Overview

The PASW Statistics 19 Guide to Data Analysis is a friendly introduction to both data analysis and PASW Statistics 19 (formerly SPSS Statistics), the world's leading desktop statistical software package. Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With this book, you'll learn how to describe data, test hypotheses, and examine relationships using PASW. Author Marija Norusis incorporates a wealth of real data, including the General Social Survey and studies of Internet usage, opinions of the criminal justice system, marathon running times, library patronage, and the importance of manners, throughout the examples and expanded chapter exercises. This unique combination of examples, exercises, and contemporary data gives you hands-on experience in analyzing data and makes learning about data analysis and statistical software relevant, unintimidating, and even fun! A data CD-ROM is included with this book.

Full Product Details

Author:   Marija Norusis ,  Inc. SPSS
Publisher:   Pearson Education (US)
Imprint:   Pearson
Dimensions:   Width: 1.00cm , Height: 1.00cm , Length: 22.60cm
Weight:   0.880kg
ISBN:  

9780321748416

ISBN 10:   0321748417
Pages:   672
Publication Date:   15 February 2011
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Out of Print
Availability:   Awaiting stock  

Table of Contents

PART 1. GETTING STARTED WITH IBM SPSS STATISTICS 1. Introduction About This Book Getting Started with IBM SPSS Statistics Describing Data Testing Hypotheses Examining Relationships Lets Get Started 2. An Introductory Tour of IBM SPSS Statistics Starting IBM SPSS Statistics Help Is Always at Hand Copying the Data Files Opening a Data File Statistical Procedures The Viewer Window Viewer Objects The Data Editor Window Entering Non-Numeric Data Clearing the Data Editor without Saving Changes The IBM SPSS Statistics Online Tutorial The IBM SPSS Statistics Toolbar The IBM SPSS Statistics Help System Contextual Help What's Next? 3. Sources of Data Know Your Data Survey Data Asking the Question Measuring Time Selecting Participants Selecting a Sample General Social Survey Random-Digit Dialing Internet Surveys Designing Experiments Random Assignment Minimizing Bias Summary What's Next? Exercises PART 2. DESCRIBING DATA 4. Counting Responses Describing Variables A Simple Frequency Table Sorting Frequency Tables Pie Charts Bar Charts Summarizing Internet Time Histograms Mode and Median Percentiles Summary What's Next? How to Obtain a Frequency Table Format: Appearance of the Frequency Table Statistics: Univariate Statistics Charts: Bar Charts, Pie Charts, and Histograms Exercises 5. Computing Descriptive Statistics Summarizing Data Scales of Measurement Mode, Median, and Arithmetic Average Comparing Mean and Median Summarizing Time Spent Online Measures of Variability Range Variance and Standard Deviation The Coefficient of Variation Standard Scores Summary What's Next? How to Obtain Univariate Descriptive Statistics Options: Choosing Statistics and Sorting Variables Exercises 6. Comparing Groups Age, Education, and Internet Use Plotting Means Layers: Defining Subgroups by More than One Variable Summary What's Next? How to Obtain Subgroup Means Layers: Defining Subgroups by More than One Variable Options: Additional Statistics and Display of Labels Exercises 7. Looking at Distributions Marathon Completion Times Age and Gender Marathon Times for Mature Runners Summary What's Next? How to Explore Distributions Explore Statistics Graphical Displays Options Exercises 8. Counting Responses for Combinations of Variables Library Use and Education Row and Column Percentages Bar Charts Adding Control Variables Library Use and the Internet Summary What's Next? How to Obtain a Crosstabulation Layers: Three or More Variables at Once Cells: Percentages, Expected Counts, and Residuals Bivariate Statistics Format: Adjusting the Table Format Exercises 9. Plotting Data Examining Population Indicators Simple Scatterplots Scatterplot Matrices Overlay Plots Three-Dimensional Plots Identifying Unusual Points Rotating 3-D Scatterplots Summary What's Next? How to Obtain a Scatterplot Obtaining a Simple Scatterplot Obtaining an Overlay Scatterplot Obtaining a Scatterplot Matrix Obtaining a 3-D Scatterplot Editing a Scatterplot Exercises PART 3. TESTING HYPOTHESES 10. Evaluating Results from Samples From Sample to Population A Computer Model The Effect of Sample Size The Binomial Test Summary What's Next? Exercises 11. The Normal Distribution The Normal Distribution Samples from a Normal Distribution Means from a Normal Population Are the Sample Results Unlikely? Testing a Hypothesis Means from Non-Normal Distributions Means from a Uniform Distribution Summary What's Next? Exercises 12. Testing a Hypothesis about a Single Mean Examining the Data The T Distribution Calculating the T Statistic Confidence Intervals Other Confidence Levels Confidence Interval for a Difference Confidence Intervals and Hypothesis Tests Null Hypotheses and Alternative Hypotheses Rejecting the Null Hypothesis Summary What's Next? How to Obtain a One-Sample T Test Options: Confidence Level and Missing Data Exercises 13. Testing a Hypothesis about Two Related Means Marathon Runners in Paired Designs Looking at Differences Is the Mean Difference Zero? Two Approaches The Paired-Samples T Test Are You Positive? Some Possible Problems Examining Normality Summary What's Next? How to Obtain a Paired-Samples T Test Options: Confidence Level and Missing Data Exercises 14. Testing a Hypothesis about Two Independent Means Examining Television Viewing Distribution of Differences Standard Error of the Mean Difference Computing the T Statistic Output from the Two-Independent-Samples T Test Confidence Intervals for the Mean Difference Testing the Equality of Variances Effect of Outliers Introducing Education Can You Prove the Null Hypothesis? Interpreting the Observed Significance Level Power Monitoring Death Rates Does Significant Mean Important? Summary What's Next? How to Obtain an Independent-Samples T Test Define Groups: Specifying the Subgroups Options: Confidence Level and Missing Data Exercises 15. One-Way Analysis of Variance Hours in a Work Week Describing the Data Confidence Intervals for the Group Means Testing the Null Hypothesis Assumptions Needed for Analysis of Variance Analyzing the Variability Comparing the Two Estimates of Variability The Analysis-of-Variance Table Multiple Comparison Procedures Television Viewing, Education, and Internet Use Summary What's Next? How to Obtain a One-Way Analysis of Variance Post Hoc Multiple Comparisons: Finding the Difference Options: Statistics and Missing Data Exercises 16. Two-Way Analysis of Variance The Design Examining the Data Testing Hypotheses Degree and Gender Interaction Necessary Assumptions Analysis-of-Variance Table Testing the Degree-by-Gender Interaction Testing the Main Effects Removing the Interaction Effect Where Are the Differences? Multiple Comparison Results Checking Assumptions A Look at Television Extensions Summary What's Next? How to Obtain a GLM Univariate Analysis GLM Univariate: Model GLM Univariate: Plots GLM Univariate: Post Hoc GLM Univariate: Options GLM Univariate: Save Exercises 17. Comparing Observed and Expected Counts Freedom or Manners? Observed and Expected Counts The Chi-Square Statistic A Larger Table Does College Open Doors? A One-Sample Chi-Square Test Power Concerns Summary What's Next? Exercises 18. Nonparametric Tests Nonparametric Tests for Paired Data Sign Test Wilcoxon Test Who's Sending E-mail? Mann-Whitney Test Kruskal-Wallis Test Friedman Test Summary How to Obtain Nonparametric Tests Chi-Square Test Binomial Test Two-Independent-Samples Tests Several-Independent-Samples Tests Two-Related-Samples Tests Several-Related-Samples Tests Options: Descriptive Statistics and Missing Values Exercises PART 4. EXAMINING RELATIONSHIPS 19. Measuring Association Components of the Justice System Proportional Reduction in Error Measures of Association for Ordinal Variables Concordant and Discordant Pairs Measures Based on Concordant and Discordant Pairs Evaluating the Components Measuring Agreement Correlation-Based Measures Measures Based on the Chi-Square Statistic Summary What's Next? Exercises 20. Linear Regression and Correlation Life Expectancy and Birthrate Choosing the Best Line Calculating the Least-Squares Line Calculating Predicted Values and Residuals Determining How Well the Line Fits Explaining Variability Some Warnings Summary What's Next? How to Obtain a Linear Regression Statistics: Further Information on the Model Residual Plots: Basic Residual Analysis Linear Regression Save: Creating New Variables Linear Regression Options Exercises 21. Testing Regression Hypotheses The Population Regression Line Assumptions Needed for Testing Hypotheses Testing Hypotheses Testing that the Slope Is Zero Confidence Intervals for the Slope and Intercept Predicting Life Expectancy Predicting Means and Individual Observations Standard Error of the Predicted Mean Confidence Intervals for the Predicted Means Prediction Intervals for Individual Cases Summary What's Next? How to Obtain a Bivariate Correlation Options: Additional Statistics and Missing Data How to Obtain a Partial Correlation Options: Additional Statistics and Missing Data Exercises 22. Analyzing Residuals Residuals Standardized Residuals Studentized Residuals Checking for Normality Checking for Constant Variance Checking Linearity Checking Independence A Final Comment on Assumptions Looking for Influential Points Studentized Deleted Residuals Summary What's Next? Exercises 23. Building Multiple Regression Models Predicting Life Expectancy The Model Assumptions for Multiple Regression Examining the Variables Looking at How Well the Model Fits Examining the Coefficients Interpreting the Partial Regression Coefficients Changing the Model Partial Correlation Coefficients Tolerance and Multicollinearity Beta Coefficients Building a Regression Model Methods for Selecting Variables Summary What's Next? How to Obtain a Multiple Linear Regression Options: Variable Selection Criteria Exercises 24. Multiple Regression Diagnostics Examining Normality Scatterplots of Residuals Leverage Changes in the Coefficients Cook's Distance Plots against Independent Variables Partial Regression Plot Why Bother? Summary Exercises Appendices A. Obtaining Charts in IBM SPSS Statistics Overview Creating Bar Charts Creating a Bar Chart for Single Variable Creating a Clustered Bar Chart Creating a Chart with Multiple Variables Modifying Charts Collapsing Pie Chart Slices Changing the Scale of Histogram Saving Chart Files B. Transforming and Selecting Data Data Transformations Transformations at a Glance Saving Changes Delaying Processing of Transformations Recoding Values Computing Variables The Calculator Pad Automatic Recoding Conditional Transformations Case Selection Temporary or Permanent Selection Other Selection Methods C. The T Distribution D. Areas under the Normal Curve E. Descriptions of Data Files F. Answers to Selected Exercises

Reviews

Author Information

Marija Norusis earned a PhD in biostatistics from the University of Michigan. She was SPSS's first professional statistician. During this time, she wrote her first book, The SPSS Introductory Guide. Since then she has written numerous volumes of highly acclaimed SPSS documentation, and textbooks that demystify statistics and SPSS. Dr. Norusis has been on the faculties of the University of Chicago and Rush Medical College, teaching statistics to diverse audiences. When not working on SPSS guides, Marija analyzes real data as a statistical consultant. For more detailed information about Dr. Norusis and her SPSS guides, visit her website at www.norusis.com.

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions