Psychological methods最新文献_第2页

Bayesian regularization in multiple-indicators multiple-causes models. 多指标多原因模型中的贝叶斯正则化。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-07-27 DOI: 10.1037/met0000594

Lijin Zhang, Xinya Liang

{"title":"Bayesian regularization in multiple-indicators multiple-causes models.","authors":"Lijin Zhang, Xinya Liang","doi":"10.1037/met0000594","DOIUrl":"10.1037/met0000594","url":null,"abstract":"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 hyperparameter settings of penalty priors, and (c) investigate prediction accuracy through cross-validation. The Bayesian regularization methods examined included: ridge, lasso, adaptive lasso, spike-and-slab prior (SSP) and its variants, and horseshoe and its variants. Sparse solutions were developed for the structural coefficient matrix that contained only a small portion of nonzero path coefficients characterizing the effects of selected covariates on the latent variable. Results from the simulation study showed that compared to diffuse priors, penalty priors were advantageous in handling small sample sizes and collinearity among covariates. Priors with only the global penalty (ridge and lasso) yielded higher model convergence rates and power, whereas priors with both the global and local penalties (horseshoe and SSP) provided more accurate parameter estimates for medium and large covariate effects. The horseshoe and SSP improved accuracy in predicting factor scores, while achieving more parsimonious models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"679-703"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10241486","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

Comparing theories with the Ising model of explanatory coherence. 用解释一致性的伊辛模型比较理论。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-01 Epub Date: 2023-03-02 DOI: 10.1037/met0000543

Maximilian Maier, Noah van Dongen, Denny Borsboom

{"title":"Comparing theories with the Ising model of explanatory coherence.","authors":"Maximilian Maier, Noah van Dongen, Denny Borsboom","doi":"10.1037/met0000543","DOIUrl":"10.1037/met0000543","url":null,"abstract":"[Correction Notice: An Erratum for this article was reported in Vol 29(3) of Psychological Methods (see record 2025-28068-002). In the article, the copyright attribution was incorrectly listed, and the Creative Commons CC BY 4.0 license disclaimer was incorrectly omitted from the author note. The correct copyright is \"© 2023 The Author(s),\" and the omitted disclaimer is below: Open Access funding provided by University College London: This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/by/ 4.0). This license permits copying and redistributing the work in any medium or format, as well as adapting the material for any purpose, even commercially.] 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. However, there are possible improvements to Thagard's (1989) model and it is not available in software that psychologists typically use. Therefore, we developed a new implementation of explanatory coherence based on the Ising model. We demonstrate the capabilities of this new Ising model of Explanatory Coherence (IMEC) on several examples from psychology and other sciences. In addition, we implemented it in the R-package IMEC to assist scientists in evaluating the quality of their theories in practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"519-536"},"PeriodicalIF":7.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10805750","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

Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development. 让算法说话：如何在心理量表开发中使用神经网络自动生成项目。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-01 Epub Date: 2023-02-16 DOI: 10.1037/met0000540

Friedrich M Götz, Rakoen Maertens, Sahil Loomba, Sander van der Linden

{"title":"Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development.","authors":"Friedrich M Götz, Rakoen Maertens, Sahil Loomba, Sander van der Linden","doi":"10.1037/met0000540","DOIUrl":"10.1037/met0000540","url":null,"abstract":"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. The PIG is based on the GPT-2, a powerful generative language model, and runs on Google Colaboratory-an interactive virtual notebook environment that executes code on state-of-the-art virtual machines at no cost. Across two demonstrations and a preregistered five-pronged empirical validation with two Canadian samples (NSample 1 = 501, NSample 2 = 773), we show that the PIG is equally well-suited to generate large pools of face-valid items for novel constructs (i.e., wanderlust) and create parsimonious short scales of existing constructs (i.e., Big Five personality traits) that yield strong performances when tested in the wild and benchmarked against current gold standards for assessment. The PIG does not require any prior coding skills or access to computational resources and can easily be tailored to any desired context by simply switching out short linguistic prompts in a single line of code. In short, we present an effective, novel machine learning solution to an old psychological challenge. As such, the PIG will not require you to learn a new language-but instead, speak yours. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"494-518"},"PeriodicalIF":7.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10787831","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

Causal inference for treatment effects in partially nested designs. 部分嵌套设计中治疗效果的因果推断。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-01 Epub Date: 2023-04-13 DOI: 10.1037/met0000565

Xiao Liu, Fang Liu, Laura Miller-Graff, Kathryn H Howell, Lijuan Wang

{"title":"Causal inference for treatment effects in partially nested designs.","authors":"Xiao Liu, Fang Liu, Laura Miller-Graff, Kathryn H Howell, Lijuan Wang","doi":"10.1037/met0000565","DOIUrl":"10.1037/met0000565","url":null,"abstract":"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 has been done on causal inference for PNDs, especially for PNDs with nonrandomized treatment assignments. To reduce the research gap, in the current study, we used the expanded potential outcomes framework to define and identify the average causal treatment effects in PNDs. Based on the identification results, we formulated the outcome models that could produce treatment effect estimates with causal interpretation and evaluated how alternative model specifications affect the causal interpretation. We also developed an inverse propensity weighted (IPW) estimation approach and proposed a sandwich-type standard error estimator for the IPW-based estimate. Our simulation studies demonstrated that both the outcome modeling and the IPW methods specified following the identification results can yield satisfactory estimates and inferences of the average causal treatment effects. We applied the proposed approaches to data from a real-life pilot study of the Pregnant Moms' Empowerment Program for illustration. The current study provides guidance and insights on causal inference for PNDs and adds to researchers' toolbox of treatment effect estimation with PNDs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"457-479"},"PeriodicalIF":7.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9737459","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

We need to change how we compute RMSEA for nested model comparisons in structural equation modeling. 我们需要改变结构方程建模中嵌套模型比较的 RMSEA 计算方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-01 Epub Date: 2023-01-09 DOI: 10.1037/met0000537

Victoria Savalei, Jordan C Brace, Rachel T Fouladi

{"title":"We need to change how we compute RMSEA for nested model comparisons in structural equation modeling.","authors":"Victoria Savalei, Jordan C Brace, Rachel T Fouladi","doi":"10.1037/met0000537","DOIUrl":"10.1037/met0000537","url":null,"abstract":"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. We instead advocate computing the RMSEA associated with the chi-square difference test, which we call RMSEAD. We are not the first to propose this index, and we review numerous methodological articles that have suggested it. Nonetheless, these articles appear to have had little impact on actual practice. The modification of current practice that we call for may be particularly needed in the context of measurement invariance assessment. We illustrate the difference between the current approach and our advocated approach on three examples, where two involve multiple-group and longitudinal measurement invariance assessment and the third involves comparisons of models with different numbers of factors. We conclude with a discussion of recommendations and future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"480-493"},"PeriodicalIF":7.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10495377","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

Reconsideration of the type I error rate for psychological science in the era of replication. 复制时代对心理科学I型错误率的再思考。

IF 7 1区心理学

Psychological methods Pub Date : 2024-04-01 Epub Date: 2022-04-11 DOI: 10.1037/met0000490

Michael T Carlin, Mack S Costello, Madisyn A Flansburg, Alyssa Darden

引用次数: 0

Efficient alternatives for Bayesian hypothesis tests in psychology. 心理学中贝叶斯假设检验的有效替代方案。

IF 7 1区心理学

Psychological methods Pub Date : 2024-04-01 Epub Date: 2022-04-14 DOI: 10.1037/met0000482

Sandipan Pramanik, Valen E Johnson

引用次数: 0

Assessing measurement invariance with moderated nonlinear factor analysis using the R package OpenMx. 利用 R 软件包 OpenMx 进行调节非线性因子分析，评估测量不变性。

IF 7 1区心理学

Psychological methods Pub Date : 2024-04-01 Epub Date: 2022-07-04 DOI: 10.1037/met0000501

Laura Kolbe, Dylan Molenaar, Suzanne Jak, Terrence D Jorgensen

引用次数: 0

Harnessing the power of excess statistical significance: Weighted and iterative least squares. 利用过度统计显著性的力量：加权和迭代最小二乘法。

IF 7 1区心理学

Psychological methods Pub Date : 2024-04-01 Epub Date: 2022-05-12 DOI: 10.1037/met0000502

T D Stanley, Hristos Doucouliagos

引用次数: 0

Factor analyzing ordinal items requires substantive knowledge of response marginals. 对序数项目进行因子分析需要对反应边际有实质性的了解。

IF 7 1区心理学

Psychological methods Pub Date : 2024-02-01 Epub Date: 2022-05-19 DOI: 10.1037/met0000495

Steffen Grønneberg, Njål Foldnes

{"title":"Factor analyzing ordinal items requires substantive knowledge of response marginals.","authors":"Steffen Grønneberg, Njål Foldnes","doi":"10.1037/met0000495","DOIUrl":"10.1037/met0000495","url":null,"abstract":"In the social sciences, measurement scales often consist of ordinal items and are commonly analyzed using factor analysis. Either data are treated as continuous, or a discretization framework is imposed in order to take the ordinal scale properly into account. Correlational analysis is central in both approaches, and we review recent theory on correlations obtained from ordinal data. To ensure appropriate estimation, the item distributions prior to discretization should be (approximately) known, or the thresholds should be known to be equally spaced. We refer to such knowledge as substantive because it may not be extracted from the data, but must be rooted in expert knowledge about the data-generating process. An illustrative case is presented where absence of substantive knowledge of the item distributions inevitably leads the analyst to conclude that a truly two-dimensional case is perfectly one-dimensional. Additional studies probe the extent to which violation of the standard assumption of underlying normality leads to bias in correlations and factor models. As a remedy, we propose an adjusted polychoric estimator for ordinal factor analysis that takes substantive knowledge into account. Also, we demonstrate how to use the adjusted estimator in sensitivity analysis when the continuous item distributions are known only approximately. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"65-87"},"PeriodicalIF":7.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9171744","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