Impact of missing data strategies in studies of parental employment and health: Missing items, missing waves, and missing mothers

Cattram D. Nguyen*, Lyndall Strazdins, Jan M. Nicholson, Amanda R. Cooklin

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Background: Understanding the long-term health effects of employment – a major social determinant – on population health is best understood via longitudinal cohort studies, yet missing data (attrition, item non-response) remain a ubiquitous challenge. Additionally, and unique to the work-family context, is the intermittent participation of parents, particularly mothers, in employment, yielding ‘incomplete’ data. Missing data are patterned by gender and social circumstances, and the extent and nature of resulting biases are unknown. Method: This study investigates how estimates of the association between work-family conflict and mental health depend on the use of four different approaches to missing data treatment, each of which allows for progressive inclusion of more cases in the analyses. We used 5 waves of data from 4983 mothers participating in the Longitudinal Study of Australian Children. Results: Only 23% had completely observed work-family conflict data across all waves. Participants with and without missing data differed such that complete cases were the most advantaged group. Comparison of the missing data treatments indicate the expected narrowing of confidence intervals when more sample were included. However, impact on the estimated strength of association varied by level of exposure: At the lower levels of work-family conflict, estimates strengthened (were larger); at higher levels they weakened (were smaller). Conclusions: Our results suggest that inadequate handling of missing data in extant longitudinal studies of work-family conflict and mental health may have misestimated the adverse effects of work-family conflict, particularly for mothers. Considerable caution should be exercised in interpreting analyses that fail to explore and account for biases arising from missing data.

    Original languageEnglish
    Pages (from-to)160-168
    Number of pages9
    JournalSocial Science and Medicine
    Volume209
    DOIs
    Publication statusPublished - Jul 2018

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