Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

Sun-Joo Cho; Sarah Brown-Schmidt; Sharice Clough; Melissa C Duff

doi:10.1007/s11336-024-09986-1

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Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

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Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

Sun-Joo Cho, Sarah Brown-Schmidt, Sharice Clough and Melissa C Duff

Psychometrika, Vol.90(2), pp.628-657

04/01/2025

DOI: 10.1007/s11336-024-09986-1

PMCID: PMC12483702

PMID: 39014288

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Abstract

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

by-variable smooth function

eye-tracking data

intensive binary longitudinal data

group comparisons

generalized additive mixed model

Details

Title: Subtitle: Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM
Creators: Sun-Joo Cho - Vanderbilt University
Sarah Brown-Schmidt - Vanderbilt University
Sharice Clough - Vanderbilt University Medical Center
Melissa C Duff - Vanderbilt University Medical Center
Resource Type: Journal article
Publication Details: Psychometrika, Vol.90(2), pp.628-657
DOI: 10.1007/s11336-024-09986-1
PMID: 39014288
PMCID: PMC12483702
NLM abbreviation: Psychometrika
ISSN: 0033-3123
eISSN: 1860-0980
Grant note: NIDCD grant R01 NIH DC017926 / NINR NIH HHS R01 DC017926 / NIDCD NIH HHS
Language: English
Date published: 04/01/2025
Academic Unit: Communication Sciences and Disorders
Record Identifier: 9985112969302771

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