Presentation on theme: "STABILITY OF HAPPINESS IN A CHANGING SOCIETY A LATENT GROWTH ANALYSIS ON A ROMANIAN PANEL DATA Sergiu Bălţătescu University of Oradea Fifth ISQOLS Conference."— Presentation transcript:
STABILITY OF HAPPINESS IN A CHANGING SOCIETY A LATENT GROWTH ANALYSIS ON A ROMANIAN PANEL DATA Sergiu Bălţătescu University of Oradea Fifth ISQOLS Conference July 20-24.2003 Frankfurt
Recent & challenging issues in quality of life research Stability of happiness Sources of changes in subjective well- being over time Quality of life in developing and transition countries
Types of stability in societal levels – stability of aggregate levels of happiness (mean, variance) (Andrews, 1991), (Diener, 1994), (Veenhoven, 1994) in structure or correlates (Andrews, 1991), (Bryant, 1982). at the individual level: relative or absolute
Stability at the individual level absolute, that is in measured levels. – This approach (using scores of of change) was criticized for its low reliability and for influences of mood biases. (Headey & al., 1995). in rank-orders (or relative stability) – defined in terms of absence of “mobility along the happiness ladder” (Ehrhardt & al., 2000) (Headey, 1992)
Stability of happiness (i.e. in rank-order) Two conflicting theories: Happiness is traitlike Happiness is statelike
Support for traitlike theory The theory was forwarded by Stones (1995) (Costa, 1987) Individual differences in happiness are more important in influencing happiness levels and explain greater variance than situational effects. (Lykken, 1996) Based on the retest of smaller samples of twins after intervals of 4.5 and 10 yrs, it is estimated that the heritability of the stable component of subjective well-being approaches 80%
Support for statelike theory Veenhoven (1994, 1998) “happiness is quite stable for the short term, but not in the long run, neither relatively nor absolutely”
Implications of the theories If happiness is a trait (i.e. at a fixed level - relative or absolute), then utilitarian social policies designed to improve well-being cannot meet their end. (Veenhoven, 1994)
Methods of testing stability Correlational studies SEM-based approaches
Correlational studies most frequently used estimation of the correlation of happiness between two points in time “The reported correlations are typically in the realm of +.50, which is usually interpreted as evidence for high stability” (Ehrhardt & al, 2000) “Classical” Pearson P criticized for being too sensible at the change in levels Tetrachoric correlation recommended.
SEM-based approaches rather recently used First use in QOL research by (Headey & al., 1985) Structural Equations are combinations of regression, factorial & reliability analysis They use latent variables and take into account the residual variance (due to errors) (Maruyama, 1998) Recent models: (Ehrhardt & al., 2000) (Saris, 2001)
SEM-based approaches for panel data: crosslaged models (markovian models, autoregressive crosslaged models) Critiques: – 1. are fixed effects models – 2. the mean structure is ignored – change between 2 points is independent of earlier changes (Curran, 2001)
Critiques of the crosslaged models as applied to happiness data 1. the fixed effects model – it is unreasonable to think that the same determination mechanism works for all individuals 2. the mean structure is ignored happiness data are organized in trends. the slope may be ascending/descending, steep or smooth, different from one group to another 3. the change between 2 points is independent of earlier or latter changes the effects of predictors for happiness changes are additive and on a rather “longue duree”
SEM studies Ehrhardt, Saris & Veenhoven, 2000 Data: German Socio-Economic Panel (11 yearly waves, 1984-1994, people aged 18 years, 5483 respondents). Year-to-year correlation started at +.45 and increased gradually to +.54. The correlation between the first and later reports declined through the years: the correlation between the first and the third report varied between.+.38 and +.53
SEM studies Ehrhardt, Saris & Veenhoven, 2000 Two models: 1. life-satisfaction of a person depends mainly on turns in life 2. life-satisfaction depends also on the person's stable stocks (body, personality and social position) Using a SEM model, authors fitted both models.
SEM-based approaches for panel data: Latent Growth Curves alternative model for the analysis of change Introduced by Meredith & Tisak (1990) They started analyzing trajectories of individual change over time. The SEM model they introduced was designed to smooth (they call it “tuckerize”) curves, to estimate the continuos trajectory that gave rise to the time specific observed measures.
Latent Growth Curves (The SEM model) Two latent factors: The stable component (the intercept). – its value is the initial mean value of the level of the studied variable The changing component (the slope). – its value is the mean value of the slope of the trajectory of the studied variable Intercept and slope – may or not be correlated – have different predictors
Latent Growth Curves (The SEM model specification) The regression coefficients of intercept to observed time variables is set to a constant (say 1) The regression coefficients of the slope factor to observed variables is linearly increased (0,1,2,3…) If the slope is supposed to be non- linear, then the regression coefficients are set a non-linear trend (ex: 0, 1, 2, 4, 8) or remain unspecified (0, a, b, c, 1)
Latent Growth Curves (Applications) In general psychology developmental processes – reading skills (Aunola & al., 2002) – self-concept, self-efficacy (Duncan & al., 1993) – antisocial behaviour (Duncan, 2002) (Li & al., 2000) – adolescent substance use (Duncan & al., 1994) experimantal designs (Muthen & Curran, 1997). In psychological well-being studies changes in positive/negative affect (Charles & al., 2001) stressor-strain relationship (Garst & al., 2000) enhancing psychological well-being of elderly (Li & al., 2001)
Latent growth curves in well-being models Benefits of the method identifies the stable and the changing components of well-being evaluates the correlations between those two. identifies the predictors of the stable component (psychological factors like personality) and the predictors of the change (life events) permit evaluations of different groups (sex, age, clinical, non-clinical, social status, etc) intercept and slope.
A latent growth curves model for the German data Data were reanalyzed from the covariance matrices and mean included in (Ehrhardt & al., 2000). We used 6 waves (1984-1989). Data shows a slight decreasing trend. In a variant of the model, I set only the first and the last regression coefficients from the slope to observed variables, letting the SEM program to estimate the others, as recommended, among others, by Curran (2001). We used AMOS 4.0 (Arbuckle, 1999) A more complicated design, recommended by (Anderson, 2000)
Two models: Linear decrease Full LGM
Fit indexes: Linear decreaseFull LGM Chi-square169,29124,374 P 00 Normed fit index0,9980,999 Relative fit index0,9980,998 Incremental fit index0,9980,999 Tucker-Lewis index0,9980,998 Comparative fit index0,9980,999 Intercept mean: 7,51 Intercept SD: 1,42 Slope Mean: -0,401 Slope SD: 1,114 Correlation SLOPE-INTERCEPT = 0,44
A latent growth curves model for the German data
Happiness levels in transition and developing countries levels are lower giving the poor socio- economic conditions.
Happiness structure in transition and developing countries income levels are higher determinants than in other countries. bottom-up and top-down effects included
Other socio-economic determinants macroeconomic instability concern for unemployment(Graham, 2001) frustrated achievers (Graham, 2002) all included, it is suggested that a higher instability of happiness will be found in rapid changing societies
Case study: Romania november 1996-1999 1997 was the beginning of a new electoral cycle dissapointing new government actions economic and social mood indicators dropped.
economic & social indicators
The life satisfaction scale Used in ‘Diagnosis of quality of life’ survey program (ICCV, 1990-1999) Sample: national, random, around 1200 cases A 5-point simple life satisfaction scale: “Considering the whole situation, how satisfied are you about your daily life? a. Very unsatisfied b.Unsatisfied c.Neither unsatisfied, nor satisfied d.Satisfied e.Very satisfied” See: Mărginean (1991), Zamfir (1992) See also: www.iccv.ro
Life satisfaction in Romania (1990-1999) Source: Diagnosis of Quality of life Data were liniarly transformed to 0- 10 scale After 1990, the mean is under the median line
Quality of life panel 1996-1998 representative sample of 700 subjects analysis of dropouts shows no significant differences with the panel.
Quality of life panel 1996-1998
LGM MODEL Additional constrains has to be imposed to fit the model: - equality of the variances of the residual terms It was used the AMOS option to fit the non-positive covariance matrices A poor fit resulted There are no additional constraints to be imposed to increase the fit of the model
Discussion Correlational study Atypical correlation They not decrease over time as predicted by Erhardt & al. Has lower levels than in Germany (and also in other advanced countries)
Correlational study Possible causes: Methodological explanations: – Coarse (ordinal level) measurement used. – It is a less reliable measurement – The coarse correlation coefficients are usually lower
Correlational study Possible causes: Theoretical : – Macroeconomic instability produce large variations in the happiness levels. – “Frustrated achievers”, as Graham (2001) found in other developing countries
Limitations of the method Minimal conditions Semi-interval scales (over 4 points) Three or more waves in the panel For 3 vawes, restrictions to be imposed are often unacceptable from the theoretical point of view Optimal conditions Semi-interval scales (over 11-200 points) More than three waves in the panel (applicable for German Socio- Economic Panel, Russian Panel, etc.)
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