Measuring Wellbeing in the SOEP

I define wellbeing as preference realization. Wellbeing can be measured with affective (the amount of pleasant versus unpleasant experiences) and cognitive (satisfaction with life in general and life domains) measures. Since its inception 25 years ago, the SOEP has included cognitive measures of wellbeing. In 2007, the SOEP included four items (happy, sad, angry, afraid) as an affective measure of wellbeing. This paper examines similarities and differences between cognitive and affective measures of wellbeing. In the end, I propose a wellbeing index that combines information from measures of life satisfaction, average domain satisfaction, and affect balance.General welfare, quality of life, happiness, wellbeing

Marriage Matters: Spousal Similarity in Life Satisfaction

Examined the concurrent and cross-lagged spousal similarity in life satisfaction over a 21-year period. Analyses were based on married couples (N = 847) in the German Socio-Economic Panel (SOEP). Concurrent spousal similarity was considerably higher than one-year retest similarity, revealing spousal similarity in the variable component of life satisfac-tion. Spousal similarity systematically decreased with length of retest interval, revealing simi-larity in the changing component of life satisfaction. Finally, there was considerable spousal similarity in the stable component of life satisfaction over 20-years. The implications of these findings for causal theories of life satisfaction and studies in line with behavioural genetics are discussedSubjective Well Being, Life Satisfaction, Marriage, Couples, Spousal Similarity, Heritability, Assortative Mating, Longitudinal Panel, SOEP

Estimating the false discovery risk of (randomized) clinical trials in medical journals based on published p-values

The influential claim that most published results are false raised concerns about the trustworthiness and integrity of science. Since then, there have been numerous attempts to examine the rate of false-positive results that have failed to settle this question empirically. Here we propose a new way to estimate the false positive risk and apply the method to the results of (randomized) clinical trials in top medical journals. Contrary to claims that most published results are false, we find that the traditional significance criterion of $\alpha = .05$ produces a false positive risk of 13%. Adjusting $\alpha$ to .01 lowers the false positive risk to less than 5%. However, our method does provide clear evidence of publication bias that leads to inflated effect size estimates. These results provide a solid empirical foundation for evaluations of the trustworthiness of medical research