Statistical Analysis for Decision Makers in Healthcare, Second Edition: Understanding and Evaluating Critical Information in Changing Times

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  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2009-05-21
  • Publisher: PRODUCTIVITY PR

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Designed for the busy health care professional who needs to collect, analyze, and/or evaluate data, this readily accessible work explains the fundamental concepts, uses, and misuses of statistics. Without jargon or mathematical formulas, the author introduces the practice of quantitative analysis to provide an understanding of the principles of scientific inquiry, applied to clinical medicine, program evaluation, policy analysis, management, accounting, and research. The book discusses practical scientific methods to show why statistics cannot compensate for bad science or bad data. It will help readers to intelligently evaluate the many numbers that cross their desk in the normal course of business.

Table of Contents

Introductionp. xi
The Scientific Foundations of Statistical Analysis
Scientific Method: The Language of Statistical Studiesp. 3
Science as a Universal Languagep. 5
The Attributes of Scientific Methodp. 8
Science Is Open-Mindedp. 8
Science Is Free of Valuesp. 9
Science Is Thoughtfulp. 10
Science Is Reproduciblep. 11
Science Is Honestp. 13
The Attributes of a Scientific Reportp. 14
Abstractp. 15
Introduction and Problem Statementp. 16
Review of Literaturep. 17
Methods (Methodology)p. 18
Datap. 19
Results and Discussionp. 20
Experimentation: The Foundation of Scientific Studiesp. 23
What Is an Experiment?p. 24
The Hypothesisp. 26
The Experimentp. 28
Randomly Select the Study Sample from the Populationp. 29
Randomly Assign the Study Sample to Experimental and Control Groupsp. 31
Establish Pretest (Baseline) Measures for Experimental and Control Groupsp. 32
Administer an Experimental Effect to an Experimental Group Onlyp. 33
Establish Posttest Measures for Experimental and Control Groupsp. 33
Analyze Experimental Data for Statistical Significancep. 33
Evaluation Studiesp. 34
The Fundamental Importance of Data
Numbers Good and Bad: How to Judge the Quality of Datap. 39
Letting the Numbers Speak for Themselvesp. 40
What Are Data?p. 42
Validityp. 43
Reliabilityp. 45
Location Biasp. 46
Sex Sampling Biasp. 47
Dollar Measurement Biasp. 47
Counting Errorp. 49
Definitional Errorp. 49
Adjustment Errorp. 49
Time Measurement Errorp. 50
Types and Levels of Measurementp. 52
Parametric Datap. 53
Nonparametric Datap. 54
Samples and Surveys: How Numbers Should Be Collectedp. 57
Sample Selectionp. 57
Sample Sizep. 58
Response Ratep. 62
Information Systemsp. 64
Survey Researchp. 65
Survey Biasp. 66
Respondent Objectivityp. 67
Survey Formatp. 69
Data Extractionp. 71
The Different Types of Statistics
Descriptive Statistics: The Foundation of Comparisonsp. 77
The First Step: What Have We Here?p. 78
Descriptive Analysisp. 78
Rangep. 80
Measures of Central Tendencyp. 83
Medianp. 84
Modep. 84
Meanp. 87
Measures of Dispersionp. 89
Overviewp. 89
Standard Deviationp. 91
Inferential Statistics: Studies of Differencesp. 97
The Normal Distributionp. 98
The Central Limit Theoremp. 103
Standard Error (of the Mean)p. 104
Quick Reviewp. 106
Hypothesis Testingp. 106
Accepting the Null Hypothesisp. 111
Rejecting the Null Hypothesisp. 111
Test Statisticsp. 114
t Testp. 116
F Test (Analysis of Variance)p. 116
Chi-Square Testp. 119
The End of the Tunnelp. 123
Relational Statistics: Studies of Relationshipsp. 125
Direction of Relationshipsp. 126
Strength of Relationshipsp. 127
Test Statisticsp. 130
Parametric Correlationp. 130
Nonparametric Correlationp. 132
Linear versus Nonlinear Relationshipsp. 132
Correlation and Causalityp. 134
Explanatory Statistics: Studies of Causalityp. 137
Statistical Modelsp. 138
The Basic Equation of Multivariate Linear Regression Modelp. 140
Specifying the Modelp. 141
Evaluating a Multivariate Linear Regression Modelp. 144
Refining Regression Modelsp. 150
Applications of Explanatory Statisticsp. 150
Postscript: Statistics in Perspectivep. 153
Indexp. 155
About the Authorp. 157
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