Statistical Discourse Analysis: Modeling Sequences of Individual Actions During Group Interactions Across Time

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Abstract

Identifying triggers of target actions within individual or social processes requires modeling individual (and group) characteristics and sequences of actions. We explicate one such method, statistical discourse analysis (SDA). SDA can model (a) pivotal actions that radically change subsequent processes, (b) effects of previous actions (or their sequences) on target actions, and (c) influences at various levels (turn, time period, individual, group, organization, etc.). SDA addresses difficulties involving data (unit of analysis, coding, interrater reliability, missing data, parallel conversations, breakpoints, time periods, statistical power), dependent variables (discrete variables, infrequency bias, nested data, multiple dependent variables), and explanatory variables (variables at earlier turns, cross-level interactions, indirect multilevel mediation, serial correlation, false positives, odds ratios, robustness). To illustrate the benefits of SDA, we test how social metacognitive actions (e.g., agree, rudely disagree) affect the likelihood of correct, new ideas (microcreativity) and justifications using 3,296 turns of talk by 80 students in 20 groups working on an algebra problem. A rude disagreement often triggered another rude disagreement, which yielded less microcreativity. After a wrong idea or in groups that solved the problem however, a rude disagreement yielded greater microcreativity. After a student with a higher mathematics grade spoke, more justifications followed; this effect differed across time periods. We also discuss limitations of SDA, which include a linear combination of explanatory variables, independent and identically distributed residuals, and a minimum sample size (20 units at the highest level).

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