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| Preface | |
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| Introduction | |
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| Focus and Overview of Topics | |
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| Some Basic Descriptive Statistics | |
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| Summation Notation | |
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| t Test for Independent Samples | |
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| t Test for Dependent Samples | |
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| Outliers | |
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| SPSS and SAS Statistical Packages | |
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| SPSS for Windows-Release 12.0 | |
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| Data Files | |
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| Data Entry | |
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| Editing a Dataset | |
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| Splitting and Merging Files | |
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| Two Ways of Running Analyses on SPSS | |
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| SPSS Output Navigator | |
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| SAS and SPSS Output for Correlations, Descriptives, and t Tests | |
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| Data Sets on Compact Disk | |
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| Obtaining the Mean and Variance on the T1-30Xa Calculator | |
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| One Way Analysis of Variance | |
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| Introduction | |
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| Rationale for ANOVA | |
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| Numerical Example | |
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| Expected Mean Squares | |
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| MS[subscript w] and MS[subscript b] as Variances | |
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| A Linear Model for the Data | |
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| Assumptions in ANOVA | |
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| The Independence Assumption | |
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| ANOVA on SPSS and SAS | |
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| Post Hoc Procedures | |
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| Tukey Procedure | |
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| The Scheffe Procedure | |
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| Heterogeneous Variances and Unequal Group Sizes | |
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| Measures of Association (Variance Accounted For) | |
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| Planned Comparisons | |
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| Test Statistic for Planned Comparisons | |
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| Planned Comparisons on SPSS and SAS | |
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| The Effect of an Outlier on an ANOVA | |
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| Multivariate Analysis of Variance | |
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| Summary | |
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| Appendix | |
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| Power Analysis | |
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| Introduction | |
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| t Test for Independent Samples | |
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| A Priori and Post Hoc Estimation of Power | |
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| Estimation of Power for One Way Analysis of Variance | |
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| A Priori Estimation of Subjects Needed for a Given Power | |
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| Ways of Improving Power | |
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| Power Estimation on SPSSM ANOVA | |
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| Summary | |
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| Factorial Analysis of Variance | |
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| Introduction | |
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| Numerical Calculations for Two Way ANOVA | |
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| Balanced and Unbalanced Designs | |
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| Higher Order Designs | |
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| A Comprehensive Computer Example Using Real Data | |
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| Power Analysis | |
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| Fixed and Random Factors | |
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| Summary | |
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| Doing a Balanced Two Way ANOVA With a Calculator | |
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| Repeated Measures Analysis | |
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| Introduction | |
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| Advantages and Disadvantages of Repeated Measures Designs | |
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| Single Group Repeated Measures | |
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| Completely Randomized Design | |
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| Univariate Repeated Measures Analysis | |
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| Assumptions in Repeated Measures Analysis | |
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| Should We Use the Univariate or Multivariate Approach? | |
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| Computer Analysis on SAS and SPSS for Example | |
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| Post Hoc Procedures in Repeated Measures Analysis | |
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| One Between and One Within Factor-A Trend Analysis | |
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| Post Hoc Procedures for the One Between and One Within Design | |
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| One Between and Two Within Factors | |
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| Totally Within Designs | |
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| Planned Comparisons in Repeated Measures Designs | |
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| Summary | |
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| Simple and Multiple Regression | |
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| Simple Regression | |
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| Assumptions for the Errors | |
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| Influential Data Points | |
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| Multiple Regression | |
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| Breakdown of Sum of Squares in Regression and F Test for Multiple Correlation | |
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| Relationship of Simple Correlations to Multiple Correlation | |
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| Multicollinearity | |
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| Model Selection | |
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| Two Computer Examples | |
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| Checking Assumptions for the Regression Model | |
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| Model Validation | |
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| Importance of the Order of Predictors in Regression Analysis | |
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| Other Important Issues | |
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| Outliers and Influential Data Points | |
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| Further Discussion of the Two Computer Examples | |
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| Sample Size Determination for a Reliable Prediction Equation | |
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| ANOVA as a Special Case of Regression Analysis | |
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| Summary of Important Points | |
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| The PRESS Statistic | |
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| Analysis of Covariance | |
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| Introduction | |
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| Purposes of Covariance | |
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| Adjustment of Posttest Means | |
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| Reduction of Error Variance | |
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| Choice of Covariates | |
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| Numerical Example | |
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| Assumptions in Analysis of Covariance | |
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| Use of ANCOVA with Intact Groups | |
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| Computer Example for ANCOVA | |
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| Alternative Analyses | |
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| An Alternative to the Johnson-Neyman Technique | |
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| Use of Several Covariates | |
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| Computer Example with Two Covariates | |
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| Summary | |
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| Hierarchical Linear Modeling | |
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| Introduction | |
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| Problems Using Single-Level Analyses of Multilevel Data | |
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| Formulation of the Multilevel Model | |
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| Two-Level Model-General Formulation | |
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| HLM6 Software | |
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| Two Level Example-Student and Classroom Data | |
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| HLM Software Output | |
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| Adding Level One Predictors to the HLM | |
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| Addition of a Level Two Predictor to a Two Level HLM | |
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| Evaluating the Efficacy of a Treatment | |
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| Final Comments on Hlm | |
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| Data Sets | |
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| Clinical Data | |
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| Alcoholics Data | |
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| Sesame Street Data | |
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| Headache Data | |
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| Cartoon Data | |
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| Attitude Data | |
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| National Academy of Sciences Data | |
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| Agresti Home Sales Data | |
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| Statistical Tables | |
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| Critical Values for F | |
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| Percentile Points of Studentized Range Statistic | |
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| Critical Values for Dunnett's Test | |
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| Critical Values for F (max) Statistic | |
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| Critical Values for Bryant-Paulson Procedure | |
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| Power Tables | |
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| Power of F Test at [alpha] = .05, u = 1 | |
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| Power of F Test at [alpha] = .05, u = 2 | |
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| Power of F Test at [alpha] = .05, u = 3 | |
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| Power of F Test at [alpha] = .05, u = 4 | |
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| Power of F Test at [alpha] = .10, u = 1 | |
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| Power of F Test at [alpha] = .10, u = 2 | |
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| Power of F Test at [alpha] = .10, u = 3 | |
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| Power of F Test at [alpha] = .10, u = 4 | |
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| References | |
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| Answers to Selected Exercises | |
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| Author Index | |
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| Subject Index | |