Correlational finding on Happiness and subject: What one eats

StudyWarner et al. (2017): study US New England 2013
TitleFruit and Vegetable Intake Predicts Positive Affect
SourceJournal of Happiness Studies,2017, Vol.18, 809 - 826
URLhttps://link.springer.com/article/10.1007/s10902-016-9749-6
DOIDOI: 10.1007/s10902-016-9749-6
PublicStudents, New England, United States of America, 2013
SampleSampling not reported
Non-Response
Respondents N =1263

Correlate
Author's labelFruit and Vergetable Intake (FVI)
Page in Source 819
Our classificationWhat one eats
Operationalization
Self report on questions on typical daily servings of 
fruits and vegetables in the last month (FVI)
Rated 0 to 8+
Observed distributionM: 2,02, SD: 2,38

Observed Relation with Happiness
Happiness
Measure
StatisticsElaboration/Remarks
A-BW-?-mq-v-5-aAoC=+ p < .014
Positive Affect only.
Negative affect ns

Unaffected by gender
Linear relationship


Appendix 1: Happiness measures used
CodeFull Text
A-BW-?-mq-v-5-aSelfreport on 20 questions:

Lead item not reported.
A nervous
B distressed
C afraid
D jittery
E irritable
F upset
G scared
H excited
I ashamed
J guilty
K hostile
L active
M determined
N inspired
O enthusiastic
P alert
Q attentive
R proud
S strong
T interested

Rated:
1: slightly/not at all
.
.
5 extremely

Negative affect score (NAS): A to K
Positive affect score (PAS): L to T
Affect Balance Score (ABS): PAS - NAS

Name: Watson's PANAS version not reported


Appendix 2: Statistics used
SymbolExplanation
AoCANALYSIS of COVARIANCE (ANCOVA)
Type: statistical procedure
Measurement level: Correlates: at least one nominal and at least one metric, Happiness: metric.

Just as in an ANOVA, in an ANCOVA the total happiness variability, expressed as the sum of squares, is partitioned into several parts, each of which is assigned to a source of variability. At least two of those sources are the variability of the correlates, in case there is one for each correlate, 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.

In an Analysis of Covariance, the treatment means for all levels of the nominal correlate are 'adjusted' for differences in the mean values of the metric correlate.
Source:
Ruut Veenhoven, World Database of Happiness, Collection of Correlational Findings, Erasmus University Rotterdam.
https://worlddatabaseofhappiness.eur.nl