Structural Equation Models for Interchangeable Dyads: Being the Same Makes a Difference.

Structural equation modeling (SEM) offers a flexible method for studying the patterns of interdependence in partners' behavior, which lie at the heart of interactions and relationships. Although SEM has been applied to the study of distinguishable dyads, in which partners are distinguishable by type, such as male and female, it has rarely been applied to the study of interchangeable dyads, such as male-male or female-female pairs. The authors integrate a wide range of dyadic interdependence models--including actor-partner interdependence models, mutual-influence models, and common-fate or dyadic personality models--into an SEM framework for use with interchangeable dyads. The authors also address the use of latent variables at both the dyadic and individual levels, whereby substantive relationships in these models can be corrected for errors of measurement. Furthermore, the authors discuss the conceptual underpinnings of dyadic models and give examples of their application. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An introduction to using structural equation models in rehabilitation psychology.

Objective: To provide an overview of structural equation modeling (SEM) using an example drawn from the rehabilitation psychology literature. Design: To illustrate the 5 steps in SEM (model specification, identification, estimation methods, interpretation of results, and model modification), an example is presented, with details on determining whether alternative models result in a significant improvement to fit to the observed data. Data are from a sample of 274 people with spinal cord injury. Issues commonly encountered in preparing data for SEM analyses (e.g., missing data, nonnormality) are reviewed, as is the debate surrounding some aspects of SEM (e.g., acceptable sample size). Conclusion: SEM can be a powerful procedure for empirically representing complex and sophisticated theoretical models of interest to rehabilitation psychologists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mixed-effects logistic regression for estimating transitional probabilities in sequentially coded observational data.

This article demonstrates the use of mixed-effects logistic regression (MLR) for conducting sequential analyses of binary observational data. MLR is a special case of the mixed-effects logit modeling framework, which may be applied to multicategorical observational data. The MLR approach is motivated in part by G. A. Dagne, G. W. Howe, C. H. Brown, & B. O. Muthén (2002) advances in general linear mixed models for sequential analyses of observational data in the form of contingency table frequency counts. The advantage of the MLR approach is that it circumvents obstacles in the estimation of random sampling error encountered using Dagne and colleagues' approach. This article demonstrates the MLR model in an analysis of observed sequences of communication in a sample of young adult same-sex peer dyads. The results obtained using MLR are compared with those of a parallel analysis using Dagne and colleagues' linear mixed model for binary observational data in the form of log odds ratios. Similarities and differences between the results of the 2 approaches are discussed. Implications for the use of linear mixed models versus mixed-effects logit models for sequential analyses are considered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Social learning and teenage drug use: An analysis of family dyads.

This analysis of drug use in family dyads draws on data from a series of nationwide studies in which interviews were conducted separately with teenagers and with older members of their families (i.e., their mothers, fathers, older siblings). The interview schedule for these studies examines each individual's personal experience with a broad range of psychoactive substances. Thus, to the extent that behavioral similarities do occur in family dyads, "same drug links" can be compared to "cross drug links," providing a basis for differentiating evidence of direct imitation from less specific patterns of behavioral similarity. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structural Equation Modeling of Paired-Comparison and Ranking Data.

L. L. Thurstone's (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart summarizes the model selection process. The authors show how to embed these models within a more familiar structural equation modeling (SEM) framework. The different special cases of Thurstone's model can be estimated with a popular SEM statistical package, including factor analysis models for paired comparisons and rankings. Only minor modifications are needed to accommodate both types of data. As a result, complex models for comparative judgments can be both estimated and tested efficiently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using Diary Methods to Study Marital and Family Processes.

Diary methods allow researchers to study marital and family processes within the context of daily life in a way that is not possible with more traditional methods. The authors review applications of diary designs in marital and family research and detail the types of research questions that can uniquely be asked of dyadic/family diary data. Technological developments for the use of electronic palm-top devices for implementing diary methods are also reviewed. Additionally, the authors discuss specific issues relevant to the analysis of diary data that come from dyads or families. Last, the authors raise unresolved issues and directions for future research in the use of diary methods for studying marital and family processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Enhanced co-orientation in the perception of friends: A social relations analysis.

Newcomb's (1953) idea of co-orientation (interdependence between two persons' attitudes or perceptions) is used as a framework within which interpersonal perception between friends and acquaintances is examined. The principal question is whether co-orientation effects are stronger for friendship dyads than for acquaintance dyads. More specifically, the study examines the degree to which consensus, assimilation, self–other agreement, and assumed similarity differ. The social relations model is used to analyze a data set that included 16 living groups with 119 friend dyads and 1,668 acquaintance dyads. Results indicate that co-orientation effects are more pronounced in friendship dyads. The increment in co-orientation effects is largely due to similarities in the unique or idiosyncratic perceptions that people have of friendship pairs as well as the unique agreement about others that friends have with one another. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling heterogeneity in social interaction processes using multilevel survival analysis.

More than 15 years ago, survival or hazard regression analyses were introduced to psychology (W. Gardner & W. A. Griffin, 1989; W. A. Griffin & W. Gardner, 1989) as powerful methodological tools for studying real time social interaction processes among dyads. Almost no additional published applications have appeared, although such data are commonly collected and the applicable questions are central to many important theoretical perspectives. To revisit the basic methods, the authors use an example from emotion regulation theory in which the level of child antisocial behavior is hypothesized to be positively associated with the hazard rate of angry emotions and negatively associated with sad, fearful emotions in the face of parental negative behavior (scolding). The authors discuss the limitations of traditional approaches to the analysis of social interaction and demonstrate improvements in the ability to model individual differences now available in existing software. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Splitting the reciprocity correlation.

Reciprocity of attraction has been called a fundamental principle of social relations. However, correlational data provide only meager support for actual reciprocity. A formal analysis reveals that the reciprocity correlation contains a mixture of 2 correlations: reciprocity at the individual and at the dyadic level. These 2 correlations can be estimated from a round robin design. Data from 48 college students and 336 dyads, measured at 5 time points, showed a –.210 individual level correlation and a .617 dyadic level correlation. The necessity for an appropriate design, specification of a theoretical and statistical model that includes 2 levels of analysis, and use of an indirect estimation procedure to calculate the model parameters are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hierarchical continuous-time sequential analysis: A strategy for clinical research.

Presents a strategy for analyzing interdyadic differences in sequential data on social interactions. The social interactive data could be, for example, a nonverbal behavior such as eye gazes within dyads, with measurement of both the sequence of behaviors and their durations. This article shows (1) how one can statistically describe an interactional structure within each dyad governing the stream of that dyad's social interactive behavior and (2) how scores describing dyadic structures can be related to covariate information about the dyads. The covariates could include, for example, ratings of therapist skill or client psychopathology. Methods for relating measures of within-dyad structures in interactive behavior to between-dyad covariates could be a powerful tool for research on psychotherapy process or interpersonal relationships. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Are we on the same wavelength? Interpersonal complementarity as shared cyclical patterns during interactions.

The present study addressed hypotheses about cyclical entrainment between interacting dyad members in their moment-to-moment levels of dominance and affiliation. Using a computer joystick technique, observers recorded the continuous stream of behavior for each partner in 50 mixed-sex dyads, and the data for each dyad were submitted to time-series analyses, including cross-spectral analysis. Although potentially interesting individual differences emerged, in most dyads, partners shared behavior cycles of roughly the same frequency with strongly correlated variations in amplitude (coherence). Consistent with interpersonal theory, partners’ affiliation behaviors were very strongly in phase, whereas their dominance behaviors were equally strongly out of phase. In addition, these cyclical forms of interpersonal complementarity were distinguishable from other forms, such as mutual adjustment in overall levels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Similarity in depressive symptoms in adolescents' friendship dyads: Selection or socialization?

This study examined friendship selection and socialization as mechanisms explaining similarity in depressive symptoms in adolescent same-gender best friend dyads. The sample consisted of 1,752 adolescents (51% male) ages 12–16 years (M = 13.77, SD = 0.73) forming 487 friend dyads and 389 nonfriend dyads (the nonfriend dyads served as a comparison group). To test our hypothesis, we applied a multigroup actor–partner interdependence model to 3 friendship types that started and ended at different time points during the 2 waves of data collection. Results showed that adolescents reported levels of depressive symptoms at follow-up that were similar to those of their best friends. Socialization processes explained the increase in similarity exclusively in female dyads, whereas no evidence for friendship selection emerged for either male or female dyads. Additional analyses revealed that similarity between friends was particularly evident in the actual best friend dyads (i.e., true best friends), in which evidence for socialization processes emerged for both female and male friend dyads. Findings highlight the importance of examining friendship relations as a potential context for the development of depressive symptoms. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A Meta-Analytic Review of Racial-Ethnic Matching for African American and Caucasian American Clients and Clinicians.

A meta-analysis was conducted to evaluate the effectiveness of client-clinician matching on the basis of race-ethnicity on overall functioning, service retention, and total number of sessions attended for African American and Caucasian American adult populations in mental health services. The analysis included 10 published and unpublished studies between 1991 and 2001. A random effects meta-analysis model showed no significant differences between client-clinician dyads matched racially-ethnically and those dyads not matched with respect to overall functioning, service retention, and total number of sessions attended. The finding was consistent for both African American and Caucasian American clients. Further implications of the findings are discussed. The authors also encourage more complete reporting of data suitable for quantitative research synthesis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stages of successful time-limited counseling: An interactional examination.

Six counselor/undergraduate dyads (the 3 most successful and the 3 least successful, as rated by client and counselor) were selected for intensive study from a sample pool of 15 dyads representative of time-limited counseling. It was hypothesized that successful counseling dyads would have 3 fairly distinct stages—high, low, and again high levels of complementarity—and that unsuccessful counseling dyads would not demonstrate this pattern. The sequence of topic-following/topic-initiation responses was used as an index of complementarity and was analyzed using a Markov chain model to test for differences in the sequence of topic responses over time. Moderate support was found for the hypothesized general stage model of high–low–high complementarity. The 3 successful dyads loosely fit the general pattern, but each showed individual variation in the length of each stage and the abruptness of stage changes. All 3 unsuccessful dyads had constant levels of complementarity over the course of counseling and thus did not fit the hypothesized model. (47 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Where is the relationship in research on the alliance? Two methods for analyzing dyadic data.

Researchers examining the therapy relationship are encouraged "to study both patients' and therapists' contribution to the relationship and the ways in which these contributions combine to impact treatment outcome" (Steering Committee, 2002, p. 443). Research on the therapeutic alliance, however, is dominated by studies that examine the individual contributions of the counselor and client. Relationship researchers have developed alternative ways to analyze dyadic data that do take into account the relationship. One alternative paradigm is to model the interdependence in dyadic alliance data with the Actor-Partner Interdependence Model (APIM; D. A. Kashy & D. A. Kenny, 2000). The APIM examines interdependence by modeling the impact of 1 dyad member's alliance ratings on the other member's session impact rating. APIM can also examine how alliance agreement interacts with alliance ratings to predict session impact. The other alternative paradigm is to use the latent group model (R. Gonzalez & D. Griffin, 2002) to examine the individual-level and dyad-level covariance in alliance and session impact ratings. The APIM and latent group models are illustrated with alliance and session impact measures from 53 client-counselor dyads. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling.

Meta-analysis and structural equation modeling (SEM) are two important statistical methods in the behavioral, social, and medical sciences. They are generally treated as two unrelated topics in the literature. The present article proposes a model to integrate fixed-, random-, and mixed-effects meta-analyses into the SEM framework. By applying an appropriate transformation on the data, studies in a meta-analysis can be analyzed as subjects in a structural equation model. This article also highlights some practical benefits of using the SEM approach to conduct a meta-analysis. Specifically, the SEM-based meta-analysis can be used to handle missing covariates, to quantify the heterogeneity of effect sizes, and to address the heterogeneity of effect sizes with mixture models. Examples are used to illustrate the equivalence between the conventional meta-analysis and the SEM-based meta-analysis. Future directions on and issues related to the SEM-based meta-analysis are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Interdependence in women with breast cancer and their partners: An interindividual model of distress.

Objective: The aim of this investigation was to test whether interdependence in dyads living with breast cancer could account for person–partner crossover effects in distress outcomes. Method: The sample consisted of 95 dyads with early-stage breast cancer. By using reciprocal dyadic data from women with breast cancer and their partners, we fit a structural equation model of the actor–partner interdependence model to examine the interaction of participants’ depression and stress in predicting their partner’s health outcomes. Results: Results revealed a pattern of influence whereby the interaction of high levels of depression coupled with high levels of stress in women with breast cancer was associated with lowered physical health and well-being in their partners. Although depression seemed to be the key mechanism in predicting distressing outcomes, when depression was combined with any additional stress, the level of physical distress was significantly greater. Results provided preliminary empirical support for crossover effects in the physical well-being of close relational partners in a cancer-related context. Further, results showed that distressing outcomes need not be limited to emotional distress but can also include physical distress. Conclusions: The findings from this study illustrate why it is not sufficient to concentrate care solely on the patient with cancer. Monitoring the social well-being of patients as they go through the cancer process could be as important as assessing their psychological state or other peripheral biomarkers. This line of inquiry would be advanced by including methods other than self-report in assessments of psychological and physical health. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks.

Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation modeling (SEM) perspective has focused only on the covariance structure, but growth curve models have four potential sources of misspecification: within-individual covariance matrix, between-individuals covariance matrix, marginal mean structure, and conditional mean structure. (c) Growth curve models can be estimated in both the SEM and multilevel modeling (MLM) frameworks; these have different emphases for the evaluation of model fit. In this article, the authors discuss the challenges presented by these 3 issues in the calculation and interpretation of SEM- and MLM-based fit indices for growth curve models and conclude by identifying some lines for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Leader–member agreement: A vertical dyad linkage approach.

The hypothesis derived from the vertical dyad linkage model of leadership development and investigated in this study is that agreement between a leader and a member regarding the meaning of certain mutually experienced events and situations will vary as a positive function of the quality of their dyadic exchanges. By employing dyadic measurement procedures, 109 university managerial dyads were assessed at 3 separate times with 3-mo intervals between assessments. Two different measures of dyadic agreement were used. Results show moderately high agreement within high- and intermediate-quality dyads and low agreement within low-quality dyads. Results were replicated on an independent sample of 41 managerial dyads. Implications are discussed. (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)