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How to Run an ANCOVA in GraphPad Prism (and How It Compares to Doing It with Sophie)
- 7 minutes ago
- 6 min read
If you have hunted through Prism's Analyze menu looking for ANCOVA, stop: it isn't there. GraphPad has never shipped a dedicated ANCOVA command, and their own support FAQ confirms it. That trips up a lot of people who need to compare treatment groups while adjusting for a baseline measurement.
The good news is that Prism can absolutely do it, through two documented routes that most users never find. The catch is that you have to know which analysis to open and how to enter your data, because nothing in the interface points you there.
Here are both Prism routes, step by step, and an honest comparison with running the same analysis in plain language instead.
ANCOVA in GraphPad Prism: Why Prism has no ANCOVA button
ANCOVA is not a separate technique. It's multiple regression with your groups entered as dummy variables plus a continuous covariate, which is why GraphPad's guidance is to reach it through regression rather than a dedicated command. Prism 8 introduced multiple regression precisely so this was possible: encode your categorical variable as dummy variables and you get the same results as ANCOVA.
So the absence of an ANCOVA button isn't a gap in Prism. It's a reflection of the statistics. You just have to know the translation.
Prism route 1: comparing regression lines (the ANCOVA slopes test)
This is the older, narrower route, and it's the right one when your question is do these groups have different slopes or intercepts? Prism has a built-in test for exactly this, and GraphPad describes it as equivalent to ANCOVA.
Create an XY table, entering each group as a separate Y dataset. All the data must be on one table.
Analyze → Simple linear regression.
In the parameters dialog, tick "Test whether slopes and intercepts are significantly different."
Read the "Are lines different?" results page.
Why this matters for ANCOVA: this is the parallel-slopes assumption test. If slopes differ significantly, your covariate's effect isn't the same in every group, so a single adjusted difference doesn't exist and you should report the interaction instead. Run this before trusting any adjusted means.
Caution: GraphPad's other documented trick, entering slopes and their standard errors into a grouped table to run an ANOVA, is for comparing three or more slopes with multiple-comparison correction. It does not produce covariate-adjusted group means. Don't substitute one for the other.
Prism route 2: ANCOVA via multiple regression with dummy-coded groups
This is the full ANCOVA, and it's the route GraphPad points to.
Create a multiple variable data table (not an XY table). One row per observation, one column per variable: outcome, group, covariate.
Enter the grouping variable as a categorical variable. Prism encodes categorical variables automatically using dummy coding, so you don't need to build indicator columns by hand. You can also enter them as coded numbers if you prefer.
Analyze → Multiple linear regression, from the multiple variable analyses.
On the Model tab, set your outcome as the dependent variable and include both the group variable and the covariate as independent variables.
For the assumption check, run it once with the group × covariate interaction (Prism adds interactions from the same tab, no manual coding needed) and confirm the interaction is non-significant.
For the ANCOVA itself, re-run without the interaction. The group coefficient is your covariate-adjusted difference from the reference group.
Note: Prism reports coefficients, not adjusted means, because you're in a regression framing. That's the same information in different currency: each group coefficient is that group's adjusted difference from the reference. If your figure needs adjusted means per group, compute them by evaluating the fitted model at the covariate's mean.
Tip: the data-entry format is the step people get wrong. ANCOVA needs the multiple variable table (one row per observation), not the XY layout you'd use for a dose-response curve.
Running the same ANCOVA in Soφ platform
The Prism routes work, but notice what they require: knowing that ANCOVA lives under multiple regression, choosing the right table type, remembering to run the interaction model first, and converting coefficients into adjusted means yourself. None of that is statistics. It's software translation.
The alternative is to describe the experiment and let the tool handle the translation. In Sophie, you paste the data and write what you actually want:
"Compare post-treatment migration across three groups (vehicle, low dose, high dose), controlling for baseline confluence measured before treatment. Test homogeneity of regression slopes first and tell me whether ANCOVA is appropriate. Report adjusted and unadjusted means, the effect size, and the assumption checks."
That request covers the slopes test, the model, the adjusted means, and the diagnostics in one pass, and it doesn't require you to know that the answer lives behind a regression menu.
Where each one wins. Prism is the better choice when you need publication-grade figures in a house style your lab already uses, or when a collaborator expects a Prism file. The plain-language route is better when you're unsure which model your design calls for, when you want the assumption checks run without remembering to ask, or when you'd rather spend your attention on whether the covariate belongs in the model at all.
Note: that last question is the one neither tool decides for you. If your covariate was measured after treatment and the treatment could have changed it, adjusting for it removes part of the effect you're measuring. Ask it out loud, in either tool, before you fit anything.
Pick the tool, but know the model either way
Prism has no ANCOVA command because ANCOVA isn't a separate model: use the simple-linear-regression slopes test to check parallel slopes, then multiple linear regression with a categorical group variable on a multiple variable table to get the adjusted comparison. Or describe the design in plain language and let the tool pick the model. Either path produces the same numbers, because it's the same general linear model underneath. What changes is how much software translation you do yourself, and how easy it is to skip the assumption check that decides whether your adjusted means mean anything.
FAQ
Does GraphPad Prism have an ANCOVA option? No. Prism has never had a dedicated ANCOVA command. GraphPad's own FAQ directs users to multiple regression with dummy-coded categorical variables, which produces the same results, or to the linear-regression option that compares slopes and intercepts.
How do I do ANCOVA in Prism step by step? Create a multiple variable table with one row per observation and columns for outcome, group, and covariate. Enter the group as a categorical variable so Prism dummy-codes it. Run Analyze → Multiple linear regression, include the group and covariate on the Model tab, test the group × covariate interaction first, then re-run without it.
How do I test homogeneity of regression slopes in Prism? Two ways. On an XY table, run simple linear regression and tick "Test whether slopes and intercepts are significantly different," then read the "Are lines different?" page. Or in multiple regression, add the group × covariate interaction term and check whether it is significant.
Why does Prism give coefficients instead of adjusted means? Because the multiple regression route uses a regression framing. Each group coefficient is that group's covariate-adjusted difference from the reference group, which is the same information. To get adjusted means, evaluate the fitted model for each group at the covariate's mean value.
Which data table should I use for ANCOVA in Prism? A multiple variable table, with one row per observation. The XY table is used for the slopes-comparison route, but the full ANCOVA needs the multiple variable format.
