Measurement Invariance: Testing for It and Explaining Why It is Absent

被引:23
|
作者
Meitinger, Katharina [1 ]
Davidov, Eldad [2 ,3 ,4 ]
Schmidt, Peter [5 ]
Braun, Michael [6 ]
机构
[1] Univ Utrecht, Fac Social & Behav Sci, Utrecht, Netherlands
[2] Univ Cologne, Fac Management Econ & Social Sci, Cologne, Germany
[3] Univ Zurich, URPP Social Networks, Zurich, Switzerland
[4] Univ Zurich, Dept Sociol, Zurich, Switzerland
[5] Univ Giessen, Ctr Int Dev & Environm Res ZEU, Giessen, Germany
[6] GESIS Leibniz Inst Social Sci, Mannheim, Germany
来源
SURVEY RESEARCH METHODS | 2020年 / 14卷 / 04期
关键词
measurement equivalence; comparability; bias; approximate measurement invariance; alignment; BSEM; MEASUREMENT EQUIVALENCE; BIAS;
D O I
10.18148/srm/2020.v14i4.7655
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
There has been a significant increase in cross-national and longitudinal data production in social science research in recent decades. Before drawing substantive conclusions based on cross-national and longitudinal survey data, researchers need to assess whether the constructs are measured in the same way across countries and time-points. If cross-national data are not tested for comparability, researchers risk confusing methodological artefacts as "real" substantive differences across countries. However, researchers often find it particularly difficult to establish the highest level of measurement invariance, that is, exact scalar invariance. When measurement invariance is rejected, it is crucial to understand why this was the case and to address its absence with approaches, such as alignment optimization or Bayesian structural equation modelling.
引用
收藏
页码:345 / 349
页数:5
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