• 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

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

Differences in Model Building Between Explanatory and Predictive Models

by Jeff Meyer 8 Comments

by Jeff Meyer, MPA, MBA

Suppose you are asked to create a model that will predict who will drop out of a program your organization offers. You decide to use a binary logistic regression because your outcome has two values: “0” for not dropping out and “1” for dropping out.

Most of us were trained in building models for the purpose of understanding and explaining the relationships between an outcome and a set of predictors. But model building works differently for purely predictive models. Where do we go from here?

Explanatory Modeling

In explanatory modeling we are interested in identifying variables that have a scientifically meaningful and statistically significant relationship with an outcome.

The primary goal is to test the theoretical hypotheses so there is an emphasis on both theoretically meaningful relationships and determining whether each relationship is statistically significant (you know that wonderful feeling you get when your predictors have p values less than 0.05).

Some of the steps in explanatory modeling include fitting potentially theoretically important predictors, checking for statistical significance, evaluating effect sizes, and running diagnostics.

Predictive Modeling

In predictive modeling our interest is different. Here the goal is to use the associations between predictors and the outcome variable to generate good predictions for future outcomes.

As a result, predictive models are created very differently than explanatory models. The primary goal is predictive accuracy.

Being able to explain why a variable “fits” in the model is left for discussion over beers after work. This gives you the latitude to use predictors that may not have any theoretical value.

Variables that are used in a predictive model are based on association, not statistical significance or scientific meaning.

There are times when statistically significant variables will not be included in a predictive model. A significant predictor that adds no predictive benefit is excluded.

If the predictor is significant but only observable immediately before or at the time of the observed outcome, it cannot be used for predictions.

For example, theoretical models have shown that water temperatures are a highly significant factor in determining whether a tropical storm turns into a hurricane. That variable is not useful in a prediction model of the expected number of hurricanes during the upcoming season because it can only be measured immediately before an impending hurricane.

That’s too late.

A key strategy for successful predictive modeling is to explore. Changing the effect of a continuous predictor by squaring or taking the square root of its value is one approach. The primary limitation for including a predictor in the model is its availability for future model running.

The primary risk when creating a predictive model is to avoid “overfitting.” Overfitting is a result of creating a model that fits the current sample so perfectly that it may not be a good representation of the population.

How can you reduce this risk?

Use half of your data to create your model. Then test your model on the other half.

The data used to create the model is generally known as the “training set.” The data used for testing the model is called the “testing or validation set.” This process is known as “cross validation.”

You may find that you will need to modify your model to better fit both sets of data.

Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.

Four Critical Steps in Building Linear Regression Models
While you’re worrying about which predictors to enter, you might be missing issues that have a big impact your analysis. This training will help you achieve more accurate results and a less-frustrating model building experience.

Tagged With: explanatory models, Model Building, overfitting, predictive models, predictors, significance testing, Training Data, validation data

Related Posts

  • Overfitting in Regression Models
  • What It Really Means to Remove an Interaction From a Model
  • Simplifying a Categorical Predictor in Regression Models
  • Descriptives Before Model Building

Reader Interactions

Comments

  1. mahdi azhdari says

    November 5, 2020 at 9:31 am

    Hi ,where can we find concept of the structural equations models in this topic? are they subset of the predictive models?

    Reply
  2. Inekwe Murumba says

    September 13, 2020 at 2:30 am

    Thanks Jeff for the article. It is quite revealing. How does one cite this material? It does not include year which is a very vital aspect of citation.

    Reply
    • TAF Support says

      September 14, 2020 at 12:17 pm

      Hi Inekwe,
      Generally, all of our pages can be cited by leaving out the published date.
      ​For APA, simply omit the publish date.
      ​For MLA, specifically, add “Accessed 14 Sep. 2020.” to the end, changed to the day you accessed the material.

      Reply
  3. Teena says

    July 4, 2020 at 4:46 pm

    Thank you Jeff, great article, my doubts got cleared after this!

    Reply
  4. Odwa Nondlozi says

    October 11, 2018 at 5:30 am

    I found this article very informative as I had little knowledge on this topic, thank you

    Reply
  5. Rebecca says

    October 10, 2018 at 4:24 pm

    Thanks Jeff, this was very helpful! Is overfitting only a potential problem in predictive models then, or can it be a problem in explanatory models, too?

    Reply
    • Jeff Meyer says

      October 10, 2018 at 5:44 pm

      Hi, glad you found it worth reading. Yes, over fitting can be an issue with an exploratory model as well. Keeping predictors with a very small effect size (in a continuous model) or odds ratio close to one (logistic model) or IRR close to one (count model) can be problematic if you try to replicate your results with a different data set.

      Reply
      • Rebecca says

        October 11, 2018 at 12:31 pm

        That makes sense, thanks Jeff!

        Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Please note that, due to the large number of comments submitted, any questions on problems related to a personal study/project will not be answered. We suggest joining Statistically Speaking, where you have access to a private forum and more resources 24/7.

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Assumptions of Linear Models

Upcoming Free Webinars

The Pathway: Steps for Staying Out of the Weeds in any Data Analysis

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

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