Test Bank Statistics for the Behavioral and Social Sciences A Brief Course 6th Edition by Aron J. Coups
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Test Bank Statistics for the Behavioral and Social Sciences A Brief Course 6th Edition by Aron J. Coups
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Test Bank Statistics for the Behavioral and Social Sciences A Brief Course 6th Edition by Aron J. Coups
Arthur Aron, Elliot J. Coups, Elaine N. Aron
The 6th edition of this book offers a practical approach to demystifying statistics for students in the behavioral and social sciences. The text focuses on underlying logic rather than rote memorization and includes contemporary topics such as effect size, statistical power, and the role of computers in analysis.
ISBN-10: 0134899024
ISBN-13: 9780134899022
Table of Contents
Preface to the Instructor
New to this Edition
What We Do Differently
Support Materials for Instructors
About this Brief Course
Keep in Touch
Introduction to the Student
Chapter 1 Displaying the Order in a Group of Numbers Using Tables and Graphs
Learning Objectives
What Is Statistics?
The Two Branches of Statistical Methods
Some Basic Concepts: Variables, Values, and Scores
1.3.1 Kinds of Variables
1.3.2 Levels of Measuring Variables
Frequency Tables
1.4.1 Example: Returning to the Stress Rating Study
1.4.2 How to Make a Frequency Table
1.4.3 Frequency Tables for Categorical Variables
1.4.4 An Example about Student Social Interactions
1.4.5 Grouped Frequency Tables
Histograms
1.5.1 How to Make a Histogram
1.5.2 An Example of Histograms for the Social Interaction Study
1.5.3 Bar Graphs and Categorical Variables
Shapes of Frequency Distributions
1.6.1 Unimodal and Bimodal Frequency Distributions
1.6.2 Symmetrical and Skewed Distributions
Floor and Ceiling Effect
1.6.3 Normal, Heavy-Tailed, and Light-Tailed Distributions
Frequency Tables and Histograms in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Making a Frequency Table
Making a Histogram
Practice Problems
Practice Set 1-1
Practice Set 1-2
Using SPSS
Creating a Frequency Table
Creating a Histogram
Answers to “How are you doing?”
I. Answers
II. Answers
III. Answers
IV. Answers
Chapter 2 The Mean, Variance, Standard Deviation, and Z Scores
Learning Objectives
Representative Values
2.1.1 The Mean
The Mean as the Balance Point
Formula for the Mean and Statistical Symbols
Two Additional Examples of Figuring the Mean
2.1.2 The Mode
2.1.3 Comparing the Mode and the Mean
2.1.4 The Median
2.1.5 Comparing Representative Values
Variability
2.2.1 The Variance
2.2.2 The Standard Deviation
Formulas for the Variance and the Standard Deviation
Example 1: Figuring the Variance and the Standard Deviation for the Mayors
Example 2: Figuring the Variance and Standard Deviation for the Students’ Social Interactions
Computational and Definitional Formulas
2.2.3 The Importance of Variability in Behavioral and Social Sciences Research
2.2.4 The Variance as the Sum of Squared Deviations Divided by N−1
Z Scores
2.3.1 What Is a Z Score?
Z Scores as a Scale
2.3.2 Z Scores as Providing a Generalized Standard of Comparison
2.3.3 Formula to Change a Raw Score to a Z Score
2.3.4 Formula to Change a Z Score to a Raw Score
2.3.5 Additional Examples of Changing Z Scores to Raw Scores and Vice Versa
Mean, Variance, Standard Deviation, and Z Scores in Research Articles
2.4.1 Example: Mean and Median of Enrollment Statistics for Psychology Doctoral Programs
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Figuring the Mean
Answer
Finding the Median
Answer
Figuring the Sum of Squared Deviations and the Variance
Answer
Figuring the Standard Deviation
Answer
Changing a Raw Score to a Z Score
Answer
Changing a Z Score to a Raw Score
Answer
Outline for Writing Essays on Finding the Mean, Variance, and Standard Deviation
Outline for Writing Essays Involving Z Scores
Practice Problems
Practice Set 2-1
Practice Set 2-2
Using SPSS
Finding the Mean, Mode, and Median
Finding the Variance and Standard Deviation
Changing Raw Scores to Z Scores
Answers to “How are you doing?”
I. Answers
II. Answers
III. Answers
Chapter 3 Correlation and Prediction
Learning Objectives
Graphing Correlations
3.1.1 The Scatter Diagram
How to Make a Scatter Diagram
An Example of Studying the Relationship of Sleep to Mood
Patterns of Correlation
3.2.1 Linear Correlations
3.2.2 Curvilinear Correlations
3.2.3 No Correlation
3.2.4 Positive and Negative Linear Correlations
3.2.5 Strength of the Correlation
3.2.6 Importance of Identifying the Pattern of Correlation
The Correlation Coefficient
3.3.1 Logic of Figuring the Exact Linear Correlation
Cross-Product of Z Scores
3.3.2 The Correlation Coefficient
3.3.3 Correlation Coefficients for Different Degrees of Correlation
3.3.4 Formula for the Correlation Coefficient
3.3.5 Steps for Figuring the Correlation Coefficient
3.3.6 Returning to the Sleep and Mood Example
3.3.7 An Example of Graphing and Figuring a Correlation
Issues in Interpreting the Correlation Coefficient
3.4.1 Three Possible Directions of Causality
3.4.2 Ruling Out Some Possible Directions of Causality
3.4.3 The Statistical Significance of a Correlation Coefficient
3.4.4 The Correlation Coefficient and the Proportion of Variance Accounted For
Prediction
3.5.1 Predictor (X) and Criterion (Y) Variables
3.5.2 The Prediction Model Using Z Scores
Formula for the Prediction Model Using Z Scores
Steps for the Prediction Model Using Z Scores
3.5.3 The Standardized Regression Coefficient (β)
Revisiting the Sleep and Mood Example
Steps for Prediction Model Using Z Scores for the Sleep and Mood Example
3.5.4 Why Prediction Is Also Called Regression
Prediction Using Raw Scores
3.6.1 Steps for Raw-Score Prediction
3.6.2 Using Raw-Score Prediction with the Sleep and Mood Example
Correlation and Prediction in Research Articles
3.7.1 The Correlation Matrix
Advanced Topic: Multiple Regression
3.8.1 Multiple Regression Prediction Models
3.8.2 The Difference between Multiple Regression and Prediction Using One Predictor Variable
Advanced Topic: Multiple Regression in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Making a Scatter Diagram and Describing the General Pattern of Association
Answer
Figuring the Correlation Coefficient
Answer
Outline for Writing Essays on the Logic and Figuring of a Correlation Coefficient
Prediction Using Z Scores
Answer
Prediction Using Raw Scores
Answer
Advanced Topic: Multiple Regression Predictions
Answer
Practice Problems
Practice Set 3-1
Practice Set 3-2
Using SPSS
Creating a Scatter Diagram
Finding the Correlation Coefficient
Prediction with a Single Predictor Variable
Advanced Topic: Multiple Regression
Answers to “How are you doing?”
I. Answers
II. Answers
III. Answers
IV. Answers
V. Answers
VI. Answers
VII. Answers
Chapter 4 Some Key Ingredients for Inferential Statistics The Normal Curve, Sample Versus Population, and Probability
Learning Objectives
The Normal Curve
4.1.1 Why the Normal Curve Is So Common in Nature
4.1.2 The Percentage of Scores Between the Mean and 1 and 2 Standard Deviations from the Mean
4.1.3 Figuring a Person’s Number of Standard Deviations from the Mean from a Percentage
4.1.4 Figuring the Exact Percentage of Scores Between Any Two Points on the Normal Curve
The Normal Curve Table and Z Scores
4.2.1 Using the Normal Curve Table
4.2.2 Figuring the Percentage of Scores Above or Below a Particular Score Using the Normal Curve Table
IQ Scores Example 1: Use the Normal Curve Table to Figure the Percentage of Scores Above or Below a Particular Score
IQ Scores Example 2: Use the Normal Curve Table to Figure the Percentage of Scores Above or Below a Particular Score
4.2.3 Figuring Z Scores and Raw Scores from Percentages Using the Normal Curve Table
IQ Example 1: Use the Normal Curve Table to Figure Z Scores and Raw Scores from Percentages
IQ Example 2: Use the Normal Curve Table to Figure the Z Scores and Raw Scores from Percentages
IQ Example 3: Use the Normal Curve Table to Figure a Range of Scores from Percentages
Sample and Population
4.3.1 Why Samples Instead of Populations are Studied
4.3.2 Methods of Sampling
4.3.3 Statistical Terminology for Samples and Populations
Probability
4.4.1 Key Concepts in Probability
4.4.2 Figuring Probabilities