Small Sample Size Solutions : A Guide for Applied Researchers and Practitioners
Bibliographic record
Abstract
Researchers often have difficulties collecting enough data to test their hypotheses, \neither because target groups are small or hard to access, or because data collection \nentails prohibitive costs. Such obstacles may result in data sets that are too small for \nthe complexity of the statistical model needed to answer the research question. This \nunique book provides guidelines and tools for implementing solutions to issues \nthat arise in small sample research. Each chapter illustrates statistical methods that \nallow researchers to apply the optimal statistical model for their research question \nwhen the sample is too small. \nThis essential book will enable social and behavioral science researchers to test \ntheir hypotheses even when the statistical model required for answering their \nresearch question is too complex for the sample sizes they can collect. The statistical \nmodels in the book range from the estimation of a population mean to models with \nlatent variables and nested observations, and solutions include both classical and \nBayesian methods. All proposed solutions are described in steps researchers can \nimplement with their own data and are accompanied with annotated syntax in R. \nThe methods described in this book will be useful for researchers across the social \nand behavioral sciences, ranging from medical sciences and epidemiology to psychology, \nmarketing, and economics.
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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 | 0.051 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.033 | 0.031 |
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".