Correlational finding on Happiness and Sex (male vs female)
Subject code: G01aa

StudyFeather & O'Brien (1986): study AU 1980
TitleA Longitudinal Study of the Effects of Employment and Unemployment on School-Leavers.
SourceJournal of Occupational Psychology, 1986, Vol. 59, 121 -144
PublicSchool-leavers, Australia, followed 2 years,1980-1982
Non-ResponseT2: 25%, T3: 34%
Respondents N =5446

Author's labelMale sex
Page in Source 136
Our classificationSex (male vs female), code G01aa
0  Females 
1  Males
Level of happiness at T1(1980), T2(1981) and T3(1982).

T1 happiness by male sex (sample A):
                 female     male       difference

Observed Relation with Happiness
happiness at T1  Mt'= 6.58  Mt'= 6.67     +.09

T3 happiness by male sex (sample D):
                 female     male       difference
happiness at T3  Mt'= 6.54  Mt'= 6.50     -.04

T2 and T3 happiness by male sex (sample A+B):
                 female     male       difference
A-CP-g-mq-n-24-aAoV=+ ns
happiness at T2  Mt'= 6.18  Mt'= 6.21     +.03
happiness at T3  Mt'= 6.25  Mt'= 6.26     +.01
Interaction sex x time (non-significant):
T2 to T3              +.07       +.05     -.02

Significance assessed by variance-analysis, also 
involving employment.

The original depression score is ranging from 
4 to 28. Mt' is standardized into a happpiness 
score (0-10 and reversed)

Possible testing effects checked by comparing 
three sub-samples:
- sample A: selected at T1, tested at T1, T2, T3
- sample B: selected at T1, tested at T2 and T3
- sample C: selected at T1, tested at T3 only
At T3 happiness appeared lower in sample A than in 
sample B, but there is little evidence for 
systematic testing effects.

Appendix 1: Happiness measures used
CodeFull Text
A-CP-g-mq-n-24-aSelfreport on 4 questions:

How do you see yourself in general....?
A 'happy' to 'sad',
B 'elated' to 'depressed',
C 'tensed' to 'relaxed'
D 'satisfied' to 'dissatisfied'.

Scoring: each item scored on bi-polar 7 step numerical scale.
Summation: scores added; possible range 4-28

Appendix 2: Statistics used
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)
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.