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paired samples t-test
comparing 2 means from the same people
the dependent variables must be interval/ratio
each person must be in both groups
within-subjects research design or repeated measures research design
working with difference scores — difference in means is compared within each person
Within-Subjects Research Design
main advantage: the exact same people are in both groups
no risk that people in group 1 are substantially different than group 2
we always want to reduce “noise” to find our true “effect”
this design removes some of that extra “noise)
Hypotheses
Null Hypothesis: H0 : μD = 0
Alternative Hypothesis: H1 : μD ≠ 0
Large Effect
0.80 and above
Medium Effect
0.50 and above
Small Effect
0.20 and above
Results Statement Example
Participants’ self-esteem scores were significantly higher before performing a comedy routine compared to after the routine, t(8) = 2.99, p < .05, d = 1.00
significance
direction
t statement: t(df) = t-statistic, alpha level, d = effect size
Assumptions of independent t-tests
scores are independent within each sample
e.g., one person’s score is not dependent on another person’s score
normality
Independent vs Paired Samples
generally, more pros to within-subjects (paired) design
uses participants more efficiently (often require lower n because 1 person is used for 2 measurements)
can study change within individuals
reduces problems with individual diffenrences → increases the chance of finding a significant effect (removing noise)
Cons to within-subjects designs
time may have an effect on the score
adding more noise by allowing time to pass
ex; 1 week goes by and now it’s exam season so people are stressed
order effects
could response on one measure affect the other
ex; respond differently when looking at child first or adult first
practice effects
repeating the same test means scores could be improving because you remember the test and are getting better at it not because of manipulation