Using Sequence Analysis to Identify Conversational Motifs in Supportive Interactions

This study demonstrates how sequence analysis, which is a method for identifying common patterns in categorical time series data, illuminates the nonlinear dynamics of dyadic conversations by describing chains of behavior that shift categorically, rather than incrementally. When applied to interpersonal interactions, sequence analysis supports the identification of conversational motifs, which can be used to test hypotheses linking patterns of interaction to conversational antecedents or outcomes. As an illustrative example, this study evaluated 285 conversations involving stranger, friend, and dating dyads in which one partner, the discloser, communicated about a source of stress to a partner in the role of listener. Using sequence analysis, we identified three five-turn supportive conversational motifs that had also emerged in a previous study of stranger dyads: discloser problem description, discloser problem processing, and listener-focused dialogue. We also observed a new, fourth motif: listener-focused, discloser questioning. Tests of hypotheses linking the prevalence and timing of particular motifs to the problem discloser’s emotional improvement and perceptions of support quality, as moderated by the discloser’s pre-interaction stress, offered a partial replication of previous findings. The discussion highlights the value of using sequence analysis to illuminate dynamic patterns in dyadic interactions.

Analysis of variability of functionals of recombinant protein production trajectories based on limited data

We propose a method for analyzing the variability in smooth, possibly nonlinear, functionals associated with a set of product production trajectories measured under different experimental conditions. The key challenge is to make meaningful inference on these parameters across different experimental conditions when only a limited number of measurements in time are collected for each treatment, and when there are only a few, or no, replicates available. For this purpose we adopt a modeling approach by representing the production trajectories in a B-spline basis, and develop a bootstrap-based inference procedure for the parameters of interest, also accounting for multiple comparisons. The methodology is applied to study two types of quantities of interest - "time to harvest" and "maximal productivity" in the context of an experiment on the production of certain recombinant proteins under laboratory conditions. We complement the findings by extensive numerical experiments that look into the effects of different types of bootstrap procedures and associated schemes for computing p-values for tests of hypotheses.

Test of Unit Root for Bounded AR (2) Model

In this paper, the test of unit root for bounded AR (2) model with constant term and dependent errors has been derived. Asymptotic distributions of OLS estimators and t-type statistics under different tests of hypotheses have been derived. A simulation study has been established to compare between different tests of the unit root. Mean squared error (MSE) and Thiel's inequality coefficient (Thiel’s U) have been considered as criteria of comparison.

Introduction to the Special Issue on Marketing Science and Field Experiments

This editorial introduces the special issue on marketing science and field experiments. We compare the characteristics of the papers that were submitted and accepted for the special issue and provide several recommendations for researchers. In general, we find field experiment research is greater in the areas of advertising and pricing with digital being the most common channel. We suggest that, beyond the estimation of effects and tests of hypotheses, field experiments can complement structural models; help train targeting policies; and also contribute to the nascent area of real-time, adaptive experimentation. We also discuss how field experiment research with a marketing science orientation can enhance and contribute in the areas of behavioral research and marketing strategy.