Research scenarios

 

 

Order Description

 

 

Scenario 1.
A University asks a sport and exercise psychologist to introduce a motivational
training scheme to the swimming team. At the start of the season she times the swimmers
over 100 m in their chosen style. She then begins the motivational training before each
twice- weekly training session for the season. During the week before the final event of
the season she times the swimmers again over 100m in their original style.
Scenario 2.
An occupational psychologist was contracted to assess attitudes towards work and
the workplace in two similar size branches of a company where productivity of the older
branch had reached a plateau while the newer branch increased output monthly. The older
branch was located in an industrialised urban location whereas the newer branch was
situated in a newly built trading estate in a rural location. The psychologist predicted that
employees of the most productive branch would have the most positive attitude to work.
All employees were asked to complete a questionnaire. Higher scores indicated a positive
attitude to work and the workplace.
What you need to do
For each scenario you need to:
? Decide what the design is (one is within subjects (repeated measures) the other is
between subjects (independent groups))
? Enter the data into SPSS and perform the following analyses
o Descriptive statistics (Means/SDs/95% CI) for each condition
? For one of the scenarios you should report the descriptive statistics
in a table (see Week 4 lecture)
? For the other scenario report the descriptive statistics in a bar
chart with error bars showing 95% confidence intervals and
precise statistical values given in text (see Week 4 lecture)
? So you end up with one results section with a table and one with a
bar chart (see Week 4 lecture)
o Inferential statistics (t(df), p, d)
t-tests
For both scenarios:
You need to report the results of the t-test in the following
format (t (df), p), like this (t (43) = 5.23, p < .001)
OR (t, df, p), like this (t = 5.23, df = 43, p < .001)
Effect size (statistical power)
For both scenarios (see Week 2 Lecture; Week 4 Lecture)
calculate Cohen’s d and report in the brackets with the ttest
results (just have a go at it even if you’re unsure, the
worst that can happen is some (hopefully) helpful feedback
on what went wrong)
Like this (t(df), p, d), e.g, (t (43), = 5.23 (p < .001, d = 1.62)
For both scenarios you need to comment on:
Whether the results was significant or not (p < 0.05?)
The direction of any difference (refer back to the direction
of the mean differences)
The effect size d = .2 was a small effect size, d = .5 a
medium effect size, and d =/> .8 a large effect size
For the between subjects t-test remember to check if Levene’s
test for equality of variances is significant or not
IF Levene’s test IS significant (p < 0.05) then you need to
report the fact that it is and give the statistical values on the
‘Equal variances not assumed’ line
IF Levene’s test ISN’T significant (p > 0.05) report the
statistical values on the ‘Equal variances assumed’ lin

 

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