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The A-Z of Error-Free Research

By Phillip I. Good

Chapman and Hall/CRC – 2012 – 269 pages

Purchasing Options:

  • Add to CartPaperback: $52.95
    978-1-43-989737-9
    July 31st 2012

Description

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.

Reviews

"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

Contents

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

Author Bio

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.

Name: The A-Z of Error-Free Research (Paperback)Chapman and Hall/CRC 
Description: By Phillip I. Good. A Practical Guide with Step-by-Step Explanations, Numerous Worked Examples, and R CodeThe A–Z of Error-Free Research describes the design, analysis, modeling, and reporting of experiments, clinical trials, and surveys. The book shows you when...
Categories: Statistics & Probability, Statistics for the Biological Sciences, Regression Analysis and Multivariate Statistics