Stability and Change of Well Being: An Experimentally Enhanced Latent State-Trait-Error Analysis

This study uses longitudinal panel data and short-term retest data from the same respondents in the German Socio-economic Panel to estimate the contribution of state and trait variance to the reliable variance in judgments of life satisfaction and domain satisfaction. The key finding is that state a...

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Bibliographic Details
Published in:Social indicators research 2010-01, Vol.95 (1), p.19-31
Main Authors: Schimmack, Ulrich, Krause, Peter, Wagner, Gert G, Schupp, Jürgen
Format: Article
Language:English
Subjects:
Bias
Cohort studies
Correlation
Correlations
Cross Cultural Studies
Cultures and civilizations
Effect Size
Environmental Influences
Error analysis
Error Analysis (Language)
Error of Measurement
Estimate reliability
Experimental Colleges
Federal Republic of Germany
Foreign Countries
Germany
History, theory and methodology
Human Geography
Intervals
Life Satisfaction
Lifestyles. Everyday life
Longitudinal Studies
Measures (Individuals)
Meta Analysis
Methodology
Microeconomics
Musical intervals
Parametric models
Public Health
Quality of Life Research
Satisfaction
Social Indicators
Social research
Social Sciences
Social surveys
Socioeconomic Status
Sociology
State
Statistical discrepancies
Statistical variance
Twins
Variance
Well Being
Wellbeing
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Summary:This study uses longitudinal panel data and short-term retest data from the same respondents in the German Socio-economic Panel to estimate the contribution of state and trait variance to the reliable variance in judgments of life satisfaction and domain satisfaction. The key finding is that state and trait variance contribute approximately equally to the reliable variance in well being measures. Most of the occasion specific variance is random measurement error, although occasion-specific variation in state variance makes a reliable contribution for some measures. Moreover, the study shows high similarity in life satisfaction and average domain satisfaction for the stable trait component (r = .97), indicating that these two measures are influenced by the same stable dispositions. In contrast, state variance of the two measures is distinct, although still highly correlated (r = .77). Error variances of the two measures are only weakly correlated, indicating that most of the error component is indeed due to random measurement error.
ISSN:0303-8300
1573-0921
DOI:10.1007/s11205-009-9443-8