How to Manage, Analyze, and Interpret Survey Data
2nd Edition
Published on 14. January 2003
Book
Paperback/Softback
152 pages
978-0-7619-2576-7 (ISBN)
Description
"A useful and readable introduction to data analysis and valuable resource for the nonspecialist."
--Cameron Lee, Fuller Theological Seminary
Clearly written with useful checklists, guidelines, and examples, How to Manage, Analyze, and Interpret Survey Data shows readers how to manage survey data and become better users and consumers of statistical and qualitative survey information. Fink explains the basic vocabulary of data management and statistics, and then demonstrates the principles and logic behind the selection and interpretation of commonly used statistical and qualitative methods to analyze survey data: from cleaning the data to measurement scales through to how to read computer output and judge significance using confidence intervals. Thoroughly reorganized and revised, the book now includes coverage of:
* How to organize and manage data for analysis
* How to draft an analysis plan
* How to define and format a data file
* How to create a complete code book, including how to establish the reliability of the coding
* How to calculate the odds ratio and risk ratio
* How to do the basic steps in a content analysis of qualitative data
* How to recognize and deal with missing data and outliers for recoding
* How to enter data accurately into spreadsheets, database management programs, and statistical programs
"The author provides an excellent introductory overview to selecting appropriate statistical tests--the purposes and prerequisites for using various statistical methods."
--Kathy Sexton-Radek, Elmhurst College
--Cameron Lee, Fuller Theological Seminary
Clearly written with useful checklists, guidelines, and examples, How to Manage, Analyze, and Interpret Survey Data shows readers how to manage survey data and become better users and consumers of statistical and qualitative survey information. Fink explains the basic vocabulary of data management and statistics, and then demonstrates the principles and logic behind the selection and interpretation of commonly used statistical and qualitative methods to analyze survey data: from cleaning the data to measurement scales through to how to read computer output and judge significance using confidence intervals. Thoroughly reorganized and revised, the book now includes coverage of:
* How to organize and manage data for analysis
* How to draft an analysis plan
* How to define and format a data file
* How to create a complete code book, including how to establish the reliability of the coding
* How to calculate the odds ratio and risk ratio
* How to do the basic steps in a content analysis of qualitative data
* How to recognize and deal with missing data and outliers for recoding
* How to enter data accurately into spreadsheets, database management programs, and statistical programs
"The author provides an excellent introductory overview to selecting appropriate statistical tests--the purposes and prerequisites for using various statistical methods."
--Kathy Sexton-Radek, Elmhurst College
Reviews / Votes
"A useful and readable introduction to data analysis and valuable resource for the nonspecialist." -- Cameron LeeMore details
Edition
2nd Revised edition
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 229 mm
Width: 152 mm
Weight
227 gr
ISBN-13
978-0-7619-2576-7 (9780761925767)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Arlene Fink (PhD) is Professor of Medicine and Public Health at the University of California, Los Angeles, and president of the Langley Research Institute. Her main interests include evaluation and survey research and the conduct of research literature reviews as well as the evaluation of their quality. Dr. Fink has conducted scores of evaluation studies in public health, medicine, and education. She is on the faculty of UCLA's Robert Wood Johnson Clinical Scholars Program and is a scientific and evaluation advisor to UCLA's Gambling Studies and IMPACT (Improving Access, Counseling & Treatment for Californians with Prostate Cancer) programs. She consults nationally and internationally for agencies such as L'institut de Promotion del la Prevention Secondaire en Addictologie (IPPSA) in Paris, France, and Peninsula Health in Victoria, Australia. Professor Fink has taught and lectured extensively all over the world and is the author of more than 130 peer-reviewed articles and 15 textbooks.
Content
How to Manage, Analyze, and Interpret Survey Data:
Learning Objectives
Ch 1. Data Management
Drafting an Analysis Plan
Creating a Codebook
Establishing Reliable Coding
Measuring Agreement Between Two Coders:
The Kappa Statistic
Reviewing Surveys for Missing Data
Entering the Data
Cleaning the Data
Some Surveys Have Not Been Returned
Some Returned Surveys Have Data Missing
Some People Are Outliers
Some Data Need to Be Recoded
Ch 2. What Statistics Do for Surveys
Measurement Scales: Nominal, Ordinal,
and Numerical
Nominal Scales
Ordinal Scales
Numerical (Interval and Ratio) Scales
Independent and Dependent Variables
Checklist for Choosing a Method to Analyze Survey
Data
Descriptive Statistics and Measures
of Central Tendency:
Numerical and Ordinal Data
Mean
Median
Mode
Distributions: Skewed and Symmetric
Checklist: When to Use the Mean, Median,
and Mode
Measures of Spread
Range
Standard Deviation
Percentile
Interquartile Range
Guidelines for Selecting Measures of Dispersion
Guidelines for Selecting Measures of Dispersion 000
Descriptive Statistics and Nominal Data
Proportion and Percentage
Ratio and Rate
Ch 3. Relationships and Comparisons
Numerical Data
Calculating the Correlation Coefficient
Size of the Correlation
Ordinal Data and Correlation
Regression
A Note on the Relationship Between Two Nominal
Characteristics
The Normal Distribution
Comparisons: Hypothesis Testing, p Values, and Confidence Levels
Confidence Levels
Guide to Hypothesis Testing,
Statistical Significance, and p Values
Risk and Odds
Odds Ratios and Relative Risk
Ch 4. Selecting Commonly Used Statistical Methods
for Surveys
Reading Computer Output
Chi-Square
t Test
Analysis of Variance (ANOVA)
Practical Significance: Using Confidence Intervals
Content Analysis of Qualitative Survey Data
Assemble the Data
Learn the Contents of the Data
Create a Codebook
Create a Codebook 000
Enter and Clean the Data
Do the Analysis
Relational Databases
Analysis of Open-Ended Questions: Best
and Least Liked
Exercises
Answers
Suggested Readings
Glossary
About the Author
Learning Objectives
Ch 1. Data Management
Drafting an Analysis Plan
Creating a Codebook
Establishing Reliable Coding
Measuring Agreement Between Two Coders:
The Kappa Statistic
Reviewing Surveys for Missing Data
Entering the Data
Cleaning the Data
Some Surveys Have Not Been Returned
Some Returned Surveys Have Data Missing
Some People Are Outliers
Some Data Need to Be Recoded
Ch 2. What Statistics Do for Surveys
Measurement Scales: Nominal, Ordinal,
and Numerical
Nominal Scales
Ordinal Scales
Numerical (Interval and Ratio) Scales
Independent and Dependent Variables
Checklist for Choosing a Method to Analyze Survey
Data
Descriptive Statistics and Measures
of Central Tendency:
Numerical and Ordinal Data
Mean
Median
Mode
Distributions: Skewed and Symmetric
Checklist: When to Use the Mean, Median,
and Mode
Measures of Spread
Range
Standard Deviation
Percentile
Interquartile Range
Guidelines for Selecting Measures of Dispersion
Guidelines for Selecting Measures of Dispersion 000
Descriptive Statistics and Nominal Data
Proportion and Percentage
Ratio and Rate
Ch 3. Relationships and Comparisons
Numerical Data
Calculating the Correlation Coefficient
Size of the Correlation
Ordinal Data and Correlation
Regression
A Note on the Relationship Between Two Nominal
Characteristics
The Normal Distribution
Comparisons: Hypothesis Testing, p Values, and Confidence Levels
Confidence Levels
Guide to Hypothesis Testing,
Statistical Significance, and p Values
Risk and Odds
Odds Ratios and Relative Risk
Ch 4. Selecting Commonly Used Statistical Methods
for Surveys
Reading Computer Output
Chi-Square
t Test
Analysis of Variance (ANOVA)
Practical Significance: Using Confidence Intervals
Content Analysis of Qualitative Survey Data
Assemble the Data
Learn the Contents of the Data
Create a Codebook
Create a Codebook 000
Enter and Clean the Data
Do the Analysis
Relational Databases
Analysis of Open-Ended Questions: Best
and Least Liked
Exercises
Answers
Suggested Readings
Glossary
About the Author