One with many design estimation
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One-with-Many Design: Estimation. David A. Kenny. What You Should Know. Introduction to the One-with-Many Design. The One-with-Many Provider-Patient Data. Terminology. People Focal person (the one) Partners (the many) Source of Data Focal persons (1PMT) Partners (MP1T)

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One-with-Many Design: Estimation

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One with many design estimation

One-with-Many Design:Estimation

David A. Kenny

What you should know

What You Should Know

  • Introduction to the One-with-Many Design

The one with many provider patient data

The One-with-Many Provider-Patient Data



  • People

    • Focal person (the one)

    • Partners (the many)

  • Source of Data

    • Focal persons (1PMT)

    • Partners (MP1T)

    • Both (reciprocal design: 1PMT & MP1T)

Analysis strategies

Analysis Strategies

  • Multilevel analysis

    • Indistinguishable partners

    • Many partners

    • Different numbers of partners per focal person

  • Confirmatory factor analysis

    • Distinguishable partners

    • Few partners

    • Same number of partners per focal person

Multilevel analyses nonreciprocal design

Multilevel Analyses: Nonreciprocal Design

  • Each record a partner

  • Levels

    • Lower level: partner

    • Upper level: focal person

  • Random intercepts model (nonindependence)

  • Lower level effects can be random

Data analytic approach for the non reciprocal one with many design

Data Analytic Approach for the Non-Reciprocal One-with-Many Design

Estimate a basic multilevel model in which

There are no fixed effects with a random intercept.

Yij = b0j + eij

b0j = a0 + dj

Note the focal person is Level 2 and partners Level 1.






Could add predictors here.

Spss output

SPSS Output

Fixed Effects

Covariance Parameters

So the actor variance is .791, and ICC is .791/(.791+1.212) = .395

Fixed effects nonreciprocal design

Fixed Effects: Nonreciprocal Design

  • Can add to the model

    • Focal person characteristics

      • Would be actor if 1PMT design

      • Would be partner if MP1T design

    • Partner characteristics

      • Would be partner if 1PMT design

      • Would be actor if MP1T design

      • Can be random: The coefficient may vary by focal person

    • Important to make zero interpretable

Reciprocal one with many design

Reciprocal One-with-Many Design

Sources of nonindependence

  • More complex…

Sources of nonindependence in the reciprocal design

Sources of Nonindependence in the Reciprocal Design

  • Individual-level effects for the focal person:

    • Actor & Partner variances

    • Actor-Partner correlation

  • Relationship effects

    • Dyadic reciprocity corelation

Data analytic approach for estimating variances covariances the reciprocal design

Data Analytic Approach for Estimating Variances & Covariances: The Reciprocal Design

Data Structure: Two records for each dyad; outcome is the same variable for focal person and partner.

Variables to be created:

role = 1 if data from focal person; -1 if from partner

focalcode = 1 if data from focal person; 0 if from partner

partcode = 1 if data from partner; 0 if from the focal person

Data analytic approach for estimating variances covariances the reciprocal design1

Data Analytic Approach for Estimating Variances & Covariances: The Reciprocal Design

A fairly complex multilevel model…


outcome BY role WITH focalcodepartcode

/FIXED = focalcodepartcode | NOINT


/RANDOM focalcodepartcode | SUBJECT(focalid) covtype(UNR)

/REPEATED = role | SUBJECT(focalid*dyadid) COVTYPE(UNR).



  • Taken from Chapter 10 of Kenny, Kashy, & Cook (2006).

  • Focal person: mothers

  • Partners: father and two children

  • Outcome: how anxious the person feels with the other

  • Distinguishability of partners is ignored.


Output fixed effects

Output: Fixed Effects

The estimates show the intercept is the mean of the ratings made by the mother (focalcode estimate is 1.808). The partcode estimate indicates the average outcome score across partners of the mother which is smaller than mothers’ anxiety. This difference is statistically significant.

One with many design estimation

  • The relationship variance for the partners is .549. (Role = -1) and for mothers (Role = 1) is .423.

  • The correlation of the two relationship effects is .24: If the mother is particularly anxious with a particular family member, that member is particularly anxious with the mother.

  • Var(1) (focalcode is the first listed random variable) is the actor variance of mothers and is .208.

  • Var(2) is the partner variance for mothers (how much anxiety she tends to elicit across family members) and is .061. (p = .012; p values for variances in SPSS are cut in half).

Output nonindependence

Output: Nonindependence

  • The ICC for actor is .208/(.208+.423) = .330 and the ICC for partner is .061/(.061+.549) = .100.

  • The actor partner correlation is .699, so if mothers are anxious with family members, they are anxious with her.

Fixed effects reciprocal design

Fixed Effects: Reciprocal Design

  • Two ways to think about fixed effects

    • Standard way

      • Focal person characteristics (fx)

      • Partner characteristics (px)

    • APIM way (the same variable must be measured for the focal person and partners)

      • Actor characteristics (ax)

      • Partner characteristics (ptx)

Fixed effects reciprocal design1

Fixed Effects: Reciprocal Design

/FIXED = focalcodepartcodefX*focalcode

fX*partcodepX*focalcodepX* partcode| NOINT


/FIXED = focalcodepartcodeaX*focalcode

aX*partcodeptX*focalcodeptX*partcode| NOINT

Note: fX*focalcode = aX*focalcode

fX*partcode = ptX*partcode

pX*focalcode = ptX*focalcode

pX*partcode = aX*partcode



Thanks to Deborah Kashy

Reading: Chapter 10 in Dyadic Data Analysis by Kenny, Kashy, and Cook

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