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Contamination is a methodological phenomenon occurring in child maltreatment research when individuals in an established comparison condition have, in reality, been exposed to maltreatment during childhood. The current paper: (1) provides a conceptual and methodological introduction to contamination in child maltreatment research, (2) reviews the empirical literature demonstrating that the presence of contamination biases causal estimates in both prospective and retrospective cohort studies of child maltreatment effects, (3) outlines a dual measurement strategy for how child maltreatment researchers can address contamination, and (4) describes modern statistical methods for generating causal estimates in child maltreatment research after contamination is controlled. Our goal is to introduce the issue of contamination to researchers examining the effects of child maltreatment in an effort to improve the precision and replication of causal estimates that ultimately inform scientific and clinical decision-making as well as public policy.more » « lessFree, publicly-accessible full text available December 26, 2024
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Solomon, Denise Haunani ; Brinberg, Miriam ; Bodie, Graham ; Jones, Susanne ; Ram, Nilam (Ed.)Conversations between people are where, among other things, stressors are amplified and attenuated, conflicts are entrenched and resolved, and goals are advanced and thwarted. What happens in dyads’ back-and-forth exchanges to produce such consequential and varied outcomes? Although numerous theories in communication and in social psychology address this question, empirical tests of these theories often operationalize conversational behavior using either discrete messages or overall features of the conversation. Dynamic systems theories and methods provide opportunities to examine the interdependency, self-stabilization, and self-organization processes that manifest in conversations over time. The dynamic dyadic systems perspective exemplified by the articles in this special issue (a) focuses inquiry on the turn-to-turn, asynchronous exchange of messages between two partners, (b) emphasizes behavioral patterns within and the structural and temporal organization of conversations, and (c) adapts techniques used in analysis of intensive longitudinal data to identify and operationalize those dynamic patterns. As an introduction to the special issue, this paper describes a dynamic dyadic systems perspective on conversation and discusses directions for future research, such as applications to humancomputer interaction, family communication patterns, health care interventions, and group deliberation.more » « less
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Abstract As the metaverse expands, understanding how people use virtual reality to learn and connect is increasingly important. We used the Transformed Social Interaction paradigm (Bailenson et al., 2004) to examine different avatar identities and environments over time. In Study 1 (n = 81), entitativity, presence, enjoyment, and realism increased over 8 weeks. Avatars that resembled participants increased synchrony, similarities in moment-to-moment nonverbal behaviors between participants. Moreover, self-avatars increased self-presence and realism, but decreased enjoyment, compared to uniform avatars. In Study 2 (n = 137), participants cycled through 192 unique virtual environments. As visible space increased, so did nonverbal synchrony, perceived restorativeness, entitativity, pleasure, arousal, self- and spatial presence, enjoyment, and realism. Outdoor environments increased perceived restorativeness and enjoyment more than indoor environments. Self-presence and realism increased over time in both studies. We discuss implications of avatar appearance and environmental context on social behavior in classroom contexts over time.
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Background When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual‐measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems.
Methods This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self‐report assessment at ages 12, 14, and 16 or by a one‐time retrospective self‐report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self‐reports, and when it was controlled for by retrospective self‐reports.
Results Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used.
Conclusions The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.
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Abstract This article articulates conceptual and methodological strategies for studying the dynamic structure of dyadic interaction revealed by the turn-to-turn exchange of messages between partners. Using dyadic time series data that capture partners’ back-and-forth contributions to conversations, dynamic dyadic systems analysis illuminates how individuals act and react to each other as they jointly construct conversations. Five layers of inquiry are offered, each of which yields theoretically relevant information: (a) identifying the individual moves and dyadic spaces that set the stage for dyadic interaction; (b) summarizing conversational units and sequences; (c) examining between-dyad differences in overall conversational structure; (d) describing the temporal evolution of conversational units and sequences; and (e) mapping within-dyad dynamics of conversations and between-dyad differences in those dynamics. Each layer of analysis is illustrated using examples from research on supportive conversations, and the application of dynamic dyadic systems analysis to a range of interpersonal communication phenomena is discussed.more » « less
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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.
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This study examines messages that problem disclosers and supportive listeners enact during conversations about everyday stressors. We coded 402 dyadic interactions between strangers, friends, and romantic couples using Stiles’s (1992) verbal response modes (VRM) and Burleson’s (1982) verbal person centeredness (PC) typology to explore whether (a) listener and discloser utterances coalesce into types of speaking turns, (b) listener turn types vary in person-centered quality, (c) listener turns relate to discloser responses, and (d) discloser responses relate to listener turns. Analyses revealed a typology for both listener and discloser turns: acknowledgment, advisement, question, elaboration, hedged disclosure, and reflection. The relative proportion of those types varied as a function of conversational role and relationship context, and these speech acts varied only minimally in PC. Configural frequency analyses revealed four greater-than-chance contingencies across data sets. The discussion highlights implications for a dyadic and dynamic understanding of supportive communication.
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This study describes when and how adolescents engage with their fast-moving and dynamic digital environment as they go about their daily lives. We illustrate a new approach— screenomics—for capturing, visualizing, and analyzing screenomes, the record of individuals’ day-to-day digital experiences. Sample includes over 500,000 smartphone screenshots provided by four Latino/Hispanic youth, age 14 to 15 years, from low-income, racial/ethnic minority neighborhoods. Screenomes collected from smartphones for 1 to 3 months, as sequences of smartphone screenshots obtained every 5 seconds that the device is activated, are analyzed using computational machinery for processing images and text, machine learning algorithms, human labeling, and qualitative inquiry. Adolescents’ digital lives differ substantially across persons, days, hours, and minutes. Screenomes highlight the extent of switching among multiple applications, and how each adolescent is exposed to different content at different times for different durations—with apps, food-related content, and sentiment as illustrative examples. We propose that the screenome provides the fine granularity of data needed to study individuals’ digital lives, for testing existing theories about media use, and for generation of new theory about the interplay between digital media and development.