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Summary
James Stevens' best-selling textis written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured. The assumptions underlying each analysis are emphasized, and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises. The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include: *a new chapter on hierarchical linear modeling using HLM6; *a CD containing all of the book's data sets; *new coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Ch. 6); *more exercises in each chapter. Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. AnInstructor's Solutions CDis available to adopters.
Table of Contents
| Preface | p. xi |
| Introduction | p. 1 |
| Focus and Overview of Topics | |
| Some Basic Descriptive Statistics | |
| Summation Notation | |
| t Test for Independent Samples | |
| t Test for Dependent Samples | |
| Outliers | |
| SPSS and SAS Statistical Packages | |
| SPSS for Windows-Release 12.0 | |
| Data Files | |
| Data Entry | |
| Editing a Dataset | |
| Splitting and Merging Files | |
| Two Ways of Running Analyses on SPSS | |
| SPSS Output Navigator | |
| SAS and SPSS Output for Correlations, Descriptives, and t Tests | |
| Data Sets on Compact Disk | |
| Obtaining the Mean and Variance on the T1-30Xa Calculator | |
| One Way Analysis of Variance | p. 45 |
| Introduction | |
| Rationale for ANOVA | |
| Numerical Example | |
| Expected Mean Squares | |
| MS[subscript w] and MS[subscript b] as Variances | |
| A Linear Model for the Data | |
| Assumptions in ANOVA | |
| The Independence Assumption | |
| ANOVA on SPSS and SAS | |
| Post Hoc Procedures | |
| Tukey Procedure | |
| The Scheffe Procedure | |
| Heterogeneous Variances and Unequal Group Sizes | |
| Measures of Association (Variance Accounted For) | |
| Planned Comparisons | |
| Test Statistic for Planned Comparisons | |
| Planned Comparisons on SPSS and SAS | |
| The Effect of an Outlier on an ANOVA | |
| Multivariate Analysis of Variance | |
| Summary | |
| Appendix | |
| Power Analysis | p. 105 |
| Introduction | |
| t Test for Independent Samples | |
| A Priori and Post Hoc Estimation of Power | |
| Estimation of Power for One Way Analysis of Variance | |
| A Priori Estimation of Subjects Needed for a Given Power | |
| Ways of Improving Power | |
| Power Estimation on SPSSM ANOVA | |
| Summary | |
| Factorial Analysis of Variance | p. 123 |
| Introduction | |
| Numerical Calculations for Two Way ANOVA | |
| Balanced and Unbalanced Designs | |
| Higher Order Designs | |
| A Comprehensive Computer Example Using Real Data | |
| Power Analysis | |
| Fixed and Random Factors | |
| Summary | |
| Doing a Balanced Two Way ANOVA With a Calculator | |
| Repeated Measures Analysis | p. 181 |
| Introduction | |
| Advantages and Disadvantages of Repeated Measures Designs | |
| Single Group Repeated Measures | |
| Completely Randomized Design | |
| Univariate Repeated Measures Analysis | |
| Assumptions in Repeated Measures Analysis | |
| Should We Use the Univariate or Multivariate Approach? | |
| Computer Analysis on SAS and SPSS for Example | |
| Post Hoc Procedures in Repeated Measures Analysis | |
| One Between and One Within Factor-A Trend Analysis | |
| Post Hoc Procedures for the One Between and One Within Design | |
| One Between and Two Within Factors | |
| Totally Within Designs | |
| Planned Comparisons in Repeated Measures Designs | |
| Summary | |
| Simple and Multiple Regression | p. 219 |
| Simple Regression | |
| Assumptions for the Errors | |
| Influential Data Points | |
| Multiple Regression | |
| Breakdown of Sum of Squares in Regression and F Test for Multiple Correlation | |
| Relationship of Simple Correlations to Multiple Correlation | |
| Multicollinearity | |
| Model Selection | |
| Two Computer Examples | |
| Checking Assumptions for the Regression Model | |
| Model Validation | |
| Importance of the Order of Predictors in Regression Analysis | |
| Other Important Issues | |
| Outliers and Influential Data Points | |
| Further Discussion of the Two Computer Examples | |
| Sample Size Determination for a Reliable Prediction Equation | |
| ANOVA as a Special Case of Regression Analysis | |
| Summary of Important Points | |
| The PRESS Statistic | |
| Analysis of Covariance | p. 285 |
| Introduction | |
| Purposes of Covariance | |
| Adjustment of Posttest Means | |
| Reduction of Error Variance | |
| Choice of Covariates | |
| Numerical Example | |
| Assumptions in Analysis of Covariance | |
| Use of ANCOVA with Intact Groups | |
| Computer Example for ANCOVA | |
| Alternative Analyses | |
| An Alternative to the Johnson-Neyman Technique | |
| Use of Several Covariates | |
| Computer Example with Two Covariates | |
| Summary | |
| Hierarchical Linear Modeling | p. 321 |
| Introduction | |
| Problems Using Single-Level Analyses of Multilevel Data | |
| Formulation of the Multilevel Model | |
| Two-Level Model-General Formulation | |
| HLM6 Software | |
| Two Level Example-Student and Classroom Data | |
| HLM Software Output | |
| Adding Level One Predictors to the HLM | |
| Addition of a Level Two Predictor to a Two Level HLM | |
| Evaluating the Efficacy of a Treatment | |
| Final Comments on Hlm | |
| Data Sets | p. 365 |
| Clinical Data | |
| Alcoholics Data | |
| Sesame Street Data | |
| Headache Data | |
| Cartoon Data | |
| Attitude Data | |
| National Academy of Sciences Data | |
| Agresti Home Sales Data | |
| Statistical Tables | p. 399 |
| Critical Values for F | |
| Percentile Points of Studentized Range Statistic | |
| Critical Values for Dunnett's Test | |
| Critical Values for F (max) Statistic | |
| Critical Values for Bryant-Paulson Procedure | |
| Power Tables | p. 413 |
| Power of F Test at [alpha] = .05, u = 1 | |
| Power of F Test at [alpha] = .05, u = 2 | |
| Power of F Test at [alpha] = .05, u = 3 | |
| Power of F Test at [alpha] = .05, u = 4 | |
| Power of F Test at [alpha] = .10, u = 1 | |
| Power of F Test at [alpha] = .10, u = 2 | |
| Power of F Test at [alpha] = .10, u = 3 | |
| Power of F Test at [alpha] = .10, u = 4 | |
| References | p. 423 |
| Answers to Selected Exercises | p. 431 |
| Author Index | p. 453 |
| Subject Index | p. 457 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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