Correlational finding on Happiness and Socio-economic status of vinicity
Subject code: L17ab04

StudyCsikszentmihalyi & Hunter (2003): study US 1998
TitleHappiness in Everyday Life: the Uses of Experience Sampling.
SourceJournal of Happiness Studies, 2003, Vol. 4, 185 - 199
DOIDOI:10.1023/A:1024409732742
PublicTeenagers, USA, 199?
SampleNon-probability purposive sample
Non-Response
Respondents N =828

Correlate
Author's labelSocial class of community
Page in Source 192,195
Our classificationSocio-economic status of vinicity, code L17ab04
Operationalization
1 Poor
2 Working class
3 Middle class
4 Upper-middle class
5 Upper class

Observed Relation with Happiness
Happiness
Measure
StatisticsElaboration/Remarks
A-ARE-mi-sqr-n-7-aF=+8.1 p < .0001
Participants were beeped at random moments eight 
times a day from 7:30 am to 10:0 pm for one week. 
At each beep they answered questions about:
a: what activity they where doing on the moment
b: whom they were with 
c: how they felt at that moment (various feelings, 
one of which happiness)
A-ARE-mi-sqr-n-7-aAoV=-.08 p < .002
ANOVA controlling for: 
- age 
- gender
- SES
- % time in flow


Appendix 1: Happiness measures used
CodeFull Text
A-ARE-mi-sqr-n-7-aSelfreport on single question repeated several times a day.

" .. mood .."
Full lead question not reported
7 happy
6
5
4
3
2
1 sad


Appendix 2: Statistics used
SymbolExplanation
AoVANALYSIS of VARIANCE (ANOVA)
Type: statistical procedure
Measurement level: Correlate(s): nominal, Happiness: metric.
In an ANOVA, the total happiness variability, expressed as the sum of squares, is split into two or more parts, each of which is assigned to a source of variability. At least one of those sources is the variability of the correlate, in case there is only one, and always one other is the residual variability, which includes all unspecified influences on the happiness variable. Each sum of squares has its own number of degrees of freedom (df), which sum up to Ne -1 for the total variability. If a sum of squares (SS) is divided by its own number of df, a mean square (MS) is obtained. The ratio of two correctly selected mean squares has an F-distribution under the hypothesis that the corresponding association has a zero-value.

NOTE: A significantly high F-value only indicates that, in case of a single correlate, the largest of the c mean values is systematically larger than the smallest one. Conclusions about the other pairs of means require the application of a Multiple Comparisons Procedure (see e.g. BONFERRONI's MULTIPLE COMPARISON TEST, DUNCAN's MULTIPLE RANGE TEST or STUDENT-NEWMAN-KEULS)
FF-STATISTIC
Type: asymmetric standard test statistic.
Range: nonnegative unlimited

Meaning : the test statistic is also called the "Variance Ratio" and is the ratio of two independent estimators of the same variance with n1 and n2 degrees of freedom respectively. The critical values of its probability distribution are tabulated extensively in almost any textbook on Statistics
Source:
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.
https://worlddatabaseofhappiness.eur.nl