Integrating Covariates into Social Relations Models: A Plausible Values Approach for Handling Measurement Error in Perceiver and Target Effects

The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. However, the estimated individual SRM effects might not provide a very reliable measure of the true, unobserved SRM effects, resulting in distorted estimates of associations with other variables. This article introduces a plausible values approach that allows users to correct for measurement error when assessing the association of individual SRM effects with other individual difference variables. In the plausible values approach, the latent, true individual SRM effects are treated as missing values and are imputed from an imputation model by applying Bayesian estimation techniques. In a simulation study, the statistical properties of the plausible values approach are compared with two approaches that have been used in previous research. A data example from educational psychology is presented to illustrate how the plausible values approach can be implemented with the software WinBUGS.     

The measurement of reliability of social relations components from round‐robin designs

The social relations model (SRM) is a useful tool for measuring relationship effects, defined as the unique perceptions or behaviors of 2 people. The sources of variance in SRM studies are persons (actors and partners), groups, and items; the relationship effect is defined as the actor–partner interaction. By removing variance because of persons and groups, a measure of a “pure” relationship effect is obtained. In this article, generalizability theory (G Theory) is applied to estimate the reliability of SRM components from round‐robin data structures. Using G Theory, reliability formulas for actor, partner, group, and relationship are developed and interpretations for the reliability estimates are provided. The authors also discuss how these formulas can be used in both planning and interpreting results from relationship research.     

The Social Relations Model: How to Understand Dyadic Processes

The social relations model (SRM) is an intriguing tool both to conceptualize and to analyze dyadic processes. We begin with explaining why interpersonal phenomena in everyday life are more complex than often considered. We then show how the SRM accounts for these complexities by decomposing interpersonal perceptions and behaviors into three independent components and describe the designs required to investigate these components. We then provide a step-by-step introduction into social relations analyses, thereby showing how the SRM can be used to investigate a multitude of exciting research questions. Finally, we summarize the existing software solutions for conducting social relations analyses. Resources for further information are suggested.     

Interpersonal feedback: a social relations perspective

The social relations model (SRM; Kenny, 1994) is a statistical model for studying interpersonal perception and behavior that can be meaningfully applied to the group therapy situation. It consists of four primary components that correspond to (a) the general emotional or attitudinal climate of the group, (b) the perspective of the perceiver providing the feedback, (c) the group's consensual view of the target who receives the feedback, and (d) the unique relationship between the perceiver and the target. Because of its rigor and specificity, the components of the SRM may serve as a heuristic for thinking about interpersonal feedback in group therapy. A case example is used to demonstrate how an SRM perspective could guide therapists' ways of conceptualizing feedback.     

A family assessment based on the Social Relations Model

One purpose of family assessment is to formulate hypotheses that can guide clinical interventions. Family assessment is based on models about family functioning. In this paper the Social Relations Model ( Kenny and La Voie, 1984 ; SRM) is presented as such a model about family dynamics. Moreover, SRM provides statistical tools to underpin empirical hypotheses about family functioning. An SRM family assessment of a family with a child in child psychiatric care exemplifies the possibilities and limitations of this SRM approach to family assessment. The subject of the family assessment is family members' sense of influence in their family relationships.     

Stepwise Latent Class Models for Explaining Group-Level Outcomes Using Discrete Individual-Level Predictors

Explaining group-level outcomes from individual-level predictors requires aggregating the individual-level scores to the group level and correcting the group-level estimates for measurement errors in the aggregated scores. However, for discrete variables it is not clear how to perform the aggregation and correction. It is shown how stepwise latent class analysis can be used to do this. First, a latent class model is estimated in which the scores on a discrete individual-level predictor are used to construct group-level latent classes. Second, this latent class model is used to aggregate the individual-level predictor by assigning the groups to the latent classes. Third, a group-level analysis is performed in which the aggregated measures are related to the remaining group-level variables while correcting for the measurement error in the class assignments. This stepwise approach is introduced in a multilevel mediation model with a single individual-level mediator, and compared to existing methods in a simulation study. We also show how a mediation model with multiple group-level latent variables can be used with multiple individual-level mediators and this model is applied to explain team productivity (group level) as a function of job control (individual level), job satisfaction (individual level), and enriched job design (group level).     

Studying Multivariate Change Using Multilevel Models and Latent Curve Models

In longitudinal research investigators often measure multiple variables at multiple points in time and are interested in investigating individual differences in patterns of change on those variables. In the vast majority of applications, researchers focus on studying change in one variable at a time. In this article we consider methods for studying relations1.1ips between patterns of change on different variables. We show how the multilevel modeling framework, which is often used to study univariate change, can be extended to the multivariate case to yield estimates of covariances of parameters representing aspects of change on different variables. We illustrate this approach using data from a study of physiological response to marital conflict in older married couples, showing a substantial correlation between rate of linear change on different stress-related hormones during conflict. We also consider how similar issues can be studied using extensions of latent curve models to the multivariate case, and we show how such models are related to multivariate multilevel models.     

基于不对称区间估计的有调节的中介模型检验

有调节的中介模型是中介过程受到调节变量影响的模型。评介了基于Bootstrap不对称置信区间和贝叶斯不对称可靠区间进行有调节的中介模型检验的3种方法, 包括亚组分析法、差异分析法和系数乘积法。模拟研究发现, 偏差校正的百分位Bootstrap置信区间和无先验信息的贝叶斯可靠区间在有调节的中介模型检验中表现相当, 都优于百分位Bootstrap置信区间的表现。建议使用系数乘积法进行第一阶段或第二阶段被调节的中介模型检验, 使用差异分析法进行两阶段被调节的中介模型检验, 并用一个实际例子演示如何用不对称区间估计检验有调节的中介模型。随后评述了3种有调节的中介模型检验方法在国内心理学的应用现状, 并展望了检验的拓展方向。     

Perceptions by and of lonely people in social networks

Although past research has associated loneliness with negative perceptions of others, P. Christensen and D. A. Kashy (1998), using the social relations model (SRM), found that among new acquaintances, loneliness was associated with more positive other‐perceptions. SRM was used to examine loneliness and interpersonal perception within existing social networks. Seventy‐three university students in the United States rated themselves and others on 19 attributes and also judged how they thought others saw them (meta‐perceptions). Results indicated that lonely people tended to be more negative toward closer targets and somewhat positive toward less close acquaintances. Lonely people had lower self‐regard and perceived self‐regard, invariant across acquaintanceship, and were rated more negatively by others. Relationship contexts thus moderate effects of loneliness on other‐perceptions.     

Social dynamics in the preschool

In this paper, we consider how concepts from dynamic systems (such as attractors, repellors, and self-organization) can be applied to the study of young children’s peer relationships. We also consider how these concepts can be used to explore basic issues involving early peer processes. We use the dynamical systems approach called state space grid (SSG) analysis and consider how it can be expanded beyond the study of dyads to the study of larger social groups and networks. In particular, we explore the role of homophily—that is, behavioral and sex similarity—as factors in the self-organization of young children’s social groups. A dynamic systems approach allows for consideration of peer processes difficult to assess using more traditional approaches.     

Modeling the personality of dyads and groups

The paper presents a methodological approach for assessing the personality of a dyad or a group, a concept that is not equivalent to the sum, or mean, of the individual scores. We illustrate how the logic of the multitrait multimethod approach, which is a familiar technique for establishing construct validity, can be extended to assess the construct of a relationship "personality." The model, which we call the latent group model, provides a decomposition and comparison of individual-level and group-level variance in a given trait, and the individual-level and group-level covariance or correlation between two traits. The model is also extended to the assessment of stability of the individual and group level traits. Throughout the paper, we draw connections between related methods and show how the latent group model can be estimated through hierarchical linear modeling.     

PLS model building: A multivariate approach to personality test data

The aim of this study was to demonstrate how personality test data can be plotted with a multivariate method known as Partial Least Squares of Latent Structures (PLS). The basic methodology behind PLS modeling is presented and the example demonstrates how a PLS model of personality test data can be used for diagnostic prediction. Principles for validating the models are also presented. The conclusion is that PLS modeling appears to be a powerful method for extracting clinically relevant information from complex personality test data matrixes. It could be used as a complement to more hard modeling methods in the process of examining a new area of interest.     

The Contribution of Neuroimaging to the Study of Language and Aphasia

New structural and functional imaging methods continue to be developed at a rapid pace. In the last 25 years, advanced imaging techniques have provided insights into how language is represented and processed in the brain and how it can be disrupted by damage to, or dysfunction of, various parts of the brain. Imaging studies have also yielded new information regarding how individuals recover language after stroke. We briefly review the strengths and weaknesses of the various radiological methods currently used to study language and aphasia.     

A Model of Knower-Level Behavior in Number Concept Development

Abstract We develop and evaluate a model of behavior on the Give- N task, a commonly used measure of young children's number knowledge. Our model uses the knower-level theory of how children represent numbers. To produce behavior on the Give- N task, the model assumes that children start out with a base rate that makes some answers more likely a priori than others but is updated on each experimental trial in a way that depends on the interaction between the experimenter's request and the child's knower level. We formalize this process as a generative graphical model, so that the parameters—including the base rate distribution and each child's knower level—can be inferred from data using Bayesian methods. Using this approach, we evaluate the model on previously published data from 82 children spanning the whole developmental range. The model provides an excellent fit to these data, and the inferences about the base rate and knower levels are interpretable and insightful. We discuss how our modeling approach can be extended to other developmental tasks and can be used to help evaluate alternative theories of number representation against the knower-level theory.     

Big toy models

We pursue a model-oriented rather than axiomatic approach to the foundations of Quantum Mechanics, with the idea that new models can often suggest new axioms. This approach has often been fruitful in Logic and Theoretical Computer Science. Rather than seeking to construct a simplified toy model, we aim for a ??big toy model??, in which both quantum and classical systems can be faithfully represented??as well as, possibly, more exotic kinds of systems. To this end, we show how Chu spaces can be used to represent physical systems of various kinds. In particular, we show how quantum systems can be represented as Chu spaces over the unit interval in such a way that the Chu morphisms correspond exactly to the physically meaningful symmetries of the systems??the unitaries and antiunitaries. In this way we obtain a full and faithful functor from the groupoid of Hilbert spaces and their symmetries to Chu spaces. We also consider whether it is possible to use a finite value set rather than the unit interval; we show that three values suffice, while the two standard possibilistic reductions to two values both fail to preserve fullness.     

Expanding the Political Psychology Toolkit: The Potential of Discursive Psychology for Understanding Contentious Political Debate at a Grassroots Level

In its location at the intersection of political science and psychology, political psychology draws on many of the research techniques of both disciplines in its exploration of power, voting behavior, leadership, attitudes, and values. One hitherto relatively underutilized approach for understanding public policy debate is discursive psychology (DP). Applying this perspective to a contentious policy issue in Australia, we seek to demonstrate that this approach can add richness and depth to our understanding of how ordinary citizens engage in public policy debates. We suggest that this type of analysis can augment insights obtained from more traditional methods—such as focus groups, experimental approaches, and opinion polling—by analyzing how debates are constructed and presented at the grassroots level. This research is innovative in two ways. First, it applies a rigorous, empirical research approach to an area in which it has not previously been used: the study of public policy issues. Second, rather than analyzing the communicative practices of political leaders, we consider the rhetorical arguments made by ordinary citizens in their engagement with political issues and how they negotiate what counts as evidence. This can provide insights into how public debate can be conducted more productively and respectfully.     

Don't fear the social relations model: reply to Piper, Ogrodniczuk, Lamarche, and Joyce

By studying actual therapy groups and using a sophisticated design to determine that clients who are held in higher esteem by their fellow group members show greater improvement over the course of therapy, Piper, Ogrodniczuk, Lamarche, and Joyce (this issue) have made a significant contribution to the group psychotherapy literature. Unfortunately, Piper et al. focused much of their discussion on the difficulties they faced using the social relations model (SRM) and the statistical program SOREMO. Although the SRM is not simple and it takes practice to run SOREMO, the analytic alternative proposed by Piper et al. has a number of serious limitations that prevents it from being a viable alternative. There is much to be gained from learning the SRM; for those group therapists and researchers who do not want to learn the SRM, they can always consult or collaborate with SRM researchers.     

Validating the Relationship Qualities of Influence and Persuasion With the Family Social Relations Model

Influence and persuasion are related, fundamental constructs in interpersonal communication. Both display the relationship qualities of interdependence, bidirectionality, reciprocity, and multiple levels of analysis. Yet, empirical validation of these relationship qualities is lacking, largely due to an absence of appropriate methods and statistical procedures. This article uses the family social relations model (SRM) to test for the personal relationship qualities of influence and persuasion in the family decision‐making context of buying a new car. New relationship measures of influence and persuasion were developed because, historically, measures have been at the individual level. The sample size of 110 families proved sufficient for stable parameter estimates. The results uncovered patterns in the relationship qualities of influence and persuasion across 3 decisions families make when buying a new car (i.e., how much to spend, car model choice, final decision). The findings confirm that both influence and persuasion are truly relational. The novel use of the model across decisions allowed the patterns of relationships among family members to be compared, and demonstrated the importance of the relationship qualities of influence and persuasion in decision making. Predictions were examined across decisions as well so as to check the consistency of hypotheses. The results provide further insight into the meaning of influence and persuasion, and of SRM terms.     

On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.     

Using Mixed Methods Approach in Cross-Cultural Studies: Lessons of a Research Experience

The primary purpose of this paper is to share the lessons the first author learnt as a novice researcher while carrying out a comparative culture study focused on exploring differences in organizational culture in units located in two societies in two distinct culture zones, using mixed methods approach, as a part of his doctoral research program. The authors have shared “why” and “how” the first author chose and applied mixed methods approach to conduct the above study, which might be of help to budding researchers, given that mixed methods approach is rarely used by doctoral students in India. The authors have presented three kinds of lessons in this paper, learnt by the first author. The first kind of lessons deals with the methodology, the second one is about the research approach and the last one is related to the qualities of an effective researcher. The authors have also presented “do’s and don’ts” in researching for the benefit of budding researchers. The insights shared in this paper, the authors hope, would be useful and relevant for the researchers employing mixed methods approach. Besides, it might also definitely help budding research scholars make an informed choice of research method to effectively deal with research problems under investigation.