Data Analysis with SPSS
Published on 9. November 1998
Book
Paperback/Softback
204 pages
978-0-205-26556-5 (ISBN)
Description
Many students find statistics courses alienating, boring, intimidating, or all of the above! Data Analysis with SPSS (R) is designed to help students develop an appreciation and hopefully an excitement for quantitative inquiry. Written in nine manageable chapters, this book first orients students to the approach researchers use to frame research questions and the logic of establishing causal relations. Students are then oriented to the SPSS (R) program and how to examine data sets. They are then guided through univariate analysis, bivariate analysis, graphic analysis, and multivariate analysis. Students conclude their course by learning how to write a research report and by engaging in their own research project.
This text is designed to teach students how to explore data in a systematic manner using the most popular professional statistics program for social scientists on the market today, SPSS (R) (Statistical Package for the Social Sciences). The book is organized to guide students through the logic of data analysis, from exploring data sets all the way through multivariate analysis and the writing of a research report.
Each book is packaged with a disk containing the GSS (General Social Survey) file and the States file. The GSS file contains 100 variables generated from interviews with 2900 people concerning their behaviors and attitudes on a wide variety of issues such as abortion, aid to the poor, religion, prejudice, sexuality, and politics. The States data allows comparison of all 50 states with 400 variables indicating issues such as unemployment, earnings, environment, criminality, population, corrections, and education. Students will ultimately use these data to conduct their own independent research project with SPSS (R).
This text is designed to teach students how to explore data in a systematic manner using the most popular professional statistics program for social scientists on the market today, SPSS (R) (Statistical Package for the Social Sciences). The book is organized to guide students through the logic of data analysis, from exploring data sets all the way through multivariate analysis and the writing of a research report.
Each book is packaged with a disk containing the GSS (General Social Survey) file and the States file. The GSS file contains 100 variables generated from interviews with 2900 people concerning their behaviors and attitudes on a wide variety of issues such as abortion, aid to the poor, religion, prejudice, sexuality, and politics. The States data allows comparison of all 50 states with 400 variables indicating issues such as unemployment, earnings, environment, criminality, population, corrections, and education. Students will ultimately use these data to conduct their own independent research project with SPSS (R).
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 280 mm
Width: 215 mm
Thickness: 15 mm
Weight
642 gr
ISBN-13
978-0-205-26556-5 (9780205265565)
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
Other editions
New editions
Stephen A. Sweet | Karen A. Grace-Martin
Data Analysis with SPSS
Book
06/2002
2nd Edition
Pearson
€63.43
Article exhausted; check for reprint
Content
All chapters begin with an "Overview" and conclude with "Summary," "Key Terms," "References and Further Reading," and "Exercises."
1.Key Concepts in Social Science Research.
Developing Research Questions.
Theory and Hypothesis.
Indicators.
Variables.
Causality.
Data Sets.
2.Getting Started: Accessing, Examining, and Saving Data.
The Layout of SPSS (R).
Defining and Saving New Data Set.
Loading and Examining an Existing File.
Dropping and Adding Variables in the Data Editor.
Naming and Labeling Variables.
Merging and Importing Files.
3.Univariate Analysis.
Sorting Data.
Measures of Central Tendency.
Exploring Distributions of Data.
Computing New Variables.
Recoding Existing Variables.
4.Bivariate Analysis.
Statistical Significance.
Analyzing Bivariate Relationships.
Cross Tabulations.
Comparison of Means.
Correlations.
5.Graphing.
Univariate Graphing.
Bivariate Graphing.
6.Multivariate Analysis: Regression.
The Regression Equation: A Bivariate Example.
Data and Multiple Regression.
Multivariate Regression: An Example.
Graphing a Multivariate Regression.
7.Multivariate Analysis: Logistic Regression.
Logistic Regression.
Logistic Regression: A Bivariate Example.
Multivariate Logistic Regression: An Example.
8.Writing a Research Report.
The Title.
The Abstract.
The Introduction.
The Literature Review.
The Methods.
The Findings.
The Conclusion.
9.Research Projects.
Secondary Data Analysis.
Publicly Available Data.
Potential Research Projects.
Research Project 1: Racism.
Research Project 2: Suicide.
Research Project 3: Politics.
Research Project 4: Criminality.
Research Project 5: Welfare.
Research Project 6: Sexual Behavior.
Research Project 7: Education.
Research Project 8: Your Topic.
Appendix 1.
STATES.SAV Descriptives.
Appendix 2.
GSS96.SAV File Information.
Appendix 3.
Variable Label Abbreviations.
1.Key Concepts in Social Science Research.
Developing Research Questions.
Theory and Hypothesis.
Indicators.
Variables.
Causality.
Data Sets.
2.Getting Started: Accessing, Examining, and Saving Data.
The Layout of SPSS (R).
Defining and Saving New Data Set.
Loading and Examining an Existing File.
Dropping and Adding Variables in the Data Editor.
Naming and Labeling Variables.
Merging and Importing Files.
3.Univariate Analysis.
Sorting Data.
Measures of Central Tendency.
Exploring Distributions of Data.
Computing New Variables.
Recoding Existing Variables.
4.Bivariate Analysis.
Statistical Significance.
Analyzing Bivariate Relationships.
Cross Tabulations.
Comparison of Means.
Correlations.
5.Graphing.
Univariate Graphing.
Bivariate Graphing.
6.Multivariate Analysis: Regression.
The Regression Equation: A Bivariate Example.
Data and Multiple Regression.
Multivariate Regression: An Example.
Graphing a Multivariate Regression.
7.Multivariate Analysis: Logistic Regression.
Logistic Regression.
Logistic Regression: A Bivariate Example.
Multivariate Logistic Regression: An Example.
8.Writing a Research Report.
The Title.
The Abstract.
The Introduction.
The Literature Review.
The Methods.
The Findings.
The Conclusion.
9.Research Projects.
Secondary Data Analysis.
Publicly Available Data.
Potential Research Projects.
Research Project 1: Racism.
Research Project 2: Suicide.
Research Project 3: Politics.
Research Project 4: Criminality.
Research Project 5: Welfare.
Research Project 6: Sexual Behavior.
Research Project 7: Education.
Research Project 8: Your Topic.
Appendix 1.
STATES.SAV Descriptives.
Appendix 2.
GSS96.SAV File Information.
Appendix 3.
Variable Label Abbreviations.