All of these programs are written in R, but to run them, the user need not download R nor be at all familiar with R. One simply goes to the website (click on the name above) and is prompted about the data and the analysis that is desired. The results of the analysis -- computer output, tables, and text -- appear on the right side of the screen and for some programs the results can be downloaded to a file. These programs were written by myself with assistance from Thomas Ledermann, Robert Ackerman, Lara Stas, John Sakaluk, and Axel Mayer. Moreover, SRM_R was co-written with Man-Nok Wong. They all use RStudio's shiny web application framework. I eagerly looking for others to help me in this endeavor. Help is needed not only in writing programs, but also in creating documentation.

These programs are all very much in development and there are likely bugs and errors. Results should be carefully checked!

These programs have difficulty with very large datasets, i.e., those with hundreds of variables. It is advisable to create a smaller dataset before running these programs.

To view a dated powerpoint that describes an application of two of the programs click here.

Other Web-based Shiny Apps

RDDD.htm (click to see these programs): Three programs written to re-structure dyadic data written by David A. Kenny and Thomas Ledermann.

apimpower (click to run): A program for power analyses for Actor-Partner Interdependence Model with both indistinguishable and indistinguishable dyads written by Rob Ackerman and David A. Kenny. To download a description of methods used in this program click here.

APIM_SEM: Actor-Partner Interdependence Model estimated by SEM written by Lara Stas of Ghent University.

Warning!

All of these apps, except apimpower, send the data to an external server. If there are restrictions about the dissemination of the data and privacy issues, I cannot guarantee to you that they are satisfied should you use the app. You may need to email me to obtain a copy of the app for you to run the app locally. There is a small charge for this service.

General Points about These Apps

(Not all the programs are currently written this way, but most are.) The opening screen contains at least two upper tabs. The one on the left is for the app itself and the next one on the right, called "Help and Contact," which some documentation, links to further documentation, and an email address for enquires. The apps, APIM_MM and SRM_R, contain an additional link that describes changes in the program. Finally, the app GAPIM_I has formulas used in the app.

The main page contains a set of vertical tabs on the right. The user begins by completing the green tabs. At the top always is the green "Select Data" tab which reads a dataset in either SPSS or csv format. One then proceeds down the green tabs completing them in order. After the green tabs is the set of black tabs, which contain optional information (e.g., change alpha). Once the run is set up, the user hits the blue tab and the blue button which says something like "Begin the Analysis" to run the analysis. Eventually, the results appear on the right side of screen and the user can select text, tables, or computer output. Some apps allow for the display of figures. If one wants to save the output, the user hits the bottom purple tab of "Download Output" that saves the text, tables, computer output, and figures (if there are any) in docx, html, or pdf formats. Finally, for some of the apps, the user can save a transformed dataset.

The apps are written to be "reactive." That is, after the run is completed and even after the files are downloaded, the user can make changes in the analysis. This feature is especially useful if the user has made an error in that the error can be corrected without having to start all over (e.g., read the dataset, enter the variables, and so on). Though not fully "idiot proofed," the programs do not always crash when the user makes an error; on the right-side of the screen a message may appear telling the user what the problem is.

This app conducts an Group Actor-Partner Interdependence Model Analysis of group data where each person is measured. I thank Randi Garcia who assisted me earlier on this project and T. M. L. Kaufman and Leon Mait who assisted me more recently. Man-Nok Wong also provided key technical advice. THIS PROGRAM IS EXPERIMENTAL AND ONE SHOULD BE VERY CAUTIOUS ABOUT THE RESULTS PRODUCED BY THIS APP. The estimation used is based on a method presented in Kenny, D. A., & Garcia, R. (2012), Using the Actor-Partner Interdependence Model to Study the Effects of Group Composition, Small Group Research, 43, 468-496, which can be downloaded here.

The basic model analysis allows for four effects. Consider gender as the group composition variable. First, there is X, the gender of the actor or respondent or the Actor Effect. Second, there is X' the gender of the other members in the group or the Others Effect. Third, there is I, the match between the gender of the actor and the others in the group or Actor-Others Similarity. Fourth, there is the similarity of the others in the group or Others Similarity. Each variable is scaled when 1 is the maximum and -1 the theoretical minimum. The program allow for non-dichotomous X variables and covariates. Groups must contain at least three persons.

Besides the Complete Model with the four above terms, 14 different submodels can be estimated. Among these models are the Contrast (aka as the frogpond effect), Group, Diversity, and Person Fit Models.

When a non-dichotomous group composition variable is used, it is rescaled where the largest is coded as +1 and the smalles at -1. In this case, Actor-Others Similarity can be measured by squared, absolute, or square root of the absolute difference averaged across all others. The product of Actor and each Other can also be used. For Others Similarity, the options are squared, absolute, and square root of the absolute difference. The program allows for nonindependence in members' responses.

When GAPIM_I is opened, one begins by completing in order the first three green tabs on the left. At the top is the "Select Data" tab which reads a dyad dataset in either SPSS or csv format. One can analyze, the data gathered by Randi Garcia, in which Gender is the distinguishing variable and outcome is Group Identification. One selects "Garcia Example."

To learn more about the program's options, open the program and click on the blue "Help and Contact" tab.

The Garcia example dataset can be downloaded at davidakenny.net\doc\GAPIM.csv and the GAPIM-I output for that dataset can be downloaded at davidakenny.net\doc\GAPIM_I_Example.pdf.

A paper describing this app,, written with ManNok Wong and Andrew Knight, can be downloaded here. This app conducts an Social Relations Analysis of directed dyadic data and estimates the fixed and random effects for that model. The code that estimates the SRM variances and covariance and creates the necessary codes for that analysis was written by Andrew Knight of Washington University (code at https:\\apknight.or\pdSRM.R). The estimation algorithm is slow and has difficulty converging with small datasets, datasets with a lot of missing data, and models with small actor or partner variances or large actor-partner correlations. The hope is to improve the algorithm in the near future.

The method involves the Snijders and Kenny (1999). The social relations model for family data: A multilevel approach. Personal Relationships, 6, 471-486) approach which creates dummy variables for each actor and partner. The presumption is that the design is round robin, everyone paired with everyone. There can be missing data and the symmetric block design can be estimated with this this app. The program can analyze the half block design, i.e., a design in which the perceivers rate targets, but the targets do not rate the perceivers.

The input file must be for each score or a "narrow format." For instance assuming no missing data, if there were a round-robin with 5 persons, 20 records would be read. On the record is the outcome variable, the actor number, partner number, and group number. Note the actor and partner numbers should refer to the same person. Thus, for person 521 should have both an actor and partner number of 521. The SRM with indistinguishable dyads is discussed in Chapter 8 of Dyadic Data Analysis, (Kenny, Kashy, & Cook, 2006). For an webinar (small charge) introduction to the SRM view a webinar and for SRM designs view another webinar.

When SRM_R is opened, one begins by completing in order the first two green tabs on the left. At the top is the "Select Data" tab which reads a dyad dataset in either SPSS or csv format. Next one goes to green "SRM Variables and Terms" tab and gives the name from the dataset of the outcome variable, actor number, partner number, and group number (see above paragraph). SRM_R creates the necessary dummy variables for analysis. One can also change "Actor" to "Perceiver" and "Partner" to "Target" or to whatever names the user desires, e.g., "Receiver and Sender" or "Victim and Aggressor." One can change the name of group to team or whatever the user prefers.

The next three black tabs are optional. Then first is the "Predictor Variables" which can be used to pick and name the covariates or fixed variables for the analysis. The black "Group Effect" tab allows the user to pick how group effects are modeled. They can be treated as random as is done in TripleR, not included in the model, or fixed as is done in SOREMO which is similar to "group-mean centering" the data. The black "Miscellaneous" tab allows the user to change the alpha and the estimation method.

Once the run is set up, one hits on the top the blue tab labelled "Run Estimation of SRM Now." The results appear on the left side of screen and the user can select text, tables, or computer output. Finally, if one wants to save the output to file (docx, html, or pdf formats), one hits the bottom purple tab on the left labelled "Download Output" and selects the options on the left.

The program displays in text, tables, and computer output. All of the output can be saved in an output in either docx, html, or pdf formats.

APIM_MM

This program conducts an Actor-Partner Interdependence analysis of dyadic data. Data must be in a pairwise structure. There must be a numeric dyad identification variable on each record and no more than two records can have the same score on the dyad identification variable. The program is NOT for the analysis of longitudinal dyadic analysis nor does it allow for non-normal outcomes. There is 30 pages of documentation for this program with screenshots and three examples (click here).

For details on the APIM, read Chapter 7 of Dyadic Data Analysis by Kenny, Kashy, and Cook (2006). Also webinars (small charge) on the APIM and APIM Estimated by Multilevel Modeling might be helpful. The defining feature of the APIM is measuring the effect of one mixed variable on another mixed variable: Measured is the effect of a person's own X on his or her Y (the actor effect) and the effect of a person's partner's X on his or her Y (the partner effect).

The program requires at least one predictor mixed variable and one outcome mixed variable. Multiple mixed predictors are allowed. Dyad members can be distinguishable or indistinguishable. Covariates can be included and an analysis of actor-partner interactions for each mixed variable can be estimated using either a product score or a discrepancy score. With distinguishability, pooled actor and partner effects across members are presented, as well as tests of distinguishability.

The residuals in the outcome variable are assumed to be normally distributed, and outliers can be removed. The user can specify removing from the analysis standardized residuals values that exceed a certain value.

The program can be relatively easily adapted to test for between-dyads moderators of actor and partner effects. Consult the third example of the documentation.

The program displays in text, tables, computer output, and figures. All of the output can be saved in an output in either docx, html, or pdf formats.

APIMeM

The program conducts an APIM mediation analysis using structural equation modeling. It requires three mixed variables in a dyad dataset. One variable, X, is presumed to cause M which in turn is presumed to cause Y. Estimates and tests of direct, indirect, and total effects are estimated and tested. The structural equation modeling program lavaan is used to estimate mediation models. One can use Monte Carlo or traditional non-parametric bootstrap confidence intervals for indirect effects. The structural equation modeling program lavaan is used to estimate mediation models. Brief documentation of the program is available here.

The program is limited to indistinguishable dyads. However, if one has distinguishable dyads, one can allow the means and variances of the two members to vary (unceck on the Basic Information page: "Equal Means for the Two Members for Each Variable (uncheck if unequal)" and "Equal Variances for the Two Members for Each Variable (uncheck if unequal)"). The program can then test whether the actor and partner effects (and, therefore, indirect effects) vary by the distinguishing variable, which if can be gauged to determine if those effects can be safely assumed to be the same for the two members. If this analysis results in a good-fitting model (e.g., a low RMSEA or a non-significant chi-square test), then it is reasonable to use this app, even with distinguishable dyads.

To use APIMeM, one goes through the tabs on the left of the opening screen:

First the green "Select Data" tab is clicked. Either an SPSS or csv (comma separated variables) can be read.

Next the green "Basic Information" tab is clicked. The user names the X, Y, and M variables, as well as the the two members. The defaults can be accepted. The user also decides whether to allow for mean and variances differences between the members.

The green "Variables" tab selects the six variables for the analysis from the dataset. They are chosen from a variable list. (Sometimes one may need to click the "Check to View Variable List" box to be able to see the entire list.

The optional black tab of "Miscellaneous" allows the user to either change alpha or the bootstrapping method. Moreover, the user can set k, the ratio of the partner effect to actor effect, for both the M and Y equations to estimate a simpler model.

To begin the run, the blue "Run Analysis" tab is clicked and then the "Click Here to Begin the APIM Mediation Analysis" button. If one runs a non-parametric bootstrap, it may take several minutes before the results appear on the right.

To download the results, the text, tables, computer output, and figure(s), the user clicks the purple "Download Output" and then chooses the name of the file and its format. The output can be saved in an output in either docx, html, or pdf formats. An example of such pdf output can be viewed here.

The program is currently limited to indistinguishable dyads but the user can allow mean and variances to differ. The user can enter k values (the ratio of partner to actor effects) to test specialized models. For instance, there might be only actor effects from X to M (k_M = 0), and the actor and partner effects from M to Y might be equal (k_Y = 1).

Currently no covariates are permitted and again dyad members must be indistinguishable. THIS PROGRAM HAS NOT BEEN FULLY TESTED.

The program conducts an APIM moderation analysis using SEM. It requires three mixed variables in a dyad dataset. One variable, X, is presumed to cause Y and those effects are presumed to vary (i.e., to moderate) due to M. Simple effects of the effect of X on Y at various levels of M are estimated. The structural equation modeling program lavaan is used to estimate moderation models. Brief documentation of the program is available here.

The program is currently limited to indistinguishable dyads but the user can allow mean and variances to differ. The user can estimate specialized models that place constraints on the four interaction effects. The constraints involve k which is the ratio of partner to actor interaction effects. One approach is to use the k ratios from the X and M main effects to constrain the interaction effects. A second approach is to estimate the k ratios from the interaction effects. For both of these approaches, a test of the constraints is provided, as well as a Monte Carlo estimate of the confidence interval for the k's. The third approach is have the user specify the k values. (See the technical appendix of Garcia et al. for more details on k and tests of moderation.)

Currently no covariates are permitted. THIS PROGRAM HAS NOT BEEN FULLY TESTED.

To use APIMoM, one goes through the tabs on the left of the opening screen:

First the green "Select Data" tab is clicked. Either an SPSS or csv (comma separated variables) can be read.

Next the green "Basic Information" tab is clicked. The user names the X or the predictor, Y of the outcome , and M or the moderator variables, as well as the the two members. The defaults can be accepted. The user also decides whether to allow for mean and variances differences between the members.

The green "Variables" tab selects the six variables for the analysis from the dataset. They are chosen from a variable list. (Sometimes one may need to click the "Check to View Variable List" box to be able to see the entire list.

The optional black tab of "Miscellaneous" allows the user to change alpha. Moreover, the user can set k, the ratio of the partner to actor variables, for both the X and M variables. One can empirically estimate the k's, input a priori k values, or use the main effect k values. (See the technical appendix of Garcia et al. for more details.) A statistical test is used to evaluate whether the k's, however chosen, can explain the pattern of moderation effects. (It is advisable to only use this option if there is detectable interaction in the data.)

To begin the run, the blue "Run Analysis" tab is clicked and then the "Click Here to Begin the APIM Moderation Analysis" button.

To download the results, the text, tables, computer output, and figure(s), the user clicks the purple "Download Output" and then chooses the name of the file and its format. The output can be saved in an output in either docx, html, or pdf formats. An example of such pdf output can be viewed here.

The program is currently limited to indistinguishable dyads but the user can allow mean and variances to differ.

One can download the output, the text, tables, and computer output (the indistinguishable run) to a file. An example file is contained here.

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