Descriptive statistics
- to describe data
- central tendency, variability, skewness, kertosis
Inferential statistics
- to make inferences about population from sample
- attempt to find out causation
If IV has 2 levels or conditions
Do z ratio or t test
If IV has >2 levels or conditions
do ANOVA
Do z ratio for:
proportions and large samples
Do t test for:
means and small samples
Z score table
1. find first 2 digits of z score on left side of table
2. look up remaining # on top of table
3. find corresponding area which translates to %
4. % is area left of z score, meaning score is that % better than general population (almost like percentile)
T distribution table
* need to know degrees of freedom of t test, # tails of t test, alpha level of t test
1. degrees of freedom is n-1
2. find degree freedom on left
3. find alpha level at top
4. find corresponding area for both
Independent t test
for comparing 2 independent groups
Dependent t test
aka paired sample; use when one sample of participants is measured twice under 2 different conditions (ex. pre & post test) OR when samples are pair matched
Mann-Whitney U test
nonparametric independent t test
Wilcoxon t test
nonparametric dependent t test
ANOVA
- test for differences among several means
- IV has >2 levels or conditions
- F ratio
- trying to answer if there is a difference among a set of group means --> in order to say yes, variance between groups needs to be significantly larger than variance wi
1 way ANOVA
effect of 1 IV on DV
2 way ANOVA
effect of 2 IV on DV
4 x 2 ANOVA
2 IV, 1 has 4 levels & other has 2 levels
3 x 3 x 3 ANOVA
3 IV, each has 3 levels
If there is more than 1 DV, use:
MANOVA
If studying effect of 1 IV on 1 DV but think other factor(s) could effect DV too:
ANCOVA
Criteria for parametric
- population normally distributed
- interval or ratio measurement
- variances of data about same
- large enough sample
Coefficient determination
r^2, estimates effect size
Eta squared
n^2, interpreted same as coefficient determination
Partial eta square
used for multiple IVs on 1 DV
Typical effect sizes
0.2 = small
0.5 = medium
0.8 = large
Power
related to type II error (accepting a false null)
power = 1 - type II
Increased power leads to
increased probability of correctly rejecting a false null
A priori
done before data collection, determines sample size
Post hoc
further evaluates completed research to determine if failure to reject was due to inefficient sample size