# Improving precision in modeling childhood executive function trajectories using psychometrics

> **NIH NIH K25** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $129,882

## Abstract

Project Summary:
Executive functions are high-level supervisory cognitive skills vital for a child’s social and academic success.
Their core components include working memory, inhibitory control and cognitive flexibility. The development of
executive functions in children can be adversely affected by many early life risk factors, such as lead exposure
and low socioeconomic status. However, there are two difficulties to precisely measuring and modeling
executive functions, which hamper our understanding of how risk factors can predict shifts in executive
functioning: 1) It is common to use a single child performance task to tap into an executive function
component. While this can yield important information, it can also lead to bias, because executive functions
can be difficult to measure precisely, since they are interdependent and also rely on non-executive function
skills. 2) It can be challenging to model the longitudinal development of executive functions because the set of
tasks used to assess executive functions can change as a child grows. Some tasks may be specific to a
particular age range and not appropriate for other ages. To address the first difficulty, we will improve the
precision of measuring an executive function component by using well-established latent variable approaches
which integrate output from multiple tasks. Preliminary analyses demonstrate that this approach can yield
improved interpretability of a risk factor’s impact on an executive function component, compared to separate
analyses of each task. To address the second difficulty, we propose to use advanced psychometric and item
response theory methods to create a longitudinal scale of executive functioning with optimized precision and
construct validity that span all the ages of interest. This will allow us to identify how risk factors can predict
changes in executive function over time (e.g. developmental trajectory). This proposal leverages data from two
large, NIH-funded prospective birth cohorts with extensive longitudinal data and repeated assessments of
many executive function tasks, and detailed measures of risk factors (e.g. lead exposure, low socioeconomic
status). Open-source, user-friendly software for the developed approaches will be developed, including a web
application for non-statistical users. I (Dr. Shelley Liu) am a biostatistician and an Assistant Professor in
Population Health Science and Policy at the Icahn School of Medicine at Mount Sinai (ISMMS). ISMMS
provides an excellent environment for this K25; all mentors and advisory committee members are based at
ISMMS. My training will involve 1) formal graduate level coursework on executive function development, child
neuropsychology, item response theory and psychometric scaling; 2) tutorials in neurodevelopment and
psychometrics with my mentoring team; and 3) clinical rotations to observe neurobehavioral assessments. This
training will complement my existing expertise in biostatistics. I aim to bec...

## Key facts

- **NIH application ID:** 10880567
- **Project number:** 5K25HD104918-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Shelley Han Liu
- **Activity code:** K25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $129,882
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880567

## Citation

> US National Institutes of Health, RePORTER application 10880567, Improving precision in modeling childhood executive function trajectories using psychometrics (5K25HD104918-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10880567. Licensed CC0.

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