Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Preface | |
Acknowledgments | |
Review of Basic Concepts | |
Introduction | |
A Simple Example of a Research Problem | |
Discrepancies Between Real and Ideal Research Situations | |
Samples and Populations | |
Descriptive Versus Inferential Uses of Statistics | |
Levels of Measurement and Types of Variables | |
The Normal Distribution | |
Research Design | |
Parametric Versus Nonparametric Statistics | |
Additional Implicit Assumptions | |
Selection of an Appropriate Bivariate Analysis | |
Summary | |
Comprehension Questions | |
Introduction to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals | |
Introduction | |
Research Example: Description of a Sample of HR Scores | |
Sample Mean (M) | |
Sum of Squared Deviations and Sample Variance (s2) | |
Degrees of Freedom (df) for a Sample Variance | |
Why Is There Variance? | |
Sample Standard Deviation (s) | |
Assessment of Location of a Single X Score Relative to a Distribution of Scores | |
A Shift in Level of Analysis: The Distribution of Values of M Across Many Samples From the Same Population | |
An Index of Amount of Sampling Error: The Standard Error of the Mean (oM) | |
Effect of Sample Size (N) on the Magnitude of the Standard Error (oM ) | |
Sample Estimate of the Standard Error of the Mean (SEM) | |
The Family of t Distributions | |
Confidence Intervals | |
Summary | |
Appendix on SPSS | |
Comprehension Questions | |
Statistical Significance Testing | |
The Logic of Null Hypothesis Significance Testing (NHST) | |
Type I Versus Type II Error | |
Formal NHST Procedures: The z Test for a Null Hypothesis About One Population Mean | |
Common Research Practices Inconsistent With Assumptions and Rules for NHST | |
Strategies to Limit Risk of Type I Error | |
Interpretation of Results | |
When Is a t Test Used Instead of a z Test? | |
Effect Size | |
Statistical Power Analysis | |
Numerical Results for a One-Sample t Test Obtained From SPSS | |
Guidelines for Reporting Results | |
Summary | |
Comprehension Questions | |
Preliminary Data Screening | |
Introduction: Problems in Real Data | |
Quality Control During Data Collection | |
Example of an SPSS Data Worksheet | |
Identification of Errors and Inconsistencies | |
Missing Values | |
Empirical Example of Data Screening for Individual Variables | |
Identification and Handling of Outliers | |
Screening Data for Bivariate Analyses | |
Nonlinear Relations | |
Data Transformations | |
Verifying That Remedies Had the Desired Effects | |
Multivariate Data Screening | |
Reporting Preliminary Data Screening | |
Summary and Checklist for Data Screening | |
Comprehension Questions | |
Comparing Group Means Using the Independent Samples t Test | |
Research Situations Where the Independent Samples t Test Is Used | |
A Hypothetical Research Example | |
Assumptions About the Distribution of Scores on the Quantitative Dependent Variable | |
Preliminary Data Screening | |
Issues in Designing a Study | |
Formulas for the Independent Samples t Test | |
Conceptual Basis: Factors That Affect the Size of the t Ratio | |
Effect Size Indexes for t | |
Statistical Power and Decisions About Sample Size for the Independent Samples t Test | |
Describing the Nature of the Outcome | |
SPSS Output and Model Results Section | |
Summary | |
Comprehension Questions | |
One-Way Between-Subjects Analysis of Variance | |
Research Situations Where One-Way Between-Subjects Analysis of Variance (ANOVA) Is Used | |
Hypothetical Research Example | |
Assumptions About Scores on the Dependent Variable for One-Way Between-S ANOVA | |
Issues in Planning a Study | |
Data Screening | |
Partition of Scores Into Components | |
Computations for the One-Way Between-S ANOVA | |
Effect Size Index for One-Way Between-S ANOVA | |
Statistical Power Analysis for One-Way Between-S ANOVA | |
Nature of Differences Among Group Means | |
SPSS Output and Model Results | |
Summary | |
Comprehension Questions | |
Bivariate Pearson Correlation | |
Research Situations Where Pearson r Is Used | |
Hypothetical Research Example | |
Assumptions for Pearson r | |
Preliminary Data Screening | |
Design Issues in Planning Correlation Research | |
Computation of Pearson r | |
Statistical Significance Tests for Pearson r | |
Setting Up CIs for Correlations | |
Factors That Influence the Magnitude and Sign of Pearson r | |
Pearson r and r2 as Effect Size Indexes | |
Statistical Power and Sample Size for Correlation Studies | |
Interpretation of Outcomes for Pearson r | |
SPSS Output and Model Results Write-Up | |
Summary | |
Comprehension Questions | |
Alternative Correlation Coefficients | |
Correlations for Different Types of Variables | |
Two Research Examples | |
Correlations for Rank or Ordinal Scores | |
Correlations for True Dichotomies | |
Correlations for Artificially Dichotomized Variables | |
Assumptions and Data Screening for Dichotomous Variables | |
Analysis of Data: Dog Ownership and Survival After a Heart Attack | |
Chi-Square Test of Association (Computational Methods for Tables of Any Size) | |
Other Measures of Association for Contingency Tables | |
SPSS Output and Model Results Write-Up | |
Summary | |
Comprehension Questions | |
Bivariate Regression | |
Research Situations Where Bivariate Regression Is Used | |
A Research Example: Prediction of Salary From Years of Job Experience | |
Assumptions and Data Screening | |
Issues in Planning a Bivariate Regression Study | |
Formulas for Bivariate Regression | |
Statistical Significance Tests for Bivariate Regression | |
Setting Up Confidence Intervals Around Regression Coefficients | |
Factors That Influence the Magnitude and Sign of b | |
Effect Size/Partition of Variance in Bivariate Regression | |
Statistical Power | |
Raw Score Versus Standard Score Versions of the Regression Equation | |
Removing the Influence of X From the Y Variable by Looking at Residuals From Bivariate Regression | |
Empirical Example Using SPSS | |
Summary | |
Comprehension Questions | |
Adding a Third Variable: Preliminary Exploratory Analyses | |
Three-Variable Research Situations | |
First Research Example | |
Exploratory Statistical Analyses for Three-Variable Research Situations | |
Separate Analysis of X1, Y Relationship for Each Level of the Control Variable X2 | |
Partial Correlation Between X1 and Y Controlling for X2 | |
Understanding Partial Correlation as the Use of Bivariate Regression to Remove Variance Predictable by X2 From Both X1 and Y | |
Computation of Partial r From Bivariate Pearson Correlations | |
Intuitive Approach to Understanding Partial r | |
Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations | |
Interpretation of Various Outcomes for rY1.2 and rY1 | |
Two-Variable Causal Models | |
Three-Variable Models: Some Possible Patterns of Association Among X1, Y, and X2 | |
Mediation Versus Moderation | |
Model Results | |
Summary | |
Comprehension Questions | |
Multiple Regression With Two Predictor Variables | |
Research Situations Involving Regression With Two Predictor Variables | |
Hypothetical Research Example | |
Graphic Representation of Regression Plane | |
Semipartial (or "Part") Correlation | |
Graphic Representation of Partition of Variance in Regression With Two Predictors | |
Assumptions for Regression With Two Predictors | |
Formulas for Regression Coefficients, Significance Tests, and Confidence Intervals | |
SPSS Regression Results | |
Conceptual Basis: Factors That Affect the Magnitude and Sign of B and b Coefficients in Multiple Regression With Two Predictors | |
Tracing Rules for Causal Model Path Diagrams | |
Comparison of Equations for B, b, pr, and sr | |
Nature of Predictive Relationships | |
Effect Size Information in Regression With Two Predictors | |
Statistical Power | |
Issues in Planning a Study | |
Use of Regression With Two Predictors to Test Mediated Causal Models | |
Results | |
Summary | |
Comprehension Questions | |
Dummy Predictor Variables and Interaction Terms in Multiple Regression | |
Research Situations Where Dummy Predictor Variables Can Be Used | |
Empirical Example | |
Screening for Violations of Assumptions | |
Issues in Planning a Study | |
Parameter Estimates and Significance Tests for Regressions With Dummy Variables | |
Group Mean Comparisons Using One-Way Between-S ANOVA | |
Three Methods of Coding for Dummy Variables | |
Regression Models That Include Both Dummy and Quantitative Predictor Variables | |
Tests for Interaction (or Moderation) | |
Interaction Terms That Involve Two Quantitative Predictors | |
Effect Size and Statistical Power | |
Nature of the Relationship and/or Follow-Up Tests | |
Results | |
Summary | |
Comprehension Questions | |
Factorial Analysis of Variance | |
Research Situations and Research Questions | |
Screening for Violations of Assumptions | |
Issues in Planning a Study | |
Empirical Example: Description of Hypothetical Data | |
Computations for Between-S Factorial ANOVA | |
Conceptual Basis: Factors That Affect the Size of Sums of Squares and F Ratios in Factorial ANOVA | |
Effect Size Estimates for Factorial ANOVA | |
Statistical Power | |
Nature of the Relationships, Follow-Up Tests, and Information to Include in the Results | |
Factorial ANOVA Using the SPSS GLM Procedure | |
Summary | |
Appendix: Nonorthogonal Factorial ANOVA (ANOVA With Unbalanced Numbers of Cases in the Cells or Groups) | |
Comprehension Questions | |
Multiple Regression With More Than Two Predictors | |
Research Questions | |
Empirical Example | |
Screening for Violations of Assumptions | |
Issues in Planning a Study | |
Computation of Regression Coefficients With k Predictor Variables | |
Methods of Entry for Predictor Variables | |
Variance Partitioning in Regression for Standard or Simultaneous Regression Versus Regressions That Involve a Series of Steps | |
Significance Test for an Overall Regression Model | |
Significance Tests for Individual Predictors in Multiple Regression | |
Effect Size | |
Changes in F and R as Additional Predictors Are Added to a Model in Sequential or Statistical Regression | |
Statistical Power | |
Nature of the Relationship Between Each X Predictor and Y (Controlling for Other Predictors) | |
Assessment of Multivariate Outliers in Regression | |
SPSS Example and Results | |
Summary | |
A Review of Matrix Algebra Notation and Operations and Application of Matrix Algebra to Estimation of Slope Coefficients for Regression With More Than k Predictor Variables | |
Tables for Wilkinson and Dallal (1981) Test of Significance of Multiple R2 in Method = Forward Statistical Regression | |
Comprehension Questions | |
Analysis of Covariance | |
Research Situations and Research Questions | |
Empirical Example | |
Screening for Violations of Assumptions | |
Variance Partitioning in ANCOVA | |
Issues in Planning a Study | |
Formulas for ANCOVA | |
Computation of Adjusted Effects and Adjusted Y* Means | |
Conceptual Basis: Factors That Affect the Magnitude of SSAadj and SSresidual and the Pattern of Adjusted Group Means | |
Effect Size | |
Statistical Power | |
Nature of the Relationship and Follow-Up Tests: Information to Include in the Results Section | |
SPSS Analysis and Model Results | |
Additional Discussion of ANCOVA Results | |
Summary | |
Appendix: Alternative Methods for the Analysis of Pretest/Posttest Data | |
Comprehension Questions | |
Discriminant Analysis | |
Research Situations and Research Questions | |
Introduction of an Empirical Example | |
Screening for Violations of Assumptions | |
Issues in Planning a Study | |
Equations for Discriminant Analysis | |
Conceptual Basis: Factors That Affect the Magnitude of Wilks's Lambda | |
Effect Size | |
Statistical Power and Sample Size Recommendations | |
Follow-Up Tests to Assess What Pattern of Scores Best Differentiates Groups | |
Results | |
One-Way ANOVA on Scores on Discriminant Functions | |
Summary | |
Appendix: Eigenvalue/Eigenvector Problem | |
Comprehension Questions | |
Multivariate Analysis of Variance | |
Research Situations and Research Questions | |
Introduction of the Initial Research Example: A One-Way MANOVA | |
Why Include Multiple Outcome Measures? | |
Equivalence of MANOVA and DA | |
The General Linear Model | |
Assumptions and Data Screening | |
Issues in Planning a Study | |
Conceptual Basis of MANOVA and Some Formulas for MANOVA | |
Multivariate Test Statistics | |
Factors That Influence the Magnitude of Wilks's Lambda | |
Effect Size for MANOVA | |
Statistical Power and Sample Size Decisions | |
SPSS Output for a One-Way MANOVA: Career Group Data From Chapter 16 | |
A 2 x 3 Factorial MANOVA of the Career Group Data | |
A Significant Interaction in a 3 x 6 MANOVA | |
Comparison of Univariate and Multivariate Follow-Up Analyses for MANOVA | |
Summary | |
Comprehension Questions | |
Principal Components and Factor Analysis | |
Research Situations | |
Path Model for Factor Analysis | |
Factor Analysis as a Method of Data Reduction | |
Introduction of an Empirical Example | |
Screening for Violations of Assumptions | |
Issues in Planning a Factor Analytic Study | |
Computation of Loadings | |
Steps in the Computation of Principal Components or Factor Analysis | |
Analysis 1: Principal Components Analysis of Three Items Retaining All Three Components | |
Analysis 2: Principal Component Analysis of Three Items Retaining Only the First Component | |
Principal Components Versus Principal Axis Factoring | |
Analysis 3: PAF of Nine Items, Two Factors Retained, No Rotation | |
Geometric Representation of Correlations Between Variables and Correlations Between Components or Factors | |
The Two Multiple Regressions | |
Analysis 4: PAF With Varimax Rotation | |
Questions to Address in the Interpretation of Factor Analysis | |
Results Section for Analysis 4: PAF With Varimax Rotation | |
Factor Scores Versus Unit-Weighted Composites | |
Summary of Issues in Factor Analysis | |
Optional: Brief Introduction to Concepts in Structural Equation Modeling | |
Appendix: The Matrix Algebra of Factor Analysis | |
Comprehension Questions | |
Reliability, Validity, and Multiple-Item Scales | |
Assessment of Measurement Quality | |
Cost and Invasiveness of Measurements | |
Empirical Examples of Reliability Assessment | |
Concepts From Classical Measurement Theory | |
Use of Multiple-Item Measures to Improve Measurement Reliability | |
Three Methods for the Computation of Summated Scales | |
Assessment of Internal Homogeneity for Multiple-Item Measures | |
Correlations Among Scores Obtained Using Different Methods of Summing Items | |
Validity Assessment | |
Typical Scale Development Study | |
Summary | |
Appendix: The CESD Scale | |
Comprehension Questions | |
Analysis of Repeated Measures | |
Introduction | |
Empirical Example: Experiment to Assess Effect of Stress on Heart Rate | |
Discussion of Sources of Within-Group Error in Between-S Versus Within-S Data | |
The Conceptual Basis for the Paired Samples t Test and One-Way Repeated Measures ANOVA | |
Computation of a Paired Samples t Test to Compare Mean HR Between Baseline and Pain Conditions | |
SPSS Example: Analysis of Stress/HR Data Using a Paired Samples t Test | |
Comparison Between Independent Samples t Test and Paired Samples t Test | |
SPSS Example: Analysis of Stress/HR Data Using a Univariate One-Way Repeated Measures ANOVA | |
Using the SPSS GLM Procedure for Repeated Measures ANOVA | |
Screening for Violations of Assumptions in Univariate Repeated Measures | |
The Greenhouse-Geisser e and Huynh Feldt e Correction Factors | |
MANOVA Approach to Analysis of Repeated Measures Data | |
Effect Size | |
Statistical Power | |
Planned Contrasts | |
Results | |
Design Problems in Repeated Measures Studies | |
More Complex Designs | |
Alternative Analyses for Pretest and Posttest Scores | |
Summary | |
Comprehension Questions | |
Binary Logistic Regression | |
Research Situations | |
Simple Empirical Example: Dog Ownership and Odds of Death | |
Conceptual Basis for Binary Logistic Regression Analysis | |
Definition and Interpretation of Odds | |
A New Type of Dependent Variable: The Logit | |
Terms Involved in Binary Logistic Regression Analysis | |
Analysis of Data for First Empirical Example: Dog Ownership/Death Study | |
Issues in Planning and Conducting a Study | |
More Complex Models | |
Binary Logistic Regression for Second Empirical Analysis: Drug Dose and Gender as Predictors of Odds of Death | |
Comparison of Discriminant Analysis to Binary Logistic Regression | |
Summary | |
Comprehension Questions | |
Proportions of Area Under Standard Normal Curve | |
Critical Values for t Distribution | |
Critical Values of F | |
Critical Values of Chi-Square | |
Critical Values of the Correlation Coefficient | |
Critical Values of the Studentized Range Statistic | |
Transformation of r (Pearson Correlation) to Fisher Z | |
Glossary | |
References | |
Index | |
About the Author | |
Table of Contents provided by Ingram. All Rights Reserved. |