1 IV. 2 groupst-test 3+ groupsANOVA
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Transcript of 1 IV. 2 groupst-test 3+ groupsANOVA
1 IV. 2 groups t-test
3+ groups ANOVA
Group Differences:1 Independent Variable
• IV: male, female• DV: happiness
• There is a difference• Due to chance?
TheoryTheory
• Confidence Interval• 95% within interval
• If Overlap– No real differences
(non-significant)
• If no Overlap– Difference is real
(significant)
TheoryTheory
• Can make significant:
• Move points away(larger between group variation)
TheoryTheory
TheoryTheory
• Can make significant:
• Smaller intervals(less within group variation)
TheoryTheory
• Summarizing theory behind “group differences”:
between group variation
within group variation
• Notice how that translates into formulas:
T-test: independent
T-test: repeated
ANOVA (I’ll show you later)
TheoryTheory
• A single pairwise comparison (e.g., males to females)• Is there a significant difference (e.g., p = .025)• What are group means (e.g., 4.84, 5.61)• What is the effect size (e.g., by hand, website)
2 groups2 groups
• Same as before:
95% CI
Overlap = n.s.
No overlap = sig
• Difference:
Multiple pairwise comparisons
So FIRST see if “overall”
Then SECOND test each pairwise
3+ groups3+ groups
(1) Overall “F”
(2) Post-hoc or Planned Comparisons
• Overall “F”– F = between group variability
within group variability
– Can’t simply start with multiple pairwise comparisons because must control for familywise error
3+ groups3+ groups
(1) Overall “F”
(2) Post-hoc or Planned Comparisons
• Overall “F”– F = between group variability
within group variability
– Can’t simply start with multiple pairwise comparisons because must control for familywise error
3+ groups3+ groups
.95 x .95 x .95 =.857 1-.857=.14
(1) Overall “F”
(2) Post-hoc or Planned Comparisons
• Overall “F”– F = between group variability
within group variability
– Can’t simply start with multiple pairwise comparisons because must control for familywise error
3+ groups3+ groups
1 - (1 - )C = 1 - (1 -.05)3=.14
.95 x .95 x .95 =.857 1-.857=.14
(1) Overall “F”
(2) Post-hoc or Planned Comparisons
• Post-hoc– Testing all possible pairwise comparisons– Control for familywise error (keep alpha=.05)– Many options. I suggest LSD or Tukey
• Planned Comparisons– Testing only specific pairwise comparisons– Must be “apriori hypotheses”– Can do one-tailed tests
3+ groups3+ groups
• Overall F (e.g., p = .003) • Post-hoc (e.g., see table)• What are group means (e.g., see table)• What is the effect size (e.g., by hand, website)
3+ groups3+ groups