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Restructuring Dyadic Data

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1 Restructuring Dyadic Data
David A. Kenny

2 Background Dyadic Data Structures Individual One record for each person Own person’s variables Dyad One record for the dyad Both persons’ variables Pairwise View:

3 The Problem You have one data structure and you want to convert to another. individual to dyad individual to pairwise dyad to pairwise Other conversions are trivial and can be accomplished either by deleting cases or renaming variables. 3

4 Dyad ID For restructuring individual data, a unique identification number for each pair of persons is needed. For longitudinal standard design, the “DyadID” is for each time point for each dyad. 4

5 Usual Strategies to Restructure
Restructuring by entering the data the “right” way. Cut and paste Computer programs Built in routines to restructure SPSS:

6 New Strategies to Restructure
R programs Individual to pairwise Individual to dyad Dyad to pairwise Apps SPSS macro (no longer maintained)

7 R Restructuring Programs
Written in R Co-written with Thomas Ledermann of Utah State University Information available at

8 Details Installing R Three programs
ItoP.R: Individual to pairwise ItoD.R: Individual to dyad DtoP.R: Dyad to pairwise

9 General R Program RDDD Description:


11 Descriptive Information for Dyad Dataset
Descriptive Information for Dyad Dataset The dataset c:/ai.sav has been transformed from an individual to a dyad dataset called c:/dyad.csv. The distinguishing variable is Gender, and it has two levels, Wives (-1) and Husbands (1). There are 148 dyads and 296 individuals, 148 Wives and 148 Husbands. There are no missing data on any of the variables in the dataset. There are 7 variables, 1 between-dyad variable, 1 within-dyad variable, and 5 mixed variables. The one between-dyad variable is Years Married, and the one within-dyads variable is Gender. The within-dyads variable, Gender, is a dichotomy and could be used as a distinguishing variable. The descriptive statistics for the variables as individuals are contained in Table 1 and the descriptive and inferential statistics as dyads are contained in Table 2.

12 Table 1: Descriptive Statistics for Individuals (All Variables)
Variable Mean sd Minimum Maximum Intra. r Years Married Gender Self Positivity Other Positivity Satisfaction Tension Similar Hobbies

13   Table 2: Inferential and Descriptive Statistics for Dyads (Mixed Variables)
Mean sd Variable Wives Husbands p Wives Husbands p r p Self Positivity < Other Positivity Satisfaction <.001 Tension <.001 Similar Hobbies < <.001 All calculations are based on 148 cases. Degrees of freedom for the test of mean difference are 147 and for the test of standard deviation difference and the test of the correlation are 146.

14 Restructuring Apps Uses the Ledermann & Kenny R programs
Adaptation to apps done with the assistance of Robert Ackerman Web-based, no need to download R or to install R. Answer prompts Results Text on the screen Restructured data that can be downloaded Link:

15 ItoP Illustration




19 SPSS Macros Steps Download the macro. Run the macro. Open the dataset.
Create the call. Run the call. Macros pairwise.sps indtodyad.sps

20 Calls (red is required)
pairwise.sps Pairwise dyadid = dyad i1 = 'A' i2 = 'P' directory = 'c:\'. indtodyad.sps IndToDyad dyadid = dyad distvar = gender i1 = 'F' i2 = 'M' directory = 'c:\'.

21 Issues 2 records per dyad No string variables for most methods
The “Individual to Dyad” restructuring programs always produce a new variable called “partnum” (one member is given a “1” and the other a “2”) which can be useful in analyses.

22 Thomas Ledermann & Rob Ackerman!
Additional Readings Kenny, D. A., Kashy, D. A., & Cook, W. L. Dyadic data analysis. New York: Guilford Press, Chapter 1. Ledermann, T., & Kenny, D. A. (2014). A toolbox with programs to restructure and describe dyadic data. Journal of Social and Personal Relationships, online. View as a webinar (small charge) Special thanks to Thomas Ledermann & Rob Ackerman! 22

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