Interaction between two continuous variables

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Statistical programs, like SPSS, do not always have "point-and-click" commands for every possible statistical test. This page is a description of how to test the interaction between two continuous variables. Two approaches are described below:<br>
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Statistical programs, like SPSS, do not always have "point-and-click" commands for every possible statistical test. This page is a description of how to test the interaction between two continuous variables. Three approaches are described below:<br>
(1) '''[[#Three Steps Using SPSS | three steps to conduct the interaction using commands within SPSS]]''', and<br>
(1) '''[[#Three Steps Using SPSS | three steps to conduct the interaction using commands within SPSS]]''', and<br>
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(2) '''[[#Interaction! software]]''' by Daniel S. Soper that performs statistical analysis and graphics for interactions between dichotomous, categorical, and continuous variables.
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(2) '''[[#Interaction! software | Interaction! software]]''' by Daniel S. Soper that performs statistical analysis and graphics for interactions between dichotomous, categorical, and continuous variables.<br>
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(3)  '''[[#R commands | R commands]]''' for executing the analysis.
<nowiki>*</nowiki>For a description of what is an interaction and main effects, please see the accompanying page about [[What is an Interaction?]].
<nowiki>*</nowiki>For a description of what is an interaction and main effects, please see the accompanying page about [[What is an Interaction?]].
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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 two new centered variables.
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*#Simply multiply together the two new centered variables.
*#In our example, multiple IQ_c x study_c
*#In our example, multiple IQ_c x study_c
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*#In SPSS this is accomplished using the "compute" command and typing IQ_c * study_c in the open box.
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*#In SPSS this is accomplished using the "compute" command and typing "IQ_c * study_c" 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 new centered variables as the IVs in the regression analysis
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*#Enter the newly centered variables as the IVs in the regression analysis.
*#Click "next" and enter both centered variables AND the new interaction variable as the IVs.
*#Click "next" and enter both centered 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==
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*Given the time and effort involved in
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*Given the tedious nature of using the [[#Three Steps using SPSS | three steps described above]] every time you need to test interactions between 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. There is also a good [http://www.danielsoper.com/Interaction/help.aspx Help section] on the website.
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*There is an SPSS macro for conducting cross-product regressions [http://www.ilstu.edu/~wjschne/tests.html here].
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==R commands==
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*Assuming you have your data in a comma delimited text file called 'myGreatData.csv' and the first line (header) labels the three columns 'y, x1, x2', the following command will generate your regression.  Note that these commands are the minimum and assume the same things are true as are true in the SPSS example above (centering, assumptions of the regression are met, etc.).
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*#setwd( 'dataDir' ) #Set the working director to the path to your data file.  You could skip this step and just enter the full path into the next step.
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*#dat <- read.csv( 'myGreatData.csv', header = TRUE ) #load your data file into the variable 'dat'
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*#m <- lm( y ~ x1 * x2, data = 'dat') #do the regression
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*#summary(m) #view the results
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◄ Back to [[Research_Tools |Research Tools mainpage]]
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◄ Back to [[Analyzing Data]] page

Latest revision as of 16:49, 5 August 2011

Statistical programs, like SPSS, do not always have "point-and-click" commands for every possible statistical test. This page is a description of how to test the interaction between two continuous variables. Three 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.
(3) R commands for executing the analysis.

*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 two continuous variables


Create the interaction term


Conduct Regression



Interaction! software

R commands


◄ Back to Analyzing Data page

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