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