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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]].
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]].
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==Purpose==
==Purpose==
* Assesses the statistical significance of differences between three or more group means for a single dependent variable
* Assesses the statistical significance of differences between three or more group means for a single dependent variable
* Extension of a ''t''-test
* Extension of a ''t''-test
** Use a [[one-way ANOVA]] in preference to multiple pairwise comparisons (''t''-tests) because:
** Use one-way ANOVA in preference to multiple pairwise comparisons (''t''-tests) because:
*** Computationally easier
*** Computationally easier
*** Limits the probability of Type I and II errors
*** 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 expct to get five Type I errors with a .05 level of significance
**** With multiple comparisons, if they are all independent (which is unlikley) in a series of 100 tests we would expct 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
**** By simultaneously computing all possible comparisons in a single significance test, the ANOVA avoids these inflated error rates
** However, it limits error rates at the expsense 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)
** 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==
==Examples==
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==See also==
==See also==
* [[Analysis of variance/Data analysis tutorial]] (3rd year tutorial)
* [[Analysis of variance]]
* [[w:Analysis of variance|Analysis of variance]] (Wikipedia)
* [[One-way ANOVA]]


==External links==
==External links==

Latest revision as of 05:49, 27 May 2009

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

  • 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 expct 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

  • 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

  1. Establish hypothesis/hypotheses
  2. Examine [[../Assumptions|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

Error bar graphs

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

  1. AQUES.sav
  2. Motiv.sav

See also

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