• 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

interaction

What Is Specification Error in Statistical Models?

by Karen Grace-Martin  Leave a Comment

When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance. The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.

What is this assumption of no specification error? [Read more…] about What Is Specification Error in Statistical Models?

Tagged With: curvilinear effect, interaction, Model Building, predictors, specification error, statistical model, transformation

Related Posts

  • Member Training: Model Building Approaches
  • Differences in Model Building Between Explanatory and Predictive Models
  • Overfitting in Regression Models
  • What It Really Means to Remove an Interaction From a Model

What It Really Means to Remove an Interaction From a Model

by Karen Grace-Martin  3 Comments

When you’re model building, a key decision is which interaction terms to include. And which interactions to remove.Stage 2

As a general rule, the default in regression is to leave them out. Add interactions only with a solid reason. It would seem like data fishing to simply add in all possible interactions.

And yet, that’s a common practice in most ANOVA models: put in all possible interactions and only take them out if there’s a solid reason. Even many software procedures default to creating interactions among categorical predictors.

[Read more…] about What It Really Means to Remove an Interaction From a Model

Tagged With: categorical predictor, interaction, Model Building

Related Posts

  • Simplifying a Categorical Predictor in Regression Models
  • Differences in Model Building Between Explanatory and Predictive Models
  • Should I Specify a Model Predictor as Categorical or Continuous?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

Member Training: Explaining Logistic Regression Results to Non-Researchers

by TAF Support 

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?

[Read more…] about Member Training: Explaining Logistic Regression Results to Non-Researchers

Tagged With: categorical variable, graphing, interaction, logistic regression, numeric variable

Related Posts

  • Member Training: Multinomial Logistic Regression
  • When Linear Models Don’t Fit Your Data, Now What?
  • Member Training: Logistic Regression for Count and Proportion Data
  • Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation

A Useful Graph for Interpreting Interactions between Continuous Variables

by Jeff Meyer  4 Comments

What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?Stage 2

Let’s start by looking at a model without an interaction.  In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.

[Read more…] about A Useful Graph for Interpreting Interactions between Continuous Variables

Tagged With: continuous predictor, continuous variable, explaining statistics, interaction, interpreting, predictor variable

Related Posts

  • A Strategy for Converting a Continuous to a Categorical Predictor
  • Recoding a Variable from a Survey Question to Use in a Statistical Model
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case
  • What is Multicollinearity? A Visual Description

Member Training: Model Building Approaches

by TAF Support 

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box? 

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

[Read more…] about Member Training: Model Building Approaches

Tagged With: centering, interaction, lasso, Missing Data, Model Building, Model Fit, Multicollinearity, overfitting, Research Question, sample size, specification error, statistical model, Stepwise

Related Posts

  • What Is Specification Error in Statistical Models?
  • Member Training: The LASSO Regression Model
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Member Training: Centering

Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

by Jeff Meyer  24 Comments

We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?

What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? [Read more…] about Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

Tagged With: categorical variable, continuous variable, interaction, Interpreting Interactions, linear regression

Related Posts

  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
  • Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction
  • When NOT to Center a Predictor Variable in Regression

  • Go to page 1
  • Go to page 2
  • Go to page 3
  • Go to page 4
  • Go to Next Page »

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Multinomial Logistic Regression

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