Illustrating instrumental variable regressions using the career adaptability – job satisfaction relationship
|Titre||Illustrating instrumental variable regressions using the career adaptability – job satisfaction relationship|
|Type de publication||Journal Article|
|Year of Publication||2019|
|Auteurs||Bollmann, G, Rouzinov, S, Berchtold, A, Rossier, J|
|Journal||Frontiers in Psychology|
|Mots-clés||Affect (emotion, career adaptability, causal inference, instrumental variable (IV), job satisfaction, mood, personality, personality)|
This article illustrates instrumental variable (IV) estimation by examining an unexpected finding of the research on career adaptability and job satisfaction. Theoretical and empirical arguments suggest that in the general population, people’s abilities to adapt their careers are beneficial to their job satisfaction. However, a recent meta-analysis unexpectedly found no effect when personality traits are controlled for. We argue that a reverse effect of job satisfaction on career adaptability, originating from affective tendencies tied to personality, might explain this null effect. Our argument implies that the estimates obtained with traditional ordinary least squares (OLS) regressions are biased by endogeneity, a correlation between an explanatory variable and the error term in a regression model. When experimental manipulations are impossible, IV estimations, such as two-stage least squares (2SLS) regressions, are one possible solution to the endogeneity problem. Analyzing three waves of data from a sample of 836 adults, the concurrent and time-lagged effect of job satisfaction on career adaptability was revealed to be more consistent than the reverse. Our results provide an explanation, rooted in affective dispositions, as to why recent meta-analytical estimates unexpectedly found that career adaptability does not predict job satisfaction at the interindividual level. We also discuss IV estimation in terms of its limits, weight the interpretation of its estimates against the temporality criterion for causal inference, and consider its possible extension to analyses of change.