Bryn Hammack-Brown, Julia A. Fulmore, Greggory L. Keiffer, Kim Nimon
{"title":"在发现非不变性时发现不变性:使用因子比率测试和列表-删除过程的自动化执行部分度量不变性测试的说明性示例","authors":"Bryn Hammack-Brown, Julia A. Fulmore, Greggory L. Keiffer, Kim Nimon","doi":"10.1002/hrdq.21452","DOIUrl":null,"url":null,"abstract":"<p>Comparisons between groups are common in human resource development (HRD) studies, yet many researchers neglect a crucial prerequisite step before analyzing and interpreting what group differences might mean. In essence, how can HRD scholars be confident in knowing that mean group differences are attributable to actual differences between groups as opposed to differences in how each group interprets the constructs of interest? Measurement invariance (MI) provides insight into whether a measure or construct has the same meaning between groups or over time and is an important precursor to the evaluation of group differences. While MI testing has gained some traction within HRD studies, steps to take when partial MI testing is needed have received very little attention. The purpose of this article is to encourage HRD researchers and practitioners to embrace and utilize two techniques when partial invariance (i.e., noninvariance) occurs. There are several techniques one could use during partial MI testing; however, the two showcased herein, the factor-ratio test and the list-and-delete procedure, are established, reliable, and proven within the confirmatory factor analysis framework. This article provides an illustrative example of how to use these techniques to identify invariance at the item level when noninvariance is found. Additionally, R syntax is included that allows for the automation of these techniques. The importance to theory and implications to researchers and practitioners of finding noninvariance and then testing for partial MI is also discussed.</p>","PeriodicalId":47803,"journal":{"name":"Human Resource Development Quarterly","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/hrdq.21452","citationCount":"2","resultStr":"{\"title\":\"Finding invariance when noninvariance is found: An illustrative example of conducting partial measurement invariance testing with the automation of the factor-ratio test and list-and-delete procedure\",\"authors\":\"Bryn Hammack-Brown, Julia A. Fulmore, Greggory L. Keiffer, Kim Nimon\",\"doi\":\"10.1002/hrdq.21452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Comparisons between groups are common in human resource development (HRD) studies, yet many researchers neglect a crucial prerequisite step before analyzing and interpreting what group differences might mean. In essence, how can HRD scholars be confident in knowing that mean group differences are attributable to actual differences between groups as opposed to differences in how each group interprets the constructs of interest? Measurement invariance (MI) provides insight into whether a measure or construct has the same meaning between groups or over time and is an important precursor to the evaluation of group differences. While MI testing has gained some traction within HRD studies, steps to take when partial MI testing is needed have received very little attention. The purpose of this article is to encourage HRD researchers and practitioners to embrace and utilize two techniques when partial invariance (i.e., noninvariance) occurs. There are several techniques one could use during partial MI testing; however, the two showcased herein, the factor-ratio test and the list-and-delete procedure, are established, reliable, and proven within the confirmatory factor analysis framework. This article provides an illustrative example of how to use these techniques to identify invariance at the item level when noninvariance is found. Additionally, R syntax is included that allows for the automation of these techniques. The importance to theory and implications to researchers and practitioners of finding noninvariance and then testing for partial MI is also discussed.</p>\",\"PeriodicalId\":47803,\"journal\":{\"name\":\"Human Resource Development Quarterly\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2021-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/hrdq.21452\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Resource Development Quarterly\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/hrdq.21452\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INDUSTRIAL RELATIONS & LABOR\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Resource Development Quarterly","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hrdq.21452","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INDUSTRIAL RELATIONS & LABOR","Score":null,"Total":0}
Finding invariance when noninvariance is found: An illustrative example of conducting partial measurement invariance testing with the automation of the factor-ratio test and list-and-delete procedure
Comparisons between groups are common in human resource development (HRD) studies, yet many researchers neglect a crucial prerequisite step before analyzing and interpreting what group differences might mean. In essence, how can HRD scholars be confident in knowing that mean group differences are attributable to actual differences between groups as opposed to differences in how each group interprets the constructs of interest? Measurement invariance (MI) provides insight into whether a measure or construct has the same meaning between groups or over time and is an important precursor to the evaluation of group differences. While MI testing has gained some traction within HRD studies, steps to take when partial MI testing is needed have received very little attention. The purpose of this article is to encourage HRD researchers and practitioners to embrace and utilize two techniques when partial invariance (i.e., noninvariance) occurs. There are several techniques one could use during partial MI testing; however, the two showcased herein, the factor-ratio test and the list-and-delete procedure, are established, reliable, and proven within the confirmatory factor analysis framework. This article provides an illustrative example of how to use these techniques to identify invariance at the item level when noninvariance is found. Additionally, R syntax is included that allows for the automation of these techniques. The importance to theory and implications to researchers and practitioners of finding noninvariance and then testing for partial MI is also discussed.
期刊介绍:
Human Resource Development Quarterly (HRDQ) is the first scholarly journal focused directly on the evolving field of human resource development (HRD). It provides a central focus for research on human resource development issues as well as the means for disseminating such research. HRDQ recognizes the interdisciplinary nature of the HRD field and brings together relevant research from the related fields, such as economics, education, management, sociology, and psychology. It provides an important link in the application of theory and research to HRD practice. HRDQ publishes scholarly work that addresses the theoretical foundations of HRD, HRD research, and evaluation of HRD interventions and contexts.