Download full text
(external source)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://doi.org/10.18148/srm/2020.v14i4.7655
Exports for your reference manager
Measurement Invariance: Testing for It and Explaining Why It is Absent
[journal article]
Abstract 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 sa... view more
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 artifacts 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 modeling.... view less
Keywords
survey research; data capture; comparative research; longitudinal study
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
measurement invariance; comparability; bias; approximate measurement invariance; alignment; BSEM
Document language
English
Publication Year
2020
Page/Pages
p. 345-349
Journal
Survey Research Methods, 14 (2020) 4
ISSN
1864-3361
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications