Psychological methods最新文献_第5页

Selecting scaling indicators in structural equation models (sems). 在结构方程模型（sems）中选择比例指标。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-10-01 Epub Date: 2022-10-06 DOI: 10.1037/met0000530

Kenneth A Bollen, Adam G Lilly, Lan Luo

{"title":"Selecting scaling indicators in structural equation models (sems).","authors":"Kenneth A Bollen, Adam G Lilly, Lan Luo","doi":"10.1037/met0000530","DOIUrl":"10.1037/met0000530","url":null,"abstract":"It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, the choice of which indicator to use for this purpose receives little attention, and many analysts use the first indicator without considering whether there are better choices. When all indicators of the latent variable have essentially the same properties, then the choice matters less. But when this is not true, we could benefit from scaling indicator guidelines. Our article first demonstrates why latent variables need a scale. We then propose a set of criteria and accompanying diagnostic tools that can assist researchers in making informed decisions about scaling indicators. The criteria for a good scaling indicator include high face validity, high correlation with the latent variable, factor complexity of one, no correlated errors, no direct effects with other indicators, a minimal number of significant overidentification equation tests and modification indices, and invariance across groups and time. We demonstrate these criteria and diagnostics using two empirical examples and provide guidance on navigating conflicting results among criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"868-889"},"PeriodicalIF":7.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9650749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares. 变量众多的大型研究中缺失数据的多重估算：使用偏最小二乘法的全条件规范方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-30 DOI: 10.1037/met0000694

Simon Grund, Oliver Lüdtke, Alexander Robitzsch

{"title":"Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.","authors":"Simon Grund, Oliver Lüdtke, Alexander Robitzsch","doi":"10.1037/met0000694","DOIUrl":"https://doi.org/10.1037/met0000694","url":null,"abstract":"Multiple imputation (MI) is one of the most popular methods for handling missing data in psychological research. However, many imputation approaches are poorly equipped to handle a large number of variables, which are a common sight in studies that employ questionnaires to assess psychological constructs. In such a case, conventional imputation approaches often become unstable and require that the imputation model be simplified, for example, by removing variables or combining them into composite scores. In this article, we propose an alternative method that extends the fully conditional specification approach to MI with dimension reduction techniques such as partial least squares. To evaluate this approach, we conducted a series of simulation studies, in which we compared it with other approaches that were based on variable selection, composite scores, or dimension reduction through principal components analysis. Our findings indicate that this novel approach can provide accurate results even in challenging scenarios, where other approaches fail to do so. Finally, we also illustrate the use of this method in real data and discuss the implications of our findings for practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352684","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 estimation and comparison of idiographic network models. 成因网络模型的贝叶斯估计和比较。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-30 DOI: 10.1037/met0000672

Björn S Siepe, Matthias Kloft, Daniel W Heck

{"title":"Bayesian estimation and comparison of idiographic network models.","authors":"Björn S Siepe, Matthias Kloft, Daniel W Heck","doi":"10.1037/met0000672","DOIUrl":"https://doi.org/10.1037/met0000672","url":null,"abstract":"Idiographic network models are estimated on time series data of a single individual and allow researchers to investigate person-specific associations between multiple variables over time. The most common approach for fitting graphical vector autoregressive (GVAR) models uses least absolute shrinkage and selection operator (LASSO) regularization to estimate a contemporaneous and a temporal network. However, estimation of idiographic networks can be unstable in relatively small data sets typical for psychological research. This bears the risk of misinterpreting differences in estimated networks as spurious heterogeneity between individuals. As a remedy, we evaluate the performance of a Bayesian alternative for fitting GVAR models that allows for regularization of parameters while accounting for estimation uncertainty. We also develop a novel test, implemented in the tsnet package in R, which assesses whether differences between estimated networks are reliable based on matrix norms. We first compare Bayesian and LASSO approaches across a range of conditions in a simulation study. Overall, LASSO estimation performs well, while a Bayesian GVAR without edge selection may perform better when the true network is dense. In an additional simulation study, the novel test is conservative and shows good false-positive rates. Finally, we apply Bayesian estimation and testing in an empirical example using daily data on clinical symptoms for 40 individuals. We additionally provide functionality to estimate Bayesian GVAR models in Stan within tsnet. Overall, Bayesian GVAR modeling facilitates the assessment of estimation uncertainty which is important for studying interindividual differences of intraindividual dynamics. In doing so, the novel test serves as a safeguard against premature conclusions of heterogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352683","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

Percentage of variance accounted for in structural equation models: The rediscovery of the goodness of fit index. 结构方程模型所占的变异百分比：重新发现拟合优度指数。

IF 7 1区心理学

Psychological methods Pub Date : 2024-09-26 DOI: 10.1037/met0000680

Alberto Maydeu-Olivares,Carmen Ximénez,Javier Revuelta

{"title":"Percentage of variance accounted for in structural equation models: The rediscovery of the goodness of fit index.","authors":"Alberto Maydeu-Olivares,Carmen Ximénez,Javier Revuelta","doi":"10.1037/met0000680","DOIUrl":"https://doi.org/10.1037/met0000680","url":null,"abstract":"This article delves into the often-overlooked metric of percentage of variance accounted for in structural equation models (SEM). The goodness of fit index (GFI) provides the percentage of variance of the sum of squared covariances explained by the model. Despite being introduced over four decades ago, the GFI has been overshadowed in favor of fit indices that prioritize distinctions between close and nonclose fitting models. Similar to R² in regression, the GFI should not be used to this aim but rather to quantify the model's utility. The central aim of this study is to reintroduce the GFI, introducing a novel approach to computing the GFI using mean and mean-and-variance corrected test statistics, specifically designed for nonnormal data. We use an extensive simulation study to evaluate the precision of inferences on the GFI, including point estimates and confidence intervals. The findings demonstrate that the GFI can be very accurately estimated, even with nonnormal data, and that confidence intervals exhibit reasonable accuracy across diverse conditions, including large models and nonnormal data scenarios. The article provides methods and code for estimating the GFI in any SEM, urging researchers to reconsider the reporting of the percentage of variance accounted for as an essential tool for model assessment and selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"24 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324987","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 computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals. 估算具有相关残差的探索性因子分析模型的高效稳健计算方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-23 DOI: 10.1037/met0000609

Guangjian Zhang, Dayoung Lee

引用次数: 0

A computational method to reveal psychological constructs from text data. 从文本数据中揭示心理结构的计算方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-19 DOI: 10.1037/met0000700

Alina Herderich, Heribert H Freudenthaler, David Garcia

{"title":"A computational method to reveal psychological constructs from text data.","authors":"Alina Herderich, Heribert H Freudenthaler, David Garcia","doi":"10.1037/met0000700","DOIUrl":"https://doi.org/10.1037/met0000700","url":null,"abstract":"When starting to formalize psychological constructs, researchers traditionally rely on two distinct approaches: the quantitative approach, which defines constructs as part of a testable theory based on prior research and domain knowledge often deploying self-report questionnaires, or the qualitative approach, which gathers data mostly in the form of text and bases construct definitions on exploratory analyses. Quantitative research might lead to an incomplete understanding of the construct, while qualitative research is limited due to challenges in the systematic data processing, especially at large scale. We present a new computational method that combines the comprehensiveness of qualitative research and the scalability of quantitative analyses to define psychological constructs from semistructured text data. Based on structured questions, participants are prompted to generate sentences reflecting instances of the construct of interest. We apply computational methods to calculate embeddings as numerical representations of the sentences, which we then run through a clustering algorithm to arrive at groupings of sentences as psychologically relevant classes. The method includes steps for the measurement and correction of bias introduced by the data generation, and the assessment of cluster validity according to human judgment. We demonstrate the applicability of our method on an example from emotion regulation. Based on short descriptions of emotion regulation attempts collected through an open-ended situational judgment test, we use our method to derive classes of emotion regulation strategies. Our approach shows how machine learning and psychology can be combined to provide new perspectives on the conceptualization of psychological processes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293946","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

Cross-lagged panel modeling with binary and ordinal outcomes. 二元和序数结果的跨滞后面板建模。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-19 DOI: 10.1037/met0000701

Bengt Muthén, Tihomir Asparouhov, Katie Witkiewitz

引用次数: 0

Thinking clearly about time-invariant confounders in cross-lagged panel models: A guide for choosing a statistical model from a causal inference perspective. 清晰思考交叉滞后面板模型中的时变混杂因素：从因果推论角度选择统计模型指南》。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-19 DOI: 10.1037/met0000647

Kou Murayama, Thomas Gfrörer

引用次数: 0

Scaling and estimation of latent growth models with categorical indicator variables. 使用分类指标变量对潜在增长模型进行缩放和估计。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-19 DOI: 10.1037/met0000679

Kyungmin Lim, Su-Young Kim

{"title":"Scaling and estimation of latent growth models with categorical indicator variables.","authors":"Kyungmin Lim, Su-Young Kim","doi":"10.1037/met0000679","DOIUrl":"https://doi.org/10.1037/met0000679","url":null,"abstract":"Although the interest in latent growth models (LGMs) with categorical indicator variables has recently increased, there are still difficulties regarding the selection of estimation methods and the interpretation of model estimates. However, difficulties in estimating and interpreting categorical LGMs can be avoided by understanding the scaling process. Depending on which parameter constraint methods are selected at each step of the scaling process, the scale applied to the model changes, which can produce significant differences in the estimation results and interpretation. In other words, if a different method is chosen for any of the steps in the scaling process, the estimation results will not be comparable. This study organizes the scaling process and its relationship with estimation methods for categorical LGMs. Specifically, this study organizes the parameter constraint methods included in the scaling process of categorical LGMs and extensively considers the effect of parameter constraints at each step on the meaning of estimates. This study also provides evidence for the scale suitability and interpretability of model estimates through a simple illustration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293950","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 should we model the effect of "change"-Or should we? 我们应该如何模拟 "变化 "的效果？

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-09-19 DOI: 10.1037/met0000663

Ethan M McCormick, Daniel J Bauer

{"title":"How should we model the effect of \"change\"-Or should we?","authors":"Ethan M McCormick, Daniel J Bauer","doi":"10.1037/met0000663","DOIUrl":"https://doi.org/10.1037/met0000663","url":null,"abstract":"There have been long and bitter debates between those who advocate for the use of residualized change as the foundation of longitudinal models versus those who utilize difference scores. However, these debates have focused primarily on modeling change in the outcome variable. Here, we extend these same ideas to the covariate side of the change equation, finding similar issues arise when using lagged versus difference scores as covariates of interest in models of change. We derive a system of relationships that emerge across models differing in how time-varying covariates are represented, and then demonstrate how the set of logical transformations emerges in applied longitudinal settings. We conclude by considering the practical implications of a synthesized understanding of the effects of difference scores as both outcomes and predictors, with specific consequences for mediation analysis within multivariate longitudinal models. Our results suggest that there is reason for caution when using difference scores as time-varying covariates, given their propensity for inducing apparent inferential inversions within different analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293949","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