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E-book: Essentials of Statistics for the Behavioral Sciences

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  • Format: 548 pages
  • Pub. Date: 01-Jan-2021
  • Publisher: Worth Publishers Inc.,U.S.
  • Language: eng
  • ISBN-13: 9781319417567
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Essentials of Statistics for the Behavioral SciencesLarger Image
  • Format: 548 pages
  • Pub. Date: 01-Jan-2021
  • Publisher: Worth Publishers Inc.,U.S.
  • Language: eng
  • ISBN-13: 9781319417567
Other books in subject:

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GO DIGITAL WITH LAUNCHPAD





Behavioral statisticsas its actually practiced today





Using Macmillan's highly touted LaunchPad to deliver superior content online, the brief version of Nolan and Heinzens text introduces students to the role of statistics in the behavioral sciences today. It is a thoroughly up-to-date presentation written specifically for behavioral science students, anchored by real-world stories, a highly visual approach to presenting data, helpful mathematical and formula support, and unique immersive learning activities in LaunchPad (Which Test is Best and the new Interpreting Statistical Results). Now with a focus in every chapter on open science and data ethics, and a new final chapter on reporting and interpreting results!
Preface ix
Chapter 1 An Introduction to Statistics and Research Design
1(28)
The Two Branches of Statistics
2(2)
Descriptive Statistics
3(1)
Inferential Statistics
3(1)
Distinguishing Between a Sample and a Population
3(1)
How to Transform Observations into Variables
4(3)
Discrete Observations
5(1)
Continuous Observations
5(2)
Variables and Research
7(3)
Independent, Dependent, and Confounding Variables
7(1)
Reliability and Validity
8(2)
Introduction to Hypothesis Testing
10(19)
Correlational Studies and the Danger of Confounding Variables
11(1)
Conducting Experiments to Control for Confounding Variables
12(2)
Between-Groups Design Versus Within-Groups Design
14(1)
Introduction to Data Ethics
15(14)
Chapter 2 Frequency Distributions
29(24)
Frequency Distributions
31(10)
Frequency Tables
32(3)
Grouped Frequency Tables
35(3)
Histograms
38(3)
Shapes of Distributions
41(12)
Normal Distributions
42(1)
Skewed Distributions
43(10)
Chapter 3 Visual Displays of Data
53(30)
Common Types of Graphs
55(8)
Scatterplots
55(1)
Line Graphs
56(2)
Bar Graphs
58(2)
Dot Plots
60(1)
Pictorial Graphs
61(1)
Pie Charts
62(1)
How to Build a Graph
63(20)
Choosing the Appropriate Type of Graph
63(1)
How to Read a Graph
64(1)
Guidelines for Creating a Graph
65(1)
The Future of Graphs
66(17)
Chapter 4 Central Tendency and Variability
83(28)
Central Tendency
84(10)
Mean: The Arithmetic Average
85(3)
Median: The Middle Score
88(1)
Mode: The Most Common Score
89(1)
How Outliers Affect Measures of Central Tendency
90(4)
Measures of Variability
94(17)
Range
94(1)
The Interquartile Range
95(1)
Variance
96(3)
Standard Deviation
99(12)
Chapter 5 Sampling and Probability
111(32)
Samples and Their Populations
112(6)
Random Sampling
113(1)
Convenience Sampling
114(2)
Convenience Samples Can Create Generalizability Problems
116(1)
Random Assignment
117(1)
Probability
118(5)
Coincidence and Probability
119(1)
Expected Relative-Frequency Probability
120(2)
Independence and Probability
122(1)
Inferential Statistics
123(4)
Developing Hypotheses
123(2)
Making a Decision About a Hypothesis
125(2)
Type I and Type II Errors
127(16)
Type I Errors
127(1)
Type II Errors
128(1)
The Shocking Prevalence of Type I Errors
128(15)
Chapter 6 The Normal Curve, Standardization, and z Scores
143(36)
The Normal Curve
144(4)
Standardization, z Scores, and the Normal Curve
148(10)
The Need for Standardization
148(1)
Transforming Raw Scores into z Scores
149(3)
Transforming z Scores into Raw Scores
152(3)
Using z Scores to Make Comparisons
155(1)
Transforming z Scores into Percentiles
156(2)
The Central Limit Theorem
158(21)
Creating a Distribution of Means
160(2)
Characteristics of the Distribution of Means
162(3)
Using the Central Limit Theorem to Make Comparisons with z Scores
165(14)
Chapter 7 Hypothesis Testing with z Tests
179(32)
The z Table
180(9)
Raw Scores, z Scores, and Percentages
181(6)
The z Table and Distributions of Means
187(2)
The Assumptions and Steps of Hypothesis Testing
189(4)
The Three Assumptions for Conducting Analyses
189(2)
The Six Steps of Hypothesis Testing
191(2)
The z Test
193(18)
An Example of the z Test
193(6)
HARKing and p-Hacking
199(12)
Chapter 8 Confidence Intervals, Effect Size, and Statistical Power
211(30)
The New Statistics
213(1)
Confidence Intervals
214(5)
Interval Estimates
214(1)
Calculating Confidence Intervals with z Distributions
215(4)
Effect Size
219(7)
The Effect of Sample Size on Statistical Significance
219(1)
What Effect Size Is
220(2)
Cohen's d
222(1)
Meta-Analysis
223(3)
Statistical Power
226(15)
Making Correct Decisions
226(3)
Sample Size Planning
229(12)
Chapter 9 The Single-Sample t Test and the Paired-Samples t Test
241(46)
The t Distributions
243(5)
Estimating Population Standard Deviation from a Sample
243(2)
Calculating Standard Error for the t Statistic
245(1)
Using Standard Error to Calculate the t Statistic
246(2)
The Single-Sample t Test
248(8)
The t Table and Degrees of Freedom
248(2)
The Six Steps of the Single-Sample t Test
250(3)
Calculating a Confidence Interval for a Single-Sample t Test
253(2)
Calculating Effect Size for a Single-Sample t Test
255(1)
The Paired-Samples t Test
256(31)
Distributions of Mean Differences
257(1)
The Six Steps of the Paired-Samples t Test
258(4)
Calculating a Confidence Interval for a Paired-Samples t Test
262(3)
Calculating Effect Size for a Paired-Samples t Test
265(1)
Replication and Reproducibility
265(22)
Chapter 10 The Independent-Samples t Test
287(32)
Conducting an Independent-Samples t Test
288(11)
A Distribution of Differences Between Means
288(2)
The Six Steps of the Independent-Samples t Test
290(7)
Reporting the Statistics
297(2)
Beyond Hypothesis Testing
299(20)
Calculating a Confidence Interval for an Independent-Samples t Test
299(3)
Calculating Effect Size for an Independent-Samples t Test
302(17)
Chapter 11 One-Way Between-Groups ANOVA
319(44)
Using the F Distributions with Three or More Samples
320(5)
Type I Errors When Making Three or More Comparisons
321(1)
The F Statistic as an Expansion of the z and t Statistics
321(1)
The F Distributions for Analyzing Variability to Compare Means
322(1)
The F Table
323(1)
The Language and Assumptions for ANOVA
323(2)
One-Way Between-Groups ANOVA
325(16)
Everything About ANOVA But the Calculations
325(5)
The Logic and Calculations of the F Statistic
330(8)
Making a Decision
338(3)
Beyond Hypothesis Testing for the One-Way Between-Groups ANOVA
341(22)
R2 and Omega Squared, Effect Sizes for ANOVA
341(1)
Post Hoc Tests
342(1)
The Tukey HSD Test
343(20)
Chapter 12 Two-Way Between-Groups ANOVA
363(46)
Two-Way Anova
365(4)
Why We Use Two-Way ANOVA
366(1)
The More Specific Vocabulary of Two-Way ANOVA
367(1)
Two Main Effects and an Interaction
367(2)
Understanding Interactions in ANOVA
369(9)
Interactions and Public Policy
370(1)
Interpreting Interactions
370(8)
Conducting a Two-Way Between-Groups ANOVA
378(31)
The Six Steps of Two-Way ANOVA
379(5)
Identifying Four Sources of Variability in a Two-Way ANOVA
384(5)
Effect Size for Two-Way ANOVA
389(2)
Variations on ANOVA
391(18)
Chapter 13 Correlation
409(32)
The Meaning of Correlation
410(6)
The Characteristics of Correlation
410(4)
Correlation Is Not Causation
414(2)
The Pearson Correlation Coefficient
416(25)
Calculating the Pearson Correlation Coefficient
416(4)
Hypothesis Testing with the Pearson Correlation Coefficient
420(2)
Correlation, Causation, and Big Data
422(19)
Chapter 14 Regression
441(40)
Simple Linear Regression
442(13)
Prediction Versus Relation
443(1)
Regression with z Scores
444(3)
Determining the Regression Equation
447(4)
The Standardized Regression Coefficient and Hypothesis Testing with Regression
451(4)
Interpretation and Prediction
455(9)
Regression and Error
455(6)
Applying the Lessons of Correlation to Regression
461(1)
Regression to the Mean
461(3)
Multiple Regression
464(17)
Understanding the Equation
464(2)
Multiple Regression in Everyday Life
466(1)
Ethical Landmines in Predicting Individual Behavior
467(14)
Chapter 15 Chi-Square Tests
481(40)
Nonparametric Statistics
483(2)
An Example of a Nonparametric Test
483(1)
When to Use Nonparametric Tests
483(2)
Chi-Square Tests
485(15)
Chi-Square Test for Goodness of Fit
485(6)
Chi-Square Test for Independence
491(6)
Adjusted Standardized Residuals
497(3)
Beyond Hypothesis Testing
500(21)
Cramer's V, the Effect Size for Chi Square
500(1)
Graphing Chi-Square Percentages
501(2)
Relative Risk
503(18)
Chapter 16 Choosing and Reporting Statistics
521(1)
Choosing the Right Statistical Test
522(2)
Category 1 Two Scale Variables
524(5)
Category 2 Nominal Independent Variable(s) and a Scale Dependent Variable
525(2)
Category 3 Only Nominal Variables
527(2)
Reporting Statistics
529(1)
Overview of Reporting Statistics
530(1)
Justifying the Study
531(3)
Reporting the Traditional and the New Statistics
534(4)
Open Data Practices
538
APPENDIX A Reference for Basic Mathematics
1(1)
A.1 Diagnostic Test: Skills Evaluation
1(1)
A.2 Symbols and Notation: Arithmetic Operations
2(1)
A.3 Order of Operations
3(1)
A.4 Proportions: Fractions, Decimals, and Percentages
4(2)
A.5 Solving Equations with a Single Unknown Variable
6(1)
A.6 Answers to Diagnostic Test and Self-Quizzes
7
APPENDIX B Statistical Tables
1(1)
TABLE B-1 The z Distribution
1(4)
TABLE B-2 The t Distributions
5(1)
TABLE B-3 The F Distributions
6(5)
TABLE B-4 The Chi-Square Distributions
11(1)
TABLE B-5 The q Statistic (Tukey HSD Test)
11(2)
TABLE B-6 The Pearson Correlation Coefficient
13(1)
TABLE B-7 Random Digits
14
APPENDIX C Solutions to Odd-Numbered End-of-Chapter Problems
1(1)
APPENDIX D Check Your Learning Solutions
1(1)
APPENDIX E The Bayesian Approach to Statistics
1(1)
Glossary 1(1)
References 1(1)
Index 1(1)
Formulas 1
Susan A. Nolan is a Professor in the Department of Psychology at Seton Hall University. She received her Ph.D. from Northwestern University. Susan studies the stigma associated with psychological disorders and the role of gender in science, technology, engineering, and mathematics (STEM) education, the latter funded in part by the National Science Foundation. Her favorite classes to teach are introductory psychology, abnormal psychology, international psychology, and statistics. Susan is the 2021 President of the Society for the Teaching of Psychology (STP), an Associate Editor of the international journal Psychology Learning and Teaching, and a Consulting Editor of the American Psychological Association (APA) journal Scholarship of Teaching and Learning in Psychology. She previously served as President of the Eastern Psychological Association (EPA), Chair of the 2012 STP Presidential Task Force on Statistical Literacy, and a representative from APA to the United Nations. Susan is a Fellow of EPA, APA, and the Association for Psychological Science, and was a 2015-2016 U.S. Fulbright Scholar in Bosnia and Herzegovina where she researched psychology higher education.





Tom Heinzen was a 29-year-old college freshman and a magna cum laude graduate of Rockford College. He earned his PhD in social psychology at the State University of New York at Albany in just 3 years. He published his first book on frustration and creativity in government 2 years later; was a research associate in public policy until he was fired for arguing over the shape of a graph; and then began a teaching career at William Paterson University of New Jersey. He founded the psychology club, established an undergraduate research conference, and has been awarded various teaching honors while continuing to write journal articles, books, plays, and two novels that support the teaching of general psychology and statistics. He is also the editor of Many Things to Tell You, a volume of poetry by elderly writers. Tom is a member of numerous professional societies, and is a Fellow of the APA, the EPA, the APS, and the New York Academy of Science. His wife, Donna, is a physician assistant who has volunteered her time in relief work following hurricanes Mitch and Katrina; and their daughters work in public health, teaching, and medicine.
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