Bayesian Hierarchical Multidimensional Item Response Modeling of Small Sample, Sparse Data for Personalized Developmental Surveillance
Bibliographic Data
| ID | 20282739 |
|---|---|
| Authors | Patricia Gilholm (0000-0001-8135-1520, Queensland University of Technology, Brisbane, Queensland, Australia, corresponding author), Kerrie Mengersen (0000-0001-8625-9168, Queensland University of Technology, Brisbane, Queensland, Australia), Helen Thompson (0000-0001-5366-3172, Queensland University of Technology, Brisbane, Queensland, Australia) |
| Year | 2021 |
| Volume | 81 |
| Issue | 5 |
| Pages | 936-956 |
| Publication date | 2021-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164420987582 |
| PMID | 34565812 |
| OpenAlex | W3124123577 |
| Language | EN |
| References cited | 36 |
Developmental surveillance tools are used to closely monitor the early development of infants and young children. This study provides a novel implementation of a multidimensional item response model, using Bayesian hierarchical priors, to construct developmental profiles for a small sample of children ( N = 115) with sparse data collected through an online developmental surveillance tool. The surveillance tool records 348 developmental milestones measured from birth to three years of age, within six functional domains: auditory, hands, movement, speech, tactile, and vision. The profiles were constructed in three steps: (1) the multidimensional item response model, embedded in the Bayesian hierarchical framework, was implemented in order to measure both the latent abilities of the children and attributes of the milestones, while retaining the correlation structure among the latent developmental domains; (2) subsequent hierarchical clustering of the multidimensional ability estimates enabled identification of subgroups of children; and (3) information from the posterior distributions of the item response model parameters and the results of the clustering were used to construct a personalized profile of development for each child. These individual profiles support early identification of, and personalized early interventions for, children with developmental delay
Bayesian hierarchical modeling · Bayesian inference · Bayesian probability · Cluster analysis · Construct (python library) · Data mining · Developmental psychology · Hierarchical clustering · Hierarchical database model · Identification (biology) · Item response theory · Machine learning · Multilevel model · Prior probability · Psychometrics · Sample (material) · Artificial Intelligence · Computer Science · Early Childhood Education and Development · Infant Development and Preterm Care · Language Development and Disorders · Psychology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |