Interaction between categorical and continuous variables

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<div style="position: relative; top: -5px; left: 0px;"> <table cellpadding=2 align=center border=0 style="background-color: #FFFFFF; border-bottom: 1px #A0A0A0 solid; border-left: 1px #A0A0A0 solid; border-right: 1px #A0A0A0 solid;"><td>Hi, I am your <span style="text-color:#C0C0C0;background-color:#FFD700;padding:1px">[[Image:Guide6.png]]</span>Doug Stenstrom<br><center><sub>[[Doug Stenstrom | What is a "Guide"?]]</sub><br><sup>[[Doug Stenstrom | Where can I go if I have a question?]]</sup></center></td></table><br></div>Testing interactions between categorical and continuous variables follows the same basic steps as testing [[Interaction between two continuous variables | interactions between two continuous variables]] so there is content overlap between this page and the page describing [[Interaction between two continuous variables | interactions between two continuous variables]].
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Testing interactions between categorical and continuous variables follows the same basic steps as testing [[Interaction between two continuous variables | interactions between two continuous variables]] so there is content overlap between this page and the page describing [[Interaction between two continuous variables | interactions between two continuous variables]].
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===► '''Create the interaction term'''===
===► '''Create the interaction term'''===
*How to create the interaction term?
*How to create the interaction term?
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*#Simply multiple together the newly centered variable and the categorical variable.
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*#Simply multiply together the newly centered variable and the categorical variable.
*#In our example, multiple IQ_c x study (e.g., "study" is the variable name for whether the subjects studied for the exam or not).
*#In our example, multiple IQ_c x study (e.g., "study" is the variable name for whether the subjects studied for the exam or not).
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*#In SPSS this is accomplished using the "compute" command and typing IQ_c * study in the open box.
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*#In SPSS this is accomplished using the "compute" command and typing "IQ_c * study" in the open box.
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*How to conduct the regression analysis?
*How to conduct the regression analysis?
*#In SPSS, click on "linear regression" and enter the test score variable as the DV.
*#In SPSS, click on "linear regression" and enter the test score variable as the DV.
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*#Enter the newly centered continuous variable and the categorical variable as the IVs in the regression analysis
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*#Enter the newly centered continuous variable and the categorical variable as the IVs in the regression analysis.
*#Click "next" and enter the same two variables AND the new interaction variable as the IVs.
*#Click "next" and enter the same two variables AND the new interaction variable as the IVs.
*#Run the analysis.
*#Run the analysis.
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*#In the output, look at the second model in the "Coefficients" box. An interaction is depicted as a significant value for the interaction variable, and a significant value for the centered variables can be conceptualized as a "main effect".
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*#In the output, look at the second model in the "Coefficients" box. An interaction is depicted as a significant value for the interaction variable. A significant value for the centered variables can be conceptualized as a "main effect".
*#If your interaction term is then significant it is recommended you produce plots to assist the interpretation of your interaction.
*#If your interaction term is then significant it is recommended you produce plots to assist the interpretation of your interaction.
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==Interaction! software==
==Interaction! software==
*Given the tedious nature of using the [[#Three Steps using SPSS | three steps described above]] every time you need to test interactions between categorical and continuous variables, I was happy to find Windows-based software which analyzes statistical interactions between dichotomous, categorical, or continuous variables, AND plots the interaction graphs.
*Given the tedious nature of using the [[#Three Steps using SPSS | three steps described above]] every time you need to test interactions between categorical and continuous variables, I was happy to find Windows-based software which analyzes statistical interactions between dichotomous, categorical, or continuous variables, AND plots the interaction graphs.
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*The software is called [http://www.danielsoper.com/Interaction/default.aspx Interaction!] from a graduate student in the Information Systems department at Arizona State University. I found it very easy to use.
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*The software is called [http://www.danielsoper.com/Interaction/default.aspx Interaction!] from a graduate student in the Information Systems department at Arizona State University. I found it very easy to use. There is also a good [http://www.danielsoper.com/Interaction/help.aspx Help section] on the website.
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*When using the software to test the interaction between a categorical and continuous variable, you should center the continuous variable first in SPSS before using the Interaction! software to analyze the data.
   
   
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◄ Back to [[Research_Tools |Research Tools mainpage]]
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◄ Back to [[Analyzing Data]] page

Latest revision as of 20:55, 7 September 2009

Testing interactions between categorical and continuous variables follows the same basic steps as testing interactions between two continuous variables so there is content overlap between this page and the page describing interactions between two continuous variables.


Two approaches are described below:
(1) three steps to conduct the interaction using commands within SPSS, and
(2) Interaction! software by Daniel S. Soper that performs statistical analysis and graphics for interactions between dichotomous, categorical, and continuous variables.

*For a description of what is an interaction and main effects, please see the accompanying page about What is an Interaction?.


Contents


Three Steps using SPSS

There are three steps involved to calculate the interaction between two continuous variables.

Center the continuous variable


Create the interaction term


Conduct Regression



Interaction! software





◄ Back to Analyzing Data page

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