Project Abstract/Summary
Researchers, educators, psychologists, and medical clinicians often want to know how individuals grow and develop. Further, there is interest in whether there are distinct–and oftentimes unseen–groups of children or adults based on their development. For example, researchers might want to understand whether a given child’s development of self-control is typical or not, or whether there are distinct patterns in how groups of students learn math as they move through school. Growth mixture models (GMMs) are statistical tools designed for exactly that purpose: identifying distinct growth patterns, including groups of individuals who follow those patterns, when such groupings remain unseen. Although GMMs have seen widespread use in developmental research in recent decades, best practices for such tools are not fully established. In particular, one important question remains unaddressed: when common forms of measurement bias are present in scores from self-report measures administered over time (e.g., self- control surveys given to students throughout middle school), how trustworthy are GMM results using these scores? Among others, one outstanding issue in GMM is how they are affected by response style bias, which may occur when respondents have characteristic patterns of responding to questions regardless of question content, such as always picking the highest or lowest response option. Failure to apply scoring models which account for response styles may yield biased score estimates–but the extent to which this impacts GMM results based on these scores is unknown.
The current project uses advances in item response theory (IRT) to examine the effects of scoring–including failure to account for response styles specifically, as well as mis-specification of the scoring model more broadly–on class recovery and parameter estimates in GMM. This is accomplished both through Monte Carlo simulation and analysis of empirical data. In the simulation study, the generation of artificial data with different features–including sample size, number of classes in the GMM, and different measurement issues such as response style bias–allows a comprehensive examination of when and under what conditions scoring model mis-specification matters for GMMs. The empirical study, which applies GMMs to scores of socioemotional development from two large studies, permits insight into the real-world consequences of scoring decisions in developmental science. Finally, guidance for researchers will be made available in the form of a ‘measurement checklist’ informed by the study results.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.
Principal Investigator
James Soland – University of Virginia Main Campus located in CHARLOTTESVILLE, VA
Co-Principal Investigators
Veronica Cole
Funders
Funding Amount
$420,000.00
Project Start Date
09/01/2022
Project End Date
08/31/2026
Will the project remain active for the next two years?
The project has more than two years remaining
Source: National Science Foundation
Please be advised that recent changes in federal funding schemes may have impacted the project’s scope and status.
Updated: April, 2025