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MANOVA Calculator

Test whether groups differ on a combination of dependent variables, then see which variables drive the difference.

Free · No sign-upReplaces SPSS GLM MultivariateWorks with your data
Updated · By Bibin Xavier

Data 120 × 6

Paste from Excel, or drop a CSV, TSV or Excel file here.
Example data 120 rows, 6 variables. Data stays in this browser tab.
Plottext · 120 levelsCultivartext · 3 levelsLeaf_LengthnumericLeaf_WidthnumericStem_HeightnumericYield_kgnumeric
L001Amber5.343.2518.41.19
L002Amber4.663.5913.91.15
L003Amber5.143.39161.4
L004Amber4.733.6314.41.13
L005Amber5.142.598.51.18
L006Amber4.823.9515.91.23
L007Amber4.913.2315.11.19
L008Amber5.733.7416.51.01

Variables and options

Dependent variables 4 selected · min 2
Results update automatically when you change an option.

Results

120 rows · example data
Table 1

Multivariate tests

TestValueFHypothesis dfError dfpPartial η²
Pillai's trace1.08434.068230< .001.542
Wilks' lambda.11057.278228< .001.668
Hotelling's trace6.29288.878226< .001.759
Roy's largest root5.997172.424115< .001.857

Note. Pillai’s trace is the most robust when group sizes are unequal or assumptions are doubtful. Roy’s statistic is an upper bound on F.

Table 2

Follow-up univariate ANOVAs

Dependent variableSSdfMSFppBonfPartial η²
Leaf_Length46.00223.00158.83< .001< .001.731
Leaf_Width9.3924.7055.18< .001< .001.485
Stem_Height971.202485.6096.36< .001< .001.622
Yield_kg11.7225.86106.37< .001< .001.645
Table 3

Group means

GroupnLeaf_LengthLeaf_WidthStem_HeightYield_kg
Amber405.023.4114.181.21
Basil405.942.7718.001.55
Cedar406.522.9021.141.97

Standardized group means

Interpretation

There was a significant multivariate effect of Cultivar on the combined dependent variables (Pillai’s trace = 1.084, partial η² = .542). The follow-up ANOVAs show which variables differ between groups.

Pillai’s trace = 1.084, F(8, 230) = 34.06, p < .001, η²p = .542.

How to cite this tool

Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.

APA 7

Xavier, B. (2026). MANOVA Calculator [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/manova-calculator/

BibTeX 

@software{xavier_manova_2026,
  author = {Xavier, Bibin},
  title = {MANOVA Calculator},
  publisher = {FreeStatisticalTools.com},
  year = {2026},
  url = {https://freestatisticaltools.com/manova-calculator/},
  note = {Web application}
}

Suggested sentence for your methods section: “The analysis was carried out with the free MANOVA Calculator tool on FreeStatisticalTools.com (Xavier, 2026).”

What is MANOVA?

Multivariate analysis of variance (MANOVA) tests whether groups differ on a combination of two or more dependent variables. It reports all four multivariate statistics and follow-up univariate ANOVAs with a Bonferroni correction.

When to use MANOVA

Use MANOVA when you have two or more related continuous outcomes and want to test group differences on them together, controlling the overall error rate.

Assumptions

  • Multivariate normality.
  • Homogeneity of covariance matrices (Box’s M in SPSS). Pillai’s trace is robust when this is doubtful.
  • No multicollinearity among the dependent variables, and more cases per group than dependent variables.

How to use this tool

  1. Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
  2. Choose the variables. Select the dependent variables and factor (groups).
  3. Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.

How to report MANOVA in APA style

Report the multivariate statistic (usually Pillai’s trace or Wilks’ lambda), then the significant follow-up ANOVAs.

Frequently asked questions

Which MANOVA statistic should I report?

Pillai’s trace is the most robust to unequal group sizes and assumption violations, so it is a safe default. Wilks’ lambda is the most commonly reported.

Why not run separate ANOVAs?

Several ANOVAs inflate the chance of a false positive and ignore correlations between outcomes. MANOVA tests them together first.

What are the assumptions of MANOVA?

Independent observations, multivariate normality, homogeneous covariance matrices, linear relationships among the outcomes and no extreme multicollinearity.

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