Extending the actor–partner interdependence model for binary outcomes: A multilevel logistic approach

Relationship researchers face many challenges when samples consist of dyads. The actor–partner interdependence model provides a framework for the analysis of dyadic data (D. A. Kashy & D. A. Kenny, 2000). Binary variables like discrete health diagnoses present additional challenges that are not easily handled using existing models. The analysis was demonstrated using SAS PROC GLIMMIX and HLM6. An example is presented where couple's personality traits are used to predict discrete health outcomes. A series of Monte Carlo simulations were performed to compare PROC GLIMMIX to HLM. GLIMMIX performed acceptably compared to HLM for small dyad‐level sample sizes (100 or fewer), but HLM clearly outperformed in larger samples. Both had problems estimating the variance of the random intercept and its standard error.     

On Virtual Transparency

Virtual worlds are notable for their realism, both for the sense of psychological immersion they provide and the apparent potential for users to perform activities and interact with objects and people “within” such worlds. This article asks whether the concept of “photographic transparency,” introduced by Kendall Walton to account for the inherent realism of photography, can be extended to account for the realism of virtual media. Specifically, I investigate whether virtual media allow for a kind of prosthetic seeing that enables users to see the real world in potentially expanded ways. The complications for the thesis of “virtual transparency” arise from the analysis of the objects and egos involved in virtual seeing. These complications show that virtual reality as a medium is not transparent, even though token uses may be.     

GETTING CREDIT FOR PROACTIVE BEHAVIOR:SUPERVISOR REACTIONS DEPEND ON WHAT YOU VALUE AND HOW YOU FEEL

Although proactive behavior is important in organizations, it is not always appreciated by supervisors. To explain when supervisors reward proactivity with higher overall performance evaluations, we draw on attribution theory. We propose that employees' values and affect send signals about their underlying intentions, which influence supervisors' attributions about whether employees deserve credit for proactive behaviors. More specifically, we hypothesize that if employees express strong prosocial values or low negative affect, the proactive behaviors of voice, issue-selling, taking charge, and anticipatory helping will have stronger relationships with supervisors' performance evaluations. We test these hypotheses with samples of 103 managers and their direct supervisors (Study 1) and 55 firefighters and their platoon supervisors (Study 2). The hypotheses were supported in both studies, suggesting that proactive behaviors are more likely to contribute to higher supervisor performance evaluations when employees express strong prosocial values or low negative affect.