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Introduction to Analysis of Variance - Coggle Diagram
Introduction to Analysis of Variance
Post Hoc Tests or Posttests
Follow-up tests after significant ANOVA to find which groups differ.
Single-Factor, independent-Measures Design-
A study with one independent variable where different participants are in each group.
Single-Factor Design
A study with only one independent variable.
eta squared
Effect size measure: proportion of variance explained by treatment (SS_between / SS_total).
Treatment Effect
Systematic differences caused by the independent variable.
Within-Treatments Variance
Systematic differences caused by the independent variable.
Levels
- The specific categories or conditions within a factor.
Error Term
Another name for within-treatments variance.
Two-Factor Design or Factorial Design
A study with two or more independent variables.
Experimentwise Alpha Level
The probability of making at least one Type I error across multiple tests.
Between-Treatments Variance
Variability between different treatment groups; includes treatment effects and random error.
F-ratio
Test statistic in ANOVA: between-treatments variance divided by within-treatments variance.
Factor
-The independent variable that defines the groups being compared.
Analysis of Variance (ANOVA)
A statistical test that compares means across two or more groups by analyzing variance.
Testwise Alpha Level
The probability of Type I error for a single test (usually 0.05).
Distribution of F-ratios
Sampling distribution of F values; positive and positively skewed.
ANOVA summary table
Table showing sources of variation, SS, df, MS, and F-ratio.
Mean Square (MS
)
Variance estimate: sum of squares divided by degrees of freedom.
Pairwise Comparisons
Comparing two groups at a time.
Tukey’s HSD test
Post hoc test comparing all pairs of means while controlling error rate.
Scheffé Test
Conservative post hoc test for any combination of group comparisons.