Sabo R., Boone E. Statistical Research Methods: A Guide for Non-Statisticians

Springer, 2013. — 223 p. — ISBN: 1461487072.This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.Statistical Methods as a Part of the Research Process
The Statistical Method
Writing in the IMRaD Format
The R Statistical Software Package
One-Sample Proportions
Introduction: Qualitative Data
Establishing Hypotheses
Summarizing Categorical Data (with R Code)
Assessing Assumptions
Hypothesis Test for Comparing a Population Proportion
Performing the Test and Decision Making (with R Code)
Formal Decision Making
Contingency Methods (with R Code)
Communicating the Results (IMRaD Write-Up)
Process
Exercises
Two-Sample Proportions
Summarizing Categorical Data with Contingency Tables (with R Code)
Hypothesis Test for Comparing Two Population Proportions
Performing the Test and Decision Making (with R Code)
Contingency Methods (with R Code)
Odds Ratio (with R Code)
Communicating the Results (IMRaD Write-Up)
Process
Exercises
Multi-category Data
Introduction: Types of Multi-categorical Data
Summarizing Categorical Data (with R Code)
Establishing Hypotheses: Difference Between Comparisons and Association
Assessing Assumptions (with R Code)
Performing the Test and Decision Making (with R Code)
Contingency Methods (with R Code)
Communicating the Results (IMRaD Write-Up)
Process
Exercises
Summarizing Continuous Data
Representative Values (with R Code)
Measures of Variability (with R Code)
Assessing Normality (with R Code)
Rounding and Reporting Conventions
Exercises
One-Sample Means
Behavior of the Sample Mean
Establishing Hypotheses
Assessing Assumptions (with R Code)
Summarizing Data (with R Code)
Performing the Test and Decision Making (with R Code)
Contingency Methods (with R Code)
Communicating the Results
Process
Exercises
Two-Sample Means
Introduction: Independent Groups or Paired Measurements
Independent Groups
Paired Measurements
Analysis of Variance
Establishing Hypotheses
Assessing Assumptions (with R Code)
Summarizing Data (with R Code)
Performing the Test and Decision Making (with R Code)
Contingency Methods (with R Code)
Communicating the Results
Process
Exercises
Power
Making Mistakes with Statistical Tests
Determinants of Sample Size
Categorical Outcomes
Continuous Outcomes
Post-hoc Power Analysis
Exercises
Association and RegressionCorrelation Coefficients
Simple Linear Regression
Exercises