Skip to main content

NS5108 Research Methods Handbook

9 JASP workshop – one way ANOVA

In the video below, I work through example analyses of both independent and repeated measures one-way ANOVAs. If you want to follow along, please find the corresponding question sheets and datasets available in the NS5108 module on Moodle.

This video walks you through an example of a Repeated measures ANOVA in JASP:

9.0.1 Conducting a Repeated Measures ANOVA

  1. Open JASP and Load Your Data:
    • As before, open your data file in JASP.
  2. Visualise the Data:
    • Go to Descriptives -> Descriptive Statistics.
    • Move all levels of your repeated measure into the Variables box.
    • Use plots to visualise distributions for each condition.
  3. Check Assumptions:
    • Normality:
      • Skewness and Kurtosis:
        • In the Descriptive Statistics window, under Statistics, check Skewness and Kurtosis for each condition.
      • Shapiro-Wilk Test:
        • Under the same Statistics tab, check Shapiro-Wilk to perform the Shapiro-Wilk test for each condition.
      • When to Use:
        • Use the Shapiro-Wilk test when sample sizes per condition are small (less than 50). For larger samples, rely more on skewness, kurtosis, and graphical assessments.
    • Sphericity:
      • Navigate to ANOVA -> Repeated Measures ANOVA.
      • Under Assumption Checks, check Sphericity tests to perform Mauchly's Test.
  4. Run the Repeated Measures ANOVA:
    • Define your repeated measures factor in the Repeated Measures Factors box (e.g., Time with levels Time1, Time2, Time3).
    • Move the corresponding variables into the Repeated Measures Cells.
    • Click OK to run the analysis.
  5. Post Hoc Tests:
    • Select your repeated measures factor for pairwise comparisons.
    • Choose an appropriate correction method, such as Bonferroni.

**This video walks you through a one-way independent samples ANOVA in JASP

9.1 “Where to Click” Guide - Conducting One-Way independent samples ANOVAs in JASP

Sometimes you just want to know where to click to run the test. Below is a step-by-step guide for performing independent and repeated measures one-way ANOVAs in JASP. Refer to the video above for more context regarding these steps.

Conducting an Independent Samples One-Way ANOVA ### Conducting an Independent Samples One-Way ANOVA

  1. Open JASP and Load Your Data:
    • Click on the File tab at the top left.
    • Select Open, and navigate to the folder containing your data file.
  2. Visualise the Data:
    • Go to Descriptives -> Descriptive Statistics.
    • Move your dependent variable into the Variables box and your independent variable (factor) into the Split box.
    • Use Plots to create histograms or boxplots for each group to check the distribution and identify potential outliers.
  3. Check Assumptions:
    • Normality:
      • Skewness and Kurtosis:
        • In the Descriptive Statistics window, under Statistics, check Skewness and Kurtosis.
      • Shapiro-Wilk Test:
        • Under the same Statistics tab, check Shapiro-Wilk to perform the Shapiro-Wilk test for normality for each group.
        • The Shapiro-Wilk test assesses whether the data in each group are normally distributed.
      • When to Use:
        • Use the Shapiro-Wilk test when your sample size is relatively small (typically less than 50 per group). For larger samples, the test becomes overly sensitive, and minor deviations from normality can lead to significant results.
    • Homogeneity of Variance:
      • Navigate to ANOVA -> ANOVA.
      • Move your dependent variable to Dependent Variable and your independent variable to Fixed Factors.
      • Under Assumption Checks, check Homogeneity tests to perform Levene's Test.
      • A non-significant p-value (p > .05) in Levene's Test indicates that the assumption of homogeneity of variances is met.
  4. Post Hoc Tests:
    • Go to the Post Hoc Tests tab.
    • Move your independent variable into the Post Hoc Tests box.
    • Select a correction method (e.g. Bonferroni) for multiple comparisons.

9.2 JASP Lab Exercises

Further practice with additional exercises can be found in the Moodle course area. Each exercise comes with a dataset and an answer sheet. Work through the exercises to solidify your understanding and then compare your results with the provided answers.

9.3 APA Style Guide for Reporting One-Way ANOVAs

Here's how to format your hypotheses and report results from your one-way ANOVAs in APA style.

Hypotheses for a Repeated Measures One-Way ANOVA (Two-Tailed/Non-Directional)

  • Null Hypothesis (H₀): There is no significant difference in [dependent variable] across [the different conditions].
  • Alternative Hypothesis (H₁): There is a significant difference in [dependent variable] across [the different conditions].
  • Continue to specify specific group difference predictions

Example APA Report for a Repeated Measures One-Way ANOVA

"A repeated measures one-way ANOVA was conducted to examine the effect of [independent variable] on [dependent variable] over [number] conditions. Mauchly's Test indicated that the assumption of sphericity had been met/violated (χ²(df) = value, p = p-value). The results revealed a significant effect of [independent variable] on [dependent variable], F(df_between, df_error) = F-value, p = p-value, η² = effect size. Using the Greenhouse-Geisser correction (if sphericity is violated), the effect remained significant/non-significant. Post hoc pairwise comparisons showed that [specific condition differences], indicating that [interpretation of results]."

Hypotheses for an Independent Samples One-Way ANOVA (Two-Tailed/Non-Directional)

  • Null Hypothesis (H₀): There is no significant difference in [dependent variable] among [the different groups].
  • Alternative Hypothesis (H₁): There is a significant difference in [dependent variable] among [the different groups].
  • Continue to specify specific group difference predictions

Example APA Report for an Independent Samples One-Way ANOVA

"An independent samples one-way ANOVA was conducted to compare the effect of [independent variable] on [dependent variable] across [number] groups. Assumption checks confirmed normality and equal variances. The results showed a significant effect of [independent variable] on [dependent variable], F(df_between, df_within) = F-value, p = p-value, η² = effect size. Post hoc analyses, using a Bonferroni correction, indicated that [specific group differences], suggesting that [interpretation of results]."

Note: Remember to replace placeholders (e.g., [dependent variable], [independent variable], df values, F-values, p-values) with your actual data when reporting your results.