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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
Summary:

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
(OCoLC)762682581
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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