ArticlePsychometrika2025
Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.
Article in Psychometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hidden Markov models (HMMs) are popular for modeling complex, longitudinal data. Existing identifiability theory for conventional HMMs assume emission probabilities are constant over time and the Markov chain governing transitions among the hidden states is irreducible, which are assumptions that may not be applicable in all educational and psychological research settings. We generalize existing conditions on homogeneous HMMs by considering heterogeneous HMMs with time-varying emission probabilities and the potential for absorbing states. Researchers are investigating a family of models known as restricted HMMs (RHMMs), which combine HMMs and restricted latent class models (RLCMs) to provide fine-grained classification of educationally and psychologically relevant attribute profiles over time. These RHMMs leverage the benefits of RLCMs and HMMs to understand changes in attribute profiles within longitudinal designs. The identifiability of RHMM parameters is a critical issue for ensuring successful applications and accurate statistical inference regarding factors that impact outcomes in intervention studies. We establish identifiability conditions for RHMMs. The new identifiability conditions for heterogeneous HMMs and RHMMs provide researchers insights for designing interventions. We discuss different types of assessment designs and the implications for practice. We present an application of a heterogeneous HMM to daily measures of positive and negative affect.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.