using statistics in research psych 231: research methods in psychology
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Using Statistics in Research
Psych 231: Research Methods in Psychology
“Generic” statistical test Tests the question:
Are there differences between groups due to a treatment?
Two possibilities in the “real world”
XBXA
H0 is true (no treatment effect)
One population
Two samples
“Generic” statistical test Tests the question:
Are there differences between groups due to a treatment?
Two possibilities in the “real world”
XBXA XBXA
H0 is true (no treatment effect) H0 is false (is a treatment effect)
Two populations
Two samples
“Generic” statistical test
Why might the samples be different?(What is the source of the variability between groups)?
ER: Random sampling error ID: Individual differences (if between subjects
factor) TR: The effect of a treatment
XBXA
“Generic” statistical test
The generic test statistic - is a ratio of sources of variability
Observed difference
Difference from chance=
TR + ID + ER
ID + ER=Computed
test statistic
XBXA
ER: Random sampling error ID: Individual differences (if between
subjects factor) TR: The effect of a treatment
“Generic” statistical test
The generic test statistic distribution To reject the H0, you want a computed test statistics
that is large• This large difference, reflects a large Treatment Effect (TR)
What’s large enough? The alpha level gives us the decision criterionDistributi
on of the test statistic
-level determines where these boundaries go
“Generic” statistical test
Distribution of the test statistic
Reject H0
Fail to reject H0
The generic test statistic distribution To reject the H0, you want a computed test statistics
that is large• This large difference, reflects a large Treatment Effect (TR)
What’s large enough? The alpha level gives us the decision criterion
“Generic” statistical test
Things that affect the computed test statistic Size of the treatment effect
• The bigger the effect, the bigger the computed test statistic
Difference expected by chance (sample error)• Sample size• Variability in the population
Some inferential statistical tests
1 factor with two groups T-tests
• Between groups: 2-independent samples • Within groups: Repeated measures samples (matched, related)
1 factor with more than two groups Analysis of Variance (ANOVA) (either between groups or repeated measures)
Multi-factorial Factorial ANOVA
T-test Design
2 separate experimental conditions Degrees of freedom
• Based on the size of the sample and the kind of t-test Formula:
T =
X1 - X2Diff by chance
Based on sample error
Observed difference
Computation differs for between and within t-tests
T-test Reporting your results
The observed difference between conditions Kind of t-test Computed T-statistic Degrees of freedom for the test The “p-value” of the test
“The mean of the treatment group was 12 points higher than the control group. An independent samples t-test yielded a significant difference, t(24) = 5.67, p < 0.05.”
“The mean score of the post-test was 12 points higher than the pre-test. A repeated measures t-test demonstrated that this difference was significant significant, t(12) = 5.67, p < 0.05.”
Analysis of Variance
Designs More than two groups
• 1 Factor ANOVA, Factorial ANOVA• Both Within and Between Groups Factors
Test statistic is an F-ratio Degrees of freedom
Several to keep track of The number of them depends on the design
XBXA XC
Analysis of Variance
More than two groups Now we can’t just compute a simple difference score since there are more than one difference
So we use variance instead of simply the difference• Variance is essentially an average difference
Observed variance
Variance from chanceF-ratio =
XBXA XC
1 factor ANOVA
1 Factor, with more than two levels Now we can’t just compute a simple difference score since there are more than one difference• A - B, B - C, & A - C
XBXA XC
1 factor ANOVA
Null hypothesis: H0: all the groups are
equalXA = XB = XCAlternative hypotheses
HA: not all the groups are equal
XA ≠ XB ≠ XC
XA ≠ XB = XCXA = XB ≠
XC
XA = XC ≠ XB
The ANOVA tests this one!!
Do further tests to pick between these
XBXA XC
1 factor ANOVA
Planned contrasts and post-hoc tests:
- Further tests used to rule out the different Alternative
hypothesesXA ≠ XB ≠ XC
XA ≠ XB = XC
XA = XB ≠ XC
XA = XC ≠ XB
Test 1: A ≠ BTest 2: A ≠ CTest 3: B = C
1 factor ANOVA Reporting your results
The observed differences Kind of test Computed F-ratio Degrees of freedom for the test The “p-value” of the test Any post-hoc or planned comparison results
“The mean score of Group A was 12, Group B was 25, and Group C was 27. A 1-way ANOVA was conducted and the results yielded a significant difference, F(2,25) = 5.67, p < 0.05. Post hoc tests revealed that the differences between groups A and B and A and C were statistically reliable (respectively t(1) = 5.67, p < 0.05 & t(1) = 6.02, p <0.05). Groups B and C did not differ significantly from one another”
Factorial ANOVAs
We covered much of this in our experimental design lecture
More than one factor Factors may be within or between Overall design may be entirely within, entirely
between, or mixed Many F-ratios may be computed
An F-ratio is computed to test the main effect of each factor
An F-ratio is computed to test each of the potential interactions between the factors
Factorial ANOVAs Reporting your results
The observed differences• Because there may be a lot of these, may present them in a table instead of directly in the text
Kind of design• e.g. “2 x 2 completely between factorial design”
Computed F-ratios• May see separate paragraphs for each factor, and for interactions
Degrees of freedom for the test• Each F-ratio will have its own set of df’s
The “p-value” of the test• May want to just say “all tests were tested with an alpha level of 0.05”
Any post-hoc or planned comparison results• Typically only the theoretically interesting comparisons are presented
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