• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • our programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • statistical resources
  • blog
  • about
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • contact
  • login

Median Split

3 Situations when it makes sense to Categorize a Continuous Predictor in a Regression Model

by Karen Grace-Martin  1 Comment

Stage 2In many research fields, particularly those that mostly use ANOVA, a common practice is to categorize continuous predictor variables so they work in an ANOVA. This is often done with median splits—splitting the sample into two categories—the “high” values above the median and the “low” values below the median. There are many reasons why this isn’t such a good idea:

  • the median varies from sample to sample, making the categories in different samples have different meanings
  • all values on one side of the median are considered equivalent—any variation within the category is ignored, and two values right next to each other on either side of the median are considered different
  • the categorization is completely arbitrary. A ‘High” score isn’t necessarily high. If the scale is skewed, as many are, even a value near the low end can end up in the “high” category.

But it can be very useful and legitimate to be able to choose whether to treat an independent variable as categorical or continuous. Knowing when it is appropriate [Read more…] about 3 Situations when it makes sense to Categorize a Continuous Predictor in a Regression Model

Tagged With: ANOVA, categorical predictor, continuous predictor, General Linear Model, Median Split, Regression

Related Posts

  • Continuous and Categorical Variables: The Trouble with Median Splits
  • 3 Reasons Psychology Researchers should Learn Regression
  • Same Statistical Models, Different (and Confusing) Output Terms
  • What are Sums of Squares?

Beyond Median Splits: Meaningful Cut Points

by Karen Grace-Martin  Leave a Comment

I’ve talked a bit about the arbitrary nature of median splits and all the information they just throw away.Stage 2

But I have found that as a data analyst, it is incredibly freeing to be able to choose whether to make a variable continuous or categorical and to make the switch easily.  Essentially, this means you need to be [Read more…] about Beyond Median Splits: Meaningful Cut Points

Tagged With: ANOVA, categorical variable, continuous variable, linear regression, Median Split

Related Posts

  • 3 Reasons Psychology Researchers should Learn Regression
  • Can Likert Scale Data ever be Continuous?
  • Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
  • Why ANOVA and Linear Regression are the Same Analysis

3 Reasons Psychology Researchers should Learn Regression

by Karen Grace-Martin  1 Comment

Stage 2Back when I was doing psychology research, I knew ANOVA pretty well.  I’d taken a number of courses on it and could run it backward and forward.  I kept hearing about ANCOVA, but in every ANOVA class that was the last topic on the syllabus, and we always ran out of time.

The other thing that drove me crazy was those stats professors kept saying “ANOVA is just a special case of Regression.”  I could not for the life of me figure out why or how.

It was only when I switched over to statistics that I finally took a regression class and figured out what ANOVA was all about. And only when I started consulting, and seeing hundreds of different ANOVA and regression models, that I finally made the connection.

But if you don’t have the driving curiosity about ANOVA and regression, why should you, as a researcher in Psychology, Education, or Agriculture, who is trained in ANOVA, want to learn regression?  There are 3 main reasons.

1. There a many, many continuous independent variables and covariates that need to be included in models.  Without the tools to analyze them as continuous, you are left forcing them into ANOVA using an arbitrary technique like median splits.  At best, you’re losing power.  At worst, you’re not publishing your article because you’re missing real effects.

2. Having a solid understanding of the General Linear Model in its various forms equips you to really understand your variables and their relationships.  It allows you to try a model different ways–not for data fishing, but for discovering the true nature of the relationships.  Having the capacity to add an interaction term or a squared term  allows you to listen to your data and makes you a better researcher.

3. The multiple linear regression model is the basis for many other statistical techniques–logistic regression, multilevel and mixed models, Poisson regression, Survival Analysis, and so on.  Each of these is a step (or small leap) beyond multiple regression.  If you’re still struggling with what it means to center variables or interpret interactions, learning one of these other techniques becomes arduous, if not painful.

Having guided thousands of researchers through their statistical analysis over the past 10 years, I am convinced that having a strong, intuitive understanding of the general linear model in its variety of forms is the key to being an effective and confident statistical analyst.  You are then free to learn and explore other methodologies as needed.

Tagged With: analysis of covariance, analysis of variance, ancova, ANOVA, continuous predictor, Covariate, General Linear Model, linear regression, Median Split

Related Posts

  • Why ANOVA and Linear Regression are the Same Analysis
  • The General Linear Model, Analysis of Covariance, and How ANOVA and Linear Regression Really are the Same Model Wearing Different Clothes
  • SPSS GLM: Choosing Fixed Factors and Covariates
  • 3 Situations when it makes sense to Categorize a Continuous Predictor in a Regression Model

Continuous and Categorical Variables: The Trouble with Median Splits

by Karen Grace-Martin  8 Comments

Stage 2A Median Split is one method for turning a continuous variable into a categorical one.  Essentially, the idea is to find the median of the continuous variable.  Any value below the median is put it the category “Low” and every value above it is labeled “High.”

This is a very common practice in many social science fields in which researchers are trained in ANOVA but not Regression.  At least that was true when I was in grad school in psychology.  And yes, oh so many years ago, I used all these techniques I’m going to tell you not to.

There are problems with median splits.  The first is purely logical.  When a continuum is categorized, every value above the median, for example, is considered equal.  Does it really make sense that a value just above the median is considered the same as values way at the end?  And different than values just below the median?  Not so much.

So one solution is to split the sample into three groups, not two, then drop the middle group.  This at least creates some separation between the two groups.  The obvious problem, here though, is you’re losing a third of your sample.

The second problem with categorizing a continuous predictor, regardless of how you do it, is loss of power (Aiken & West, 1991).  It’s simply harder to find effects that are really there.

So why is it common practice?  Because categorizing continuous variables is the only way to stuff them into an ANOVA, which is the only statistics method researchers in many fields are trained to do.

Rather than force a method that isn’t quite appropriate, it would behoove researchers, and the quality of their research, to learn the general linear model and how ANOVA fits into it.  It’s really only a short leap from ANOVA to regression but a necessary one.  GLMs can include interactions among continuous and categorical predictors just as ANOVA does.

If left continuous, the GLM would fit a regression line to the effect of that continuous predictor.  Categorized, the model will compare the means.  It often happens that while the difference in means isn’t significant, the slope is.

Reference: Aiken & West (1991). Multiple Regression: Testing and interpreting interactions.

Bookmark and Share

Tagged With: ANOVA, categorical predictor, continuous predictor, GLM, Median Split, Regression

Related Posts

  • 3 Situations when it makes sense to Categorize a Continuous Predictor in a Regression Model
  • 3 Reasons Psychology Researchers should Learn Regression
  • Beyond Median Splits: Meaningful Cut Points
  • Centering a Covariate to Improve Interpretability

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Moderated Mediation, Not Mediated Moderation

Upcoming Workshops

    No Events

Upcoming Free Webinars

TBA

Quick links

Our Programs Statistical Resources Blog/News About Contact Log in

Contact

Upcoming

Free Webinars Membership Trainings Workshops

Privacy Policy

Search

Copyright © 2008–2023 The Analysis Factor, LLC.
All rights reserved.

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT