So much data, so little time: Using sequential data analysis to monitor behavioral changes

0Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

Abstract

Twenty-three infants (M = 13.7 months, SD = 3.73) and their primary caregivers were observed and video-taped in three 20-min play sessions. Over the course of a month, changes in infant behaviors and caregiver responsiveness to those behaviors were monitored. Repeated-measures ANOVAs indicated that caregiver responsiveness to infant object-related and dyadic behaviors significantly increased over the course of the sessions. However, the ANOVAs did not specify exactly which caregiver behaviors changed. Sequential data analysis revealed that caregivers specifically increased their use of dyadic vocal behaviors in response to all infant behaviors. This study reveals that although ANOVAs are useful for providing information about macro, overall changes in caregiver behavior, sequential data analysis is a useful tool for evaluating micro, moment-to-moment changes in behavior. With sequential analysis, specific behavioral patterns can be examined and, if necessary, steps can be taken to modify and monitor those behaviors over time. • Sequential data analysis was used to monitor changes in caregiver behavior.• Non-culture-specific behavioral codes and techniques were used to quantify caregiver responsiveness to infant object-related and dyadic behaviors.• When compared to ANOVA, sequential data analysis is more useful for assessing micro-level behavioral changes in infant-caregiver interactions.

Cite

CITATION STYLE

APA

Walker, T. (2016). So much data, so little time: Using sequential data analysis to monitor behavioral changes. MethodsX, 3, 560–568. https://doi.org/10.1016/j.mex.2016.10.004

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free