Statistical methods for the information professional : a practical, painless approach to understanding, using, and interpreting statistics / Liwen Vaughan.

Format:

Book

Author/Creator:

Vaughan, Liwen, 1956-

Contributor:

American Society for Information Science and Technology.

Series:

ASIST monograph series

Language:

English

Subjects (All):

Commercial statistics.

Physical Description:

xxi, 209 pages : illustrations ; 23 cm.

Place of Publication:

Medford, N.J. : Published for the American Society for Information Science and Technology by Information Today, 2001.

Summary:

In this unique and useful book, Liwen Vaughan clearly explains the statistical methods used in information science research, focusing on basic logic rather than mathematical intricacies. Her emphasis is on the meaning of statistics, when and how to apply them, and how to interpret the results of statistical analysis. Through the use of real-world examples, she shows how statistics can be used to improve services, make better decisions, and conduct more effective research. More than 80 helpful figures and tables, seven appendices, bibliography, and an index are included.

Contents:

Not Just Another Stats Book xvii

Chapter 1 Getting Started

Recognizing the Types of Data 1

1.1 Nominal Data 1

1.2 Ordinal Data 2

1.3 Interval Data 3

1.4 Ratio Data 4

1.5 Data Conversion 5

Chapter 2 Avoiding Manual Calculations and Formula Manipulations

Using Software 9

2.1 Types of Software 9

2.2 Which Software to Select 10

2.3 How to Organize Data into a Computer File 11

2.4 How to Deal with Missing Data 12

Chapter 3 First Look

Using Graphs to See the Characteristics of Data 15

3.1 Variety of Graphs 15

3.2 A Special Bar Graph

Histogram 20

Chapter 4 Summarizing Messy Data into Neat Figures

Descriptive Statistics 25

4.1 Measures of Central Tendency 25

4.1.1 Mean

The Arithmetic Average 26

4.1.2 Median

The Middle Point 28

4.1.3 Mode

The Peak of the Histogram 29

4.1.4 When to Use Which Measure of Central Tendency

A Summary 31

4.2 Measures of Variability 33

4.2.1 Range 34

4.2.2 Interquartile Range (IQR) 35

4.2.3 Standard Deviation (SD) 35

4.2.4 Variance 38

4.2.5 When to Use Which Measure of Variability

A Summary 38

4.3 Tying Together Descriptive Statistics Measures

Examples 38

Chapter 5 What Is Statistically Significant?

Basic Concepts of Inferential Statistics 45

5.1 Descriptive Statistics vs. Inferential Statistics 45

5.2 Population vs. Sample 46

5.3 Parameter vs. Statistic 46

5.4 Probability and Frequency Distribution 47

5.5 Normal Distribution 50

5.6 The Z Score 51

5.7 Standard Normal Distribution 53

5.8 Confidence Interval 54

5.9 Hypothesis Testing

Statistically Significant or Not 59

5.10 Errors of Statistical Testing

Type I and Type II Errors 63

Chapter 6 How to Collect Data

Sampling Methods 67

6.1 Simple Random Sample 67

6.2 Systematic Sample 69

6.3 Stratified Sample 70

6.4 Sampling Bias 71

Chapter 7 Examining Relationships for Nominal and Ordinal Data

Chi-Square Test 75

7.1 The Logic of the Chi-Square Test 75

7.2 Calculation of Expected Frequencies 78

7.3 Chi-Square Score 80

7.4 Chi-Square Table 81

7.5 Examining the Pattern of the Relationship 83

7.6 An Example of Using Software to Carry Out a Chi-Square Test 84

7.7 Requirements for Using Chi-Square Test 88

Chapter 8 Examining Relationships for Interval and Ratio Data

Correlation and Regression 93

8.1 Types of Correlation 94

8.2 Using A Scatter Plot to View the Pattern of Relationship 95

8.3 Measuring the Strength of a Relationship

Pearson r 95

8.4 Testing the Significance of Pearson r 98

8.5 Correlation and Causation 100

8.6 Regression Equation and Regression Line 102

8.7 Prediction 107

8.8 Requirements for Doing Correlation and Regression 109

Chapter 9 Are Two Samples Significantly Different?

T Test 111

9.1 Independent T Test vs. Paired T Test 112

9.2 The Logic of the T Test 114

9.3 The Procedure of the T Test 116

9.4 Examples of T Tests Using Software 118

9.5 Requirements for Using a T Test 122

Chapter 10 Are Three or More Samples Significantly Different?

Analysis of Variance 125

10.1 The Logic of ANOVA 126

10.2 The Procedure for ANOVA 129

10.3 Example of ANOVA Using Software 131

10.4 Examining the Pattern of Difference 133

10.5 Requirements for Using ANOVA 137

Chapter 11 When Data Do Not Behave

Using Nonparametric Tests 139

11.1 Spearman Correlation Coefficient 140

11.2 The Mann-Whitney Test 143

11.3 The Wilcoxon Signed Ranks Test 146

11.4 Kruskal-Wallis Test 149

11.5 Advantages and Disadvantages of Nonparametric Tests 153

11.5.1 Advantages of Nonparametric Tests 153

11.5.2 Disadvantages of Nonparametric Tests 153

11.5.3 When to Use a Nonparametric Test 154

Chapter 12 When Should I Use Which Test?

A Road Map 157

Chapter 13 Getting Sophisticated

A Taste of Some Advanced Statistical Methods 163

13.1 Two-Way ANOVA 163

13.2 Multiple Regression 171

13.2.1 Why Do We Need Multiple Regression? 171

13.2.2 Multiple Regression Equation 172

13.2.3 Regression Coefficients 173

13.2.4 Multiple Correlation Coefficient and Multiple Coefficient of Determination 174

13.2.5 Partial Correlation Coefficient 175

13.3 LISREL 176

Appendix 1 Standard Normal Distribution 185

Appendix 2 Random Number Table 187

Appendix 3 Critical Values of Chi-Square 189

Appendix 4 Critical Values of Pearson r 191

Appendix 5 Critical Values of t 193

Appendix 6 Critical Values of F for ANOVA ([alpha] = 0.05) 195

Appendix 7 Critical Values for Tukey's HSD ([alpha] = 0.05) 197.

Notes:

Includes bibliographical references (pages 199-200) and index.

ISBN:

1573871109

OCLC:

45804857