Do conversations with virtual avatars increase feelings of social anxiety?

Virtual reality (VR) technology provides a way to conduct exposure therapy with patients with social anxiety. However, the primary limitation of current technology is that the operator is limited to pre-programed avatars that cannot be controlled to interact/converse with the patient in real time. The current study piloted new technology allowing the operator to directly control the avatar (including speaking) during VR conversations. Using an incomplete repeated measures (VR vs. in vivo conversation) design and random starting order with rotation counterbalancing, participants (N = 26) provided ratings of fear and presence during both VR and in vivo conversations. Results showed that VR conversation successfully elevated fear ratings relative to baseline (d = 2.29). Participants also rated their fear higher during VR conversation than during in vivo conversation (d = 0.85). However, in vivo conversation was rated as more realistic than VR conversation (d = 0.74). No participants dropped out and 100% completed both VR and in vivo conversations. Qualitative participant comments suggested that the VR conversations would be more realistic if they did not meet the actor/operator and if they were not in the same room as the participant. Overall, the data suggest that the novel technology allowing real time interaction/conversation in VR may prove useful for the treatment of social anxiety in future studies

Contemporary quantitative statistical methods for family psychology

This chapter discusses some contemporary statistical methods and their application in the field of family psychology. It focuses on longitudinal data analyses, because the author believes that family psychology benefits from longitudinal designs. The chapter outlines some key issues in operationalizing constructs - that is, measurement models, including latent factor modeling and measurement invariance. It turns to basic and advanced statistical methods for describing and explaining the associations between constructs. Basic statistical methods include moderation and mediation analyses, whereas advanced statistical methods include developmental cascade models, growth curve models, latent difference score models, and growth mixture models. The discussion of each method begins with a description of the statistical techniques, followed by a relevant study in the field of family psychology, used as an illustration of that particular method. The chapter concludes with a general discussion of statistical methods and future directions for their use in the field of family psychology

Contemporary quantitative statistical methods for family psychology

This chapter discusses some contemporary statistical methods and their application in the field of family psychology. It focuses on longitudinal data analyses, because the author believes that family psychology benefits from longitudinal designs. The chapter outlines some key issues in operationalizing constructs - that is, measurement models, including latent factor modeling and measurement invariance. It turns to basic and advanced statistical methods for describing and explaining the associations between constructs. Basic statistical methods include moderation and mediation analyses, whereas advanced statistical methods include developmental cascade models, growth curve models, latent difference score models, and growth mixture models. The discussion of each method begins with a description of the statistical techniques, followed by a relevant study in the field of family psychology, used as an illustration of that particular method. The chapter concludes with a general discussion of statistical methods and future directions for their use in the field of family psychology