SPSS 6.1 Guide to Data Analysis
Published on 23. March 1995
Book
Paperback/Softback
600 pages
978-0-13-437054-5 (ISBN)
Description
The SPSS Guide to Data Analysis is a clear, practical, systematic presentation of basic statistical techniques. This easy-to-follow book provides a comprehensive overview of the entire research process-from question formulations through sampling, data collection and data analysis. In clear, descriptive language, this book explains how to design research for computer analysis, get data into the computer and perform simple analyses. Actual survey data sets are used to explain these fundamental concepts. The data sets are available on diskette, and a corresponding instructor's manual is also available.
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 230 mm
Width: 185 mm
Thickness: 20 mm
Weight
866 gr
ISBN-13
978-0-13-437054-5 (9780134370545)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
I. GETTING STARTED. 1. Answering a Question. 2. Finding Your Way in SPSS. 3. Basics of Creating a Data File. II. DESCRIBING DATA. 1. Examining Frequencies. 2. Computing Descriptive Statistics. 3. Comparing Groups. 4. Looking at Distributions. 5. Crosstabulations. 6. Plotting Data. III. TESTING HYPOTHESES. 1. What Does it Mean to Test Statistical Hypotheses? 2. The Normal Distribution. 3. Drawing Conclusions About a Single Mean. 4. Testing Hypotheses About Two Related Means. 5. Testing Hypotheses About Two Independent Means. 6. One Way Analysis of Variance. 7. Two Way Analysis of Variance. 8. Testing Hypotheses About Independence. 9. Nonparametric Tests. IV. LOOKING FOR RELATIONSHIPS. 1. Measuring Association in Crosstabulations. 2. Measuring Association for Interval Data. 3. Testing Bivariate Regression Hypotheses. 4. Analyzing Residuals. 5. Building Multiple Regression Models. 6. Diagnostics for Multiple Regression Models.