Correlational finding on Happiness and subject: Current area of residence

StudyNoble (2009): study UA Kharkiv oblast 2007
TitleSubjective Well-Being: A Ukrainian Case Study.
SourceThesis, 2009, University College London, UK
Public18-85 aged, general public, 2 districts in city Kyiv, Ukraine, 2007
SampleNon-probability accidental sample
Respondents N =136

Author's labelCity district
Page in Source 143
Our classificationCurrent area of residence
District in city of Kyev
a: Borshegovka
b: Perchersk
Observed distributionN= a: 86, b: 50

Observed Relation with Happiness
a: Borshegovka M = 2.76 SD = 0.49
b: Perchersk   M = 2.92 SD = 0.72
- difference       0.16
Happiness remains higher in Prerchrsk after 
control for
- age
- gender
- perceived health
- material welfare

Perceived health explains most of

Appendix 1: Happiness measures used
CodeFull Text
O-HL-m-sq-v-4-aSelfreport on single question:

Taking all into account, right now would you say that you are:
4 very happy
3 quite happy
2 not very happy
1 not at all happy

Appendix 2: Statistics used
Type: descriptive statistic only.
Measurement level: Correlate: dichotomous, Happiness: metric
Range: depending on the happiness rating scale of the author; range symmetric about zero.

Meaning: the difference of the mean happiness, as measured on the author's rating scale, between the two correlate levels.
LRCDLRCD: Regression coefficient in binary logistic regression.
Only the sign of the computed coefficient is informative.

Happiness is a binary or dichotomous variable with Happy =1 and Unhappy=0.

LRCD < 0 indicates that the odds of being happy-to-being unhappy
decreases when

1) the corresponding metric correlate increases
2) the corresponding category of a categorical correlate is compared to the reference category.

LRCD > 0 indicates an increase in the odds for both the above cases.
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.