{"title":"因子得分总和与估算因子得分:在使用观测分数时考虑不确定性。","authors":"Yang Liu, Jolynn Pek","doi":"10.1037/met0000644","DOIUrl":null,"url":null,"abstract":"<p><p>Observed scores (e.g., summed scores and estimated factor scores) are assumed to reflect underlying constructs and have many uses in psychological science. Constructs are often operationalized as latent variables (LVs), which are mathematically defined by their relations with manifest variables in an LV measurement model (e.g., common factor model). We examine the performance of several types of observed scores for the purposes of (a) estimating latent scores and classifying people and (b) recovering structural relations among LVs. To better reflect practice, our evaluation takes into account different sources of uncertainty (i.e., sampling error and model error). We review psychometric properties of observed scores based on the classical test theory applied to common factor models, report on a simulation study examining their performance, and provide two empirical examples to illustrate how different scores perform under different conditions of reliability, sample size, and model error. We conclude with general recommendations for using observed scores and discuss future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Summed versus estimated factor scores: Considering uncertainties when using observed scores.\",\"authors\":\"Yang Liu, Jolynn Pek\",\"doi\":\"10.1037/met0000644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Observed scores (e.g., summed scores and estimated factor scores) are assumed to reflect underlying constructs and have many uses in psychological science. Constructs are often operationalized as latent variables (LVs), which are mathematically defined by their relations with manifest variables in an LV measurement model (e.g., common factor model). We examine the performance of several types of observed scores for the purposes of (a) estimating latent scores and classifying people and (b) recovering structural relations among LVs. To better reflect practice, our evaluation takes into account different sources of uncertainty (i.e., sampling error and model error). We review psychometric properties of observed scores based on the classical test theory applied to common factor models, report on a simulation study examining their performance, and provide two empirical examples to illustrate how different scores perform under different conditions of reliability, sample size, and model error. We conclude with general recommendations for using observed scores and discuss future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>\",\"PeriodicalId\":20782,\"journal\":{\"name\":\"Psychological methods\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-02-08\",\"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/met0000644\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000644","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
摘要
观测分数(如总分和估计因子分数)被认为反映了潜在的结构,在心理科学中有许多用处。构念通常被操作化为潜变量(LVs),在潜变量测量模型(如共因子模型)中,潜变量与显变量的关系对其进行了数学定义。我们研究了几类观察分数的性能,目的是:(a)估计潜在分数并对人进行分类;(b)恢复 LV 之间的结构关系。为了更好地反映实际情况,我们的评估考虑了不同的不确定性来源(即抽样误差和模型误差)。我们回顾了基于经典测试理论、应用于常见因子模型的观察分数的心理测量特性,报告了对其性能进行检验的模拟研究,并提供了两个实证例子,以说明不同分数在不同可靠性、样本大小和模型误差条件下的表现。最后,我们提出了使用观察分数的一般性建议,并讨论了未来的研究方向。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
Summed versus estimated factor scores: Considering uncertainties when using observed scores.
Observed scores (e.g., summed scores and estimated factor scores) are assumed to reflect underlying constructs and have many uses in psychological science. Constructs are often operationalized as latent variables (LVs), which are mathematically defined by their relations with manifest variables in an LV measurement model (e.g., common factor model). We examine the performance of several types of observed scores for the purposes of (a) estimating latent scores and classifying people and (b) recovering structural relations among LVs. To better reflect practice, our evaluation takes into account different sources of uncertainty (i.e., sampling error and model error). We review psychometric properties of observed scores based on the classical test theory applied to common factor models, report on a simulation study examining their performance, and provide two empirical examples to illustrate how different scores perform under different conditions of reliability, sample size, and model error. We conclude with general recommendations for using observed scores and discuss future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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.