Intermediate Statistics: A Modern Approach, Third Edition

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  • Edition: 3rd
  • Format: Paperback
  • Copyright: 7/5/2007
  • Publisher: Lawrence Erlbau

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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

Prefacep. xi
Introductionp. 1
Focus and Overview of Topics
Some Basic Descriptive Statistics
Summation Notation
t Test for Independent Samples
t Test for Dependent Samples
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 Variancep. 45
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
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
Power Analysisp. 105
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
Factorial Analysis of Variancep. 123
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
Doing a Balanced Two Way ANOVA With a Calculator
Repeated Measures Analysisp. 181
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
Simple and Multiple Regressionp. 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
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 Covariancep. 285
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
Hierarchical Linear Modelingp. 321
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 Setsp. 365
Clinical Data
Alcoholics Data
Sesame Street Data
Headache Data
Cartoon Data
Attitude Data
National Academy of Sciences Data
Agresti Home Sales Data
Statistical Tablesp. 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 Tablesp. 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
Referencesp. 423
Answers to Selected Exercisesp. 431
Author Indexp. 453
Subject Indexp. 457
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