SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

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. Contents......Page 3 Preface......Page 6 Review of Essential Statistical Principles......Page 8 Variables and Types of Data......Page 9 Significance Tests and Hypothesis Testing......Page 10 Significance Levels and Type I and Type II Errors......Page 11 Sample Size and Power......Page 12 Model Assumptions......Page 13 How to Communicate with SPSS......Page 16 Data View vs. Variable View......Page 17 Missing Data in SPSS: Think Twice Before Replacing Data!......Page 19 Frequencies and Descriptives......Page 26 The Explore Function......Page 30 What Should I Do with Outliers? Delete or Keep Them?......Page 35 Data Transformations......Page 36 Computing a New Variable......Page 39 Selecting Cases......Page 40 Recoding Variables into Same or Different Variables......Page 42 Sort Cases......Page 43 Transposing Data......Page 44 ‐Scores in SPSS......Page 46 Correlation Coefficients......Page 49 Binomial Tests......Page 57 Chi‐square Goodness‐of‐fit Test......Page 59 ‐Test for a Mean......Page 62 ‐Test for Means......Page 64 Power Analysis and Estimating Sample Size......Page 68 Example Using G*Power: Estimating Required Sample Size for Detecting Population Correlation......Page 69 ‐Test......Page 71 ‐Test......Page 72 Analysis of Variance: Fixed and Random Effects......Page 74 Performing the ANOVA in SPSS......Page 75 ‐Test for ANOVA......Page 78 Effect Size......Page 79 Contrasts and Post Hoc Tests on Teacher......Page 80 Alternative Post Hoc Tests and Comparisons......Page 83 Random Effects ANOVA......Page 85 Fixed Effects Factorial ANOVA and Interactions......Page 87 Simple Main Effects......Page 91 Analysis of Covariance (ANCOVA)......Page 93 Power for Analysis of Variance......Page 95 One‐way Repeated Measures......Page 96 Two‐way Repeated Measures: One Between and One Within Factor......Page 104 Example of Simple Linear Regression......Page 108 Interpreting a Simple Linear Regression: Overview of Output......Page 110 Multiple Regression Analysis......Page 112 Scatterplot Matrix......Page 116 Running the Multiple Regression......Page 117 Approaches to Model Building in Regression......Page 123 Forward, Backward, and Stepwise Regression......Page 125 Interactions in Multiple Regression......Page 126 Residuals and Residual Plots: Evaluating Assumptions......Page 128 Homoscedasticity Assumption and Patterns of Residuals......Page 130 Detecting Multivariate Outliers and Influential Observations......Page 131 Mediation Analysis......Page 132 Power for Regression......Page 134 Logistic Regression......Page 136 Example of Logistic Regression......Page 137 Multiple Logistic Regression......Page 143 Power for Logistic Regression......Page 144 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis......Page 145 Example of MANOVA......Page 146 Effect Sizes......Page 150 Box’s M Test......Page 151 Discriminant Function Analysis......Page 152 Equality of Covariance Matrices Assumption......Page 156 MANOVA and Discriminant Analysis on Three Populations......Page 157 Classification Statistics......Page 163 Visualizing Results......Page 165 Power Analysis for MANOVA......Page 166 Example of PCA......Page 167 Pearson’s 1901 Data......Page 168 Component Scores......Page 170 Visualizing Principal Components......Page 171 PCA of Correlation Matrix......Page 174 The Common Factor Analysis Model......Page 178 Factor Analysis of the PCA Data......Page 179 What Do We Conclude from the Factor Analysis?......Page 182 Scree Plot......Page 183 Rotating the Factor Solution......Page 184 Is There Sufficient Correlation to Do the Factor Analysis?......Page 185 Reproducing the Correlation Matrix......Page 186 Cluster Analysis......Page 187 How to Validate Clusters?......Page 190 Hierarchical Cluster Analysis......Page 191 Nonparametric Tests......Page 193 Independent‐samples: Mann–Whitney U......Page 194 Multiple Independent‐samples: Kruskal–Wallis Test......Page 195 Repeated Measures Data: The Wilcoxon Signed‐rank Test and Friedman Test......Page 196 The Sign Test......Page 198 Closing Remarks and Next Steps......Page 201 Refs......Page 202 Index......Page 204 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. Enables readers to start doing actual data analysis fast for a truly hands-on learning experienceThis 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 researchAssuming 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