Quantitative Analysis with Small Samples: A Practical Guide for Students and Early-Career Researchers
Quantitative Analysis with Small Samples: A Practical Guide for Students and Early-Career Researchers
Small Sample Lab is a practical methods guide for studies where data are limited but rigorous quantitative analysis is still required. It is written for students and early-career researchers working across education, health, business, and resource-constrained research settings.
Current Status
UNDER PROOFREADING
Positioning
Quantitative Analysis with Small Samples addresses a common gap in methods training: what to do when the dataset is too small for comfortable large-sample assumptions, but the research question still matters. The project is grounded in applied contexts such as classroom interventions, pilot studies, small markets, community-based research, and Small Island Developing States where sample sizes are often modest by necessity.
Who It Is For
This book is being developed for:
- undergraduates, taught masters students, and early-career PhD researchers
- education, health, business, and social-science users
- analysts and practitioners working with samples of roughly 10 to 100 cases
- readers who need method guidance that is both statistically careful and operationally realistic
It is not framed as an abstract statistical monograph. The goal is to help readers choose defensible methods, run them in R, and report them honestly.
Prerequisite Knowledge
A basic introductory-statistics background is helpful, but the project is written to be teachable. The source material assumes readers may be learning some of these methods for the first time, and the repo already supports that with synthetic datasets, helper functions, lab practicals, and worked projects. R and Quarto are the core workflow tools.
Book Structure
The source project maps the teaching line into eight parts:
- Foundations
- Data Collection and Preparation
- Analysis Methods
- Reporting and Interpretation
- Worked Projects
- Technical Appendices
- Guided Lab Practicals
- Instructor’s Manual
The current Quarto build already includes the main teaching sequence, worked projects, guided labs, and instructor-facing material. Technical appendices are part of the broader project design and are being prepared alongside the main book build.
What The Book Covers
The repo materials already point to a clear methods range:
- exact tests and resampling methods
- nonparametric rank-based procedures
- penalised and Bayesian regression for limited data
- reliability and measurement-quality checks for short scales
- multi-criteria decision-making for small-case settings
- effect sizes, confidence intervals, and transparent interpretation
This is paired with complete worked projects and a guided-lab layer so the reader can move from explanation to application rather than stopping at theory.
What Already Exists In The Project
Generated Datasets
The repo already includes scripts that generate project datasets for marketing, service-quality, and process-change examples so readers can rebuild examples locally.
Helper Functions
Reusable R helpers are already defined for exact tests, bootstrap intervals, Wilcoxon procedures, and Firth logistic regression.
Guided Labs
The planned lab sequence already covers Fisher's exact test, bootstrap confidence intervals, rank tests, reliability analysis, power planning, imputation, and data screening.
Worked Projects
The source outline already includes integrated projects such as campaign evaluation, short-scale reliability assessment, and paired intervention evaluation.
Why This Project Is Useful
Small-sample work is often treated as a compromise case. This book takes the opposite view: with the right methods, small studies can still be analysed carefully and reported responsibly. That matters in exactly the contexts where researchers have the least margin for wasted effort, weak assumptions, or overclaimed findings.
Current Stage
The public status remains Proofreading, but the repo already contains a real teaching system: structured chapters, generated datasets, helper functions, lab practicals, worked projects, and an instructor-manual track. This page now reflects that actual level of project maturity.