Statistics Translated : A Step-by-Step Guide to Analyzing and Interpreting Data book cover
2nd Edition

Statistics Translated
A Step-by-Step Guide to Analyzing and Interpreting Data




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ISBN 9781462545407
Published April 5, 2021 by Guilford Press
433 Pages

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Book Description

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.

Table of Contents

- Introduction: You Do Not Need to Be a Statistician to Understand Statistics!
A Little Background
Many Students Do Not Know What They’re Getting Into
A Few Simple Steps
- Identify the Problem
- State a Hypothesis
- Identify the Independent Variable
- Identify and Describe the Dependent Variable
- Choose the Right Statistical Test
- Use Data Analysis Software to Test the Hypothesis
So, What’s New in This Edition?
Summary
Do You Understand These Key Words and Phrases?
1. Identifying a Research Problem and Stating Hypotheses
Introduction
Identify the problem
- Characteristics of a Good Problem Statement
- Finding a Good Research Problem
- The Problem Is Interesting to the Researcher
- The Scope of the Problem Is Manageable by the Researcher
- The Researcher Has the Knowledge, Time, and Resources Needed to Investigate the Problem
- The Problem Can Be Researched through the Collection and Analysis of Numeric Data
- Investigating the Problem Has Theoretical or Practical Significance
- It Is Ethical to Investigate the Problem
- Writing the Problem Statement
- Problem Statements Must Be Clear and Concise
- The Problem Statement Must Include All Variables to Be Considered
- The Problem Statement Should Not Interject the Researcher’s Bias
- Summary of Step 1: Identify the Problem
State a Hypothesis
- An Example of Stating Our Hypothesis
- A Little More Detail
- The Direction of Hypotheses
- Using Directional Hypotheses to Test a “Greater Than” Relationship
- Using Directional Hypotheses to Test a “Less Than” Relationship
- Nondirectional Hypotheses
- Hypotheses Must Be Testable via the Collection and Analysis of Data
- Research versus Null Hypotheses
- Stating Null Hypotheses for Directional Hypotheses
- Issues Underlying the Null Hypothesis for Directional Research Hypotheses
- Stating Null Hypotheses for Nondirectional Hypotheses
- A Preview of Testing the Null Hypothesis
- Where Does That Leave Us?
- Statistical Words of Wisdom
- Summary of Step 2: State a Hypothesis
Do You Understand These Key Words and Phrases?
Quiz Time!
Problem Statements
Case Studies:
The Case of Distance Therapy
- The Case of the New Teacher
- The Case of Being Exactly Right
- The Case of “Does It Really Work?”
- The Case of Advertising
- The Case of Learning to Speak
- The Case of Kids on Cruises
2. Identifying the Independent and Dependent Variables in a Hypothesis
Introduction
Identify the Independent Variable
- Nonmanipulated Independent Variables
- Another Way of Thinking about Nonmanipulated Independent Variables
- Manipulated or Experimental Independent Variables
- Levels of the Independent Variable
- Summary of Step 3: Identify the Independent Variable
Identify and Describe the Dependent Variable
- Identifying Your Dependent Variable
- What Type of Data Are We Collecting?
- Interval Data
- Data Types—What Is the Good News?
- Summary of the Dependent Variable and Data Types
- Measures of Central Tendency
- The Mean, Median, and Mode—Measures of Central Tendency
- The Mode
- Using Statistical Software to Analyze Our Data
- Summary of the First Part of Step 4: Identify and Describe the Dependent Variable
Do You Understand These Key Words and Phrases?
Do You Understand These Formulas?
Quiz Time!
3. Measures of Dispersion and Measures of Relative Standing
Introduction
Measures of Dispersion
- The Range
- The Standard Deviation
- The Variance
Measures of Relative Standing
- Percentiles
- Computing and Interpreting T-Scores
- Stanines
- Putting It All Together
- Using SPSS for T-Scores and Stanines—Not So Fast!
Summary
Do You Understand These Key Words and Phrases?
Do You Understand These Formulas?
Quiz Time!
4. Graphically Describing the Dependent Variable
Introduction
Graphical Descriptive Statistics
- Graphically Describing Nominal Data
- Pie Charts
- Bar Charts
- Graphically Describing Quantitative Data
- Scatterplots
- Histograms
- Don’t Let a Picture Tell You the Wrong Story!
- Summary of Graphical Descriptive Statistics
The Normal Distribution
- Things That Can Affect the Shape of a Distribution of Quantitative Data
Summary of the Normal Distribution
Do You Understand These Key Words and Phrases?
Quiz Time!
5. Choosing the Right Statistical Test
Introduction
The Very Basics
- The Central Limit Theorem
- The Sampling Distribution of the Means
- Summary of the Central Limit Theorem and the Sampling Distribution of the Means
How Are We Doing So Far?
Estimating Population Parameters Using Confidence Intervals
- The Alpha Value
- Type I and Type II Errors
Predicting a Population Parameter Based on a Sample Statistic Using Confidence Intervals
- Pay Close Attention Here
- Confidence Intervals for Alpha = .01 and Alpha = .10
- Another Way to Think about z Scores in Confidence Intervals
- Tying This All Together
- Be Careful When Changing Your Alpha Values
- Do We Understand Everything We Need to Know about Confidence Intervals?
Testing Hypotheses about a Population Parameter Based on a Sample Statistic
- Making a Decision about the Certification Examination Scores
- We Are Finally Going to Test Our Hypothesis!
- Testing a One-Tailed Hypothesis
Testing a One-Tailed “Less Than” Hypothesis
Summarizing What We Just Said
Be Careful When Changing Your Alpha Values
The Heart of Inferential Statistics
- Probability Values
- A Few More Examples
- Great News—We Will Always Use Software to Compute Our p Value
Choose the Right Statistical Test
- You Already Know a Few Things
- A Couple of Notes about the Table
- Summary of Step 5: Choose the Right Statistical Test
Do You Understand These Key Words and Phrases?
Do You Understand These Formulas and Symbols?
Quiz Time!
6. The One-Sample t-Test
Introduction
Welcome to the Guinness Breweries
The t Distribution
- Putting This Together
- Determining the Critical Value of t
- Degrees of Freedom
- Be Careful Computing Degrees of Freedom
- Let’s Get Back to Our Anxiety Hypothesis
- Plotting Our Critical Value of t
- The Statistical Effect Size of Our Example
Let’s Look at a Directional Hypothesis
- Using the p Value
- Check Your Mean Scores!
One More Time
- Important Note about Software Packages
Let’s Use the Six-Step Model!
- The Case of Slow Response Time
- Identify the Problem
- State a Hypothesis
- Identify the Independent Variable
- Identify and Describe the Dependent Variable
- Choose the Right Statistical Test
- Use Data Analysis Software to Test the Hypothesis
- The Case of Stopping Sneezing
- Identify the Problem
- State a Hypothesis
- Identify the Independent Variable
- Identify and Describe the Dependent Variable
- Choose the Right Statistical Test
- Use Data Analysis Software to Test the Hypothesis
- The Case of Growing Tomatoes
- Identify the Problem
- State a Hypothesis
- Identify the Independent Variable
- Identify and

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Author(s)

Biography

Steven R. Terrell, PhD, is Professor 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 Association’s Online Teaching and Learning Special Interest Group.

Reviews

"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, Professor of Special Education and Director, Center for Collaborative Solutions for Kids, Practice, and Policy, University of Missouri–Columbia

"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

"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

"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

"Terrell’s 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-