Two/Three-Way ANOVA (Updated)

1. Upload or Paste Data

Supported formats: Excel file (.xlsx, .xls) or Tab-separated text

Data format: First row should contain column headers. Each row represents one observation.

Required: One response variable (numeric) and 2-3 factor variables (categorical).

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Click to upload Excel file or drag and drop here

OR paste tab-separated data below

Or paste tab-separated data here:

2. Select ANOVA Type and Variables

Instructions: First, select your ANOVA type. Then choose which column is your response variable (the data you're measuring) and which columns are your factors (the variables you're testing).

Tip: You can also click on column headers in the data preview below to quickly select them.

Two-Way ANOVA Three-Way ANOVA

Select Variables

Response Variable (numeric): *Required The numeric data you want to analyze (e.g., K value, concentration, etc.)

Factor 1 (categorical): *Required First independent variable (e.g., oil, treatment, etc.)

Factor 2 (categorical): *Required Second independent variable (e.g., protein, group, etc.)

Factor 3 (categorical): *Required Third independent variable (e.g., pH, condition, etc.)

3. Advanced Options: Post-hoc Comparisons

Post-hoc Comparisons: Within-level pairwise comparisons with adjusted p-values; optional Excel export.

Within-Level Pairwise Comparisons:

Optional: Compare levels of one factor WITHIN each level of another factor (e.g., compare pH levels within each Protein level). Uses pooled error from the full model. Output: grouping factor, contrast, estimate, SE, df, t.ratio, p.value, Significant. Exported to Excel.

Post-hoc Comparison Type: Type of comparisons to perform for factors with 3+ levels. (Two-way ANOVA requires Pairwise only)

P-value Adjustment Method: Note: Letter assignments (cld) always use Tukey. Confidence intervals always use Bonferroni.

Data Preview

Click on any column header to quickly select it as your response variable or factor.

Generated R Script

Instructions: Copy the R script below and run it in R or RStudio. The script uses car::Anova with Type III sums of squares (matching SPSS default) and performs Tukey HSD post-hoc tests. Requires: car, multcomp, dplyr, tidyr, emmeans, openxlsx, ggplot2 (no ez package).