Individual differences in adjustment to spousal loss: A nonlinear mixed model analysis

Abstract

The study of within-person change lies at the core of developmental research. Theory and empirical data suggest that many of these developmental processes are not linear. We describe a broad class of multilevel models that allows for nonlinear change — nonlinear mixed models. To demonstrate the utility of these models, we present a nonlinear mixed model analysis of adjustment to conjugal loss. Coming from a perspective of the individual as a regulatory system, our model predicts a faster rate of adjustment immediately following the loss and diminished adjustment as time since the loss increases, approaching an equilibrium level of well-being. This model allows us to estimate various aspects of the adjustment trajectory and individual differences in these trajectories, including multiple ways that pre- and post-loss factors can explain variability in the adjustment process. The model provides new insights into an important phenomenon that cannot be gleaned from linear models and other methods of trajectory analysis. We discuss the strengths and limitations of this type of analysis relative to other methods.

The multidimensional nature of resilience to spousal loss.

“…First, latent basis models provide added flexibility in the description of and better articulation of non-linear patterns of change during each time period (see Burke, Shrout, & Bolger, 2007) and second, allow for examining whether there are between-person differences in those non-linear patterns of change. For example, separate latent factors for pre- and post-spousal loss change allows for some participants (and classes) to possibly show declines in the years preceding spousal loss, but bounce back following.…”

Parents’ adjustment following the death of their child: Resilience is multidimensional and differs across outcomes examined

“…Additionally, as we demonstrated in Table 1, estimating separate parameters for changes before and after child loss led to improved model fit in our longitudinal model of change, a key first step when using GMM (for discussion, see Ram & Grimm, 2009). This permitted for more fully examine the potential non-linearity of change (e.g., declines prior to child loss and improvements following child loss) (see Burke, Shrout, & Bolger, 2007; Infurna & Luthar, 2016; Lucas et al, 2004). Latent basis factors allow for the pattern of change to emerge from the raw data, as opposed to imposing a specific functional form on the shape of change (e.g., linear or quadratic).…”

Health and Illness Over Time

“…For example, grief following death of a spouse normally occurs and persists for some time. After consideration of several functional forms, exponential change was selected as the most reasonable way to model individual grief trajectories using repeated pre- and postloss Center for Epidemiological Studies-Depression (CES-D) scores among surviving spouses taking part in the Changing Lives of Older Couples (CLOC) study (Burke, Shrout, & Bolger, 2007). Preloss patterns of depression over time, intensity of the immediate response, shape of the grief experience over time, and eventual level of resolution varied.…”