William L. Cook

1. Basic Definitions and Overview Nonindependence Basic Definitions Data Organization A Database of Dyadic Studies 2. The Measurement of Nonindependence Interval Level of Measurement Categorical Measures Consequences of Ignoring Nonindependence What Not to Do Power Considerations 3. Analyzing Between- and Within-Dyads Independent Variables Interval Outcome Measures and Categorical Independent Variables Interval Outcome Measures and Interval Independent Variables Categorical Outcome Variables 4. Using Multilevel Modeling to Study Dyads Mixed-Model ANOVA Multilevel-Model Equations Multilevel Modeling with Maximum Likelihood Adaptation of Multilevel Models to Dyadic Data 5. Using Structural Equation Modeling to Study Dyads Steps in SEM Confirmatory Factor Analysis Path Analyses with Dyadic Data SEM for Dyads with Indistinguishable Members 6. Tests of Correlational Structure and Differential Variance Distinguishable Dyads Indistinguishable Dyads 7. Analyzing Mixed Independent Variables: The Actor-Partner Interdependence Model The Model Conceptual Interpretation of Actor and Partner Effects Estimation of the APIM: Indistinguishable Dyad Members Estimation of the APIM: Distinguishable Dyads Power and Effect Size Computation Specification Error in the APIM 8. Social Relations Designs with Indistinguishable Members The Basic Data Structures Model Details of an SRM Analysis Model Social Relations Analyses: An Example 9. Social Relations Designs with Roles SRM Studies of Family Relationships Design and Analysis of Studies The Model Application of the SRM with Roles Using Confirmatory Factor Analysis The Four-Person Design Illustration of the Four-Person Family Design The Three-Person Design Multiple Perspectives on Family Relationships Means and Factor Score Estimation Power and Sample Size 10. One-with-Many Designs Design Issues Measuring Nonindependence The Meaning of Nonindependence in the One-with-Many Design Univariate Analysis with Indistinguishable Partners Univariate Estimation with Distinguishable Partners The Reciprocal One-with-Many Design 11. Social Network Analysis Definitions The Representation of a Network Network Measures The p1 12. Dyadic Indexes Item Measurement Issues Measures of Profile Similarity Mean and Variance of the Dyadic Index Stereotype Accuracy Differential Endorsement of the Stereotype Pseudo-Couple Analysis Idiographic versus Nomothetic Analysis Illustration 13. Over-Time Analyses: Interval Outcomes Cross-Lagged Regressions Over-Time Standard APIM Growth-Curve Analysis Cross-Spectral Analysis Nonlinear Dynamic Modeling 14. Over-Time Analyses: Dichotomous Outcomes Sequential Analysis Statistical Analysis of Sequential Data: Log-Linear Analysis Statistical Analysis of Sequential Data: Multilevel Modeling Event-History Analysis 15. Concluding Comments Specialized Dyadic Models Going Beyond the Dyad Conceptual and Practical Issues The Seven Deadly Sins of Dyadic Data Analysis The Last Word

The actor–partner interdependence model (APIM) is a model of dyadic relationships that integrates a conceptual view of interdependence with the appropriate statistical techniques for measuring and testing it. In this article we present the APIM as a general, longitudinal model for measuring bidirectional effects in interpersonal relationships. We also present three different approaches to testing the model. The statistical analysis of the APIM is illustrated using longitudinal data on relationship specific attachment security from 203 mother–adolescent dyads. The results support the view that interpersonal influence on attachment security is bidirectional. Moreover, consistent with a hypothesis from attachment theory, the degree to which a child’s attachment security is influenced by his or her primary caregiver is found to diminish with age.

Abstract This article discusses the conceptual meaning of partner effects, which occur when one person is affected by the behavior or characteristics of his or her partner. We show that partner effects can be used to validate the presence of a relationship and can elaborate the particular nature of that relationship. We discuss possible moderation of partner effects and show that many theoretical variables in relationship research (e.g., similarity) can be viewed as the interactions of partner effects with other variables. We present three extended examples that illustrate the importance of partner effects.

Attachment theory (J. Bowlby, 1969) is not just about how internalized models of relationships affect interpersonal outcomes; it is primarily a theory about how interpersonal processes affect social and cognitive development. This study tested 3 hypotheses about the interpersonal sources of adult attachment security: (a) attachment security is relationship specific, (b) characteristics of partners affect attachment security, and (c) security of attachment is reciprocated. Measures of attachment security were obtained from 2 parents and 2 children (adolescent or older) in 208 middle-class families. Results of social relations model analysis (D. A. Kenny & L. La Voie, 1984) supported all 3 hypotheses. The author concludes that internal working models of relationships may not be so "internal" after all and that greater emphasis on the interpersonal sources of adult attachment security is warranted.