Handbook of Longitudinal Research Methods in Organisation and Business Studies
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Handbook of Longitudinal Research Methods in Organisation and Business Studies

Edited by Mélanie E. Hassett and Eriikka Paavilainen-Mäntymäki

This innovative Handbook demonstrates that there is no single best approach to conducting longitudinal studies. At their best, longitudinal research designs yield rich, contextualised, multilevel and deep understanding of the studied phenomenon. The lack of resources in terms of time, funding and people can pose a serious challenge to conducting longitudinal research. This book tackles many of these challenges and discusses the role of longitudinal research programmes in overcoming such obstacles.
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Chapter 3: Planned missing data designs for longitudinal organizational research

Mijke Rhemtulla and Todd D. Little


The presence of unplanned missing data has long been a nuisance to researchers. Missing data can lower the sample size available to test hypotheses, and it has the potential to introduce bias into the results of any inquiry based on those data. Any amount of missing data can turn a straightforward analysis into something messy (e.g. turning balanced samples into unbalanced ones) and it must be dealt with in some way. Too often the cases with missingness are deleted under the misguided assumption that cases with missingness are no different than those without. Given this widespread attitude toward missing data, the idea of deliberately inserting missing data into a research design may sound perverse. We aim to convince researchers in the field of organizational studies that planned missing data designs, where certain measurements are purposely omitted, are an efficient way to reduce data collection costs while producing more valid conclusions. For example, planned missing data designs can both reduce the amount of unplanned missing data due to participant nonresponse and increase effect sizes by reducing fatigue effects (thereby reducing measurement error; Harel et al. 2012). Planned missing data research designs can also enhance validity in longitudinal scenarios where test reactivity can be a threat to validity: by controlling participants’ testing occasions, researchers can estimate retest effects and separate their influence from the growth parameters of interest (McArdle and Woodcock 1997).

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