Psychological methods最新文献

Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach. 在构造测量的潜在变化中建立构造随时间变化的模型：纵向调节因子分析方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-08-29 DOI: 10.1037/met0000685

Siyuan Marco Chen, Daniel J Bauer

{"title":"Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach.","authors":"Siyuan Marco Chen, Daniel J Bauer","doi":"10.1037/met0000685","DOIUrl":"https://doi.org/10.1037/met0000685","url":null,"abstract":"In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding actual construct change with changes in measurement (i.e., differential item functioning [DIF]), threatening the validity of conclusions. An improved method that avoids such confounding is the second-order growth curve (SGC) model. It specifies a measurement model at each occasion of measurement that can be evaluated for invariance over time. The applicability of the SGC model is hindered by key limitations: (a) the SGC model treats time as continuous when modeling construct growth but as discrete when modeling measurement, reducing interpretability and parsimony; (b) the evaluation of DIF becomes increasingly error-prone given multiple timepoints and groups; (c) DIF associated with continuous covariates is difficult to incorporate. Drawing on moderated nonlinear factor analysis, we propose an alternative approach that provides a parsimonious framework for including many time points and DIF from different types of covariates. We implement this model through Bayesian estimation, allowing for incorporation of regularizing priors to facilitate efficient evaluation of DIF. We demonstrate a two-step workflow of measurement evaluation and growth modeling, with an empirical example examining changes in adolescent delinquency over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142111363","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

Supplemental Material for Modeling Construct Change Over Time Amidst Potential Changes in Construct Measurement: A Longitudinal Moderated Factor Analysis Approach 在结构测量潜在变化中建立结构随时间变化模型的补充材料：纵向调节因子分析方法

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-08-08 DOI: 10.1037/met0000685.supp

引用次数: 0

Inference with cross-lagged effects-Problems in time. 交叉滞后效应推断--时间问题。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000665

Charles C Driver

{"title":"Inference with cross-lagged effects-Problems in time.","authors":"Charles C Driver","doi":"10.1037/met0000665","DOIUrl":"https://doi.org/10.1037/met0000665","url":null,"abstract":"The interpretation of cross-effects from vector autoregressive models to infer structure and causality among constructs is widespread and sometimes problematic. I describe problems in the interpretation of cross-effects when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps (as in e.g., structural equation modeling)-zeroes in a discrete-time temporal matrix do not necessarily correspond to zero effects in the underlying continuous processes, and vice versa. This has implications for the common case when the presence or absence of cross-effects is used for inference about underlying causal processes. I demonstrate these problems via simulation, and also show that when an underlying set of processes are continuous in time, even relatively few direct causal links can result in much denser temporal effect matrices in discrete-time. I demonstrate one solution to these issues, namely parameterizing the system as a stochastic differential equation and focusing inference on the continuous-time temporal effects. I follow this with some discussion of issues regarding the switch to continuous-time, specifically regularization, appropriate measurement time lag, and model order. An empirical example using intensive longitudinal data highlights some of the complexities of applying such approaches to real data, particularly with respect to model specification, examining misspecification, and parameter interpretation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634308","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 framework for studying environmental statistics in developmental science. 发展科学环境统计研究框架。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000651

Nicole Walasek, Ethan S Young, Willem E Frankenhuis

{"title":"A framework for studying environmental statistics in developmental science.","authors":"Nicole Walasek, Ethan S Young, Willem E Frankenhuis","doi":"10.1037/met0000651","DOIUrl":"https://doi.org/10.1037/met0000651","url":null,"abstract":"Psychologists tend to rely on verbal descriptions of the environment over time, using terms like \"unpredictable,\" \"variable,\" and \"unstable.\" These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying \"unpredictability\" in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different \"unpredictability statistics\" across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634306","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

Coefficients of determination measured on the same scale as the outcome: Alternatives to R² that use standard deviations instead of explained variance. 以与结果相同的尺度衡量的决定系数：R² 的替代品，使用标准差代替解释方差。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000681

Mathias Berggren

{"title":"Coefficients of determination measured on the same scale as the outcome: Alternatives to R² that use standard deviations instead of explained variance.","authors":"Mathias Berggren","doi":"10.1037/met0000681","DOIUrl":"https://doi.org/10.1037/met0000681","url":null,"abstract":"The coefficient of determination, R², also called the explained variance, is often taken as a proportional measure of the relative determination of model on outcome. However, while R² has some attractive statistical properties, its reliance on squared variations (variances) may limit its use as an easily interpretable descriptive statistic of that determination. Here, the properties of this coefficient on the squared scale are discussed and generalized to three relative measures on the original scale. These generalizations can all be expressed as transformations of R², and alternatives can therefore also be calculated by plugging in related estimates, such as the adjusted R². The third coefficient, new for this article, and here termed the CoDSD (the coefficient of determination in terms of standard deviations), or Rπ (R-pi), equals R²/(R²+1-R²). It is argued that this coefficient most usefully captures the relative determination of the model. When the contribution of the error is c times that of the model, the CoDSD equals 1/(1 + c), while R² equals 1/(1 + c²). (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634307","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

Supplemental Material for Coefficients of Determination Measured on the Same Scale as the Outcome: Alternatives to R2 That Use Standard Deviations Instead of Explained Variance 在与结果相同的尺度上测量的决定系数的补充材料：使用标准差代替解释方差的 R2 替代方案

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-08 DOI: 10.1037/met0000681.supp

引用次数: 0

Using group level factor models to resolve high dimensionality in model-based sampling. 在基于模型的抽样中使用组级因子模型解决高维度问题。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-24 DOI: 10.1037/met0000618

Niek Stevenson, Reilly J Innes, Quentin F Gronau, Steven Miletić, Andrew Heathcote, Birte U Forstmann, Scott D Brown

{"title":"Using group level factor models to resolve high dimensionality in model-based sampling.","authors":"Niek Stevenson, Reilly J Innes, Quentin F Gronau, Steven Miletić, Andrew Heathcote, Birte U Forstmann, Scott D Brown","doi":"10.1037/met0000618","DOIUrl":"https://doi.org/10.1037/met0000618","url":null,"abstract":"Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141446886","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 comparison of random forest-based missing imputation methods for covariates in propensity score analysis. 比较倾向评分分析中基于随机森林的协变量缺失估算方法。

IF 7 1区心理学

Psychological methods Pub Date : 2024-06-13 DOI: 10.1037/met0000676

Yongseok Lee, Walter L Leite

引用次数: 0

Supplemental Material for A Comparison of Random Forest-Based Missing Imputation Methods for Covariates in Propensity Score Analysis 倾向评分分析中基于随机森林的协变量缺失估算方法比较》补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2024-06-06 DOI: 10.1037/met0000676.supp

引用次数: 0

Supplemental Material for A Framework for Studying Environmental Statistics in Developmental Science 发育科学环境统计研究框架》补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2024-06-06 DOI: 10.1037/met0000651.supp

引用次数: 0