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Essential statistics for the social and behavioral sciences : a conceptual approach

Author: Anthony Walsh; Jane C Ollenburger
Publisher: Upper Saddle River, NJ : Prentice Hall, 2001.
Edition/Format:   Print book : EnglishView all editions and formats

Designed specifically to teach statistics to social and behavioral science majors, this text features an approach that shows the continuity and interrelatedness of the techniques discussed. It is  Read more...

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Additional Physical Format: Online version:
Walsh, Anthony, 1941-
Essential statistics for the social and behavioral sciences.
Upper Saddle River, NJ : Prentice Hall, 2001
Document Type: Book
All Authors / Contributors: Anthony Walsh; Jane C Ollenburger
ISBN: 0130193399 9780130193391
OCLC Number: 44681910
Description: xii, 305 pages : illustrations ; 24 cm
Contents: Chapter 1. Introduction to Statistical Analysis --
Thinking Statistically --
Descriptive and Inferential Statistics --
Descriptive Statistics --
Inferential Statistics --
Statistics and Error --
Parametric and Nonparametric Statistics --
Operationalization --
Reliability and Validity --
Measurement --
Dependent and Independent Variables --
Nominal Level --
Ordinal Level --
Interval Level --
Ratio Level --
The Role of Statistics in Science --
Practice Application: Variables and Levels of Measurement --
Chapter 2. Presenting and Summarizing Data --
Types of Frequency Distributions --
Interpreting Cumulative Frequencies --
Frequency Distribution of Grouped Data --
Limits, Sizes, and Midpoints of Class Intervals --
Advantages and Disadvantages of Grouping Data --
Bar Graphs and Pie Charts --
Histograms and Frequency Polygons --
Numerical Summation of Data: Percentages, Proportions, and Ratios --
Practice Application: Displaying and Summarizing Data --
Chapter 3. Central Tendency and Dispersion --
Measures of Central Tendency --
Mode --
Median --
Computing the Median with Grouped Data --
The Mean --
Computing the Mean from Grouped Data --
A Research Example --
Choosing a Measure of Central Tendency --
Measures of Dispersion --
Range --
Standard Deviation --
Computational Formula for s --
Variability and Variance --
Computing the Standard Deviation from Grouped Data --
Coefficient of Variation --
Practice Application: Central Tendency and Dispersion --
Chapter 4. Probability and the Normal Curve --
Probability --
The Multiplication Rule --
The Addition Rule --
Theoretical Probability Distributions --
The Normal Curve --
Different Kinds of Curves --
The Standard Normal Curve --
The z Scores --
Finding Area of the Curve Below the Mean --
Practice Application: The Normal Curve and z Scores --
Chapter 5. The Sampling Distribution and Estimation Procedures --
Sampling --
Simple Random Sampling --
Stratified Random Sampling --
The Sampling Distribution --
The Central Limit Theorem --
Standard Error of the Sampling Distribution --
Point and Interval Estimates --
Confidence Intervals and Alpha Levels --
Calculating Confidence Intervals --
Sampling and Confidence Intervals --
Interval Estimates for Proportions --
Estimating Sample Size --
Estimating Sample Size for Proportions --
Practice Application: The Sampling Distribution and Estimation --
Chapter 6. Hypothesis Testing: Interval/Ratio Data --
The Logic of Hypothesis Testing --
The Evidence and Statistical Significance --
Errors in Hypothesis Testing --
One Sample z Test --
Decision Rule --
The t Test --
Degrees of Freedom --
The t Distribution --
Directional Hypotheses: One- and Two-Tailed Tests --
Computing t --
t Test for Correlated (Dependent) Means --
Effects of Sample Variance on H[subscript 0] Decision --
Large Sample t Test: A Computer Example --
Interpreting the Printout --
Calculating t with Unequal Variances --
Testing Hypotheses for Single-Sample Proportions --
Statistical Versus Substantive Significance, and Strength of Association --
Practice Application: t Test --
Chapter 7. Analysis of Variance --
Assumptions of Analysis of Variance --
The Basic Logic of ANOVA --
The Idea of Variance Revisited --
The Advantage of ANOVA over Multiple Tests --
The F Distribution --
An Example of ANOVA --
Determining Statistical Significance: Mean Square and the F Ratio --
ETA Squared --
Multiple Comparisons: The Scheffe Test --
Two-Way Analysis of Variance --
Determining Statistical Significance --
Significance Levels --
Understanding Interaction --
A Research Example of a Significant Interaction Effect --
Practice Application --
Chapter 8. Hypothesis Testing with Categorical Data: Chi-Square Test --
Table Construction --
Putting Percentages in Tables --
Assumptions for the Use of Chi-Square --
The Chi-Square Distribution --
Yates' Correction for Continuity --
Chi-Square Distribution and Goodness of Fit --
Chi-Square-Based Measures of Association --
Sample Size and Chi-Square --
Contingency Coefficient --
Cramer's V --
A Computer Example of Chi-Square --
Kruskal-Wallis One-Way Analysis of Variance --
Practice Application: Chi-Square --
Chapter 9. Nonparametric Measures of Association --
The Idea of Association --
Does an Association Exist? --
What Is the Strength of the Association? --
What Is the Direction of the Association? --
Proportional Reduction in Error --
The Concept of Paired Cases --
A Computer Example --
Gamma --
Lambda --
Somer's d --
Tau-B --
The Odd's Ratio and Yule's Q --
Spearman's Rank Order Correlation --
Which Test of Association Should We Use? --
Practice Application: Nonparametric Measures of Association --
Chapter 10. Elaboration of Tabular Data --
Causal Analysis --
Criteria for Causality --
Association --
Temporal Order --
Spuriousness --
Necessary Cause --
Sufficient Cause --
Necessary and Sufficient Cause --
A Statistical Demonstration of Cause-and-Effect Relationships --
Multivariate Contingency Analysis --
Introducing a Third Variable --
Explanation and Interpretation --
Illustrating Elaboration Outcomes --
Controlling for One Variable --
Further Elaboration: Two Control Variables --
Partial Gamma --
When Not to Compute Partial Gamma --
Problems with Tabular Elaboration --
Practice Application: Bivariate Elaboration --
Chapter 11. Bivariate Correlation and Regression --
Preliminary Investigation: The Scattergram --
The Slope --
The Intercept --
The Pearson Correlation Coefficient --
Covariance and Correlation --
Partitioning r Squared and Sum of Squares --
Standard Error of the Estimate --
Standard Error of r --
Significance Testing for Pearson's r --
The Interrelationship of b, r, and [beta] --
Summarizing Properties of r, b, and [beta] --
Summarizing Prediction Formulas --
A Computer Example of Bivariate Correlation and Regression --
Practice Application: Bivariate Correlation and Regression --
Practice Application: Bivariate Correlation and Regression --
Chapter 12. Multivariate Correlation and Regression --
Partial Correlation --
Computing Partial Correlations --
Computer Example and Interpretation --
Second-Order Partials: Controlling for Two Independent Variables --
The Multiple Correlation Coefficient --
Multiple Regression --
The Unstandardized Partial Slope --
The Standardized Slope ([beta]) --
A Computer Example of Multiple Regression and Interpretation --
Summary Statistics: Multiple R, R[superscript 2], s[subscript Y.X], and ANOVA --
The Predictor Variables: b, [beta], and t --
A Visual Representation of Multiple Regression --
Dummy Variable Regression --
Regression and Interaction --
Practice Application: Partial Correlation --
Chapter 13. Introduction to Logistic Regression --
An Example of Logit Regression --
Interpretation: Probabilities and Odds --
Assessing the Model Fit --
Multiple Logistic Regression --
Practice Application: Logistic Regression --
Appendix A. Statistical Tables --
Appendix B. Answers to Odd Numbered Problems.
Responsibility: Anthony Walsh, Jane C. Ollenburger.
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