markov chain Monte-Carlo, and also approximate methods based on variational Bayesian (VB) inference. The VB methods are sufficiently fast and efficient that they can address large-scale estimation problems, such as with very large data sets. We evaluate the performance of these methods in applications to data from three existing experiments. Detailed algorithmic implementations and code are freely available for all methods. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Relative importance analysis in multiple mediator models. 多中介模型的相对重要性分析。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-13 DOI: 10.1037/met0000725
Xun Zhu, Xin Gu
{"title":"Relative importance analysis in multiple mediator models.","authors":"Xun Zhu, Xin Gu","doi":"10.1037/met0000725","DOIUrl":"https://doi.org/10.1037/met0000725","url":null,"abstract":"<p><p>Mediation analysis is widely used in psychological research to identify the relationship between independent and dependent variables through mediators. Assessing the relative importance of mediators in parallel mediator models can help researchers better understand mediation effects and guide interventions. The traditional coefficient-based measures of indirect effect merely focus on the partial effect of each mediator, which may reach undesirable results of importance assessment. This study develops a new method of measuring the importance of multiple mediators. Three <i>R</i>² measures of indirect effect proposed by MacKinnon (2008) are extended to parallel mediator models. Dominance analysis, a popular method of evaluating relative importance, is applied to decompose the <i>R</i>² indirect effect and attribute it to each mediator. This offers new measures of indirect effect in terms of relative importance. Both frequentist and Bayesian methods are used to make statistical inference for the dominance measures. Simulation studies investigate the performance of the dominance measures and their inference. A real data example illustrates how the relative importance can be assessed in multiple mediator models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Missing not at random intensive longitudinal data with dynamic structural equation models. 缺少非随机密集的纵向数据与动态结构方程模型。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-10 DOI: 10.1037/met0000742
Daniel McNeish
{"title":"Missing not at random intensive longitudinal data with dynamic structural equation models.","authors":"Daniel McNeish","doi":"10.1037/met0000742","DOIUrl":"https://doi.org/10.1037/met0000742","url":null,"abstract":"<p><p>Intensive longitudinal designs are increasingly popular for assessing moment-to-moment changes in mood, affect, and interpersonal or health behavior. Compliance in these studies is never perfect given the high frequency of data collection, so missing data are unavoidable. Nonetheless, there is relatively little existing research on missing data within dynamic structural equation models, a recently proposed framework for modeling intensive longitudinal data. The few studies that exist tend to focus on methods appropriate for data that are missing at random (MAR). However, missing not at random (MNAR) data are prevalent, particularly when the interest is a sensitive outcome related to mental health, substance use, or sexual behavior. As a motivating example, a study on people with binge eating disorder that has large amounts of missingness in a self-report item related to overeating is considered. Missingness may be high because participants felt shame reporting this behavior, which is a clear case of MNAR and for which methods like multiple imputation and full-information maximum likelihood are less effective. To improve handling of MNAR intensive longitudinal data, embedding a Diggle-Kenward-type MNAR model within a dynamic structural equation model is proposed. This approach is straightforward to apply in popular software like Mplus and only requires a few extra lines of code relative to models that assume MAR. Results from the proposed approach are contrasted with results from models that assume MAR, and a simulation study is conducted to study performance of the proposed model with continuous or binary outcomes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A peculiarity in psychological measurement practices. 心理测量实践中的一个特点。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-10 DOI: 10.1037/met0000731
Mark White
{"title":"A peculiarity in psychological measurement practices.","authors":"Mark White","doi":"10.1037/met0000731","DOIUrl":"https://doi.org/10.1037/met0000731","url":null,"abstract":"<p><p>This essay discusses a peculiarity in institutionalized psychological measurement practices. Namely, an inherent contradiction between guidelines for how scales/tests are developed and how those scales/tests are typically analyzed. Best practices for developing scales/tests emphasize developing individual items or subsets of items to capture unique aspects of constructs, such that the full construct is captured across the test. Analysis approaches, typically factor analysis or related reflective models, assume that no individual item (nor a subset of items) captures unique, construct-relevant variance. This contradiction has important implications for the use of factor analysis to support measurement claims. The implications and other critiques of factor analysis are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reliability in unidimensional ordinal data: A comparison of continuous and ordinal estimators. 一维有序数据的可靠性:连续估计和有序估计的比较。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-10 DOI: 10.1037/met0000739
Eunseong Cho, Sébastien Béland
{"title":"Reliability in unidimensional ordinal data: A comparison of continuous and ordinal estimators.","authors":"Eunseong Cho, Sébastien Béland","doi":"10.1037/met0000739","DOIUrl":"https://doi.org/10.1037/met0000739","url":null,"abstract":"<p><p>This study challenges three common methodological beliefs and practices. The first question examines whether ordinal reliability estimators are more accurate than continuous estimators for unidimensional data with uncorrelated errors. Continuous estimators (e.g., coefficient alpha) can be applied to both continuous and ordinal data, while ordinal estimators (e.g., ordinal alpha and categorical omega) are specific to ordinal data. Although ordinal estimators are often argued to have conceptual advantages, comprehensive investigations into their accuracy are limited. The second question explores the relationship between skewness and kurtosis in ordinal data. Previous simulation studies have primarily examined cases where skewness and kurtosis change in the same direction, leaving gaps in understanding their independent effects. The third question addresses item response theory (IRT) models: Should the scaling constant always be fixed at the same value (e.g., 1.7)? To answer these questions, this study conducted a Monte Carlo simulation comparing four continuous estimators and eight ordinal estimators. The results indicated that most estimators achieved acceptable levels of accuracy. On average, ordinal estimators were slightly less accurate than continuous estimators, though the difference was smaller than what most users would consider practically significant (e.g., less than 0.01). However, ordinal alpha stood out as a notable exception, severely overestimating reliability across various conditions. Regarding the scaling constant in IRT models, the results indicated that its optimal value varied depending on the data type (e.g., dichotomous vs. polytomous). In some cases, values below 1.7 were optimal, while in others, values above 1.8 were optimal. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The relationship between the phi coefficient and the unidimensionality index H: Improving psychological scaling from the ground up. phi系数与单维指数H之间的关系:从根本上改善心理尺度。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-10 DOI: 10.1037/met0000736
Johannes Titz
{"title":"The relationship between the phi coefficient and the unidimensionality index H: Improving psychological scaling from the ground up.","authors":"Johannes Titz","doi":"10.1037/met0000736","DOIUrl":"https://doi.org/10.1037/met0000736","url":null,"abstract":"<p><p>To study the dimensional structure of psychological phenomena, a precise definition of unidimensionality is essential. Most definitions of unidimensionality rely on factor analysis. However, the reliability of factor analysis depends on the input data, which primarily consists of Pearson correlations. A significant issue with Pearson correlations is that they are almost guaranteed to underestimate unidimensionality, rendering them unsuitable for evaluating the unidimensionality of a scale. This article formally demonstrates that the simple unidimensionality index <i>H</i> is always at least as high as, or higher than, the Pearson correlation for dichotomous and polytomous items (φ). Leveraging this inequality, a case is presented where five dichotomous items are perfectly unidimensional, yet factor analysis based on φ incorrectly suggests a two-dimensional solution. To illustrate that this issue extends beyond theoretical scenarios, an analysis of real data from a statistics exam (<i>N</i> = 133) is conducted, revealing the same problem. An in-depth analysis of the exam data shows that violations of unidimensionality are systematic and should not be dismissed as mere noise. Inconsistent answering patterns can indicate whether a participant blundered, cheated, or has conceptual misunderstandings, information typically overlooked by traditional scaling procedures based on correlations. The conclusion is that psychologists should consider unidimensionality not as a peripheral concern but as the foundation for any serious scaling attempt. The index <i>H</i> could play a crucial role in establishing this foundation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reassessing the fitting propensity of factor models. 重新评估因子模型的拟合倾向。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-02-10 DOI: 10.1037/met0000735
Wes Bonifay, Li Cai, Carl F Falk, Kristopher J Preacher
{"title":"Reassessing the fitting propensity of factor models.","authors":"Wes Bonifay, Li Cai, Carl F Falk, Kristopher J Preacher","doi":"10.1037/met0000735","DOIUrl":"https://doi.org/10.1037/met0000735","url":null,"abstract":"<p><p>Model complexity is a critical consideration when evaluating a statistical model. To quantify complexity, one can examine fitting propensity (FP), or the ability of the model to fit well to diverse patterns of data. The scant foundational research on FP has focused primarily on proof of concept rather than practical application. To address this oversight, the present work joins a recently published study in examining the FP of models that are commonly applied in factor analysis. We begin with a historical account of statistical model evaluation, which refutes the notion that complexity can be fully understood by counting the number of free parameters in the model. We then present three sets of analytic examples to better understand the FP of exploratory and confirmatory factor analysis models that are widely used in applied research. We characterize our findings relative to previously disseminated claims about factor model FP. Finally, we provide some recommendations for future research on FP in latent variable modeling. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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