THE POWER OF ESTIMATED DATA

Every serious researcher eventually meets the same wall: the data you need cannot be reached. The agency never published it. The records were never kept. The fieldwork is impossible, too costly, or too late. Most researchers respond in one of two wrong ways — they quietly narrow the question until it no longer matters, or they invent the numbers and hope no examiner looks too closely.

There is a third road, and this book is the map.

The Power of Estimated Data in Academic Research teaches you how to produce figures that are estimated yet fully defensible — figures that survive examiners, reviewers, and your own conscience. It rests on one clear idea: data lives on a continuum from measured fact to assumed value, and estimation is the disciplined work in the middle. Done openly, it lets a field know more than it otherwise could. Done in the dark, it becomes fabrication. The line between the two is the line this book teaches you to respect.

Every method in the book is held to three integrity tests: transparency, defensibility, and traceability. If a figure cannot pass all three, it does not enter your study.

What you'll learn:

• How to decide whether a data gap can be estimated — or whether the question must be narrowed honestly
• Imputation, including multiple imputation, for missing values you can defend
• Time-series methods — forecasting, backcasting, and ARIMA — to extend data across years you don't have
• Bayesian estimation that puts your prior knowledge to work, formally and openly
• Structured expert elicitation (anonymous, iterative) that resists anchoring and groupthink
• Disciplined proxy use — and how to disclose the proxy relationship instead of hiding it
• Sensitivity analysis that stress-tests your assumptions before a reviewer does
• How to build an audit trail another researcher could follow from source to final figure
• How to write the disclosure section that turns estimation into a strength on the page
• The fabrication boundary in Chapter 14 — the practices no amount of disclosure can rescue, named precisely so you never cross them by accident

Each method chapter closes with a disclosure section, a full glossary defines every term at a working-researcher level, and worked cases mirror the studies African research institutions produce every day.

Who it's for: postgraduate students writing theses and dissertations, supervisors guiding them, journal authors, and any researcher who has ever stared at a data gap and wondered what an honest scholar is allowed to do next.

Written and published by Veristzon Global Research Lab, Abuja — the research house behind years of postgraduate and journal work across African universities.

Stop narrowing your best questions to fit the data you can find. Learn to estimate the data you need — defensibly, transparently, and on the record.