Essential Statistics for the Social and Behavioral Sciences A Conceptual Approach

ISBN-10: 0130193399

ISBN-13: 9780130193391

Edition: 2001

List price: $99.80
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For undergraduate-level courses in Introductory Statistics in any social/behavioral science program. Designed specifically to teach statistics to social and behavioral science majors, this text features a conceptual, intuitive approach that clearly shows the continuity and interrelatedness of the techniques discussed. It helps students become intelligent consumers of the social/behavioral science literature and provides them with the fundamental tools of research.
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Book details

List price: $99.80
Copyright year: 2001
Publisher: Prentice Hall PTR
Publication date: 10/2/2000
Binding: Paperback
Pages: 320
Size: 6.75" wide x 9.00" long x 0.75" tall
Weight: 0.990
Language: English

Introduction to Statistical Analysis
Why Study Statistics?
Thinking Statistically
Descriptive and Inferential Statistics
Descriptive Statistics
Inferential Statistics
Statistics and Error
Parametric and Nonparametric Statistics
Reliability and Validity
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
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
Central Tendency and Dispersion
Measures of Central Tendency
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
Standard Deviation
Computational Formula for s
Variability and Variance
Computing the Standard Deviation from Grouped Data
Coefficient of Variation
Index of Qualitative Variation
Practice Application: Central Tendency and Dispersion
Probability and the Normal Curve
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
The Sampling Distribution and Estimation Procedures
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
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
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
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
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
Somer's d
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
Elaboration of Tabular Data
Causal Analysis
Criteria for Causality
Temporal Order
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
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
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
Introduction to Logistic Regression
An Example of Logit Regression
Interpretation: Probabilities and Odds
Assessing the Model Fit
Multiple Logistic Regression
Practice Application: Logistic Regression
Statistical Tables
Answers to Odd Numbered Problems
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
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