The LIVES Daily Hassles Scale and Its Relation to Life Satisfaction.

Although daily hassles have been of interest since the 1980s, only a few tools have been developed to assess them. Most of them are checklists or open-ended questions that are demanding for participants in panel surveys. Therefore, to facilitate daily hassles integration into large surveys, the aim of this study was to present a new tool assessing daily hassles, the LIVES-Daily Hassles Scale (LIVES-DHS), and to examine its relation to life satisfaction, in a sample of 1,170 French- and German-speaking adults living in Switzerland. In a first random subsample, we conducted a principal axis factor analysis, and the results suggested a five-factor solution. Furthermore, we conducted a confirmatory factor analysis on a second random subsample, and it supported the hierarchical factor structure of the scale. The LIVES-DHS consists of 18 items represented by five factors that describe five sources of daily hassles: financial, physical, relational, environmental, and professional. The bivariate correlations showed that the LIVES-DHS could differentiate the concept of daily hassles from associated concepts. Finally, the hierarchical regression showed that daily hassles negatively predicted life satisfaction and added a significant incremental variance beyond that accounted for by age, gender, household income, education level, and personality traits.

Illustrating Instrumental Variable Regressions Using the Career Adaptability - Job Satisfaction Relationship.

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.