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More info on Analysis of variance/Follow-up tests

Analysis of variance/Follow-up tests: Wikis

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Study guide

Up to date as of January 14, 2010
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From Wikiversity

There are two types of follow-up tests: planned contrasts (when you have hypothesised specific group comparisions) and post hoc tests (when you haven't hypothesised specific differences - tests all pairs of groups).

To learn more about conducting follow-up tests for ANOVA, consult:

  1. Allen & Bennett (SPSS for the health and behavioural sciences):
    1. Chapter 7.3.3 Follow up analyses (One-way ANOVA Example 1)
    2. Chapter 7.4.3 Follow up analyses (One-way ANOVA Example 2)
    3. Chapter 8.4.2 Follow up analyses (Factor between groups ANOVA Example 2)
    4. Chapter 9.4.3 Follow up analyses (One-way repeated measures ANOVA)
  2. Howell (Fundamental Statistics):
    1. Section 16.5: Multiple comparison procedures (375-383)
  3. Howell (Statistical Methods): Chapter 12: Multiple comparison among treatment means (343-389)
  4. Francis (Introduction to SPSS for Windows):
    1. Section 3.3.6.1: Post hoc tests and planned contrasts (61-63)
  5. Francis (Introduction to SPSS for Windows):
    1. Section 3.3.8.4: Planned contrasts for within subjects ANOVA (71-71)

Planned contrasts

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Post-hoc tests

For a factorial ANOVA, if you get a significant F for an IV which has more then 2 groups and you had made no hypotheses, then your main options are to followup with post-hoc tests, choosing among:

  1. Fisher's Least Significant Difference (LSD) (or protected t test))
  2. Bonferroni
  3. Tukey's test (or Tukey's Honestly Significant Difference (HSD)):
    1. Particularly useful for comparing groups of unequal cell sizes
  4. Scheffé's method

In order to get these analyses:
SPSS

> Analyze
> General Linear Model
> Univariate
> Insert DV and Fixed Factors (IV)
> Post-hoc
> Insert Factors for post-hoc analysis
> Tick the boxes for the post-hoc tests you want ---> OK

You only need to report one set of post-hoc analyses.

Once you get the results, interpretation is pretty straightforward, because you will have a series of comparison tests between each pair of means, showing either significant or non-significant differences.

See also


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