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Group Dynamics: Theory, Research, and Practice publishes original empirical articles, theoretical analyses, literature reviews, and brief reports dealing with basic and applied topics in the field of group research and application. We construe the phrase

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Copyright: Copyright 2016 American Psychological Association

Statistical methods in group psychology and group psychotherapy: Introduction to the special issue.


Data analyses of people nested in groups has evolved over the past 2 decades. Group members interact with each other, they share common experiences within their group that may be different across groups, and each group may be affected by different compositions and histories. These factors make groups and group research exciting, but they also complicate the analyses of grouped data. This special issue of Group Dynamics: Theory, Research, and Practice gathers 8 articles that are structured as tutorials for conducting statistical analyses that are appropriate to capture the unique and emergent properties of groups. The articles are geared toward new researchers, students, and interested readers who want to learn to conduct or evaluate a study using these statistical methods. Statistical methods reviewed in this special issue include the latent group model, multilevel methods to assess between and within-leader variance, multilevel confirmatory factor analysis, the relational event model, the social relations model, sequential analysis, recurrence analysis, and statistical discourse analysis. Each article presents the main concepts, a running example, instructions on how to run the analyses and interpret outputs, suggestions on when to use the technique, and common problems that may be encountered when using these methods. Most of the articles provide equations that are concretely explained, computer syntax, example data, annotated bibliographies, and website links. The articles in this special section represent a sampling of the state of the art in statistical methodology for group psychology and group psychotherapy research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Estimation and application of the latent group model.


Group scholars often describe or conceptualize groups as having characteristics similar to those of individuals. For example, groups can be thought of as motivated or satisfied in degree. Describing groups in such terms assumes that members possess similar levels of the characteristic in question and that latent group-level processes influence convergence on that characteristic. The latent group model (LGM) offers conceptual and statistical means for assessing the structure of latent, group-level factors based on the convergence of variables measured at the individual level. This paper outlines the conceptual issues related to the LGM and offers a detailed example using interaction data from 4-person groups for estimating the model in SAS—SPSS, Mplus, and R syntax are provided in the appendixes. Applications and extensions of the LGM are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Examining between-leader and within-leader processes in group therapy.


In the present article we illustrate a method for simultaneously examining between-leader (between-person) and within-leader (within-person) relationships in group therapy research. To do so, we introduce Hoffman and Stawski’s (2009); Curran and Bauer’s (2011), and Wang and Maxwell’s (2015) method for partitioning longitudinal data into between-person and within-person variance components, review the existing psychotherapy research that has used this method, and provide an example of the application of this method to group therapy data. Specifically, using an archival data set, we model between-leader and within-leader dominance and empathy ratings as predictors of group engagement. Additionally, we model time and how time interacts with the within- and between-leader effects, to examine the relationship between within-leader and between-leader empathy and dominance and group climate over time. We conclude by discussing the strengths and limitations of variance partitioning as well as future application in group therapy research. It is our hope that the present article familiarizes group psychotherapy researchers with an effective method for examining multilevel and complex group phenomenon. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Evaluating factor structures of measures in group research: Looking between and within.


Data from psychotherapy groups are nested by nature. Ignoring this nesting when performing statistical tests can result in inflated Type I error rates and spurious significant results. This presents a serious problem for interpreting research that does not account for members nested within a specific group. Factor analytic methods are not immune to the negative effects of nesting, and when it is ignored, a poorly fitting factor structure can be hidden if one does not examine model fit at both the between or within-group level. Multilevel confirmatory factor analysis allows the researcher to account for nesting by separately examining both the between- and within-group structures of measures used in group research. This article presents an overview of methods for evaluating the level of group dependency using the intraclass correlation coefficient (ICC) and a comparison of 2 methods for calculating ICCs. It then provides an overview and an example of multilevel factor analysis as a method for testing the model fit at the between and within levels separately by using partially saturated models. The authors end by reviewing common problems and offering guidelines for interpreting differences in within- and between-level fit in group research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

An illustration of the relational event model to analyze group interaction processes.


A fundamental assumption in the study of groups is that they are constituted by various interaction processes that are critical to survival, success, and failure. However, there are few methods available sophisticated enough to empirically analyze group interaction. To address this issue, we present an illustration of relational event modeling (REM). A relational event is a “discrete event generated by a social actor and directed toward 1 or more targets” (Butts, 2008, p. 159). Because REM provides a procedure to model relational event histories, it has the ability to figure out which patterns of group interaction are more or less common than others. For instance, do past patterns of interaction influence future interactions, (e.g., reciprocity), do individual attributes make it more likely that individuals will create interactions (e.g., homophily), and do specific contextual factors influence interaction patterns (e.g., complexity of a task)? The current paper provides an REM tutorial from a multiteam system experiment in which 2 teams navigated a terrain to coordinate their movement to arrive at a common destination point. We use REM to model the dominant patterns of interactions, which included the principle of inertia (i.e., past contacts tended to be future contacts) and trust (i.e., group members interacted with members they trusted more) in the current example. An online appendix that includes the example data set and source code is available as supplemental material in order to demonstrate the utility REM, which mainly lies in its ability to model rich, time-stamped trace data without severely simplifying it (e.g., aggregating interactions into a panel). (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Using the social relations model to understand dyadic perceptions within group therapy.


A central goal of group-based psychotherapy is for participants to gain insight into how they perceive others and how others perceive them. However, such interpersonal perceptions are challenging to study because any given perception could be a function of the perceiver (some people see everyone as friendly), the target (some people are seen as friendly by everyone), or both. The present article provides an introduction and brief tutorial for how the social relations model (SRM) can be applied to studying such interpersonal perceptions within psychotherapy groups. The SRM is a theoretical and statistical model for understanding the possible sources of dyadic perceptions and behaviors. Specifically, any interpersonal perception within a group can be partitioned into variance due to the person making the rating (perceiver effect), the target of the rating (target effect), the relationship between perceiver and target (relationship effect), and the group as a whole. Research on group psychotherapy is especially amenable to a SRM analysis because the interpersonal context allows multiple perceivers to rate multiple targets, which is a requirement of any SRM analysis. A fictitious study of wilderness therapy is used to highlight the conceptual, methodological, and statistical issues that are addressed with the SRM. Supplementary data and output files are provided to elucidate the analytic process using the WinSoReMo software. Although there are multiple ways that SRM studies and analyses can be conducted, the WinSoReMo program is specifically designed for round-robin data in which group members rate, and are rated by, other group members. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Group interactions and time: Using sequential analysis to study group dynamics in project meetings.


Video-recorded observations of group interactions present a unique challenge for group researchers. This article presents methodological advice on how to perform sequential analysis when collecting observational timed-event data of group discussions. Sequential analyses is a statistical method that examines dynamic behavioral sequences in group interactions. To exemplify the method, the authors present data from 1 industry project team that was video-taped during 24 consecutive meetings. Meeting behaviors were coded into different categories (e.g., procedural and action-oriented communication). They compared sequential behavioral patterns in meetings from the first and second half of the project and provided guidelines on the topic of interrater reliability and reported a detailed psychometric analysis of the observational instrument. Overall, the authors showed that positive procedural communication can inhibit dysfunctional communication patterns in group meetings. Their results also show that communication patterns of negative action-orientation only appeared in the second half of the project. This study extends previous group research on microsequential patterns with respect to larger scale macrotemporal group dynamics. Overall, they provided practical suggestions for researchers who aim to run observational research and aim to look for sequential dynamics in video-recorded team interactions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Using recurrence analysis to examine group dynamics.


This article provides an accessible introduction to recurrence analysis—an analytical approach that has great promise for helping researchers understand group dynamics. Recurrence analysis is a technique with roots in the systems dynamics literature that was developed to reveal the properties of complex, nonlinear systems. By tracking when a system visits similar states at multiple points in its life—and the form or pattern of these recurrences over time—recurrence analysis equips researchers with a set of new metrics for assessing the properties of group dynamics, such as recurrence rate (i.e., stability), determinism (i.e., predictability), and entropy (i.e., complexity). Recent work has shown the potential value of recurrence analysis across a number of different disciplines. To extend its use within the domain of group dynamics, the authors present a conceptual overview of the technique and give a step-by-step tutorial on how to use recurrence analysis to study groups. An exemplar application of recurrence analysis using dialogue-based data from 63 three-person student groups illustrates the use of recurrence analysis in examining how groups change their focus on different processes over time. This is followed by a discussion of variations of recurrence analysis and implications for research questions within the literature on groups. When group researchers track group processes or emergent states over time, and thus compile a time series dataset, recurrence analysis can be a useful technique for measuring the properties of groups as dynamic systems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)

Statistical discourse analysis: Modeling sequences of individual actions during group interactions across time.


Identifying triggers of target actions within individual or social processes requires modeling individual (and group) characteristics and sequences of actions. We explicate one such method, statistical discourse analysis (SDA). SDA can model (a) pivotal actions that radically change subsequent processes, (b) effects of previous actions (or their sequences) on target actions, and (c) influences at various levels (turn, time period, individual, group, organization, etc.). SDA addresses difficulties involving data (unit of analysis, coding, interrater reliability, missing data, parallel conversations, breakpoints, time periods, statistical power), dependent variables (discrete variables, infrequency bias, nested data, multiple dependent variables), and explanatory variables (variables at earlier turns, cross-level interactions, indirect multilevel mediation, serial correlation, false positives, odds ratios, robustness). To illustrate the benefits of SDA, we test how social metacognitive actions (e.g., agree, rudely disagree) affect the likelihood of correct, new ideas (microcreativity) and justifications using 3,296 turns of talk by 80 students in 20 groups working on an algebra problem. A rude disagreement often triggered another rude disagreement, which yielded less microcreativity. After a wrong idea or in groups that solved the problem however, a rude disagreement yielded greater microcreativity. After a student with a higher mathematics grade spoke, more justifications followed; this effect differed across time periods. We also discuss limitations of SDA, which include a linear combination of explanatory variables, independent and identically distributed residuals, and a minimum sample size (20 units at the highest level). (PsycINFO Database Record (c) 2016 APA, all rights reserved)(image)