PSYCH 100A Lecture Notes - Lecture 6: Sampling Distribution, F-Distribution, Null Hypothesis

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Published on 28 Mar 2018
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Lecture 6
Within-group research design: seeks to compare two or more measurements of variables from
the same participants
Does’t euie itevetios
Scores don't need to be from the same person
A classic within-group design collects two or more repeated measurements from the same
group of individuals (pretest and posttest)
Paired-samples t-test
Appropriate for within-group designs with two measurements
Outcome variable must be continuous
Difference (change) scores:
The paired-samples t test requires two scores per individual (pretest and posttest)
Hypotheses and computations involve difference scores that subtract the two variables
Diff = x2 - x1 = posttest - pretest
o If increase from pre --> post then positive
o If decrease from pre --> post then negative
Null hypothesis
Vast majority of pretest-posttest designs specify a null hypothesis of no change
States that the mean different or change in the full population is zero
H: μ_diff = 
Alternate hypothesis
What kind of effect can we expect to see?
Requires a two-tailed p-value
S_xdiff = s_diff
One- factor ANOVA: appropriate for research scenarios with a categorical independent variable
and one numeric (continuous) dependent variable
One-factor = one independent variable
Independent-samples t test was used to compare means of two groups
ANOVA is appropriate for between-group designs with two or more groups
ANOVA tests whether the package of group differences have anything significant
Two step analytic procedure
Package of group differences: an experiment with 3+ groups that can be thought of as a
package of two-group tests
o With three groups, there are three pairs of mean differences: µ1 vs. µ2, µ2 vs. µ3, µ3
vs. µ1 With four groups, there are six pairs, etc.
ANOVA Procedure
First step: examines whether any of the group means differ
o If overall test is significant, we use t tests to examine specific group differences
Hypothesis
Null hypothesis of no effect predicts that group means are identical in population
o H0: µ1 = µ2 = µ3
o The alternate hypothesis states that at least one pair of means differs
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Document Summary

Paired-samples t-test: appropriate for within-group designs with two measurements, outcome variable must be continuous. Difference (change) scores: the paired-samples t test requires two scores per individual (pretest and posttest, hypotheses and computations involve difference scores that subtract the two variables, diff = x2 - x1 = posttest - pretest. If increase from pre --> post then positive. If decrease from pre --> post then negative. Null hypothesis: vast majority of pretest-posttest designs specify a null hypothesis of no change, states that the mean different or change in the full population is zero, h(cid:1004): _diff = (cid:1004) Alternate hypothesis: what kind of effect can we expect to see, requires a two-tailed p-value. One- factor anova: appropriate for research scenarios with a categorical independent variable and one numeric (continuous) dependent variable. Independent-samples t test was used to compare means of two groups: one-factor = one independent variable, anova is appropriate for between-group designs with two or more groups.

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