Creates a summary object that produces detailed output when printed, including group descriptives, ANOVA table with Welch test, and effect sizes.
Usage
# S3 method for class 'oneway_anova'
summary(
object,
descriptives = TRUE,
anova_table = TRUE,
effect_sizes = TRUE,
digits = 3,
...
)Arguments
- object
A
oneway_anovaresult object.- descriptives
Logical. Show group descriptive statistics? (Default: TRUE)
- anova_table
Logical. Show ANOVA results table and Welch test? (Default: TRUE)
- effect_sizes
Logical. Show effect size measures? (Default: TRUE)
- digits
Number of decimal places for formatting (Default: 3).
- ...
Additional arguments (not used).
See also
oneway_anova for the main analysis function.
Examples
result <- oneway_anova(survey_data, life_satisfaction, group = education)
summary(result)
#> One-Way ANOVA Results
#> ---------------------
#>
#> - Dependent variable: life_satisfaction
#> - Grouping variable: education
#> - Confidence level: 95.0%
#> Null hypothesis: All group means are equal
#> Alternative hypothesis: At least one group mean differs
#>
#>
#> --- life_satisfaction ---
#>
#> Descriptive Statistics by Group:
#> Basic Secondary: mean = 3.204, sd = 1.243, n = 809
#> Intermediate Secondary: mean = 3.701, sd = 1.112, n = 618
#> Academic Secondary: mean = 3.853, sd = 0.998, n = 607
#> University: mean = 4.047, sd = 0.957, n = 387
#>
#> ANOVA Results:
#> --------------------------------------------------------------------------------
#> Source Sum_Squares df Mean_Square F p_value sig
#> Between Groups 247.347 3 82.449 67.096 <.001 1
#> Within Groups 2970.080 2417 1.229
#> Total 3217.428 2420
#> --------------------------------------------------------------------------------
#>
#> Assumption Tests:
#> ----------------
#> Assumption Statistic df1 df2 p_value sig
#> Welch 64.489 3 1229 <.001 ***
#>
#> Effect Sizes:
#> ------------
#> Variable Eta_Squared Epsilon_Squared Omega_Squared Effect_Size
#> life_satisfaction 0.077 0.076 0.076 medium
#>
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
#>
#> Effect Size Interpretation:
#> - Eta-squared: Proportion of variance explained (biased upward)
#> - Epsilon-squared: Less biased than eta-squared
#> - Omega-squared: Unbiased estimate (preferred for publication)
#> - Small effect: eta-squared ~ 0.01, Medium effect: eta-squared ~ 0.06, Large effect: eta-squared ~ 0.14
#>
#> Post-hoc tests: Use tukey_test() for pairwise comparisons
summary(result, effect_sizes = FALSE)
#> One-Way ANOVA Results
#> ---------------------
#>
#> - Dependent variable: life_satisfaction
#> - Grouping variable: education
#> - Confidence level: 95.0%
#> Null hypothesis: All group means are equal
#> Alternative hypothesis: At least one group mean differs
#>
#>
#> --- life_satisfaction ---
#>
#> Descriptive Statistics by Group:
#> Basic Secondary: mean = 3.204, sd = 1.243, n = 809
#> Intermediate Secondary: mean = 3.701, sd = 1.112, n = 618
#> Academic Secondary: mean = 3.853, sd = 0.998, n = 607
#> University: mean = 4.047, sd = 0.957, n = 387
#>
#> ANOVA Results:
#> --------------------------------------------------------------------------------
#> Source Sum_Squares df Mean_Square F p_value sig
#> Between Groups 247.347 3 82.449 67.096 <.001 1
#> Within Groups 2970.080 2417 1.229
#> Total 3217.428 2420
#> --------------------------------------------------------------------------------
#>
#> Assumption Tests:
#> ----------------
#> Assumption Statistic df1 df2 p_value sig
#> Welch 64.489 3 1229 <.001 ***
#>
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
#>
#> Post-hoc tests: Use tukey_test() for pairwise comparisons