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E-raamat: SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 31-Jul-2018
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119465782
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SPSS Data Analysis for Univariate, Bivariate, and Multivariate StatisticsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 31-Jul-2018
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119465782
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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.

Preface   ix  
  1 Review of Essential Statistical Principles
  1 (8)
  1.1 Variables and Types of Data
  2 (1)
  1.2 Significance Tests and Hypothesis Testing
  3 (1)
  1.3 Significance Levels and Type I and Type II Errors
  4 (1)
  1.4 Sample Size and Power
  5 (1)
  1.5 Model Assumptions
  6 (3)
  2 Introduction to SPSS
  9 (10)
  2.1 How to Communicate with SPSS
  9 (1)
  2.2 Data View vs. Variable View
  10 (2)
  2.3 Missing Data in SPSS: Think Twice Before Replacing Data!
  12 (7)
  3 Exploratory Data Analysis, Basic Statistics, and Visual Displays
  19 (14)
  3.1 Frequencies and Descriptives
  19 (4)
  3.2 The Explore Function
  23 (5)
  3.3 What Should I Do with Outliers? Delete or Keep Them?
  28 (1)
  3.4 Data Transformations
  29 (4)
  4 Data Management in SPSS
  33 (8)
  4.1 Computing a New Variable
  33 (1)
  4.2 Selecting Cases
  34 (2)
  4.3 Recoding Variables into Same or Different Variables
  36 (1)
  4.4 Sort Cases
  37 (1)
  4.5 Transposing Data
  38 (3)
  5 Inferential Tests on Correlations, Counts, and Means
  41 (22)
  5.1 Computing z-Scores in SPSS
  41 (3)
  5.2 Correlation Coefficients
  44 (8)
  5.3 A Measure of Reliability: Cohen's Kappa
  52 (1)
  5.4 Binomial Tests
  52 (2)
  5.5 Chi-square Goodness-of-fit Test
  54 (3)
  5.6 One-sample t-Test for a Mean
  57 (2)
  5.7 Two-sample t-Test for Means
  59 (4)
  6 Power Analysis and Estimating Sample Size
  63 (6)
  6.1 Example Using G*Power: Estimating Required Sample Size for Detecting Population Correlation
  64 (2)
  6.2 Power for Chi-square Goodness of Fit
  66 (1)
  6.3 Power for Independent-samples t-Test
  66 (1)
  6.4 Power for Paired-samples t-Test
  67 (2)
  7 Analysis of Variance: Fixed and Random Effects
  69 (22)
  7.1 Performing the ANOVA in SPSS
  70 (3)
  7.2 The F-Test for ANOVA
  73 (1)
  7.3 Effect Size
  74 (1)
  7.4 Contrasts and Post Hoc Tests on Teacher
  75 (3)
  7.5 Alternative Post Hoc Tests and Comparisons
  78 (2)
  7.6 Random Effects ANOVA
  80 (2)
  7.7 Fixed Effects Factorial ANOVA and Interactions
  82 (4)
  7.8 What Would the Absence of an Interaction Look Like?
  86 (1)
  7.9 Simple Main Effects
  86 (2)
  7.10 Analysis of Covariance (ANCOVA)
  88 (2)
  7.11 Power for Analysis of Variance
  90 (1)
  8 Repeated Measures ANOVA
  91 (12)
  8.1 One-way Repeated Measures
  91 (8)
  8.2 Two-way Repeated Measures: One Between and One Within Factor
  99 (4)
  9 Simple and Multiple Linear Regression
  103 (28)
  9.1 Example of Simple Linear Regression
  103 (2)
  9.2 Interpreting a Simple Linear Regression: Overview of Output
  105 (2)
  9.3 Multiple Regression Analysis
  107 (4)
  9.4 Scatterplot Matrix
  111 (1)
  9.5 Running the Multiple Regression
  112 (6)
  9.6 Approaches to Model Building in Regression
  118 (2)
  9.7 Forward, Backward, and Stepwise Regression
  120 (1)
  9.8 Interactions in Multiple Regression
  121 (2)
  9.9 Residuals and Residual Plots: Evaluating Assumptions
  123 (2)
  9.10 Homoscedasticity Assumption and Patterns of Residuals
  125 (1)
  9.11 Detecting Multivariate Outliers and Influential Observations
  126 (1)
  9.12 Mediation Analysis
  127 (2)
  9.13 Power for Regression
  129 (2)
  10 Logistic Regression
  131 (10)
  10.1 Example of Logistic Regression
  132 (6)
  10.2 Multiple Logistic Regression
  138 (1)
  10.3 Power for Logistic Regression
  139 (2)
  11 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
  141 (22)
  11.1 Example of MANOVA
  142 (4)
  11.2 Effect Sizes
  146 (1)
  11.3 Box's M Test
  147 (1)
  11.4 Discriminant Function Analysis
  148 (4)
  11.5 Equality of Covariance Matrices Assumption
  152 (1)
  11.6 MANOVA and Discriminant Analysis on Three Populations
  153 (6)
  11.7 Classification Statistics
  159 (2)
  11.8 Visualizing Results
  161 (1)
  11.9 Power Analysis for MANOVA
  162 (1)
  12 Principal Components Analysis
  163 (12)
  12.1 Example of PCA
  163 (1)
  12.2 Pearson's 1901 Data
  164 (2)
  12.3 Component Scores
  166 (1)
  12.4 Visualizing Principal Components
  167 (3)
  12.5 PCA of Correlation Matrix
  170 (5)
  13 Exploratory Factor Analysis
  175 (16)
  13.1 The Common Factor Analysis Model
  175 (1)
  13.2 The Problem with Exploratory Factor Analysis
  176 (1)
  13.3 Factor Analysis of the PCA Data
  176 (3)
  13.4 What Do We Conclude from the Factor Analysis?
  179 (1)
  13.5 Scree Plot
  180 (1)
  13.6 Rotating the Factor Solution
  181 (1)
  13.7 Is There Sufficient Correlation to Do the Factor Analysis?
  182 (1)
  13.8 Reproducing the Correlation Matrix
  183 (1)
  13.9 Cluster Analysis
  184 (3)
  13.10 How to Validate Clusters?
  187 (1)
  13.11 Hierarchical Cluster Analysis
  188 (3)
  14 Nonparametric Tests
  191 (8)
  14.1 Independent-samples: Mann--Whitney U
  192 (1)
  14.2 Multiple Independent-samples: Kruskal--Wallis Test
  193 (1)
  14.3 Repeated Measures Data: The Wilcoxon Signed-rank Test and Friedman Test
  194 (2)
  14.4 The Sign Test
  196 (3)
Closing Remarks and Next Steps   199 (2)
References   201 (2)
Index   203  
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.
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