AI Article Synopsis

  • Personality and psychopathology involve complex interactions of various psychological factors that evolve over time in response to a person's environment.
  • Ambulatory assessment techniques are paving the way for more effective evaluation by collecting real-time data in individuals' natural settings, enabling better modeling of their behavior.
  • The group iterative multiple model estimation (GIMME) method allows researchers to analyze both individualized and general traits from intensive longitudinal behavioral data, as demonstrated with individuals diagnosed with personality disorder who maintained daily diaries for 100 days.

Article Abstract

Personality and psychopathology are composed of dynamic and interactive processes among diverse psychological systems, manifesting over time and in response to an individual's natural environment. Ambulatory assessment techniques promise to revolutionize assessment practices by allowing access to the dynamic data necessary to study these processes directly. Assessing manifestations of personality and psychopathology naturalistically in an individual's own ecology allows for dynamic modeling of key behavioral processes. However, advances in dynamic data collection have highlighted the challenges of both fully understanding an individual (via idiographic models) and how s/he compares with others (as seen in nomothetic models). Methods are needed that can simultaneously model idiographic (i.e., person-specific) processes and nomothetic (i.e., general) structure from intensive longitudinal personality assessments. Here we present a method, group iterative multiple model estimation (GIMME) for simultaneously studying general, shared (i.e., in subgroups), and person-specific processes in intensive longitudinal behavioral data. We first provide an introduction to the GIMME method, followed by a demonstration of its use in a sample of individuals diagnosed with personality disorder who completed daily diaries over 100 consecutive days. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380034PMC
http://dx.doi.org/10.1037/pas0000617DOI Listing

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