학술논문

A Quantitative Evaluation of Thin Slice Sampling for Parent–Infant Interactions
Document Type
Academic Journal
Author
Burgess, Romana (University of Bristol); Costantini, Ilaria (University of Bristol); Bornstein, Marc H. (Eunice Kennedy Shriver National Institute of Child Health and Human Development); Campbell, Amy (University of Bristol); Cordero Vega, Miguel A. (University of Bristol); Culpin, Iryna (University of Bristol); Dingsdale, Hayley (Cardiff University); John, Rosalind M. (Cardiff University); Kennedy, Mari-Rose (University of Bristol); Pearson, Rebecca M. (University of Bristol; Bristol NIHR Biomedical Research Centre); Nabney, Ian (University of Bristol)
Source
Journal of Nonverbal Behavior; 2023 June; 47(2): 117-210.  [Journal Detail] Springer.
Subject
relationship to parent-infant interaction
Language
ISSN
0191-5886
1573-3653 (electronic)
Abstract
Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11–14 behavioural groups, with each group comprised of 3–13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matrices (probability of transitioning between behaviours, e.g., from looking at the infant to looking at a distraction) and stationary distributions (long-term proportion of time spent within behavioural states) for 15 thin slices of full, 5-min interactions. Measures drawn from the full sessions were compared to those from 1-, 2-, 3- and 4-min slices. We identified many instances where thin slice sampling (i.e., < 5 min) was an appropriate coding method, although we observed significant variation across different behaviours. We thereby used this information to provide detailed guidance to researchers regarding how long to code for each behaviour depending on their objectives.