David A. Kenny

May 15, 2011


APIM (Indistinguishable Dyads) Macro


This macro, called APIMText, was written by David A. Kenny, Department of Psychology, University of Connecticut(please email me).  This is Version 1, completed on January 29, 2009.  It was last revised on May 15, 2001.  It is definitely advisable to return for updates.


Thank You!

I thank Linda Acitelli for the sample data.


Data Preparation

The dataset needs to be in pairwise format.  To convert an individual file to a pairwise data click here.

I have created a Frequently Asked Questions page.   If you have questions click here.



APIMText (You need SPSS to open this and the next 3 files.)

Sample Data File

Macro Call

SPSS Output

Macro Output (You do not need SPSS to open this file.) 


To understand how to run a macro return to the DataToText page. The macro may take a minute or two to run and so be patient.  Also make sure to backup the raw data file, as sometimes an error in the macro can alter the data file. If using a MAC, search for "c:\apimtext.dat" in the macro and change "c" to "Macintosh HD".  Note variables will be added to data file.  YOU SHOULD BACKUP YOUR DATA FILE! The text file is NOT contained in the SPSS output file.  It is contained in a file called “c:\apimtext”.  Look for it there!


The Macro Call


This is the statement for the sample data:


APIMText a = RSpouse/p = PSpouse /y = RSatisfied/dyadid = coupleid

xn = 'Other Positivity' yn = 'Satisfied' alpha=.05.


The defaults are as follows:

a = Actor

p = Partner

y = Outcome

dyadid = dyadid

xn = X

yn = Y

alpha =.05

oflile = c:\apimtext.txt

directory = c:\

clist =

That is, if you just say “APIMText.”, the program will assume that are variables in the SPSS data file with variables named Actor, Partner, and M.


APIMText was written on SPSS 16 and 18 there is no guarantee that it work on earlier or later versions of SPSS.


Variables in the macro:


a = the name of actor variable in the SPSS data set

p = the name of actor variable in the SPSS data set

y = the name of outcome variable in the SPSS data set

dyadid = the name of outcome variable in the SPSS data set

xn = the name for X variable (actor and partner) to be used in the text file

yn = the name for the outcome variable to be used in the text file

alpha = significance level (defaults to .05)

ofile = the name of the output file (use quotes); this is where you go to find the text; defaults to c:\apimtext.txt; if your computer does not allow you write on the c drive or does not have a c drive, you must change this

directory = the name of the directory where temporary files are written (use quotes); this must be a directory you are allowed to write on; defaults to c:\; if your computer does not have a c drive or does not allow you to write on it, you must change this

clist = the SPSS names of the covariates separated by spaces; defaults to blank.


Note carefully what terms have quotes and what do not and where the slashes are where they are not and the defaults.  The output file is currently written to a file named "c:\APIMtext.dat".

Look for updates, as there are likely to be errors.  No guarantee for accuracy.  Almost certainly you will need to edit the DataToText output in research reports.  There will be updates.  There is no guarantee for accuracy.  Examine not only DataToText output file, but also the SPSS output file.  The user needs to carefully edit the ApimText output in research reports.  Please cite this ApimText webpage if you do use it.  Moreover, you need a footnote that says: “Some of the material here was produced by the SPSS macro ApimText (Kenny, 2011).”

 If a non-English version of SPSS is being used, ModText changes the language to English.  It does not currently change the language back to the original language.


ApimText provides five possible warnings.  The user needs to pay careful attention to them.  Note that the example below produces two warnings.

 1.  The outcome variable is dichotomous and logistic regression, not multiple regression, should be used.  The output from ApimText in this case is wrong!

 2.   With covariates, ApimText can fail to remove missing cases on the covariates.  The researcher should remove those cases before undertaking the analysis. 

 3.  The actor and partner variables are highly correlated and this colinearity compromises the analysis. 

 4.   Because zero is not a possible value for actor variable ,grand-mean centering that variable should be considered. 

 5.   Because the actor variable is a dichotomy, the product term and discrepancy score are perfectly correlated and only one of the two should be reported.


Macro Output



WARNINGS:  1.  Because zero is not a possible value for Other Positivity, grand-mean centering that variable should be considered.




      The focus of this study is the investigation of the effect of Other Positivity on Satisfaction.  Both the effect of own Other Positivity (actor) and the effect of partner's Other Positivity (partner) on Satisfaction are studied. There are a total of 148 dyads and no missing data. The total number of individuals is 296.  The means and standard deviations are presented in Table 1.


      The actor effect is equal to .400 and is statistically significant (p < .001), with a medium effect size (Beta = .402). The partner effect is equal to .288 and is statistically significant (p < .001), with a small effect size (Beta = .289).  A summary of actor and partner effects is contained in Table 2. The intraclass correlation is equal to .469. Thus, the two members of the dyad are similar to one another.  The pseudo R squared (Kenny, Kashy, & Cook, 2006) is equal to .295.  There is evidence for "couple model" (Kenny & Cook, 1999) in that the actor and partner effects are not significantly different.  It may make sense to sum or average the two Other Positivity scores.


      The actor-partner interaction is equal to -.295 and is statistically significant (p = .019).  The partner effect for persons who are one standard deviation above the mean on Other Positivity is .146 and for persons who are one standard deviation below the mean on Other Positivity is .441.  Alternatively, the effect of the absolute difference of the two members on Other Positivity is equal to .019 and is not statistically significant (p = .819).  Thus, if two members have the same score on  Other Positivity, their score on Satisfaction is .019 units lower than it is for a dyad whose scores on Satisfaction differ by one unit.  There is evidence for an actor-partner interaction.


                      Table 1: Descriptive Statistics


Variable                  Mean        Standard Deviation


Other Positivity         4.264              .498

Satisfaction             3.605              .496



                      Table 2: Effect Estimates


Effect  Coefficient   Beta        df   p value 


Actor       .400      .402     273.1     <.001                     

Partner     .288      .289     273.1     <.001                     



Figure 1


APIM Diagram



         Other Positivity  1  _________________________> Satisfaction 1

                 /\   \                                /\     /\

                /        \                           /           \

               (            \                     /                \

              (                \               /                    \

             (                    \         /                        E1

            (                        \   /                             )

    .058*  [                           X                                ] .082*

            (                        /   \                             )

             (             .288*  /         \  .288*                 E2

              (                /               \                   /

               (            /                     \              /

                \        /                           \         /

                 \/   /              .400*             \/   \/

         Other Positivity  2  _________________________> Satisfaction 2


                                  * p < .05






       Kenny, D. A., & Cook, W. (1999).  Partner effects in relationship research:  Conceptual issues, analytic difficulties, and illustrations. Personal Relationships, 6, 433-448.

       Kenny, D. A., Kashy, D. A., & Cook, W. (2006).  Dyadic data analysis.  New York: Guilford.



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