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This tutorial focuses on '''one-way ANOVA''' (testing mean differences between more than two independent groups). Practical exercises are based on using [[SPSS]].
This tutorial teaches understanding and use of '''one-way ANOVA''' as a statistical technique for testing mean differences between three or more independent groups. Practical exercises are based on using [[SPSS]].
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Revision as of 03:07, 18 August 2008

File:Wikademia.logo.png Resource type: this resource contains a tutorial or tutorial notes.

Template:50%done This tutorial teaches understanding and use of one-way ANOVA as a statistical technique for testing mean differences between three or more independent groups. Practical exercises are based on using SPSS.

Introduction

  • Unit outline (updated - to upload/link)
  • [[../Assessment/]]

General steps

  1. Establish hypothesis/hypotheses
    • Make these as explicit and clear as possible
    • Break complicated hypotheses down into sub-hypotheses
    • Each hypothesis should be able to be answered as "yes" or "no"
    • It should be clear from the hypotheses what the predicted relation is between an IV and a DV
  2. Identify IV (categorical; between-subjects) and DV (at least interval)
  3. Examine assumptions:
    • The data in each cell is normally distributed
    • Homogeneity of variance (the variance in each cell is similar)
    • Cells are independent
  4. Examine descriptive statistics, particularly the four moments (M, SD, Skewness, Kurtosis) overall, and also for each group
  5. Examine graphs, e.g.,:
    • Histograms
    • Normal probability plot
    • Error-bar graph
  6. Conduct inferential test (ANOVA) and interpret significance of F
  7. Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
  8. Calculate and interpret effect sizes
    • Eta-square (omnibus - equivalent to R2)
    • Standardised mean effect size (difference b/w two means)

Visual ANOVA

Error bar graphs

  • 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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