Estimating Actor,Partner, and Interaction Effects for Dyadic Data Using PROC MIXED and HLM: A User–Friendly Guide

Data collected from both members of a dyad provide abundant opportunities as well as data analytic challenges. The Actor–Partner Interdependence Model (APIM; Kashy & Kenny, 2000) was developed as a conceptual framework for collecting and analyzing dyadic data, primarily by stressing the importance of considering the interdependence that exists between dyad members. The goal of this paper is to detail how the APIM can be implemented in dyadic research, and how its effects can be estimated using hierarchical linear modeling, including PROC MIXED in SAS and HLM (version 5.04; Raudenbush, Bryk, Cheong, & Congdon, 2001). The paper describes the APIM and illustrates how the data set must be structured to use the data analytic methods proposed. It also presents the syntax needed to estimate the model, indicates how several types of interactions can be tested, and describes how the output can be interpreted.     

Dyadic longitudinal models: A critical review

In this review, we discuss the most commonly used models to analyze dyadic longitudinal data. We start the review with a definition of dyadic longitudinal data that allows relationship researchers to identify when these models might be appropriate. Then, we go on to describe the three major models commonly used when one has dyadic longitudinal data: the dyadic growth curve model (DGCM), the actor–partner interdependence model (APIM), and the common fate growth model (CFGM). We discuss when each model might be used and strengths and weaknesses of each model. We end with additional thoughts that focus on extensions to new methods being discussed in the literature, along with some of the challenges of collecting and analyzing dyadic longitudinal data that might be helpful for future dyadic researchers.     

It takes two to tango: Using the actor-partner interdependence model for studying the coach-athlete relationship

Research has repeatedly shown the importance of the relationship between a coach and an athlete. Despite these findings, we argue that the approaches used in previous research fail to sufficiently consider the dyadic and bidirectional nature of the coach-athlete relationship. Although the Actor-Partner Interdependence Model (APIM) was already proposed as a useful methodology for investigating and understanding the coach-athlete relationship, its potential has been underexploited in sport psychology. We discuss the possibly misleading conclusions that can be drawn from studies with an individual rather than a dyadic perspective and we propose to closely examine the dyadic pattern that can be inferred from the APIM, rather than making a priori assumptions about it. Practical implementations of the APIM in user-friendly apps are envisaged, as are relevant extensions of the APIM that allow to answer more advanced research questions by using more complex designs.     

Reflections on the actor–partner interdependence model

The actor–partner interdependence model (APIM) has been widely successful and has become the default method for analyses of dyadic data in which both members of the pair have the same measurements. This article reviews the history of the development of the APIM, focusing on the original presentation of the model, a 1979 article by Kraemer and Jacklin. Also discussed are the topics of APIM patterns, whether the APIM is a dyadic model, and the automation of APIM analyses. Although the APIM is the most popular method for dyadic analysis, it should be recognized there are alternatives that should also be considered.     

Attachment insecurities and interpersonal processes in Spanish couples: A dyadic approach

Several attachment‐related phenomena in Spanish couples using dyadic‐level analyses were examined. A sample of 295 heterosexual couples completed measures of attachment‐related anxiety and avoidance, self‐esteem, social self‐efficacy, and relationship satisfaction. Results, analyzed from a dyadic perspective using the actor–partner interdependence model (APIM), indicate that (a) there are actor but no partner effects of attachment insecurities on intrapersonal variables such as self‐esteem and social self‐efficacy, (b) there are actor and partner effects of avoidant attachment on relationship satisfaction, and (c) actor anxiety is associated with partner avoidance, and actor avoidance is associated with partner anxiety. Overall, the results reveal the importance of a dyadic perspective on couple members' attachment insecurities and their associations with intrapersonal and interpersonal processes and relationship adjustment. They also show that attachment variables and correlates studied mainly in English‐speaking countries are useful in understanding Spanish couple dynamics.     

Capturing non-linear temporally embedded processes in organizations using recurrence quantification analysis

ABSTRACT

Despite the growing consensus that the majority of psychological phenomena at work are temporally embedded and highly dynamic, existing research is often based on simplified theoretical and methodological models, which take limited account of process dynamics and especially non-linear growth trajectories. In this paper, we highlight the potentials of using recurrence quantifications analysis (RQA) and an extension of RQA – cross-recurrence quantification analysis (CRQA) – for researching process dynamics in organizations. (C)RQA is a powerful technique that can be used to both visualize and quantify time-series data such as repeated measurements of psychological states or sequentially coded dyadic and team interactions. To illustrate the manifold opportunities of (C)RQA, we present three application examples focusing on individuals as systems, dyads as systems, and teams as systems. Specifically, we highlight how (C)RQA can be applied to individual diary data, to leader-follower communication dynamics observed during annual appraisal interviews, and to high-density coded team interactions observed during organizational meetings. We discuss the strengths and limitations of (C)RQA and provide recommendations for researchers interested in using the method.     

The common fate model for dyadic data: variations of a theoretically important but underutilized model

Studying dyads, very often there is a theoretical construct that has an effect on both members, such as relationship harmony or shared environment. To model such influences, the common fate model (CFM) is often the most appropriate approach. In this article, we address conceptual and statistical issues in the use of the standard CFM and present a series of variations, all of which are estimated by structural equation modeling (SEM). For indistinguishable dyad members (e.g., gay couples), we describe the use of a multilevel SEM method. Throughout the paper, we draw connections to the actor-partner interdependence model (APIM). We also discuss the analysis of hybrid models that combines both the CFM and the APIM. The models are illustrated using data from heterosexual couples.     

主客体互倚性的成对模式及其检验

主客体互倚性模型（APIM）以成对数据作为分析单元,是成对数据分析的重要方法。基于APIM中的主体效应与客体效应,Kenny等人论述了APIM的四种成对模式：主体模式、客体模式、对偶模式与对比模式。通过定义客体效应与主体效应的比值为k,Kenny等人提出了定量指标,以分析主客体互倚性的成对模式。成对模式分析有助于简化数据结果、检验理论模型、解析群体互动过程。虽然k值的检验程序较为复杂,但APIM成对模式及k值的提出,对于成对数据分析,特别是家庭研究与组织心理学研究具有重要意义。     

Giving dyadic data analysis away: A user‐friendly app for actor–partner interdependence models

The actor–partner interdependence model (APIM) is widely used for analyzing dyadic data. Although dyadic research has become immensely popular, its statistical complexity can be a barrier. To remedy this, a free user‐friendly web application, called APIM_SEM, has been developed. This app automatically performs the statistical analyses (i.e., structural equation modeling) of both simple and complex APIMs. It allows the researcher to analyze distinguishable or indistinguishable dyads, to examine dyadic patterns, to estimate actor and partner effects of one or two predictors, and to control for covariates. Results are provided in software and text format, complemented by summary tables and figures. As an illustration, the effect of perception of the partner on satisfaction is assessed by fitting APIMs with varying complexity.     

Analysis of group composition in multimember multigroup data

Data from groups often have a multimember multigroup (MMMG) structure. Examples are two‐parent families with a female or male child (three members, two groups), two same‐gender and opposite‐gender peers of different status (two members, four groups), or gay, lesbian, and heterosexual couples (two members, three groups). To analyze such data, a framework called MMMG actor–partner interdependence model (MMMG APIM) is presented considering group composition. Three models are discussed in detail: the three‐member two‐group APIM, the two‐member four‐group APIM, and the two‐member three‐group APIM. Structural equation modeling and cross‐sectional and longitudinal data are used to illustrate the approach. To ease the interpretation of APIM findings, a proposal of a general classification scheme is made.     

Integrating analogical mapping and general problem solving: the path-mapping theory

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects' incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.     

Actor‐Partner Interdependence Models (APIM) and Voting Behavior: Methodology and Applications

Recently, the social sciences have witnessed a rising interest in dyadic design, as an efficient way to disentangle mechanisms of interpersonal influence. Despite the relevance of this design to political research, few efforts have been made to collect and efficiently analyze dyadic data. In this article, we suggest the Actor‐Partner Interdependence Model as a useful tool to test bidirectional effects in dyadic data on political attitudes and behaviors. The model explicitly assumes that members of a dyad (reciprocally identified as actor and partner) involved in political communication are interdependent and influence each other. We apply the model to estimate the effect of partner's party identification on actor's vote choice, using 1996 Indianapolis–St. Louis dyadic data. Results show that partner's party identification is significantly associated with vote choice. Moreover, we show that influence between dyad members is moderated by their intimacy and that an increased difference in socioeconomic status between dyad members tips the balance of the effect in favor of the individual with more resources. Our conclusions point to the effectiveness of APIM in modeling interdependent asymmetric relations and call for increasing efforts in collecting dyadic data and in developing proper tools for their analysis.     

Mind Perception Is the Essence of Morality

Mind perception entails ascribing mental capacities to other entities, whereas moral judgment entails labeling entities as good or bad or actions as right or wrong. We suggest that mind perception is the essence of moral judgment. In particular, we suggest that moral judgment is rooted in a cognitive template of two perceived minds-a moral dyad of an intentional agent and a suffering moral patient. Diverse lines of research support dyadic morality. First, perceptions of mind are linked to moral judgments: dimensions of mind perception (agency and experience) map onto moral types (agents and patients), and deficits of mind perception correspond to difficulties with moral judgment. Second, not only are moral judgments sensitive to perceived agency and experience, but all moral transgressions are fundamentally understood as agency plus experienced suffering-that is, interpersonal harm-even ostensibly harmless acts such as purity violations. Third, dyadic morality uniquely accounts for the phenomena of dyadic completion (seeing agents in response to patients, and vice versa), and moral typecasting (characterizing others as either moral agents or moral patients). Discussion also explores how mind perception can unify morality across explanatory levels, how a dyadic template of morality may be developmentally acquired, and future directions.     

Probability Models for Duration: The Data Don′t Tell the Whole Story

Probability models for duration have been applied to a wide range of individual-level and organizational phenomena. Interestingly, seemingly similar models may produce different results. Using divorce as an illustration, we discuss a hierarchy of duration models of different complexity and show how an analysis of the results across models helps explain why different studies may have come to different conclusions. This analysis also leads to substantive insights that even the most complete model by itself does not provide.     

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.     

Individual and Dyadic Coping and Fear of Progression in Mothers and Fathers of Children with Hematologic Cancer

This study investigated the association of individual and dyadic coping strategies with fear of progression (FoP) in mothers and fathers of children with hematologic cancer. Parental couples (N = 44) whose children had been diagnosed with hematologic cancer were recruited at a university hospital and a rehabilitation clinic in Germany between 03/2017 and 08/2017. Data included parents' self-report on FoP (Fear of Progression Questionnaire—parent version, FoP-Q-SF/PR), individual coping (Coping Health Inventory for Parents, CHIP-D), and dyadic coping (Dyadic Coping Inventory, DCI). Statistical analyses were carried out for mothers and fathers individually as well as for parental couples using dyadic data analyses (e.g., actor-partner interdependence model, APIM). Individual and dyadic coping strategies were significantly correlated with FoP in mothers, but not in fathers. Fathers' evaluation of the couple's dyadic coping significantly predicted mothers' FoP. The more frequent use of familial integration (CHIP-D FAM) and maintaining social support (CHIP-D SUP) as well as a better evaluation of their partners' dyadic coping was significantly associated with lower FoP in mothers. Differences in individual and dyadic coping in parental couples were not associated with FoP. Individual and dyadic coping strategies should be addressed in the psychosocial care of mothers and fathers of children with hematologic cancer. Study results support the benefits of involving fathers in psychosocial interventions, for example, in couple-based interventions that acknowledge interpersonal effects of coping on FoP. Future research should further explore coping strategies applied by fathers of children with hematologic cancer for the regulation of FoP.     

成对数据分析之行动者-对象互依性模型(APIM)

成对关系是人际关系和人际互动的最基本的单元, 对成对关系的测量得到的成对数据具有人际关系的互依性特点, 这种互依性往往体现在对象效应上。考虑到数据的互依性以及同时估计行动者效应和对象效应的行动者-对象互依性模型(APIM)是一种相对简单但很有吸引力的指导分析的模型。成对数据中根据数据的变异源分为三种变量; 根据是否存在可区分开成对关系中的双方的变量而分为可区分和不可区分的数据; 成对数据的分析往往开始于互依性程度的检验; 在独立性不能得到满足的情况下, 考虑是否可区分, 则可以采用联合回归、多水平模型, 结构方程建模的方法, 对APIM中的各个效应进行估计。其中, 尤为详细地介绍了使用LISREL软件进行结构方程建模分析的方法。     

The actor–partner interdependence model for categorical dyadic data: A user‐friendly guide to GEE

The actor–partner interdependence model (APIM) has been widely used for the analysis of pairs of individuals who interact with each other. The goal of this article is to detail in a nontechnical way how the APIM for binary or count outcomes can be implemented and how actor and partner effects can be estimated using generalized estimating equations (GEE) methodology. Both SPSS‐ and SAS‐syntax needed to estimate the model and the interpretation of the output are illustrated using data from a study exploring the effect of satisfaction with the relationship before the breakup on unwanted pursuit behavior after the breakup in formerly married partners. The use of this GEE method will allow researchers to test a wide array of research hypotheses.     

Mind Perception Is the Essence of Morality

Mind perception entails ascribing mental capacities to other entities, whereas moral judgment entails labeling entities as good or bad or actions as right or wrong. We suggest that mind perception is the essence of moral judgment. In particular, we suggest that moral judgment is rooted in a cognitive template of two perceived minds—a moral dyad of an intentional agent and a suffering moral patient. Diverse lines of research support dyadic morality. First, perceptions of mind are linked to moral judgments: dimensions of mind perception (agency and experience) map onto moral types (agents and patients), and deficits of mind perception correspond to difficulties with moral judgment. Second, not only are moral judgments sensitive to perceived agency and experience, but all moral transgressions are fundamentally understood as agency plus experienced suffering—that is, interpersonal harm—even ostensibly harmless acts such as purity violations. Third, dyadic morality uniquely accounts for the phenomena of dyadic completion (seeing agents in response to patients, and vice versa), and moral typecasting (characterizing others as either moral agents or moral patients). Discussion also explores how mind perception can unify morality across explanatory levels, how a dyadic template of morality may be developmentally acquired, and future directions.     

动态计算模型在组织行为学研究中的应用

动态计算模型是用数学语言进行理论描述的规范表达, 能够有效地解释理论与实证结果的冲突, 整合和发展理论, 非常适合研究复杂的、非线性的、非递归的组织行为问题, 但目前在组织行为学研究中的应用有限。本研究对基于主体的建模、系统动力学模型和元胞自动机模型等主要动态计算模型进行综述, 对已有引入动态计算模型的组织行为学研究主题、研究问题的类型及动态计算模型的实施等内容进行复杂网络分析及内容综述, 并指出动态计算模型能够帮助研究者开拓新的研究领域、改变组织行为研究的模式、继承和发展已有研究成果, 形成动态计算模型在组织行为学研究领域的应用手册。