A Practical Guide with Step-by-Step Explanations, Numerous Worked Examples, and R Code
The A–Z of Error-Free Research describes the design, analysis, modeling, and reporting of experiments, clinical trials, and surveys. The book shows you when to use statistics, the best ways to cope with variation, and how to design an experiment, determine optimal sample size, and collect useable data. It also helps you choose the best statistical procedures for your application and takes you step by step through model development and reporting results for publication.
Transition from Student to Researcher
Helping you become a confident researcher, the book begins with an overview of when—and when not—to use statistics. It guides you through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure your research efforts get the proper credit. The book concludes with case-control and cohort studies.
Research Essentials
Prescription
Fundamental Concepts
Precautions
Will the Data Require Statistical Methods?
Summary
PLANNING
Hypotheses and Losses
Prescription
State the Objectives of Your Research
Gather Qualitative Data
Formulating Hypotheses
Specify the Decisions and Associated Costs
Specify the Alternatives
Summary
To Learn More
Coping with Variation
Prescription
Start with Your Reports
List All Outcomes of Interest
List All Sources of Variation
Describe How You Will Cope with Sources of Variation
Establish a Time Line
Should the Study Be Performed?
To Learn More
Experimental Design
Prescription
Define the Study Population
The Purpose of Experimental Design
K.I.S.S?
Summary
To Learn More
DATA COLLECTION
Fundamentals
Prescription
How Will You Make Your Measurements?
Formal Descriptions of Methods and Materials
Put Your Data in a Computer and Keep It There
Forestall Disaster
To Learn More
Quality Control
Prescription
Potential Sources of Error
Preventive Measures
Make Baseline Measurements
Conduct a Pilot Study
Monitor the Data Collection Process
Monitor the Data
To Learn More
ANALYZING YOUR DATA
Describing the Data
Prescription
Box and Whiskers Plot
Which Statistic?
Interval Estimates
Confidence Intervals for the Population Mean
Confidence Intervals for Proportions
Estimated from Randomized Responses
Confidence Intervals for Other Population
Characteristics
An Improved Bootstrap
Summary
To Learn More
Hypothesis Tests
Prescription
Types of Data
Analyzing Data from a Single Population
Comparing Two Populations
Comparing Three or More Populations
Experimental Designs
To Learn More
Multiple Variables and Multiple Tests
Prescription
Multiple Variables
Multiple Tests
To Learn More
Miscellaneous Hypothesis Tests
Prescription
Hypothesis Tests and Confidence Intervals
Testing for Equivalence
When Variables Are Not Identically Distributed
Testing for Trend
Sample Size Determination
Prescription
Prepare a Budget
Final Sample Size
Initial Sample Size
To Learn More
BUILDING A MODEL
Ordinary Least Squares
Prescription
Linear Regression
Improving the Fit
Increasing the Number of Predictors
Analysis of Variance
Summary
To Learn More
Alternate Regression Methods
Prescription
LAD Regression
Quantile Regression
Errors-in-Variables Regression
Generalized Linear Models
Classification
Modeling Survival Data
Principal Component Analysis
Summary
To Learn More
Decision Trees
Prescription
How Trees Are Grown
Incorporating Existing Knowledge
Using the Decision Tree as an Aid to Decision Making
Summary
To Learn More
REPORTING YOUR RESULTS
Reports
Prescription
Choose a Journal
Methods and Materials
Results
Reporting Your Analyses
Discussion
Introduction
Abstract
Bibliography
Responding to Rejection
To Learn More
Oral Presentations
Prescription
Text
Graphs
Tables
Better Graphics
Prescription
Creating Graphs with R
To Learn More
NONRANDOM SAMPLES
Cohort and Case-Control Studies
A Worked-Through Example
Prescription
Examples
To Learn More
R Primer
Bibliography
Author Index
Subject Index
R Function Index
Biography
Phillip I. Good is a mathematical statistician with nearly 40 years of experience in the field. He has published hundreds of articles on microcomputers and has authored nine books, including A Practitioner’s Guide to Resampling for Data Analysis, Data Mining, and Modeling and Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses. He was among the first to apply the bootstrap method in his analyses of 2 × 2 designs with a missing cell. He has also contributed to other areas of small sample statistics, including a uniformly most powerful unbiased permutation test for Type I censored data, an exact test for comparing variances, and an exact test for cross-over designs. He earned a PhD in mathematical statistics from the University of California at Berkeley.
"… this primer on research design helps shed light on some of the fundamental issues in experimental design, analysis, and dissemination of results. Some of the most effective chapters are those regarding logistics and planning, how one should think about data and variation, how best to present results, and even how to respond to rejection. The author assumes some basic knowledge of mathematical statistics and the R programming language, and for readers with a solid research methods background, this primer will be a useful addition to their toolbox."
—Jo A. Wick, The American Statistician, November 2014"This book is a general primer, with examples, explaining the value of statistical design and analysis of experiments. It uses the R computing language for all illustrative calculations and is written in a plain, down-to-earth, and easily understood manner."
—International Statistical Review, 2013"Making the transition from student to professional researcher can be a daunting experience. This book can serve as a valuable refresher on hypothesis testing, coping with variation, data collection, sample size decisions and more, along with cursory explanation of R output largely based on freely available data sets. … This is high-level material to aid the reader in becoming a confident researcher … . For the reader who wants to put theory to practice, and do it in R, this work can be a guide to success in analyzing and collection categorical data, detecting confounding, bootstrap approaches, case-control and cohort studies, and more."
—Tom Schulte, MAA Reviews, April 2013"… a nice primer for academic researchers. The book includes practical and straightforward information, if you like your statistics moderately seasoned with formulae and althorithms."
—Journal of Clinical Research Best Practices, June 2013
