Advanced ANOVA/Testing differences
This tutorial examines inferential techniques for 'testing differences' between the means for:
Practical exercises are based on using SPSS.
There are three types of t-test"
- Compares a sample mean with a known population mean
- Non-parametric equivalent is the chi-square goodness-of-fit test
Within-subjects t-test (also dependent samples or paired sample "t"-testEdit
- Compares two means that are repeated measures for the same participants
- Compares two means between matched samples
- Compares two treatments across blocks
- Non-parametric equivalent is the Wilcoxon t-test
- Compares two means for independent groups
- Non-parametric equivalents are Mann-Whitney U and chi-square test for two independent samples (this can be used for nominal, interval, or ratio data)
- Within-group variance = individual differences + measurement error
- Between-group variance = individual differences + measurement error + treatment effect
- What are the three types of t-test and when would you use each of them?
- What are the assumptions of t-tests?
- What are the non-parametric alternatives and when would you use each of them?
- What graphical techniques could accompany the different ways of testing differences?
- What measures of effect size are available for measuring differences?
- What should be included in a results section write-up for analyses which involve testing differences?
- What results might be derived from graphic displays for two dependent sample comparisons that could alter questions or comparisons?
- What information (e.g. comparing counts) might lead to non-linear transformations of the data used for comparison?
Using the LEQ dataset, provide analyses which demonstrate use of the each of the types of parametric and non-parametric tests of differences, including:
- Assumption testing
- Inferential analyses of differences
- Effect sizes
- APA style write-up
- See SPSS tips
- See Thesis tips
- Diekhoff Ch 6 and 7
- Howell, D. C. (2002). Statistical methods for psychology. (5th ed.). Pacific Grove CA: Duxbury. Chapter 7.
- Pruzek, R. M. and Helmreich, J. (2009) Enhancing dependent sample analyses with graphics, J. of Statistics Education 
- Testing differences (ucspace)