Tag: Nilam Ram

Bayesian Response Surface Analysis

Foundations of Response Surface Analysis for Congruence Hypothesis

Dynamic Dyadic Systems

Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study

This exploratory empirical demonstration aims to answer whether mental health effects are associated with media use experiences by...

Variational Inference as Tractable Approximation for Bayesian Models of Psychological Change Processes

Large-scale Bayesian modeling of nonlinear psychological change processes can be intractable and unsustainable. Variational inference facilitates Bayesian analysis through estimation of approximate posterior distributions...

Personality Differences Drive Conversational Dynamics: A High-Dimensional NLP Approach

This paper investigates how the topical flow of dyadic conversations emerges over time and how differences in interlocutors’ personality traits contribute to this topical flow.

Using State Space Grids to Quantify and Examine Dynamics of Dyadic Conversation

This paper illustrates how to implement state space grid analysis for analyzing the back-and-forth multi-turn dynamics that manifest in dyadic conversations. In doing so, we contribute to a dynamic dyadic systems perspective that seeks to advance theories about individual and relational antecedents and outcomes of interpersonal communication through the articulation and study of dyadic interaction dynamics.

Affective Sensitivity to Air Pollution (ASAP): Person-specific associations between daily air pollution and affective states

Individuals’ sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)...

Stanford Psychology Podcast: Learning from The Human Screenome Project

Anjie chats with Dr. Nilam Ram. Nilam is a Professor of Communications & Psychology

Modeling and managing behavior change in groups: A Boolean network method

Social influence processes can induce desired or undesired behavior change in individual members of a group. Empirical modeling of group processes and the design of network-based interventions meant to promote desired behavior change is somewhat limited...