Week 1: Introduction
What am I doing here?
Why is my evil professor making
me take statistics?
Week 1: Intro
1
Some Basic Concepts:
Getting Your Head Back into it
a database
final grades (%) from recent 3800 class
76
56
60
84
73
84
86
67
50
70
93
85
73
78
Repeated-Measures Designs
also called within-subjects designs
can be any # of factors
we'll stick to 1 factor today
> 2 levels (conditions)
or
else, would be a paired-samples t-test
Repeated Measures
1
vs. Oneway Independent
oneway independent
diff
One-way
Independent-Groups Anova
1) when is a one-way Anova used?
2) sources of variation
3) an example of a one-way analysis
of variance
4) assumptions underlying F-distribution
Week 4: One-way Anova
1
When would you use a Oneway Anova?
Example 1
looking
Monte Carlo Studies
Switch gears
Be statisticians, rather than researchers
Compare empirical vs. theoretical distributions of statistics
Look at the effect that violating assumptions has on the t-test
Do violations change the probability of Type I &Type I
Week 2: t-tests
3 types of t-tests:
1. Single sample
2. 2-sample test:
Independent groups
3. 2-sample test:
Paired groups
(correlated, dependent)
What are they and when do
you use them?
Examples of independent
groups and paired groups
t-tests
Important co
Completely Randomized Factorial Design
any number of factors (IVs)
we'll stick to 2 today:
e.g., effects of alcohol (1 vs. 6 oz)
and alcohol tolerance (low vs. high)
on a reaction time vigilance button pressing task
reaction time
(ms)
low tolerance
hig
Split-Plot Analysis of Variance
1) intro to split-plot (mixed) ANOVA
2) a short example
3) a medium length example
4) a long example
5) choosing error terms for simple main effect analyses
Split-Plot
1
Design & Terms
2 or more factors
at least 1 is indep
High-level Review
1) Background stuff
assumptions of t and F distributions
effects of violating them
Monte Carlo investigations
Type I error, Type II error
, , power
Review
1
2)
Hypothesis Testing
Difference between Means
a) t-test
1 group vs. a constant
Factor Analysis
NOT used for testing the effect of a treatment variable on some
dependent measure, as in t-test or ANOVA
in fact, no IV/DV distinction
*no significance testing involved*: no treatment being applied here
used for explaining trends in a data
Multiple Correlation and Regression
1 dependent variable, a bunch of independent variables
> 1, at least
What is the best way to predict the DV from this set of IV's?
e.g., have a bunch of personality variables
How do you best predict marital happine
Chi-square (2)
1) some background on 2
2) 2 test of an association between 2 nominal
variables
- basic 2 x 2 (2 variables, 2 levels each)
3) 2 test of association with variables with
more than 2 levels
- post hoc analyses
Chi-square
1
2
Used for testing
Correlation & Regression
1) Pearson correlation
relationship between 2 continuous variables
types of relationships
r Pearson product moment correlation coefficient
t-test for r
2) single variable linear regression
the regression line
evaluating goodness o