A Simulation Study Using a Mixed Model Framework to Analyze the Impact of Sample Size and Variability on Type I Error
Bibliographic record
Abstract
Aims: This simulation study was conducted to check the validity of a MIXED model’s statistical inference when violating the underlying assumptions – normality of random errors when there are unbalanced group sizes and inequality of variance of errors [Scheffe, 1959]. Study Design: Monte Carlo Simulation Study. Place and Duration of Study: North Dakota State University 2020-2021. Methodology: Repeated measures designs (or longitudinal studies) are commonly seen in many research fields, especially in pharmaceutical clinical trials, agricultural research, and psychology. PROC MIXED (SAS Inc.) is a well-known standard tool for analyzing repeated measures data nowadays. The MIXED procedure is based on the standard linear MIXED model, which estimates parameters by maximizing the restricted likelihood. The usual assumption for a standard linear MIXED model is normality. However, the character of data in the real world may be non- smoothed, or non-symmetric, or having heavy tails. We estimate the Type I error rates in different combinations of settings and compare them with the stated Type I error. Conclusion: The main results in this study show us that the MIXED model is reasonably robust to modest violations of the normal distribution. However, when a small sample size associated with a treatment was combined with the effects of that treatment having a large variance, a severe inflation problem on Type I error rates could occur when using the MIXED model procedure. When the Type I errors were found to be inflated, the Group= option was found to often help with this problem. A Sub-Sampling procedure was also found to help with this problem.
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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 | 0.079 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".