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Data analysis with SPSS : a first course in applied statistics

Author: Stephen A Sweet; Karen Grace-Martin
Publisher: Boston : Allyn & Bacon, ©2012.
Edition/Format:   Print book : CD for computer : English : 4th edView all editions and formats
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Document Type: Book
All Authors / Contributors: Stephen A Sweet; Karen Grace-Martin
ISBN: 9780205019670 0205019676
OCLC Number: 681481924
Description: ix, 277 pages : illustrations ; 28 cm + 1 CD-ROM (4 3/4 in.)
Contents: Machine generated contents note: ch. 1 Key Concepts in Social Science Research --
Overview --
Why Do We Need Statistics? --
Framing Topics into Research Questions --
Theories and Hypotheses --
Population and Samples --
Relationships and Causality --
Association --
Time Order --
Nonspuriousness --
Data Sets --
Parts of a Data Set --
Reliability and Validity --
Summary --
Key Terms --
Exercises --
ch. 2 Getting Started: Accessing, Examining, and Saving Data --
Overview --
Layout of SPSS --
Types of Variables --
String Variables --
Categorical Variables --
Scale Variables --
Initial Settings --
Defining and Saving a New Data Set --
Managing Data Sets: Dropping and Adding Variables, Merging Data Sets --
Dropping and Adding Variables --
Merging and Importing Files --
Loading and Examining an Exisiting File --
Summary --
Key Terms --
Exercises --
ch. 3 Univariate Analysis: Descriptive Statistics --
Overview --
Why Do Researchers Perform Univariate Analysis? --
Exploring Distributions of Scale Variables --
Listing, Summarizing, and Sorting Observations --
Histograms --
Shapes of Distributions --
Measures of Central Tendency --
Measures of Spread --
Box Plots --
Exploring Distributions of Categorical Variables --
Pie Charts --
Bar Charts --
Summary --
Key Terms --
Exercises --
ch. 4 Constructing Variables --
Overview --
Why Construct New Variables From Existing Data? --
Recoding Existing Variables --
Computing New Variables --
Recording Computations Using Syntax --
Minimizing Missing Values in Computing New Variables --
Summary --
Key Terms --
Exercises --
ch. 5 Assessing Association through Bivariate Analysis --
Overview --
Why Do We Need Significance Tests? --
Hypotheses and Significance Tests --
Significance Levels --
Sample Sizes, Magnitudes of Effect and Significance. Levels --
Analyzing Bivariate Relationships Between Two Categorical Variables --
Cross Tabulations --
Bar Charts --
Analyzing Bivariate Relationships Between Two Scale Variables --
Correlations --
Scatterplots --
Summary --
Key Terms --
Exercises --
ch. 6 Comparing Group Means through Bivariate Analysis --
Overview --
One-Way Analysis of Variance --
Post-hoc Tests --
Assumptions of ANOVA --
Independence Assumption --
Normality Assumption --
Equal Variance Assumption --
Graphing the Results of ANOVA --
Bar Charts --
Box Plots --
T tests --
Independent Samples T Test --
Paired-Samples T Test --
Summary --
Key Terms --
Exercises --
ch. 7 Modeling Relationships of Multiple Variables with Linear Regression --
Overview --
Advantages of Modeling Relationships in Multiple Regression --
Putting Theory First-When to Pursue Linear Regression --
Linear Regression: A Bivariate Example --
Interpreting The ANOVA F-test --
Interpreting Linear Regression Coefficients --
Interpreting the R-Square Statistic --
Putting the Statistics Together --
Using Linear Regression Coefficients to Make Predictions --
Using Coefficients to Graph Bivariate Regression Lines --
Multiple Linear Regression --
Interpreting Multiple Linear Regression Coefficients --
Graphing a Multiple Regression --
Other Concerns In Applying Linear Regression --
Residuals --
Constant Variation --
Normality of Residuals --
Building Multiple Variable Models --
Degrees of Freedom --
Collinearity --
Dummy Variables --
Outliers --
Causality --
Summary --
Key Terms --
Exercises --
ch. 8 Logistic Regression --
Overview --
What Is Logistic Regression? --
When Can I Use a Logistic Regression? --
Understanding Relationships through Probabilities --
Logistic Regression: A Bivariate Example --
Interpreting Odds Ratios and Logistic Regression Coefficients --
Using Logistic Regression Coefficients to Make Predictions --
Using Coefficients to Graph a Logistic Regression Line --
Model Chi-Squares and Goodness of Fit --
Multiple Variable Logistic Regression: An Example --
Interpreting Logistic Regression Output --
Using Multiple Variable Logistic Regression Coefficients to Make Predictions --
Using Multiple Variable Coefficients to Graph a Logistic Regression Line --
Summary --
Key Terms --
Exercises --
ch. 9 Writing a Research Report --
Overview --
Writing Style and Audience --
Structure of a Report --
Title --
Abstract --
Introduction --
Literature Review --
Methods --
Findings --
Conclusion --
References --
Summary --
Key Terms --
Exercises --
ch. 10 Research Projects --
Potential Research Projects --
Research Project 1 Racism --
Research Project 2 Suicide --
Research Project 3 Criminality --
Research Project 4 Welfare and Other Public Aid Consumption --
Research Project 5 Sexual Behavior --
Research Project 6 Education --
Research Project 7 Health --
Research Project 8 Happiness --
Research Project 9 Your Topic.
Responsibility: Stephen Sweet, Karen Grace-Martin.

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