Rights Contact Login For More Details
- Wiley
More About This Title SPSS Data Analysis for Univariate, Bivariate, andMultivariate Statistics
English
Enables readers to start doing actual data analysis fast for a truly hands-on learning experience
This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction.
SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to:
- Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts
- Make use of computational tools used by data analysis professionals.
- Focus on real-world application to apply concepts from the book to actual research
Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent “how-to” book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks.
English
Daniel J. Denis, PhD, is Professor of Quantitative Psychology in the Department of Psychology at the University of Montana where he teaches courses in applied univariate and multivariate statistics. He has published several articles in peer-reviewed journals and regularly serves as consultant to researchers and practitioners in a variety of fields.
English
Preface ix
1 Review of Essential Statistical Principles 1
1.1 Variables and Types of Data 2
1.2 Significance Tests and Hypothesis Testing 3
1.3 Significance Levels and Type I and Type II Errors 4
1.4 Sample Size and Power 5
1.5 Model Assumptions 6
2 Introduction to SPSS 9
2.1 How to Communicate with SPSS 9
2.2 Data View vs. Variable View 10
2.3 Missing Data in SPSS: Think Twice Before Replacing Data! 12
3 Exploratory Data Analysis, Basic Statistics, and Visual Displays 19
3.1 Frequencies and Descriptives 19
3.2 The Explore Function 23
3.3 What Should I Do with Outliers? Delete or Keep Them? 28
3.4 Data Transformations 29
4 Data Management in SPSS 33
4.1 Computing a New Variable 33
4.2 Selecting Cases 34
4.3 Recoding Variables into Same or Different Variables 36
4.4 Sort Cases 37
4.5 Transposing Data 38
5 Inferential Tests on Correlations, Counts, and Means 41
5.1 Computing z‐Scores in SPSS 41
5.2 Correlation Coefficients 44
5.3 A Measure of Reliability: Cohen’s Kappa 52
5.4 Binomial Tests 52
5.5 Chi‐square Goodness‐of‐fit Test 54
5.6 One‐sample t‐Test for a Mean 57
5.7 Two‐sample t‐Test for Means 59
6 Power Analysis and Estimating Sample Size 63
6.1 Example Using G*Power: Estimating Required Sample Size for Detecting Population Correlation 64
6.2 Power for Chi‐square Goodness of Fit 66
6.3 Power for Independent‐samples t‐Test 66
6.4 Power for Paired‐samples t‐Test 67
7 Analysis of Variance: Fixed and Random Effects 69
7.1 Performing the ANOVA in SPSS 70
7.2 The F‐Test for ANOVA 73
7.3 Effect Size 74
7.4 Contrasts and Post Hoc Tests on Teacher 75
7.5 Alternative Post Hoc Tests and Comparisons 78
7.6 Random Effects ANOVA 80
7.7 Fixed Effects Factorial ANOVA and Interactions 82
7.8 What Would the Absence of an Interaction Look Like? 86
7.9 Simple Main Effects 86
7.10 Analysis of Covariance (ANCOVA) 88
7.11 Power for Analysis of Variance 90
8 Repeated Measures ANOVA 91
8.1 One‐way Repeated Measures 91
8.2 Two‐way Repeated Measures: One Between and One Within Factor 99
9 Simple and Multiple Linear Regression 103
9.1 Example of Simple Linear Regression 103
9.2 Interpreting a Simple Linear Regression: Overview of Output 105
9.3 Multiple Regression Analysis 107
9.4 Scatterplot Matrix 111
9.5 Running the Multiple Regression 112
9.6 Approaches to Model Building in Regression 118
9.7 Forward, Backward, and Stepwise Regression 120
9.8 Interactions in Multiple Regression 121
9.9 Residuals and Residual Plots: Evaluating Assumptions 123
9.10 Homoscedasticity Assumption and Patterns of Residuals 125
9.11 Detecting Multivariate Outliers and Influential Observations 126
9.12 Mediation Analysis 127
9.13 Power for Regression 129
10 Logistic Regression 131
10.1 Example of Logistic Regression 132
10.2 Multiple Logistic Regression 138
10.3 Power for Logistic Regression 139
11 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis 141
11.1 Example of MANOVA 142
11.2 Effect Sizes 146
11.3 Box’s M Test 147
11.4 Discriminant Function Analysis 148
11.5 Equality of Covariance Matrices Assumption 152
11.6 MANOVA and Discriminant Analysis on Three Populations 153
11.7 Classification Statistics 159
11.8 Visualizing Results 161
11.9 Power Analysis for MANOVA 162
12 Principal Components Analysis 163
12.1 Example of PCA 163
12.2 Pearson’s 1901 Data 164
12.3 Component Scores 166
12.4 Visualizing Principal Components 167
12.5 PCA of Correlation Matrix 170
13 Exploratory Factor Analysis 175
13.1 The Common Factor Analysis Model 175
13.2 The Problem with Exploratory Factor Analysis 176
13.3 Factor Analysis of the PCA Data 176
13.4 What Do We Conclude from the Factor Analysis? 179
13.5 Scree Plot 180
13.6 Rotating the Factor Solution 181
13.7 Is There Sufficient Correlation to Do the Factor Analysis? 182
13.8 Reproducing the Correlation Matrix 183
13.9 Cluster Analysis 184
13.10 How to Validate Clusters? 187
13.11 Hierarchical Cluster Analysis 188
14 Nonparametric Tests 191
14.1 Independent‐ samples: Mann–Whitney U 192
14.2 Multiple Independent‐samples: Kruskal–Wallis Test 193
14.3 Repeated Measures Data: The Wilcoxon Signed‐rank Test and Friedman Test 194
14.4 The Sign Test 196
Closing Remarks and Next Steps 199
References 201
Index 203
