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Understanding Statistical Analysis and Modeling
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Understanding Statistical Analysis and Modeling

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December 2017 | 456 pages | SAGE Publications, Inc

Understanding Statistical Analysis and Modeling is a text for graduate and advanced undergraduate students in the social, behavioral, or managerial sciences seeking to understand the logic of statistical analysis. Robert Bruhl covers all the basic methods of descriptive and inferential statistics in an accessible manner by way of asking and answering research questions. Concepts are discussed in the context of a specific research project and the book includes probability theory as the basis for understanding statistical inference. Instructions on using SPSS® are included so that readers focus on interpreting statistical analysis rather than calculations. Tables are used, rather than formulas, to describe the various calculations involved with statistical analysis and the exercises in the book are intended to encourage students to formulate and execute their own empirical investigations.

 
Part I: Research Design
 
Chapter 1: “Why” Conduct Research and “Why” Use Statistics?
Learning Objectives  
Motivation  
Representation and Modeling  
A Special Case: Investigating Subjective Behavior  
Reasons for an Empirical Investigation  
Summary  
Exercises  
Some Formal Terminology  
Chapter 2. Methods of Quantitative Empirical Investigation  
Learning Objectives  
Motivation  
Instrumentation: Choosing a Tool to Assess a Property of Interest  
Limited Focus or Intent to Generalize  
Controlled or Natural Observations  
Applied versus Pure Research  
Summary  
Exercises  
 
Part II: Descriptive Statistics
 
Chapter 3. The Frequency Distribution Report: Organizing a Set of Observations
Learning Objectives  
Motivation: Comparing, Sorting, and Counting  
Constructing a Sample Frequency Distribution for a “Qualitative” Property  
Constructing a Sample Frequency Distribution for an “Ordinal” Property  
Some Important Technical Notes  
Summary  
SPSS® Tutorial  
Exercises  
 
Chapter 4. The Mode, Median, and Mean: Describing a Typical Value of a Quantitative Property Observed for a Set of Phenomena
Learning Objectives  
Motivation  
A Cautionary Note Regarding Quantitatively Assessed Properties  
Constructing a Sample Frequency Distribution for a Quantitative Property  
Identifying a Typical Phenomenon from a Set of Phenomena  
Assessing and Using the Median of a Set of Observations  
Assessing and Using the Mean of a Set of Observations  
Interpreting and Comparing the Mode, Median, and Mean  
Summary  
SPSS® Tutorial  
Exercises  
 
Chapter 5. The Variance and Standard Deviation: Describing the Variability Observed for a Quantitative Property of a Set of Phenomena
Learning Objectives  
Motivation  
A Case Example: The Frequency Distribution Report  
The Range of a Set of Observations  
The Mean Absolute Difference  
The Variance and Standard Deviation  
Interpreting the Variance and Standard Deviation  
Comparing the Mean Absolute Difference and Standard Deviation  
A Useful Note on Calculating the Variance  
A Note on Modeling and the Assumption of Variability  
Summary  
SPSS® Tutorial  
Exercises  
The Method of Moments  
A Distribution of “Squared Differences from a Mean”  
 
Chapter 6. The Z-Transformation and Standardization: Using the Standard Deviation to Compare Observations
Learning Objectives  
Motivation  
Executing the Z-Transformation  
An Example  
Summary  
An Exercise  
 
Part III: Statistical Inference and Probability
 
Chapter 7. The Concept of a Probability
Learning Objectives  
Motivation  
Uncertainty, Chance, and Probability  
Selection Outcomes and Probabilities  
Events and Probabilities  
Describing a Probability Model for a Quantitative Property  
Summary  
Exercises  
 
Chapter 8. Co-Existing Properties and Joint Probability Models
Learning Objectives  
Motivation  
Probability Models Involving Co-Existing Properties  
Models of Association, Conditional Probabilities, and Stochastic Independence  
Co-Variability in Two Quantitative Properties  
Importance of Stochastic Independence and Co-Variance in Statistical Inference  
Summary  
Exercises  
 
Chapter 9. Sampling and the Normal Probability Model
Learning Objectives  
Motivation  
Samples and Sampling  
Bernoulli Trials and the Binomial Distribution  
Representing the Character of a Population  
Predicting Potential Samples from a Known Population  
The Normal Distribution  
The Central Limit Theorem  
Normal Sampling Variability and Statistical Significance  
Summary  
Exercises  
 
Part IV: Statistical Inference
 
Chapter 10. Estimation Studies
Learning Objectives  
Motivation  
Projecting the Occurrence of a Qualitative Property for a Population  
Projecting a Parameter of a Population from a Sample Statistic  
Some Notes on Sampling  
Summary  
Exercises  
 
Chapter 11. The Chi-Square Statistic: Association Studies Involving Two Qualitative Properties
Learning Objectives  
Motivation  
An Example  
An Extension: Testing the Statistical Significance of Population Proportions  
Summary  
SPSS® Tutorial  
Exercises  
 
Chapter 12. The t-Test of Statistical Significance: Comparing a Quantitative Attribute Assessed for Two Different Group
Learning Objectives  
Motivation  
Comparing Sample Means Using the Central Limit Theorem  
The t-Test  
Summary  
SPSS® Tutorial  
Exercises  
 
Chapter 13. Analysis of Variance (ANOVA): Comparing a Quantitative Attribute Assessed for Several Different Groups
Learning Objectives  
Motivation  
Variability and “Squared Differences”  
“Typical” Variability and the F-Statistic  
Summary  
SPSS® Tutorial  
Exercises  
 
Chapter 14. Correlation Analysis and Linear Regression: Assessing the Co-Variability of Two Quantitative Properties
Learning Objectives  
Motivation  
Assessing an Association as a Co-Variance  
Step One: Visual Interpretation with a Scatter-Plot  
Step Two: Assessing the Co-Variation as the Correlation Coefficient  
Step Three: Constructing a Linear Mathematical Model  
Step 4: Assessing the Usefulness of the Model  
Summary  
SPSS® Tutorial  
Exercises  

Supplements

Instructor Site

Password-protected Instructor Resources include the following:

  • Sample syllabi to help you prepare for your course using Understanding Statistical Analysis and Modeling.
  • Editable, chapter-specific Microsoft® PowerPoint® slides offer you complete flexibility in easily creating a multimedia presentation for your course.
  • Extra exercises which reinforce the key concepts of each chapter and can be used as test questions.
Student Study Site

The open-access Student Study Site includes the following:

  • Downloadable datasets to accompany exercises and problems in the book
  • Solutions to all exercises and problems
  • EXCLUSIVE! Access to multimedia from the SAGE Research Methods platform featuring videos with the author

“This is a well-thought out and designed text that gives students an open and accessible introduction to the concepts and techniques necessary for conducting social science research.”

Scott Comparato
Political Science, Southern Illinois University

“This book presents the opportunity for those teaching statistics to present probability theory in a non-intrusive manner, allowing students to move beyond their fears of probability theory and access one of the most important aspects of really understanding statistics.”

Robert J. Eger III
Financial Management, Naval Postgraduate School

“This text takes a refreshing approach to presenting statistical concepts in a methodologically rigorous yet meaningful way that students will intuitively grasp.”

Brian Frederick
Political Science, Bridgewater State University

“This text has a competitive edge over similar textbooks. I strongly recommend it to students who want to have a clear understanding of how to develop good research questions and select statistical techniques appropriate in answering the research questions.”

Benjamin C. Ngwudike
Educational Leadership, Jackson State University

“Readers will be surprised how much they are learning about statistics and statistical analysis as they read this book. The author presents mathematical concepts by first starting with the familiar and gently guiding the reader in more unfamiliar territory.”

John David Rausch, Jr.
Political Science, West Texas A&M University
Key features

KEY FEATURES:

  • A motivation section begins each chapter explaining why the topics are important, and how these topics answer research questions.
  • SPSS® tutorials guide readers through the process of using SPSS® to conduct each type of statistical analysis.
  • Every example of using a statistical procedure begins with a research question and ends by answering that research question to show readers why that statistical result matters.
  • For non-mathematical readers, tables are used to describe statistical calculations because they are easier to follow than formulas.
  • Non-parametric statistical procedures are included for those in the social sciences and business fields.

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ISBN: 9781506317410