Correlational finding on Happiness and Married state (compared to non-married states)
Subject code: M02aa

StudyLough et al. (1985): study US 1980
TitleLife Satisfaction Following Heart Transplantation.
SourceHeart Transplantation, 1985, Vol. 4, 446 - 449
PublicHeart transplantation recipients, 7 month to 14 years after transplant, USA, 198?
SampleProbability simple random sample
Non-Response25%
Respondents N =75

Correlate
Author's labelMarital status
Page in Source 447
Our classificationMarried state (compared to non-married states), code M02aa
Operationalization
0 unmarried
1 married
Remarks
Most Ss had stable family lives. Less than 10% 
experienced a change in family makeup as a result of 
the transplantation.

Observed Relation with Happiness
Happiness
Measure
StatisticsElaboration/Remarks
O-QL?-c-sq-v-6-aAoV= ns
O-SQL-c-sq-?-5-aAoV= ns


Appendix 1: Happiness measures used
CodeFull Text
O-QL?-c-sq-v-6-aSelfreport on single question:

"....... current quality of life"
(full lead items not reported)
1
2
3
4
5
6
(response options not reported)
O-SQL-c-sq-?-5-aSelfreport on single question:

".....satisfaction with current quality of life ...."
(Full question not reported.)
1
2
3
4
+
(Response options: not reported)


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)
Source:
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.
https://worlddatabaseofhappiness.eur.nl