This paper demonstrates the usage of “Cultural Fit” concept to test wherether cultural values and descrepaces could be a deteminants for Subjective Well-Being. The “usual” SWB determinants, such as health, martial status, and education, are also tested on the ESS data. The research suggests that at country level some of the values are weak determinants for SWB, while cultural descrepancy is not.
The term “subjective well-being” (SWB), which is used by psychologists as a synonym for happiness (Lu, 2006), was promoted by Ed Diener. Since 1984 “Dr. Happiness” - as Diener is known by colleagues (Wallis, 2005) - published about 200 works, and his original work “Subjective Well-Being(1984)” has been cited over 1 200 times (Eid & Larsen, 2008).
The study of SWB focuses on how people evaluate their lives: both at the moment and during long periods of time (Diener et. al. 2003). These evaluations include emotional reactions to events, moods (affects); judgements about life satisfaction, fulfilment (cognitive judgement); and satisfaction with different life domains, such as marriage and work (domain satisfaction). SWB researchers have shown that these constructs are separable, and moreover, pleasant (e.g. joy, pride, ecstasy) and unpleasant (e.g. envy, guilt, shame) affects are moderately inversely correlated, but clearly separable (Diener et. al. 1999).
In order to measure SWB, the Satisfaction With Life Scale (SWLS) was developed (Diener et. al. 1985). SWLS consists of five items or statements, each in scale from one to seven, resulting in a five to 35 scale. Diener suggests a number of factors that influence overall life satisfaction: social relationships; work, school, or performance in an important role; satisfaction with the self, standing for spiritual life, learning, growth, and leisure (Diener, 2006). Although psychologists developed a lot of other tools to assess SWB, based on peer-reports, observations, emotion-sensitive tasks, and cognitive tasks, self-report assessments are considered reliable, thus SWLS is considered valid and reliable (Diener et. al. 1999) and used as a standard measure of SWB (Eid & Larsen, 2008).
Over decades researchers have found relationships between SWB and an individual's income (Diener et. al. 1993), health (Dolan et. al. 2008), age (Horley & Lavery, 1995), martial status, partner's education (Stutzer & Frey, 2006), self-esteem (Zhang, 2005), etc.
Chinese researcher Luo Lu claims that “cultural conceptions of happiness are critical aspects of SWB, which has largely been neglected” (Lu, 2005). Using a number of scales to measure individual's values (beliefs): Independent and Interdependent Self Scales (IISS), Primary Control Beliefs Scale (PCBS), Harmony Beliefs Scale (HBS); and Chinese Happiness Inventory (CHI) for SWB, Lu proposed a “cultural fit index”: an absolute value of the difference between the individual mean on each item and the group mean. The research showed that individual-level cultural values are consistently related to SWB.
Sagiv and Schwarz, researchers from The Hebrew University of Jerusalem in Israel, discovered two corellations between two indexes of SWB (general mental health and positive affect) and achievement, stimulation, and self-direction values (positive correlation); and traditional values (negative correlation). Further, no correlation was found between values and the cognitive index of SWB (Sagiv & Schwartz, 2000).
I use the “Cultural Fit” concept, estimate the Cultural Fit Index (CFi) and it's effect into SWB. I expect to find a consistant relationship between CFi and SWB, as I suppose that individuals who do not fit into their reference groups will be constrained in communications and social actions. I operationalize CFi as a mean of values of differences between individual and reference group values. I define an individual's reference group as a group within the same country and age group. I use the following age groups: young (18-24 years), young adult (25-34 years), adult (35-44 years), middle age (45-65 years), and old (65+ years).
I expect to find a consistant relationship between subjective health and martial status and Cognitive Judgement even without controlling for age and country.
I expect to find a consistant relationship between Creativity values (e.g. importance of being creative) and Cognitive Judgement.
I expect to find a consistant relationship between CFi and Cognitive judgement.
Health, martial status, and education level will be consistantly related to SWB
Creativity values will be consistantly related to SWB.
Cultural Fit indices will be consistantly related to SWB: traditional values discrepancy will be related with SWB in former communist countries, whereas individual values discrepancy will be related with SWB in protestant countries.
To test the first hypothesys I propose the following model:
To test the second hypothesys I propose the following model:
To test the third hypothesys I propose the following model:
I use the European Social Survey (ESS) data, that can be fetched by anyone. I use the data from the last (the fith) round and all available countries.
Dataset contains 50781 observations from 26 countries. Mean age of respondent is 48.3932.
I define two sub-groups of countries: protestant (Denmark, Great Britain, Nitherlands, and Switzerland) and former communist (Russia, Ukrain, and Bulgaria).
CFi are calculated as a mean of values of differences betwee individual and group values.
I use the confirmatory factor analisys (CFA) to test hypothesys. CFA allows us to measure or estimate the loadings of certain factors into construct we are investigating. The purpose of CFA is to determine whether the real data fits the hypothetised model or not.
I define the first model.
model.1 <- "Cognitive =~ happy + stflife\nStatus =~ health + partner + edulvla\nCognitive ~~ Status"
I test the model against the whole data.
model.1.fit <- cfa(model.1, data = cdata)
m.1.fit <- inspect(model.1.fit, "fit")
The model converged with following fit indices: CFI = 0.9713, SRMR = 0.0366, and RMSEA = 0.0833, which is not acceptable.
p.model.1.fit <- cfa(model.1, data = mature.p)
p.m.1.fit <- inspect(p.model.1.fit, "fit")
I test this model against the groups of middle age and old people and get the following fit indices: CFI = 0.9876, SRMR = 0.0215, and RMSEA = 0.0559, which is acceptable.
summary(p.model.1.fit, standardized = T)
## lavaan (0.5-11) converged normally after 47 iterations
##
## Used Total
## Number of observations 27028 27645
##
## Estimator ML
## Minimum Function Test Statistic 342.294
## Degrees of freedom 4
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Cognitive =~
## happy 1.000 1.912 0.871
## stflife 1.069 0.013 84.909 0.000 2.044 0.834
## Status =~
## health 1.000 0.602 0.631
## partner 0.242 0.008 32.073 0.000 0.146 0.303
## edulvla -0.582 0.021 -27.754 0.000 -0.351 -0.249
##
## Covariances:
## Cognitive ~~
## Status -0.769 0.013 -57.002 0.000 -0.667 -0.667
##
## Variances:
## happy 1.167 0.041 1.167 0.242
## stflife 1.824 0.048 1.824 0.304
## health 0.549 0.011 0.549 0.602
## partner 0.211 0.002 0.211 0.908
## edulvla 1.863 0.017 1.863 0.938
## Cognitive 3.656 0.056 1.000 1.000
## Status 0.363 0.012 1.000 1.000
semPaths(p.model.1.fit, "std", "std")
This gives a limited support to the first hypothesys and suggests it will hold for certain groups of people (45 + years).
I define the second model.
model.2 <- "Cognitive =~ happy + stflife\nIndValues =~ ipcrtiv + impdiff\nCognitive ~~ IndValues"
I test the model.
model.2.fit <- cfa(model.2, cdata)
m.2.fit <- inspect(model.2.fit, "fit")
Model converged normally. Fit indices: CFI = 0.9999, SRMR = 0.0017, and RMSEA = 0.0113.
summary(model.2.fit)
## lavaan (0.5-11) converged normally after 50 iterations
##
## Used Total
## Number of observations 48704 50781
##
## Estimator ML
## Minimum Function Test Statistic 7.202
## Degrees of freedom 1
## P-value (Chi-square) 0.007
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|)
## Latent variables:
## Cognitive =~
## happy 1.000
## stflife 0.978 0.019 50.532 0.000
## IndValues =~
## ipcrtiv 1.000
## impdiff 0.805 0.027 30.115 0.000
##
## Covariances:
## Cognitive ~~
## IndValues -0.459 0.012 -37.844 0.000
##
## Variances:
## happy 0.726 0.070
## stflife 2.026 0.068
## ipcrtiv 0.743 0.029
## impdiff 1.285 0.020
## Cognitive 3.604 0.075
## IndValues 0.869 0.030
semPaths(model.2.fit, "std", "std")
This gives the hypothese weak support and shows the relationship between cognitive judgement and creativity values, though this relationship should be investigated in further research.
I define two models.
model.3.a <- "Cognitive =~ happy + stflife\nStatus =~ health + partner + edulvla\nFit =~ ind.fit + status.fit\nCognitive ~~ Status + Fit"
model.3.b <- "Cognitive =~ happy + stflife\nStatus =~ health + partner + edulvla\nFit =~ trad.fit\nCognitive ~~ Status + Fit"
I test the first model against the data, containing obesrvations from protestant countries
model.3.a.fit <- cfa(model.3.a, cdata[cdata$cntry %in% protestant_cntr, ])
m.3.a.fit <- inspect(model.3.a.fit, "fit")
Fit indices (CFI = 0.9522, SRMR = 0.0395, and RMSEA = 0.068) do not allow to accept this model. I test this model against the data, containing observations from former communist countries.
model.3.a1.fit <- cfa(model.3.a, cdata[cdata$cntry %in% former_communist_cntr,
])
m.3.a1.fit <- inspect(model.3.a1.fit, "fit")
Fit indices: CFI = 0.9838, SRMR = 0.021, and RMSEA = 0.0386.
summary(model.3.a.fit, standardized = T)
## lavaan (0.5-11) converged normally after 73 iterations
##
## Used Total
## Number of observations 6995 7333
##
## Estimator ML
## Minimum Function Test Statistic 367.157
## Degrees of freedom 11
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Cognitive =~
## happy 1.000 1.386 0.850
## stflife 1.132 0.032 35.188 0.000 1.569 0.867
## Status =~
## health 1.000 0.475 0.541
## partner 0.274 0.021 13.327 0.000 0.130 0.266
## edulvla -0.914 0.063 -14.525 0.000 -0.434 -0.303
## Fit =~
## ind.fit 1.000 0.323 0.588
## status.fit 0.538 0.106 5.093 0.000 0.174 0.403
##
## Covariances:
## Cognitive ~~
## Status -0.384 0.017 -22.295 0.000 -0.583 -0.583
## Fit -0.014 0.009 -1.541 0.123 -0.031 -0.031
## Status ~~
## Fit 0.040 0.005 7.763 0.000 0.262 0.262
##
## Variances:
## happy 0.740 0.053 0.740 0.278
## stflife 0.815 0.068 0.815 0.249
## health 0.544 0.018 0.544 0.707
## partner 0.224 0.004 0.224 0.929
## edulvla 1.861 0.035 1.861 0.908
## ind.fit 0.197 0.021 0.197 0.654
## status.fit 0.155 0.006 0.155 0.837
## Cognitive 1.922 0.067 1.000 1.000
## Status 0.225 0.018 1.000 1.000
## Fit 0.104 0.021 1.000 1.000
semPaths(model.3.a.fit, "std", "std")
I test the second model against the data
model.3.b.fit <- cfa(model.3.b, cdata[cdata$cntry %in% protestant_cntr, ])
m.3.b.fit <- inspect(model.3.b.fit, "fit")
Fit indices (CFI = 0.9501, SRMR = 0.043, and RMSEA = 0.0842) do not allow to accept this model. I test this model against the data, containing observations from former communist countries.
model.3.b1.fit <- cfa(model.3.b, cdata[cdata$cntry %in% former_communist_cntr,
])
m.3.b1.fit <- inspect(model.3.b1.fit, "fit")
summary(model.3.b1.fit, standardized = T)
## lavaan (0.5-11) converged normally after 52 iterations
##
## Used Total
## Number of observations 6275 6960
##
## Estimator ML
## Minimum Function Test Statistic 124.201
## Degrees of freedom 7
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Cognitive =~
## happy 1.000 2.126 0.871
## stflife 0.907 0.027 33.732 0.000 1.928 0.754
## Status =~
## health 1.000 0.457 0.492
## partner 0.160 0.018 8.788 0.000 0.073 0.146
## edulvla -0.664 0.050 -13.212 0.000 -0.304 -0.232
## Fit =~
## trad.fit 1.000 0.466 1.000
##
## Covariances:
## Cognitive ~~
## Status -0.805 0.030 -26.635 0.000 -0.828 -0.828
## Fit -0.011 0.014 -0.775 0.439 -0.011 -0.011
## Status ~~
## Fit 0.008 0.005 1.498 0.134 0.035 0.035
##
## Variances:
## happy 1.435 0.126 1.435 0.241
## stflife 2.825 0.113 2.825 0.432
## health 0.655 0.022 0.655 0.758
## partner 0.243 0.004 0.243 0.979
## edulvla 1.615 0.030 1.615 0.946
## trad.fit 0.000 0.000 0.000
## Cognitive 4.522 0.161 1.000 1.000
## Status 0.209 0.021 1.000 1.000
## Fit 0.217 0.004 1.000 1.000
The result shows that there is no consistant relashionship between traditional values fitness and cognitive judgement.
This rejects the hypothesys, whereas suggests that creative and status values fitness has a weak relationship with cognitive judgement in the former communist countries.
Results showed that there is a consistant, yet weak relationship between creativity values and cognitive judgement. The results also showed that the value discrepancy could have a relationship with cognitive judgement, though further researches should investigate this relationship.
I assume that cultural descrepancy could be a significant determinant of SWB for individuals in certain social groups, especially closed ones. At the macro level (country) it's power as determinant is weak and can be often neglected in SWB or other researches.
The difference of cultures shows itself not only in individuals and groups values, but also in the attitude to and tolerance for discrepancy.
I suggest using “Cultural Fit” concept in further SWB (and other) researches as well as other fitness indices (e.g. ethnicity fit, religion fit, language fit, etc.). These concepts, obviosly originated from biology, are worth trying in social science.