Missing not at random intensive longitudinal data with dynamic structural equation models.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Daniel McNeish
{"title":"Missing not at random intensive longitudinal data with dynamic structural equation models.","authors":"Daniel McNeish","doi":"10.1037/met0000742","DOIUrl":null,"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":7.6000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000742","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

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).

缺少非随机密集的纵向数据与动态结构方程模型。
密集的纵向设计越来越受欢迎,用于评估情绪、情感、人际关系或健康行为的瞬间变化。由于数据收集的频率很高,这些研究的依从性从来都不是完美的,因此数据丢失是不可避免的。然而,对于动态结构方程模型中缺失数据的研究相对较少,动态结构方程模型是最近提出的一种对密集纵向数据建模的框架。现有的少数研究倾向于关注适用于随机缺失数据(MAR)的方法。然而,非随机缺失(MNAR)数据普遍存在,特别是当兴趣是与心理健康、物质使用或性行为相关的敏感结果时。作为一个鼓舞人心的例子,一项关于暴食症患者的研究被认为在与暴饮暴食相关的自我报告项目中有大量缺失。缺失可能很高,因为参与者在报告这种行为时感到羞耻,这是MNAR的一个明显例子,对于这种情况,多重归因和全信息最大似然等方法效果较差。为了提高MNAR密集纵向数据的处理能力,提出在动态结构方程模型中嵌入diggle - kenward型MNAR模型。该方法可以直接应用于Mplus等流行软件中,并且相对于假设MAR的模型只需要额外的几行代码。将所提出方法的结果与假设MAR的模型的结果进行对比,并进行仿真研究,以研究所提出模型在连续或二元结果下的性能。(PsycInfo Database Record (c) 2025 APA,版权所有)。
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
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
×
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