Examining inter-family differences in intra-family (parent-adolescent) dynamics using grid-sequence analysis

Abstract

Family systems theorists have forwarded a set of theoretical principles meant to guide family scientists and practitioners in their conceptualization of patterns of family interaction-intra-family dynamics-that, over time, give rise to family and individual dysfunction and/or adaptation. In this article, we present an analytic approach that merges state space grid methods adapted from the dynamic systems literature with sequence analysis methods adapted from molecular biology into a "grid-sequence" method for studying inter-family differences in intra-family dynamics. Using dyadic data from 86 parent-adolescent dyads who provided up to 21 daily reports about connectedness, we illustrate how grid-sequence analysis can be used to identify a typology of intrafamily dynamics and to inform theory about how specific types of intrafamily dynamics contribute to adolescent behavior problems and family members' mental health. Methodologically, grid-sequence analysis extends the toolbox of techniques for analysis of family experience sampling and daily diary data. Substantively, we identify patterns of family level microdynamics that may serve as new markers of risk/protective factors and potential points for intervention in families. (PsycINFO Database Record

Figure 1

Top portion Two dyads’ daily reports of parent-adolescent connectedness mapped in order of their occurrence onto state space grids where the x- and y- axes indicate adolescents’ and parents’ perceptions of connectedness. The state space grid on the left (Family A) is of a dyad that has unstable levels of connectedness, while the grid on the right (Family B) is of a dyad where both members consistently report high levels of connectedness. Bottom portion. Extracted sequences from Families A, B, and C (not pictured above). Gray cells indicate missing values. Distances between pairs of sequences (e.g, Distance_AB = 30) are calculated (via optimal matching algorithm) as the cost of transforming one sequence into the other, with greater distance indicating more dissimilarity between dyads.

Figure 2

Steps to conduct grid-sequence analysis.

Figure 3

Left panel Time series plots depicting the N = 86 family-level sequences extracted from the state space grid. Each colored row indicates how that family moved through the parent-adolescent connectedness grid over time (time running left to right; color indicating location of cell as per Figure 1). Right panel. A dendrogram depicting results of a hierarchical cluster analysis. The vertical red line indicates the cut point for a 5-cluster solution.

Figure 4

Five cluster-group profiles of parent-adolescent connectedness dynamics, with an accompanying exemplar family’s state space grid. Visible are within-group similarities, and between-group differences in the intra-family dynamics.