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ISBN-13 : 9798214353272
Author: Frederick Gravetter
This field-leading introduction to statistics text for students in the behavioral and social sciences continues to offer straightforward instruction, accuracy, built-in learning aids, and real-world examples. The goals of STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition are to teach the methods of statistics and convey the basic principles of objectivity and logic that are essential for science — and valuable in everyday life. Authors Frederick Gravetter and Larry Wallnau help students understand statistical procedures through a conceptual context that explains why the procedures were developed and when they should be used. Students have numerous opportunities to practice statistical techniques through learning checks, examples, step-by-step demonstrations, and problems.
Statistics for The Behavioral Sciences 10th Table of contents:
Chapter 1. Introduction to Statistics
1.1. Statistics, Science, and Observations
Definitions of Statistics
Populations and Samples
Variables and Data
Parameters and Statistics
Descriptive and Inferential Statistical Methods
Statistics in the Context of Research
1.2. Data Structures, Research Methods, and Statistics
Individual Variables: Descriptive Research
Relationships between Variables
Statistics for the Correlational Method
Limitations of the Correlational Method
Statistics for Comparing Two (or More) Groups of Scores
Experimental and Nonexperimental Methods
The Experimental Method
Terminology in the Experimental Method
Nonexperimental Methods: Nonequivalent Groups and Pre-Post Studies
1.3. Variables and Measurement
Constructs and Operational Definitions
Discrete and Continuous Variables
Scales of Measurement
The Nominal Scale
The Ordinal Scale
The Interval and Ratio Scales
1.4. Statistical Notation
Scores
Summation Notation
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 1.1
Problems
Chapter 2. Frequency Distributions
2.1. Frequency Distributions and Frequency Distribution Tables
Frequency Distribution Tables
Proportions and Percentages
2.2. Grouped Frequency Distribution Tables
Real Limits and Frequency Distributions
2.3. Frequency Distribution Graphs
Graphs for Interval or Ratio Data
Graphs for Nominal or Ordinal Data
Graphs for Population Distributions
The Shape of a Frequency Distribution
2.4. Percentiles, Percentile Ranks, and Interpolation
Cumulative Frequency and Cumulative Percentage
Interpolation
2.5. Stem and Leaf Displays
Comparing Stem and Leaf Displays with Frequency Distributions
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 2.1
Demonstration 2.2
Problems
Chapter 3. Central Tendency
3.1. Overview
3.2. The Mean
Alternative Definitions for the Mean
The Weighted Mean
Computing the Mean from a Frequency Distribution Table
Characteristics of the Mean
3.3. The Median
Finding the Median for Most Distributions
Finding the Precise Median for a Continuous Variable
The Median, the Mean, and the Middle
3.4. The Mode
3.5. Selecting a Measure of Central Tendency
When to Use the Median
When to Use the Mode
Presenting Means and Medians in Graphs
3.6. Central Tendency and the Shape of the Distribution
Symmetrical Distributions
Skewed Distributions
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 3.1
Problems
Chapter 4. Variability
4.1. Introduction to Variability
The Range
4.2. Defining Standard Deviation and Variance
4.3. Measuring Variance and Standard Deviation for a Population
The Sum of Squared Deviations (SS)
Final Formulas and Notation
4.4. Measuring Standard Deviation and Variance for a Sample
The Problem with Sample Variability
Formulas for Sample Variance and Standard Deviation
Sample Variability and Degrees of Freedom
4.5. Sample Variance as an Unbiased Statistic
Biased and Unbiased Statistics
4.6. More about Variance and Standard Deviation
Presenting the Mean and Standard Deviation in a Frequency Distribution Graph
Transformations of Scale
Standard Deviation and Descriptive Statistics
Variance and Inferential Statistics
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 4.1
Problems
Chapter 5. z-Scores: Location of Scores and Standardized Distributions
5.1. Introduction to z-Scores
5.2. z-Scores and Locations in a Distribution
The z-Score Formula
Determining a Raw Score (X) from a z-Score
5.3. Other Relationships between z, X, μ , and σ
5.4. Using z-Scores to Standardize a Distribution
Demonstration of a z-Score Transformation
Using z-Scores for Making Comparisons
5.5. Other Standardized Distributions Based on z-Scores
Transforming z-Scores to a Distribution with a Predetermined μ and σ
5.6. Computing z-Scores for Samples
Standardizing a Sample Distribution
5.7. Looking Ahead to Inferential Statistics
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 5.1
Demonstration 5.2
Problems
Chapter 6. Probability
6.1. Introduction to Probability
Defining Probability
Random Sampling
Probability and Frequency Distributions
6.2. Probability and the Normal Distribution
The Unit Normal Table
Probabilities, Proportions, and z-Scores
6.3. Probabilities and Proportions for Scores from a Normal Distribution
6.4. Probability and the Binomial Distribution
The Binomial Distribution
The Normal Approximation to the Binomial Distribution
6.5. Looking Ahead to Inferential Statistics
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 6.1
Demonstration 6.2
Problems
Chapter 7. Probability and Samples: The Distribution of Sample Means
7.1. Samples, Populations, and the Distribution of Sample Means
The Distribution of Sample Means
Characteristics of the Distribution of Sample Means
7.2. The Distribution of Sample Means for Any Population and Any Sample Size
The Central Limit Theorem
The Shape of the Distribution of Sample Means
The Mean of the Distribution of Sample Means: The Expected Value of M
The Standard Error of M
Three Different Distributions
7.3. Probability and the Distribution of Sample Means
A z-Score for Sample Means
7.4. More about Standard Error
Sampling Error and Standard Error
7.5. Looking Ahead to Inferential Statistics
Standard Error as a Measure of Reliability
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 7.1
Problems
Chapter 8. Introduction to Hypothesis Testing
8.1. The Logic of Hypothesis Testing
The Unknown Population
The Four Steps of a Hypothesis Test
A Closer Look at the z-Score Statistic
8.2. Uncertainty and Errors in Hypothesis Testing
Type I Errors
Type II Errors
Selecting an Alpha Level
8.3. More about Hypothesis Tests
A Summary of the Hypothesis Test
Factors that Influence a Hypothesis Test
Assumptions for Hypothesis Tests with z-Scores
8.4. Directional (One-Tailed) Hypothesis Tests
The Hypotheses for a Directional Test
The Critical Region for Directional Tests
Comparison of One-Tailed vs. Two-Tailed Tests
8.5. Concerns about Hypothesis Testing: Measuring Effect Size
Measuring Effect Size
8.6. Statistical Power
Calculating Power
Power and Effect Size
Other Factors that Affect Power
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 8.1
Demonstration 8.2
Problems
Chapter 9. Introduction to the t Statistic
9.1. The t Statistic: An Alternative to z
The Problem with z-Scores
Introducing the t Statistic
Degrees of Freedom and the t Statistic
The t Distribution
The Shape of the t Distribution
Determining Proportions and Probabilities for t Distributions
9.2. Hypothesis Tests with the t Statistic
Hypothesis Testing Example
Assumptions of the t Test
The Influence of Sample Size and Sample Variance
9.3. Measuring Effect Size for the t Statistic
Estimated Cohen’s d
Measuring the Percentage of Variance Explained, r 2
Confidence Intervals for Estimating μ
Constructing a Confidence Interval
Factors Affecting the Width of a Confidence Interval
9.4. Directional Hypotheses and One-Tailed Tests
The Critical Region for a One-Tailed Test
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 9.1
Demonstration 9.2
Problems
Chapter 10. The t Test for Two Independent Samples
10.1. Introduction to the Independent-Measures Design
10.2. The Null Hypothesis and the Independent-Measures t Statistic
The Hypotheses for an Independent-Measures Test
The Formulas for an Independent-Measures Hypothesis Test
Calculating the Estimated Standard Error
Pooled Variance
Estimated Standard Error
The Final Formula and Degrees of Freedom
10.3. Hypothesis Tests with the Independent-Measures t Statistic
Directional Hypotheses and One-Tailed Tests
Assumptions Underlying the Independent-Measures t Formula
Hartley’s F-Max Test
10.4. Effect Size and Confidence Intervals for the Independent-Measures t
Confidence Intervals for Estimating μ 1 – μ 2
Confidence Intervals and Hypothesis Tests
10.5. The Role of Sample Variance and Sample Size in the Independent-Measures t Test
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 10.1
Demonstration 10.2
Problems
Chapter 11. The t Test for Two Related Samples
11.1. Introduction to Repeated-Measures Designs
The Matched-Subjects Design
11.2. The t Statistic for a Repeated-Measures Research Design
Difference Scores: The Data for a Repeated-Measures Study
The Hypotheses for a Related-Samples Test
The t Statistic for Related Samples
11.3. Hypothesis Tests for the Repeated-Measures Design
Directional Hypotheses and One-Tailed Tests
Assumptions of the Related-Samples t Test
11.4. Effect Size and Confidence Intervals for the Repeated-Measures t
Cohen’s d
Descriptive Statistics and the Hypothesis Test
Sample Variance and Sample Size in the Repeated-Measures t Test
11.5. Comparing Repeated- and Independent-Measures Designs
Number of Subjects
Time-Related Factors and Order Effects
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 11.1
Demonstration 11.2
Problems
Chapter 12. Introduction to Analysis of Variance
12.1. Introduction (An Overview of Analysis of Variance)
Terminology in Analysis of Variance
Statistical Hypotheses for ANOVA
Type I Errors and Multiple-Hypothesis Tests
The Test Statistic for ANOVA
12.2. The Logic of Analysis of Variance
Between-Treatments Variance
Within-Treatments Variance
The F-Ratio: The Test Statistic for ANOVA
12.3. ANOVA Notation and Formulas
ANOVA Formulas
Analysis of Sum of Squares (SS)
The Analysis of Degrees of Freedom (df)
Calculation of Variances (MS) and the F-Ratio
12.4. Examples of Hypothesis Testing and Effect Size with ANOVA
The Distribution of F-Ratios
The F Distribution Table
An Example of Hypothesis Testing and Effect Size with ANOVA
Measuring Effect Size for ANOVA
An Example with Unequal Sample Sizes
Assumptions for the Independent-Measures ANOVA
12.5. Post Hoc Tests
Posttests and Type I Errors
Tukey’s Honestly Significant Difference (HSD) Test
The Scheffè Test
12.6. More about ANOVA
A Conceptual View of ANOVA
MS within and Pooled Variance
The Relationship between ANOVA and t Tests
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 12.1
Demonstration 12.2
Problems
Chapter 13. Repeated-Measures Analysis of Variance
13.1. Overview of the Repeated-Measures ANOVA
The Hypotheses and Logic for the Repeated-Measures ANOVA
The F-Ratio for Repeated-Measures ANOVA
The Logic of the Repeated-Measures ANOVA
13.2. Hypothesis Testing and Effect Size with the Repeated-Measures ANOVA
Notation for the Repeated-Measures ANOVA
Stage 1 of the Repeated-Measures Analysis
Stage 2 of the Repeated-Measures Analysis
Calculation of the Variances (MS Values) and the F-Ratio
Measuring Effect Size for the Repeated-Measures ANOVA
Post Hoc Tests with Repeated Measures
Assumptions of the Repeated-Measures ANOVA
13.3. More about the Repeated-Measures Design
Advantages and Disadvantages of the Repeated-Measures Design
Factors that Influence the Outcome of a Repeated-Measures ANOVA and Measures of Effect Size
Repeated-Measures ANOVA and Repeated-Measures t
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 13.1
Demonstration 13.2
Problems
Chapter 14. Two-Factor Analysis of Variance (Independent Measures)
14.1. An Overview of the Two-Factor, Independent-Measures, ANOVA: Main Effects and Interactions
Main Effects and Interactions
Main Effects
Interactions
More about Interactions
Independence of Main Effects and Interactions
14.2. An Example of the Two-Factor ANOVA and Effect Size
Stage 1 of the Two-Factor Analysis
Stage 2 of the Two-Factor Analysis
Mean Squares and F-Ratios for the Two-Factor ANOVA
Measuring Effect Size for the Two-Factor ANOVA
Interpreting the Results from a Two-Factor ANOVA
14.3. More about the Two-Factor ANOVA
Testing Simple Main Effects
Using a Second Factor to Reduce Variance Caused by Individual Differences
Assumptions for the Two-Factor ANOVA
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 14.1
Demonstration 14.2
Problems
Chapter 15. Correlation
15.1. Introduction
The Characteristics of a Relationship
15.2. The Pearson Correlation
The Sum of Products of Deviations
Calculation of the Pearson Correlation
Correlation and the Pattern of Data Points
The Pearson Correlation and z-Scores
15.3. Using and Interpreting the Pearson Correlation
Where and why Correlation Are Used
Interpreting Correlations
Correlation and Causation
Correlation and Restricted Range
Outliers
Correlation and the Strength of the Relationship
Partial Correlations
15.4. Hypothesis Tests with the Pearson Correlation
The Hypotheses
The Hypothesis Test
15.5. Alternatives to the Pearson Correlation
The Spearman Correlation
Ranking Tied Scores
Special Formula for the Spearman Correlation
Testing the Significance of the Spearman Correlation
The Point-Biserial Correlation and Measuring Effect Size with r 2
The Phi-Coefficient
Summary
Key Terms
SPSS®
Focus on Problem Solving
Demonstration 15.1
Problems
Chapter 16. Introduction to Regression
16.1. Introduction to Linear Equations and Regression
Linear Equations
Regression
The Least-Squares Solution
Using the Regression Equation for Prediction
Standardized Form of the Regression Equation
16.2. The Standard Error of Estimate and Analysis of Regression: The Significance of the Regression Equation
Relationship between the Standard Error and the Correlation
Analysis of Regression
Significance of Regression and Significance of the Correlation
16.3. Introduction to Multiple Regression with Two Predictor Variables
Regression Equations with Two Predictors
R 2 and Residual Variance
The Standard Error of Estimate
Testing the Significance of the Multiple Regression Equation: Analysis of Regression
Evaluating the Contribution of Each Predictor Variable
Multiple Regression and Partial Correlations
Summary
Key terms
SPSS®
Linear and Multiple Regression
Focus on Problem Solving
Demonstration 16.1
Problems
Chapter 17. The Chi-Square Statistic: Tests for Goodness of Fit and Independence
17.1. Introduction to Chi-Square: The Test for Goodness of Fit
Parametric and Nonparametric Statistical Tests
The Chi-Square Test for Goodness of Fit
The Null Hypothesis for the Goodness-of-Fit Test
The Data for the Goodness-of-Fit Test
Expected Frequencies
The Chi-Square Statistic
17.2. An Example of the Chi-Square Test for Goodness of Fit
The Chi-Square Distribution and Degrees of Freedom
Locating the Critical Region for a Chi-Square Test
A Complete Chi-Square Test for Goodness of Fit
Goodness of Fit and the Single-Sample t Test
17.3. The Chi-Square Test for Independence
The Null Hypothesis for the Test for Independence
Observed and Expected Frequencies
The Chi-Square Statistic and Degrees of Freedom
An Example of the Chi-Square Test for Indepenedence
17.4. Effect Size and Assumptions for the Chi-Square Tests
Cohen’s w
The Phi-Coefficient and Cramér’s V
Assumptions and Restrictions for Chi-Square Tests
17.5. Special Applications of the Chi-Square Tests
Chi-Square and the Pearson Correlation
Chi-Square and the Independent-Measures t and ANOVA
The Median Test for Independent Samples
Summary
Key terms
SPSS®
Focus on Problem Solving
Demonstration 17.1
Demonstration 17.2
Problems
Chapter 18. The Binomial Test
18.1. Introduction to the Binomial Test
Hypotheses for the Binomial Test
The Data for the Binomial Test
The Test Statistic for the Binomial Test
18.2. An Example of the Binomial Test
Score Boundaries and the Binomial Test
Assumptions for the Binomial Test
18.3. More about the Binomial Test: Relationship with Chi-Square and the Sign Test
The Sign Test
Zero Differences in the Sign Test
When to Use the Sign Test
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