A comprehensive assessment of measurement equivalence in operations management
This paper provides a comprehensive framework for treating equivalence both prior to data collection and during subsequent analyses, and assesses the extent to which equivalence is considered in survey research in six leading empirical Operations Management (OM) journals (Decision Sciences, International Journal of Operations & Production Management, International Journal of Production Research, Journal of Operations Management, Management Science and Production and Operations Management). Measurement equivalence of latent variables in survey data is an important condition that should be met in order to meaningfully pool and/or compare data stemming from apparently heterogeneous sub-groups. We assess 465 survey articles from a six-year period from 2006 to 2011 and document these articles in relation to the four main stages of our comprehensive framework: identifying sources of heterogeneity; maximising equivalence prior to data collection; testing measurement equivalence after data collection; and dealing with partial and non-equivalence. We conclude that pooling of data from heterogeneous sub-groups is common practice in OM, but that awareness and testing of equivalence remains limited. Given these findings, we further elaborate the best practices detected in those few OM studies that do address equivalence in some way. We conclude that to improve the quality of OM survey research, authors, editors and reviewers should pay greater attention to equivalence, and we provide a pragmatic checklist of measurement equivalence issues across the four stages.
|Keywords||measurement equivalence, Operations Management (OM), survey research|
|Persistent URL||dx.doi.org/10.1080/00207543.2014.944629, hdl.handle.net/1765/76376|
|Journal||International Journal of Production Research|
Knoppen, D, Ateş, M.A, Brandon-Jones, A, Luzzini, D, van Raaij, E.M, & Wynstra, J.Y.F. (2014). A comprehensive assessment of measurement equivalence in operations management. International Journal of Production Research. doi:10.1080/00207543.2014.944629