# Measuring the individual: Personalized latent variable models from ecological momentary assessments

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $227,395

## Abstract

PROJECT SUMMARY
With the increasing availability of ecological momentary assessments (EMA) such as daily dairy and
experience sampling measurements, behavioral scientists are better able to investigate the within-person
dynamic patterns (i.e., relations among variables across time) underlying symptoms, behaviors, and life
events. One prominent challenge in this endeavor is the inherent heterogeneity in individual mental health
processes. Others have demonstrated that this heterogeneity requires personalized measurement models to
accurately assess constructs of interest. By measurement model, we mean the pattern of how observed
variables relate to a latent construct. As an example, depression can be thought of as a latent construct that
psychologists often seek to measure. Individuals may differ with regards to which (observed) symptoms relate
to their overall (latent) depression levels at a given time point. For one person, the symptoms of sadness,
feelings of hopelessness, and irritability may be the best measures of depression over time whereas for
another, perhaps sadness, anhedonia, and fatigue are the symptoms that indicate depression.Allowing
individuals to have personalized assessments will enable the field to get even closer to personalized treatment
plans by better quantifying these somewhat abstract constructs.The current standard is to force all individuals
to have the same measurement model, but the field is quickly moving towards adopting personalized
measurement models for assessments. Critically, the available methods have a number of issues that prevent
reliable personalized measurement models. First, some approaches (such as simply using observed variables)
ignore the reality of measurement errors. This causes bias in the effects among latent constructs of interest
and can lead to inaccurate inferences regarding anindividuals' process. Second, the number of observations
obtained for a given individual is often too small to arrive at person-specific measurement models. Third, the
current methods require the assumption of multivariate normality to be met; this is typically not seen in many
forms of ecological momentary assessment data. Fourth, many available approaches for arriving at individual-
level models do not perform well when the model is misspecified (i.e., the pattern of relations among observed
symptoms and latent constructs is incorrect). This prevents a considerable hurdle when attempting to arrive at
model structures in an exploratory manner where by definition the correct model is unknown in the
beginning.Our project, if funded, would provide researchers with an easy-to-use tool for arriving at
personalized measurement models. This can be achieved by building an exploratory approach within a well-
understood estimation approach that has a number of desirable properties. Measurement errors would be
accounted for, the method will work well even when the number of time points (observations) is less than the
number of va...

## Key facts

- **NIH application ID:** 9906952
- **Project number:** 5R21MH119572-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Kenneth Bollen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $227,395
- **Award type:** 5
- **Project period:** 2019-04-04 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9906952

## Citation

> US National Institutes of Health, RePORTER application 9906952, Measuring the individual: Personalized latent variable models from ecological momentary assessments (5R21MH119572-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9906952. Licensed CC0.

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