Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data

Abstract

The nomothetic approach (i.e., the study of interindividual variation) dominates analyses of clinical data, even though its assumption of homogeneity across people and time is often violated. The idiographic approach (i.e., the study of intraindividual variation) is best suited for analyses of heterogeneous clinical data, but its person-specific methods and results have been criticized as unwieldy. Group iterative multiple model estimation (GIMME) combines the assets of the nomothetic and idiographic approaches by creating person-specific maps that contain a group-level structure. The maps show how intensively measured variables predict and are predicted by each other at different time scales. In this article, GIMME is introduced conceptually and mathematically, and then applied to an empirical data set containing the negative affect, detachment, disinhibition, and hostility composite ratings from the daily diaries of 25 individuals with personality pathology. Results are discussed with the aim of elucidating GIMME's potential for clinical research and practice.

Focusing Personality Assessment on the Person: Modeling General, Shared, and Person Specific Processes in Personality and Psychopathology

“…Although the current sample was highly heterogeneous by design, we anticipate based on past experience using GIMME in several data sets of different sorts (Beltz et al, 2016;Price et al, 2017) that massive heterogeneity is likely to be the rule rather than the exception.…”

“…This is particularly the case in clinical settings (i.e., mental health), although this could be extended to occupational or other healthcare settings. The main engine of the GIMME approach, uSEM, can be estimated for an individual (For exemplars see Beltz et al, 2016;Fisher et al, in press;or Foster & Beltz, in press), and the GIMME software package includes this capability. This makes it feasible to apply this model using the same tools in practical settings using easy to use and freely available software.…”

Drivers of human development: How relationships and context shape learning and development1

“…These co-influential interactions produce differential effects that can be studied and understood at both an individual and subgroup level over time (e.g., Beltz, Wright, Sprague, & Molenaar, 2016;Kremer, Flower, Huang, & Vaughn, 2016).…”

Evidence that Psychopathology Symptom Networks have Limited Replicability

“…Consistent with these expectations, time series network analyses of depression symptoms differ from cross-sectional analyses (Bringmann, Lemmens, Huibers, Borsboom & Tuerlinckx, 2015;Fried et al, 2016), and individuals have been found to have highly distinct networks of associations among domains of psychopathology (Beltz, Wright, Sprague, & Molenaar, 2016;Wright, Beltz, Gates, Molenaar & Simms, 2015). These assumptions evidently require further investigation.…”