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Statistics Translated, Second Edition: A Step-by-Step Guide to Analyzing and Interpreting Data 2nd edition [Minkštas viršelis]

(Nova Southeastern University, United States)
  • Formatas: Paperback / softback, 433 pages, aukštis x plotis: 254x178 mm, weight: 780 g
  • Išleidimo metai: 05-Apr-2021
  • Leidėjas: Guilford Press
  • ISBN-10: 1462545408
  • ISBN-13: 9781462545407
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 433 pages, aukštis x plotis: 254x178 mm, weight: 780 g
  • Išleidimo metai: 05-Apr-2021
  • Leidėjas: Guilford Press
  • ISBN-10: 1462545408
  • ISBN-13: 9781462545407
Kitos knygos pagal šią temą:
Roping the reader in with humor and real-world case examples presented as mysteries to be solved, this engaging text has been updated with new cases, the latest version of SPSS, and new coverage of multivariate analysis of variance. Steven R. Terrell prepares students and practitioners to become informed consumers of statistics so that they can make decisions based on data, and understand decisions others have made. He identifies six simple steps and guides readers to master them--from identifying a researchable problem to stating a hypothesis; identifying independent and dependent variables; and selecting, computing, and interpreting appropriate statistical tests. All techniques are demonstrated both manually and with the help of SPSS software.
 
New to This Edition
*All software instructions and examples are updated to SPSS Version 25.
*Expanded chapter on the analysis of variance (ANOVA)--now covers multivariate ANOVA.
*New and revised examples and quiz items pertaining to a broader range of fields, such as business, information systems, and medical sciences, along with education and psychology.
 
Pedagogical Features
*Examples of SPSS screenshots used for analyzing data.
*User-friendly cautionary notes, "Putting it All Together" recaps, and alerts, such as "notice the effect size" or "check the direction of the mean scores."
*End-of-chapter "Quiz Time" exercises that guide students to answer intriguing questions like whether working from home increases productivity, or whether age affects how long it takes to complete a doctoral degree.
*Lists of key terms and formulas in each chapter, plus end-of-book glossary.
 

Recenzijos

"Most undergraduates--and many junior professors--dread introductory statistics courses. Statistics Translated, Second Edition, will relieve the concerns of both students and instructors. The conversational tone, frequent examples and applications, consistent presentation of a six-step model to drive decision making, and visual demonstrations make the book easy to read. It offers clear explanations of relatively advanced ideas and infuses ethics into statistical decision making, which will appeal to teachers. I also appreciate the author's emphasis, in interpreting data, on the size of the effect rather than the magnitude of the alpha level. This user-friendly text surely will be widely adopted in college classrooms and kept as a reference guide by professionals long after they complete their required statistics course."--Matthew K. Burns, PhD, Rose and Irving Fein Endowed Professor of Special Education, University of Florida; Assistant Director, University of Florida Literacy Institute

"Terrell's overall tone and approach display his genuine desire to help every reader learn about statistics. The text's step-by-step method enables students to think through the process of research so that they understand what tools are needed to answer questions of interest. Terrell uses practical examples throughout the book to help readers anchor ideas on prior knowledge. This is a fantastic introductory book for all students who feel that they struggle to understand statistics, and it is written in such a way that they will be empowered to learn."--Andrew H. Rose, PhD, Master of Social Work Program Director, Texas Tech University

"Terrells text is noteworthy for its cheerful, straightforward approach. The entire research process is presented in six steps, from identifying a research problem to testing the final hypothesis. The author integrates some simple data exploration procedures (such as graphical display of distributions) without entering into the sometimes tiresome arguments about the philosophy of data analysis. This calm approach is used throughout the volume. This is not to say the essentials are oversimplified--students are likely to complete the volume with an understanding of statistics as a descriptive procedure, and a basic competence with some tools to aid in evaluating propositions. Useful revisions in the second edition include more examples to illustrate the techniques, and coverage of multivariate ANOVA. If you want to bring students softly into appreciating--not fearing--statistics, this book is a good place to look."--Charles M. Super, PhD, Center for the Study of Culture, Health, and Human Development, University of Connecticut

"Statistics Translated is just that--statistics, translated into highly accessible language that glides students through the logic and common sense of statistics. With a mellifluous voice, Terrell brings all the essential statistical concepts to a level anyone can understand and appreciate (with no loss of meaning or utility). This is my book of choice for both introductory and intermediate statistics courses."--Todd D. Little, PhD, Department of Educational Psychology and Leadership, College of Education, Texas Tech University

"Tremendously accessible and well written. This text is especially helpful for students who are intimidated by statistics--which includes most students in the behavioral sciences. The text clearly and simply explains the steps of quantitative research, from creating a research question to computing and interpreting statistical findings. Like the first edition, the second edition is an excellent text for psychology research methods or behavioral statistics courses, and is valuable for anyone who must use and interpret statistics."--Robin A. Barry, PhD, Department of Psychology, University of Wyoming-

Introduction: You Do Not Need to Be a Statistician to Understand Statistics! 1(6)
A Little Background
1(1)
Many Students Do Not Know What They're Getting Into
2(1)
A Few Simple Steps
3(2)
Step 1 Identify the Problem
4(1)
Step 2 State a Hypothesis
4(1)
Step 3 Identify the Independent Variable
4(1)
Step 4 Identify and Describe the Dependent Variable
5(1)
Step 5 Choose the Right Statistical Test
5(1)
Step 6 Use Data Analysis Software to Test the Hypothesis
5(1)
So, What's New in This Edition?
5(1)
Summary
6(1)
Do You Understand These Key Words and Phrases?
6(1)
Chapter 1 Identifying A Research Problem And Stating Hypotheses
7(26)
Introduction
7(1)
Step 1 Identify the Problem
7(9)
Characteristics of a Good Problem Statement
8(1)
Finding a Good Research Problem
8(1)
The Problem Is Interesting to the Researcher
9(1)
The Scope of the Problem Is Manageable by the Researcher
9(1)
The Researcher Has the Knowledge, Time, and Resources Needed to Investigate the Problem
10(1)
The Problem Can Be Researched through the Collection and Analysis of Numeric Data
11(1)
Investigating the Problem Has Theoretical or Practical Significance
11(1)
It Is Ethical to Investigate the Problem
12(1)
Writing the Problem Statement
13(1)
Problem Statements Must Be Clear and Concise
13(1)
The Problem Statement Must Include All Variables to Be Considered
14(1)
The Problem Statement Should Not Interject the Researcher's Bias
15(1)
Summary of Step 1 Identify the Problem
15(1)
Step 2 State a Hypothesis
16(13)
An Example of Stating Our Hypothesis
16(1)
A Little More Detail
17(1)
The Direction of Hypotheses
17(1)
Using Directional Hypotheses to Test a "Greater Than" Relationship
18(1)
Using Directional Hypotheses to Test a "Less Than" Relationship
18(1)
Nondirectional Hypotheses
19(1)
Hypotheses Must Be Testable via the Collection and Analysis of Data
20(1)
Research versus Null Hypotheses
20(1)
Stating Null Hypotheses for Directional Hypotheses
20(1)
Issues Underlying the Null Hypothesis for Directional Research Hypotheses
21(1)
Stating Null Hypotheses for Nondirectional Hypotheses
22(1)
A Preview of Testing the Null Hypothesis
23(3)
Where Does That Leave Us?
26(2)
Statistical Words of Wisdom
28(1)
Summary of Step 2 State a Hypothesis
28(1)
Do You Understand These Key Words and Phrases?
29(1)
Quiz Time!
29(1)
Problem Statements
29(1)
Case Studies
30(1)
The Case of Distance Therapy
30(1)
The Case of the New Teacher
30(1)
The Case of Being Exactly Right
31(1)
The Case of "Does It Really Work?"
31(1)
The Case of Advertising
31(1)
The Case of Learning to Speak
32(1)
The Case of Kids on Cruises
32(1)
Chapter 2 Identifying The Independent And Dependent Variables In A Hypothesis
33(27)
Introduction
33(1)
Step 3 Identify the Independent Variable
33(4)
Nonmanipulated Independent Variables
34(1)
Another Way of Thinking about Nonmanipulated Independent Variables
35(1)
Manipulated or Experimental Independent Variables
35(1)
Levels of the Independent Variable
36(1)
Summary of Step 3 Identify the Independent Variable
37(1)
Step 4 Identify and Describe the Dependent Variable
37(20)
Identifying Your Dependent Variable
37(2)
What Type of Data Are We Collecting?
39(1)
Interval Data
40(2)
Data Types---What Is the Good News?
42(1)
Summary of the Dependent Variable and Data Types
43(1)
Measures of Central Tendency
43(2)
The Mean, Median, and Mode---Measures of Central Tendency
45(5)
The Mode
50(1)
Using Statistical Software to Analyze Our Data
51(5)
Summary of the First Part of Step 4: Identify and Describe the Dependent Variable
56(1)
Do You Understand These Key Words and Phrases?
57(1)
Do You Understand These Formulas?
57(1)
Quiz Time!
57(3)
Chapter 3 Measures Of Dispersion And Measures Of Relative Standing
60(27)
Introduction
60(1)
Measures of Dispersion
60(10)
The Range
61(2)
The Standard Deviation
63(4)
The Variance
67(3)
Measures of Relative Standing
70(13)
Percentiles
71(6)
Computing and Interpreting T-Scores
77(1)
Stanines
78(1)
Putting It All Together
79(1)
Using SPSS for T-Scores and Stanines---Not So Fast!
80(3)
Summary
83(1)
Do You Understand These Key Words and Phrases?
84(1)
Do You Understand These Formulas?
84(1)
Quiz Time!
85(2)
Chapter 4 Graphically Describing The Dependent Variable
87(28)
Introduction
87(1)
Graphical Descriptive Statistics
87(12)
Graphically Describing Nominal Data
88(1)
Pie Charts
88(2)
Bar Charts
90(2)
Graphically Describing Quantitative Data
92(1)
Scatterplots
92(5)
Histograms
97(1)
Don't Let a Picture Tell You the Wrong Story!
97(2)
Summary of Graphical Descriptive Statistics
99(1)
The Normal Distribution
99(13)
Things That Can Affect the Shape of a Distribution of Quantitative Data
101(11)
Summary of the Normal Distribution
112(1)
Do You Understand These Key Words and Phrases?
113(1)
Quiz Time!
113(2)
Chapter 5 Choosing The Right Statistical Test
115(41)
Introduction
115(1)
The Very Basics
115(9)
The Central Limit Theorem
116(1)
The Sampling Distribution of the Means
117(7)
Summary of the Central Limit Theorem and the Sampling Distribution of the Means
124(1)
How Are We Doing So Far?
124(1)
Estimating Population Parameters Using Confidence Intervals
125(4)
The Alpha Value
125(2)
Type I and Type II Errors
127(2)
Predicting a Population Parameter Based on a Sample Statistic Using Confidence Intervals
129(8)
Pay Close Attention Here
131(1)
Confidence Intervals for Alpha = .01 and Alpha = .10
132(2)
Another Way to Think about z Scores in Confidence Intervals
134(1)
Tying This All Together
134(1)
Be Careful When Changing Your Alpha Values
135(1)
Do We Understand Everything We Need to Know about Confidence Intervals?
136(1)
Testing Hypotheses about a Population Parameter Based on a Sample Statistic
137(7)
Making a Decision about the Certification Examination Scores
138(3)
We Are Finally Going to Test Our Hypothesis!
141(1)
Testing a One-Tailed Hypothesis
141(3)
Testing a One-Tailed "Less Than" Hypothesis
144(1)
Summarizing What We Just Said
145(1)
Be Careful When Changing Your Alpha Values
146(1)
The Heart of Inferential Statistics
147(5)
Probability Values
148(1)
A Few More Examples
149(1)
Great News---We Will Always Use Software to Compute Our p Value
150(2)
Step 5 Choose the Right Statistical Test
152(2)
You Already Know a Few Things
152(1)
A Couple of Notes about the Table
153(1)
Summary of Step 5 Choose the Right Statistical Test
154(1)
Do You Understand These Key Words and Phrases?
154(1)
Do You Understand These Formulas and Symbols?
154(1)
Quiz Time!
155(1)
Chapter 6 The One-Sample F-Test
156(25)
Introduction
156(1)
Welcome to the Guinness Breweries
156(1)
The t Distribution
157(6)
Putting This Together
157(2)
Determining the Critical Value of t
159(1)
Degrees of Freedom
159(2)
Be Careful Computing Degrees of Freedom
161(1)
Let's Get Back to Our Anxiety Hypothesis
162(1)
Plotting Our Critical Value of t
162(1)
The Statistical Effect Size of Our Example
162(1)
Let's Look at a Directional Hypothesis
163(3)
Using the p Value
165(1)
Check Your Mean Scores!
165(1)
One More Time
166(2)
Important Note about Software Packages
167(1)
Let's Use the Six-Step Model!
168(8)
The Case of Slow Response Time
168(3)
The Case of Stopping Sneezing
171(3)
The Case of Growing Tomatoes
174(2)
Summary
176(1)
Do You Understand These Key Words and Phrases?
177(1)
Do You Know These Formulas?
177(1)
Quiz Time!
177(4)
Chapter 7 The Independent-Sample T-Test
181(36)
Introduction
181(1)
If We Have Samples from Two Independent Populations, How Do We Know If They Are Significantly Different from One Another?
182(15)
The Sampling Distribution of Mean Differences
182(3)
Calculating the t Value for the Independent-Sample t-Test
185(1)
Pay Attention Here
185(5)
Testing Our Hypothesis
190(1)
The p Value
191(1)
Note on Variance and the t-Test
192(1)
The Statistical Effect Size of Our Example
192(1)
Let's Try Another Example
193(2)
Remember the Effect Size
195(1)
How Does This Work for a Directional Hypothesis?
195(2)
Reminder---Always Pay Attention to the Direction of the Means!
197(1)
Putting the Independent-Sample t-Test to Work
197(12)
The Case of the Cavernous Lab
197(5)
The Case of the Report Cards
202(4)
The Case of the Anxious Athletes
206(3)
Summary
209(1)
Do You Understand These Key Words and Phrases?
210(1)
Do You Understand These Formulas?
210(1)
Quiz Time!
211(1)
The Case of the Homesick Blues
211(1)
The Case of the Cold Call
211(1)
The Case of the Prima Donnas
212(1)
The Case of the Wrong Side of the Road
213(1)
The Case of Workplace Satisfaction
214(1)
The Case of the Flower Show
215(2)
Chapter 8 The Dependent-Sample T-Test
217(27)
Introduction
217(1)
That's Great, but How Do We Test Our Hypotheses?
217(1)
Independence versus Dependence
218(8)
Computing the t Value for a Dependent-Sample t-Test
218(1)
Testing a One-Tailed "Greater Than" Hypothesis
219(3)
The Effect Size for a Dependent-Sample t-Test
222(1)
Testing a One-Tailed "Less Than" Hypothesis
223(3)
Testing a Two-Tailed Hypothesis
226(3)
Let's Move Forward and Use Our Six-Step Model
229(3)
Step 1 Identify the Problem
229(1)
Step 2 State a Hypothesis
229(1)
Step 3 Identify the Independent Variable
230(1)
Step 4 Identify and Describe the Dependent Variable
230(1)
Step 5 Choose the Right Statistical Test
231(1)
Step 6 Use Data Analysis Software to Test the Hypothesis
231(1)
The Case of the Unexcused Students
232(2)
Step 1 Identify the Problem
232(1)
Step 2 State a Hypothesis
232(1)
Step 3 Identify the Independent Variable
233(1)
Step 4 Identify and Describe the Dependent Variable
233(1)
Step 5 Choose the Right Statistical Test
233(1)
Step 6 Use Data Analysis Software to Test the Hypothesis
233(1)
The Case of Never Saying Never
234(3)
Step 1 Identify the Problem
235(1)
Step 2 State a Hypothesis
235(1)
Step 3 Identify the Independent Variable
235(1)
Step 4 Identify and Describe the Dependent Variable
235(1)
Step 5 Choose the Right Statistical Test
236(1)
Step 6 Use Data Analysis Software to Test the Hypothesis
236(1)
Just in Case---A Nonparametric Alternative
237(1)
Summary
237(1)
Do You Understand These Key Words and Phrases?
237(1)
Do You Understand These Formulas?
237(1)
Quiz Time!
238(1)
The Case of Technology and Achievement
238(1)
The Case of Worrying about Our Neighbors
239(1)
The Case of SPAM
240(1)
The Case of "We Can't Get No Satisfaction"
241(1)
The Case of "Winning at the Lottery"
242(2)
Chapter 9 Analysis Of Variance And Multivariate Analysis Of Variance
244(60)
Introduction
244(1)
Understanding the ANOVA
245(1)
The Different Types of ANOVAs
246(2)
One-Way ANOVA
246(1)
Factorial ANOVA
246(1)
Multivariate ANOVA (MANOVA)
247(1)
Assumptions of the ANOVA
248(1)
Random Samples
248(1)
Independence of Scores
248(1)
Normal Distribution of Data
248(1)
Homogeneity of Variance
248(1)
Calculating the ANOVA
249(10)
Descriptive Statistics
249(2)
The Total Variance
251(1)
The Total Sum of Squares
252(1)
The Between Sum of Squares
253(1)
The Within Sum of Squares
253(2)
Computing the Degrees of Freedom
255(1)
Computing the Mean Square
255(1)
Computing the F Value
255(1)
The F Distribution
256(1)
Determining the Area under the Curve for F Distributions
257(1)
The p Value for an ANOVA
258(1)
Effect Size for the ANOVA
258(1)
Testing a Hypothesis Using the ANOVA
259(15)
The Case of Multiple Means of Math Mastery
259(4)
The Post-Hoc Comparisons
263(1)
Multiple-Comparison Tests
263(1)
Always Observe the Means!
264(1)
The Case of Seniors Skipping School
265(3)
The Case of Quality Time
268(3)
The Case of Regional Discrepancies
271(3)
The Factorial ANOVA
274(21)
The Case of Age Affecting Ability
275(7)
Interpreting the Interaction p Value
282(2)
The Case of the Coach
284(3)
The Multivariate ANOVA (MANOVA)
287(1)
Assumptions of the MANOVA
288(1)
Using the MANOVA
288(4)
The Case of Balancing Time
292(3)
Summary
295(1)
Do You Understand These Key Words and Phrases?
296(1)
Do You Understand These Formulas?
296(1)
Quiz Time!
296(1)
The Case of Degree Completion
296(1)
The Case of Seasonal Depression
297(2)
The Case of Driving Away
299(1)
The Case of Climbing
300(1)
The Case of Employee Productivity
301(3)
Chapter 10 The Chi-Square Tests
304(31)
Introduction
304(1)
The One-Way Chi-Square Test
304(1)
The Factorial Chi-Square Test (the Chi-Square Test of Independence)
305(13)
Computing the Chi-Square Statistic
306(1)
The Chi-Square Distribution
307(2)
What about the Post-Hoc Test?
309(2)
Working with an Even Number of Expected Values
311(2)
The Case of the Belligerent Bus Drivers
313(3)
The Case of the Irate Parents
316(2)
The Chi-Square Test of Independence
318(9)
Computing Chi-Square for the Test of Independence
319(1)
Computing Expected Values for the Test of Independence
319(1)
Computing the Chi-Square Value for the Test of Independence
320(1)
Determining the Degrees of Freedom for the Test of Independence
321(1)
We Are Finally Going to Test Our Hypothesis
321(1)
Using SPSS to Check What We Just Computed
322(2)
The Case of Corporal Punishment
324(3)
Post-Hoc Tests Following the Chi-Square
327(3)
The Case of Type of Instruction and Learning Style
327(3)
Summary
330(1)
Do You Understand These Key Words and Phrases?
330(1)
Do You Understand These Formulas?
330(1)
Quiz Time!
331(1)
The Case of Prerequisites and Performance
331(1)
The Case of Getting What You Asked For
332(1)
The Case of Money Meaning Nothing
332(1)
The Case of Equal Opportunity
333(2)
Chapter 11 The Correlational Procedures
335(40)
Introduction
335(1)
Understanding the Idea of Correlations
335(4)
Interpreting Pearson's r
339(5)
A Word of Caution
340(1)
An Even More Important Word of Caution!
341(3)
A Nonparametric Correlational Procedure
344(14)
The p Value of a Correlation
345(1)
The Case of the Absent Students
346(4)
Another Example: The Case against Sleep
350(3)
The Case of Height versus Weight
353(2)
The Case of Different Tastes
355(3)
Once We Have a Linear Relationship, What Can We Do with It?
358(1)
Linear Regression
358(12)
The Regression Equation
358(1)
Computing the Slope
359(2)
Computing the Intercept
361(4)
Why Wasn't It Exactly Right?
365(2)
Using the Six-Step Model: The Case of Age and Driving
367(3)
Summary
370(1)
Do You Understand These Key Words and Phrases?
370(1)
Do You Understand These Formulas?
371(1)
Quiz Time!
371(1)
The Case of "Like Father, Like Son"
371(1)
The Case of "Can't We All Just Get Along?"
372(1)
The Case of More Is Better
373(1)
The Case of More Is Better Still
374(1)
Conclusion: Have We Accomplished What We Set Out to Do?
375(2)
Statistics in a New Light
375(1)
A Limited Set of Statistical Techniques
375(1)
The Use of Statistical Software Packages
376(1)
A Straightforward Approach
376(1)
At Long Last
376(1)
Appendix A Area under the Normal Curve Table (Critical Values of z) 377(1)
Appendix B Critical Values of t 378(1)
Appendix C Critical Values of F When Alpha = .01 379(2)
Appendix D Critical Values of F When Alpha = .05 381(2)
Appendix E Critical Values of F When Alpha = .10 383(2)
Appendix F Critical Values of Chi-Square 385(1)
Appendix G Selecting the Right Statistical Test 386(1)
Glossary 387(10)
Answers to Quiz Time! 397(24)
Index 421(12)
About the Author 433
Steven R. Terrell, PhD, is Professor Emeritus at Nova Southeastern University. He has taught quantitative and qualitative research methods since the 1980s and is the author of over 130 journal articles and conference presentations. Dr. Terrell is active in the American Psychological Association and the American Counseling Association and served as Chair of the American Educational Research Associations Online Teaching and Learning Special Interest Group.