Correlational finding on Happiness and subject: Car

StudyBreslin et al. (2013): study GB 2009
TitleSocio-Demographic and Behavioural Differences ans Associations with Happiness for Those Who Are in Good and Poor Health.
SourceInternational Journal of Happiness and Development, 2013, Vol. 1, 142 - 154
URLhttps://doi.org/10.1504/IJHD.2013.055641
Public16+ aged general public Northern Ireland, 2009
SampleProbability multi-stage random
Non-Response45,4%
Respondents N =4663

Correlate
Author's labelAccess to a car
Our classificationCar
Operationalization
Selfreport on single question; Over the ladst 12 month 
would you say your health has been
5 very good
4  good
3  average
2  poor
1  very poor

Dichotomized 1 (5+4), 0 (3+2+1)
Observed distributionGood health 60%, poor health 40%
Remarks
Order reversed by WDH team

Observed Relation with Happiness
Happiness
Measure
StatisticsElaboration/Remarks
O-HL-u-sq-n-10-hOR=1.25 p < .05
Access to a car/all respondents
CI95[1.05-1.75]

OR for respondents with poor health 1.41
CI95[1.13-1.71]

OR's controled for:
- age
- social class

Happiness dichotomized: <7 vs 7 or more


Appendix 1: Happiness measures used
CodeFull Text
O-HL-u-sq-n-10-hSelf report on single question:

In general, how happy would you say you are?
10
9
8
7
6
5
4
3
2
1
Labels of scale ends not reported

1. Least happy score
2
3
-
-
10. Most happy score


Appendix 2: Statistics used
SymbolExplanation
OROR: Odds ratio in binary logistic regression.

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

OR < 1 indicates that the odds of being happy-to-being unhappy
decreases by a factor OR when

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

OR > 1 indicates an increase by a factor OR for both the above cases.
Source:
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.
https://worlddatabaseofhappiness.eur.nl