Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies.

IF 3.4 3区 心理学 Q1 PSYCHOLOGY, DEVELOPMENTAL
Jason D Rights, Sonya K Sterba
{"title":"Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies.","authors":"Jason D Rights,&nbsp;Sonya K Sterba","doi":"10.1002/cad.20387","DOIUrl":null,"url":null,"abstract":"<p><p>Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R<sup>2</sup> ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR<sup>2</sup> ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.</p>","PeriodicalId":47745,"journal":{"name":"New Directions for Child and Adolescent Development","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cad.20387","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Directions for Child and Adolescent Development","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1002/cad.20387","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/29 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PSYCHOLOGY, DEVELOPMENTAL","Score":null,"Total":0}
引用次数: 14

Abstract

Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R2 ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR2 ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.

纵向增长分析的效应大小测量:扩展多水平模型r方的框架,以适应异方差、自相关、非线性和可选的定心策略。
发展研究人员通常使用多层次模型(MLMs)来描述和预测个体差异随时间的变化。在这种增长模型的应用中,研究人员被广泛鼓励用效应大小的测量来补充统计显著性的报告,比如r平方(R2),它传达了模型中术语解释的方差。right和Sterba最近引入了一个计算随机截点和/或斜率的传销r平方的综合框架,它将已有的传销r平方作为特殊情况纳入其中。然而,这项工作侧重于横断面应用,因此没有解决MLM r平方的计算和解释如何受到纵向设置中典型的建模考虑因素的影响:(a)时间的替代中心选择(例如,以常数为中心与以人平均为中心),(b)预测因子的非线性效应,如时间,(c)异方差1级误差和/或(d)自相关1级误差。本文通过将Rights和Sterba R-squared框架扩展到纵向上下文来解决这些差距。我们:(a)为使用任何类型居中的传销提供总体和特定水平的r平方度量的完整框架,并将其与right和Sterba的假设簇均值居中的度量进行对比,(b)解释和推导哪些度量适用于具有非线性项的传销,并扩展r平方计算以适应(c)异方差和/或(d)自相关误差。此外,我们展示了如何使用增长模型之间的r平方(ΔR2)度量的差异(例如,添加时变协变量作为一级预测因子或添加时不变协变量作为二级预测因子)来获得单个项的效应大小。我们提供R软件(r2MLMlong)和一个分析青少年自我效能增长的教学实例来说明这些方法的发展。随着这些发展,研究人员在分析和预测使用传销的变化时将有更大的能力考虑效应大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.70
自引率
3.60%
发文量
34
期刊介绍: The mission of New Directions for Child and Adolescent Development is to provide scientific and scholarly presentations on cutting edge issues and concepts in the field of child and adolescent development. Each issue focuses on a specific new direction or research topic, and is peer reviewed by experts on that topic. Any topic in the domain of child and adolescent development can be the focus of an issue. Topics can include social, cognitive, educational, emotional, biological, neuroscience, health, demographic, economical, and socio-cultural issues that bear on children and youth, as well as issues in research methodology and other domains. Topics that bridge across areas are encouraged, as well as those that are international in focus or deal with under-represented groups. The readership for the journal is primarily students, researchers, scholars, and social servants from fields such as psychology, sociology, education, social work, anthropology, neuroscience, and health. We welcome scholars with diverse methodological and epistemological orientations.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信