Using Multivariate Statistics

ISBN-10: 0205459382
ISBN-13: 9780205459384
Edition: 5th 2007 (Revised)
Description: This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous  More...
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List Price: $178.60
Edition: 5th
Copyright Year: 2007
Publisher: Allyn & Bacon, Incorporated
Publication Date: 2/21/2006
Binding: Hardcover
Pages: 1008
Size: 7.75" wide x 9.50" long x 1.25" tall
Weight: 4.026
Language: English

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS and SPSS. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.

Preface
Introduction
Multivariate Statistics: Why?
The Domain of Multivariate Statistics: Numbers of IVs and DVs
Experimental and Nonexperimental Research
Computers and Multivariate Statistics
Garbage In, Roses Out?
Some Useful Definitions
Continuous, Discrete, and Dichotomous Data
Samples and Populations
Descriptive and Inferential Statistics
Orthogonality: Standard and Sequential Analyses
Linear Combinations of Variables
Number and Nature of Variables to Include
Statistical Power
Data Appropriate for Multivariate Statistics
The Data Matrix
The Correlation Matrix
The Variance-Covariance Matrix
The Sum-of-Squares and Cross-Products Matrix
Residuals
Organization of the Book
A Guide to Statistical Techniques: Using the Book
Research Questions and Associated Techniques
Degree of Relationship among Variables
Significance of Group Differences
Prediction of Group Membership
Structure
Time Course of Events
Some Further Comparisons
A Decision Tree
Technique Chapters
Preliminary Check of the Data
Review of Univariate and Bivariate Statistics
Hypothesis Testing
One-Sample z Test as Prototype
Power
Extensions of the Model
Controversy Surrounding Significance Testing
Analysis of Variance
One-Way Between-Subjects ANOVA
Factorial Between-Subjects ANOVA
Within-Subjects ANOVA
Mixed Between-Within-Subjects ANOVA
Design Complexity
Specific Comparisons
Parameter Estimation
Effect Size
Bivariate Statistics: Correlation and Regression
Correlation
Regression
Chi-Square Analysis
Cleaning Up Your Act: Screening Data Prior to Analysis
Important Issues in Data Screening
Accuracy of Data File
Honest Correlations
Missing Data
Outliers
Normality, Linearity, and Homoscedasticity
Common Data Transformations
Multicollinearity and Singularity
A Checklist and Some Practical Recommendations
Complete Examples of Data Screening
Screening Ungrouped Data
Screening Grouped Data
Multiple Regression
General Purpose and Description
Kinds of Research Questions
Degree of Relationship
Importance of IVs
Adding IVs
Changing IVs
Contingencies among IVs
Comparing Sets of IVs
Predicting DV Scores for Members of a New Sample
Parameter Estimates
Limitations to Regression Analyses
Theoretical Issues
Practical Issues
Fundamental Equations for Multiple Regression
General Linear Equations
Matrix Equations
Computer Analyses of Small-Sample Example
Major Types of Multiple Regression
Standard Multiple Regression
Sequential Multiple Regression
Statistical (Stepwise) Regression
Choosing among Regression Strategies
Some Important Issues
Importance of IVs
Statistical Inference
Adjustment of R[superscript 2]
Suppressor Variables
Regression Approach to ANOVA
Centering when Interactions and Powers of IVs Are Included
Mediation in Causal Sequences
Complete Examples of Regression Analysis
Evaluation of Assumptions
Standard Multiple Regression
Sequential Regression
Example of Standard Multiple Regression with Missing Values Multiply Imputed
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Analysis of Covariance
General Purpose and Description
Kinds of Research Questions
Main Effects of IVs
Interactions among IVs
Specific Comparisons and Trend Analysis
Effects of Covariates
Effect Size
Parameter Estimates
Limitations to Analysis of Covariance
Theoretical Issues
Practical Issues
Fundamental Equations for Analysis of Covariance
Sums of Squares and Cross Products
Significance Test and Effect Size
Computer Analyses of Small-Sample Example
Some Important Issues
Choosing Covariates
Evaluation of Covariates
Test for Homogeneity of Regression
Design Complexity
Alternatives to ANCOVA
Complete Example of Analysis of Covariance
Evaluation of Assumptions
Analysis of Covariance
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Multivariate Analysis of Variance and Covariance
General Purpose and Description
Kinds of Research Questions
Main Effects of IVs
Interactions among IVs
Importance of DVs
Parameter Estimates
Specific Comparisons and Trend Analysis
Effect Size
Effects of Covariates
Repeated-Measures Analysis of Variance
Limitations to Multivariate Analysis of Variance and Covariance
Theoretical Issues
Practical Issues
Fundamental Equations for Multivariate Analysis of Variance and Covariance
Multivariate Analysis of Variance
Computer Analyses of Small-Sample Example
Multivariate Analysis of Covariance
Some Important Issues
MANOVA vs. ANOVAs
Criteria for Statistical Inference
Assessing DVs
Specific Comparisons and Trend Analysis
Design Complexity
Complete Examples of Multivariate Analysis of Variance and Covariance
Evaluation of Assumptions
Multivariate Analysis of Variance
Multivariate Analysis of Covariance
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Profile Analysis: The Multivariate Approach to Repeated Measures
General Purpose and Description
Kinds of Research Questions
Parallelism of Profiles
Overall Difference among Groups
Flatness of Profiles
Contrasts Following Profile Analysis
Parameter Estimates
Effect Size
Limitations to Profile Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Profile Analysis
Differences in Levels
Parallelism
Flatness
Computer Analyses of Small-Sample Example
Some Important Issues
Univariate vs. Multivariate Approach to Repeated Measures
Contrasts in Profile Analysis
Doubly-Multivariate Designs
Classifying Profiles
Imputation of Missing Values
Complete Examples of Profile Analysis
Profile Analysis of Subscales of the WISC
Doubly-Multivariate Analysis of Reaction Time
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Discriminant Analysis
General Purpose and Description
Kinds of Research Questions
Significance of Prediction
Number of Significant Discriminant Functions
Dimensions of Discrimination
Classification Functions
Adequacy of Classification
Effect Size
Importance of Predictor Variables
Significance of Prediction with Covariates
Estimation of Group Means
Limitations to Discriminant Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Discriminant Analysis
Derivation and Test of Discriminant Functions
Classification
Computer Analyses of Small-Sample Example
Types of Discriminant Function Analyses
Direct Discriminant Analysis
Sequential Discriminant Analysis
Stepwise (Statistical) Discriminant Analysis
Some Important Issues
Statistical Inference
Number of Discriminant Functions
Interpreting Discriminant Functions
Evaluating Predictor Variables
Effect Size
Design Complexity: Factorial Designs
Use of Classification Procedures
Complete Example of Discriminant Analysis
Evaluation of Assumptions
Direct Discriminant Analysis
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Logistic Regression
General Purpose and Description
Kinds of Research Questions
Prediction of Group Membership or Outcome
Importance of Predictors
Interactions among Predictors
Parameter Estimates
Classification of Cases
Significance of Prediction with Covariates
Effect Size
Limitations to Logistic Regression Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Logistic Regression
Testing and Interpreting Coefficients
Goodness-of-Fit
Comparing Models
Interpretation and Analysis of Residuals
Computer Analyses of Small-Sample Example
Types of Logistic Regression
Direct Logistic Regression
Sequential Logistic Regression
Statistical (Stepwise) Logistic Regression
Probit and Other Analyses
Some Important Issues
Statistical Inference
Effect Size for a Model
Interpretation of Coefficients Using Odds
Coding Outcome and Predictor Categories
Number and Type of Outcome Categories
Classification of Cases
Hierarchical and Nonhierarchical Analysis
Importance of Predictors
Logistic Regression for Matched Groups
Complete Examples of Logistic Regression
Evaluation of Limitations
Direct Logistic Regression with Two-Category Outcome and Continuous Predictors
Sequential Logistic Regression with Three Categories of Outcome
Comparisons of Programs
SPSS Package
SAS System
SYSTAT System
Survival/Failure Analysis
General Purpose and Description
Kinds of Research Questions
Proportions Surviving at Various Times
Group Differences in Survival
Survival Time with Covariates
Limitations to Survival Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Survival Analysis
Life Tables
Standard Error of Cumulative Proportion Surviving
Hazard and Density Functions
Plot of Life Tables
Test for Group Differences
Computer Analyses of Small-Sample Example
Types of Survival Analyses
Actuarial and Product-Limit Life Tables and Survivor Functions
Prediction of Group Survival Times from Covariates
Some Important Issues
Proportionality of Hazards
Censored Data
Effect Size and Power
Statistical Criteria
Predicting Survival Rate
Complete Example of Survival Analysis
Evaluation of Assumptions
Cox Regression Survival Analysis
Comparison of Programs
SAS System
SPSS Package
SYSTAT System
Canonical Correlation
General Purpose and Description
Kinds of Research Questions
Number of Canonical Variate Pairs
Interpretation of Canonical Variates
Importance of Canonical Variates
Canonical Variate Scores
Limitations
Theoretical Limitations
Practical Issues
Fundamental Equations for Canonical Correlation
Eigenvalues and Eigenvectors
Matrix Equations
Proportions of Variance Extracted
Computer Analyses of Small-Sample Example
Some Important Issues
Importance of Canonical Variates
Interpretation of Canonical Variates
Complete Example of Canonical Correlation
Evaluation of Assumptions
Canonical Correlation
Comparison of Programs
SAS System
SPSS Package
SYSTAT System
Principal Components and Factor Analysis
General Purpose and Description
Kinds of Research Questions
Number of Factors
Nature of Factors
Importance of Solutions and Factors
Testing Theory in FA
Estimating Scores on Factors
Limitations
Theoretical Issues
Practical Issues
Fundamental Equations for Factor Analysis
Extraction
Orthogonal Rotation
Communalities, Variance, and Covariance
Factor Scores
Oblique Rotation
Computer Analyses of Small-Sample Example
Major Types of Factor Analyses
Factor Extraction Techniques
Rotation
Some Practical Recommendations
Some Important Issues
Estimates of Communalities
Adequacy of Extraction and Number of Factors
Adequacy of Rotation and Simple Structure
Importance and Internal Consistency of Factors
Interpretation of Factors
Factor Scores
Comparisons among Solutions and Groups
Complete Example of FA
Evaluation of Limitations
Principal Factors Extraction with Varimax Rotation
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
Structural Equation Modeling
General Purpose and Description
Kinds of Research Questions
Adequacy of the Model
Testing Theory
Amount of Variance in the Variables Accounted for by the Factors
Reliability of the Indicators
Parameter Estimates
Intervening Variables
Group Differences
Longitudinal Differences
Multilevel Modeling
Limitations to Structural Equation Modeling
Theoretical Issues
Practical Issues
Fundamental Equations for Structural Equations Modeling
Covariance Algebra
Model Hypotheses
Model Specification
Model Estimation
Model Evaluation
Computer Analysis of Small-Sample Example
Some Important Issues
Model Identification
Estimation Techniques
Assessing the Fit of the Model
Model Modification
Reliability and Proportion of Variance
Discrete and Ordinal Data
Multiple Group Models
Mean and Covariance Structure Models
Complete Examples of Structural Equation Modeling Analysis
Confirmatory Factor Analysis of the WISC
SEM of Health Data
Comparison of Programs
EQS
LISREL
AMOS
SAS System
Multilevel Linear Modeling
General Purpose and Description
Kinds of Research Questions
Group Differences in Means
Group Differences in Slopes
Cross-Level Interactions
Meta-Analysis
Relative Strength of Predictors at Various Levels
Individual and Group Structure
Path Analysis at Individual and Group Levels
Analysis of Longitudinal Data
Multilevel Logistic Regression
Multiple Response Analysis
Limitations to Multilevel Linear Modeling
Theoretical Issues
Practical Issues
Fundamental Equations
Intercepts-Only Model
Model with a First-Level Predictor
Model with Predictors at First and Second Levels
Types of MLM
Repeated Measures
Higher-Order MLM
Latent Variables
Nonnormal Outcome Variables
Multiple Response Models
Some Important Issues
Intraclass Correlation
Centering Predictors and Changes in Their Interpretations
Interactions
Random and Fixed Intercepts and Slopes
Statistical Inference
Effect Size
Estimation Techniques and Convergence Problems
Exploratory Model Building
Complete Example of MLM
Evaluation of Assumptions
Multilevel Modeling
Comparison of Programs
SAS System
SPSS Package
HLM Program
MLwiN Program
SYSTAT System
Multiway Frequency Analysis
General Purpose and Description
Kinds of Research Questions
Associations among Variables
Effect on a Dependent Variable
Parameter Estimates
Importance of Effects
Effect Size
Specific Comparisons and Trend Analysis
Limitations to Multiway Frequency Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Multiway Frequency Analysis
Screening for Effects
Modeling
Evaluation and Interpretation
Computer Analyses of Small-Sample Example
Some Important Issues
Hierarchical and Nonhierarchical Models
Statistical Criteria
Strategies for Choosing a Model
Complete Example of Multiway Frequency Analysis
Evaluation of Assumptions: Adequacy of Expected Frequencies
Hierarchical Log-Linear Analysis
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
An Overview of the General Linear Model
Linearity and the General Linear Model
Bivariate to Multivariate Statistics and Overview of Techniques
Bivariate Form
Simple Multivariate Form
Full Multivariate Form
Alternative Research Strategies
Time-Series Analysis (available online at www.ablongman.com/tabachnick5e)
General Purpose and Description
Kinds of Research Questions
Pattern of Autocorrelation
Seasonal Cycles and Trends
Forecasting
Effect of an Intervention
Comparing Time Series
Time Series with Covariates
Effect Size and Power
Assumptions of Time-Series Analysis
Theoretical Issues
Practical Issues
Fundamental Equations for Time-Series ARIMA Models
Identification ARIMA (p, d, q) Models
Estimating Model Parameters
Diagnosing a Model
Computer Analysis of Small-Sample Time-Series Example
Types of Time-Series Analyses
Models with Seasonal Components
Models with Interventions
Adding Continuous Variables
Some Important Issues
Patterns of ACFs and PACFs
Effect Size
Forecasting
Statistical Methods for Comparing Two Models
Complete Example of a Time-Series Analysis
Evaluation of Assumptions
Baseline Model Identification and Estimation
Baseline Model Diagnosis
Intervention Analysis
Comparison of Programs
SPSS Package
SAS System
SYSTAT System
A Skimpy Introduction to Matrix Algebra
The Trace of a Matrix
Addition or Subtraction of a Constant to a Matrix
Multiplication or Division of a Matrix by a Constant
Addition and Subtraction of Two Matrices
Multiplication, Transposes, and Square Roots of Matrices
Matrix "Division" (Inverses and Determinants)
Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix
Research Designs for Complete Examples
Women's Health and Drug Study
Sexual Attraction Study
Learning Disabilities Data Bank
Reaction Time to Identify Figures
Field Studies of Noise-Induced Sleep Disturbance
Clinical Trial for Primary Biliary Cirrhosis
Impact of Seat Belt Law
Statistical Tables
Normal Curve Areas
Critical Values of the t Distribution for [alpha] = .05 and .01, Two-Tailed Test
Critical Values of the F Distribution
Critical Values of Chi Square (X[superscript 2])
Critical Values for Squared Multiple Correlation (R[superscript 2]) in Forward Stepwise Selection
Critical Values for F[subscript MAX] (S[superscript 2 subscript MAX]/S[superscript 2 subscript MIN]) Distribution for [alpha] = .05 and .01
References
Index

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