Time Varying Grouping Variables in Markov Latent Class Analysis: Some Problems and Solutions
Bibliographic record
Abstract
Markov latent class analysis (MLCA) is a modeling technique for panel or longitudinal data that can be used to estimate the classification error rates (e.g., false positive and false negative rates for dichotomous items) for discrete outcomes with categorical predictors when gold-standard measurements are not available. Because panel surveys collect data at multiple time points, the grouping variables in the model may either be time varying or time invariant (static). Time varying grouping variables may be more correlated with either the latent construct or the measurement errors because they are measured simultaneously with the construct during the measurement process. However, they generate a large number of model parameters that can cause problems with data sparseness, model diagnostic validity, and model convergence. In this paper we investigate whether more parsimonious grouping variables that either summarize the variation of the time varying grouping variable or assume a structure that lacks memory of previous values of the grouping variables can be used instead, without sacrificing model fit or validity. We propose a simple diagnostic approach for comparing the validity of models that use time-invariant summary variables with their time-varying counterparts. To illustrate the methodology, this approach is applied to data from the National Crime Victimization Survey (NCVS) where greater parsimony and a reduction in data sparseness were achieved with no appreciable loss in model validity for the outcome variables considered. The approach is generalized for application to essentially any MLCA using time varying group variables and its advantages and disadvantages are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".