Specifying exogeneity and bilinear effects in data-driven model searches
- PDF / 1,891,379 Bytes
- 13 Pages / 595.224 x 790.955 pts Page_size
- 10 Downloads / 149 Views
Specifying exogeneity and bilinear effects in data-driven model searches Cara Arizmendi1 · Kathleen Gates1 · Barbara Fredrickson1 · Aidan Wright2
© The Psychonomic Society, Inc. 2020
Abstract Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone’s emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME. Keywords Data-driven · Gimme · Exogenous · Moderator · Sem · Time series · Daily diary · Model search
Introduction Data-driven model searches are an increasingly popular approach for detecting relations among psychological, behavioral, and physiological variables. In particular, network searches are of interest to psychologists aiming to model relationships between variables as a grouped latent construct as is done in factor analysis (Cramer et al., 2016; Schmittmann et al., 2013; Bringmann et al., 2015; Costantini et al., 2015; Wright et al., 2018). Typically, network search approaches used in psychology treat all variables as potentially exogenous and endogenous. That is, Cara Arizmendi
[email protected] 1
The University of North Carolina Chapel Hill, CB #3270, Davie Hall, Chapel Hill, NC 27599-3270, USA
2
The University of Pittsburgh, Pittsburgh, PA 15260, USA
all variables in the model search are handled as potentially predicting or being predicted by other variables in the model. Few methods allow for a priori specification of exogenous variables, or variables that can predict but not b
Data Loading...
