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## Summary

## 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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