Advanced ANOVA/One-way ANOVA

From Wikiversity
Jump to navigation Jump to search
Wikiversity.logo.svg Resource type: this resource contains a tutorial or tutorial notes.
Progress-0500.svg Completion status: this resource is ~50% complete.

This tutorial teaches use of one-way ANOVA, a statistical technique for testing mean differences betweeen three or more independent groups on a single dependent variable. Practical exercises are based on using SPSS.

Purpose[edit]

  • Assesses the statistical significance of differences between three or more group means for a single dependent variable
  • Extension of a t-test
    • Use one-way ANOVA in preference to multiple pairwise comparisons (t-tests) because:
      • Computationally easier
      • Limits the probability of type I and type II errors.
        • With multiple comparisons, if they are all independent (which is unlikley) in a series of 100 tests we would expect to get five Type I errors with a .05 level of significance
        • By simultaneously computing all possible comparisons in a single significance test, the ANOVA avoids these inflated error rates
    • However, use of one-way ANOVA limits error rates at the expense of specificity - statistic tells us that there is a significant difference somewhere among the sample means, but does not tell us which means differ significantly (have to use post-hoc and a priori comparison procedures)

Examples[edit]

  • Experimental study: Examine reaction time under different levels of alcohol consumption by randomly assigning participants to four conditions (none, low, medium, and high alcohol)
  • Quasi-experimental study: Examine whether students with behaviour problems behave better in classrooms where teachers have a humanistic philosophy and have control of their classrooms. Classify teachers as (1) humanists with control, (2) strict disciplinarians, and (3) laissez-faire.

General steps[edit]

  1. Establish hypothesis/hypotheses
  2. Examine assumptions - If assumptions are not met, use the Kruskal-Wallis non-parametric procedure
  3. Examine descriptive statistics, particularly the four moments (M, SD, Skewness, Kurtosis) overall, and also for each group
  4. Examine graphs, e.g.,:
    • Histograms
    • Normal probability plot
    • Error-bar graph
  5. Conduct inferential test (ANOVA) and interpret significance of F
  6. Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
  7. Calculate and interpret effect sizes
    • Eta-square (omnibus - equivalent to R2)
    • Standardised mean effect size (difference b/w two means) - e.g., Cohen's d

Visual ANOVA[edit]

Error bar graphs[edit]

Error-bar graph showing mean pulse rates and 95% confidence intervals by exercise level.
  • Use any dataset
  • Conduct a one-way ANOVA and graphically present the means and confidence intervals using an Error Bar Graph - is this error bar chart consistent with the statistical results?
    • Why?
    • Why not?

Data[edit]

  1. AQUES.sav
  2. Motiv.sav

See also[edit]

External links[edit]