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

摘要

class="kwd-title">Method name: Sequential data analysis class="kwd-title">Keywords: Sequential data analysis, Family interactions, Infant behavior, Caregiver responsiveness class="head no_bottom_margin" id="abs0010title">AbstractTwenty-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. class="first-line-outdent">

• 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.