Publisher Description

This textbook is ideal for graduate or advanced undergraduate students in the social, behavioral, or managerial sciences who may need to conduct some form of statistical analysis in their future professional lives, but who are not trained in mathematics.

Author Biography

Robert Bruhl is the author of the statistics textbook Understanding Statistical Analysis and Modeling for Sage (2018).  He is currently Clinical Professor in the Department of Political Science, where his primary focus is on policy analysis and research design.  Included in his specialties are economic history, voter behavior, and Congressional behavior.  Most recently, he has focused his attention on political marketing and campaigns, and has presented papers on this subject both nationally and internationally.  He has also contributed his expertise to both local, national, and international media.  Prior to his appointment at UIC, he taught at DePaul University in their Public Service Graduate Program, and before entering the academic world, he enjoyed a fifteen-year career in the private sector, and held various positions in management consulting, marketing, and business planning.  He has a B.A. degree (Phi Beta Kappa) in Mathematics from Northwestern University, an M.S. degree in Computer and Communication Sciences from the University of Michigan, an M.B.A. in Business Economics from the University of Chicago, and a Ph.D. (Phi Kappa Phi) in Public Policy Analysis from the University of Illinois at Chicago.  He is currently at work on a book describing the political and economic development of the English-American Colonies, with a focus on the effect this development had on the U.S. Constitution.

Table of Contents

IntroductionAcknowledgmentsAbout the AuthorPART I. RESEARCH DESIGNPurpose: Making Sense of What We ObserveDeciding How to Represent Properties of a PhenomenonDescribing Differences or Explaining Differences Between Phenomena?Deciding How to Collect ObservationsChapter 1. "Why" Conduct Research, and "Why" Use Statistics?1.0 Learning Objectives1.1 Motivation1.2 Representation and Modeling1.3 A Special Case: Investigating Subjective Behavior1.4 Reasons for an Empirical Investigation1.5 Summary1.6 Exercises1.7 Some Formal Terminology (Optional)Chapter 2. Methods of Quantitative Empirical Investigation2.0 Learning Objectives2.1 Motivation2.2 Instrumentation: Choosing a Tool to Assess a Property of Interest2.3 Limited Focus or Intent to Generalize2.4 Controlled or Natural Observations2.5 Applied Versus Pure Research2.6 Summary2.7 ExercisesPART II. DESCRIPTIVE STATISTICSOrganizing and Describing a Set of ObservationsMeasuring the Variability in a Set of ObservationsDescribing a Set of Observations in Terms of Their VariabilityChapter 3. The Frequency Distribution Report: Organizing a Set of Observations3.0 Learning Objectives3.1 Motivation: Comparing, Sorting, and Counting3.2 Constructing a Sample Frequency Distribution for a "Qualitative" Property3.3 Constructing a Sample Frequency Distribution for an "Ordinal" Property3.4 Some Important Technical Notes3.5 Summary3.6 SPSS Tutorial3.7 ExercisesChapter 4. The Mode, the Median, and the Mean: Describing a Typical Value of a Quantitative Property Observed for a Set of Phenomena4.0 Learning Objectives4.1 Motivation4.2 A Cautionary Note Regarding Quantitatively Assessed Properties4.3 Constructing a Sample Frequency Distribution for a Quantitative Property4.4 Identifying a Typical Phenomenon from a Set of Phenomena4.5 Assessing and Using the Median of a Set of Observations4.6 Assessing and Using the Mean of a Set of Observations4.7 Interpreting and Comparing the Mode, the Median, and the Mean4.8 Summary4.9 SPSS Tutorial4.10 ExercisesChapter 5. The Variance and the Standard Deviation: Describing the Variability Observed for a Quantitative Property of a Set of Phenomena5.0 Learning Objectives5.1 Motivation5.2 A Case Example: The Frequency Distribution Report5.3 The Range of a Set of Observations5.4 The Mean Absolute Difference5.5 The Variance and the Standard Deviation5.6 Interpreting the Variance and the Standard Deviation5.7 Comparing the Mean Absolute Difference and the Standard Deviation5.8 A Useful Note on Calculating the Variance5.9 A Note on Modeling and the Assumption of Variability5.10 Summary5.11 SPSS Tutorial5.12 Exercises5.13 The Method of Moments (Optional)5.14 A Distribution of "Squared Differences from a Mean" (Optional)Chapter 6. The z-Transformation and Standardization: Using the Standard Deviation to Compare Observations6.0 Learning Objectives6.1 Motivation6.2 Executing the z-Transformation6.3 An Example6.4 Summary6.5 An ExercisePART III. STATISTICAL INFERENCE AND PROBABILITYWhy Probability Theory?The Concept of a ProbabilityPredicting Events Involving Two Coexisting PropertiesSampling and the Normal Probability ModelChapter 7. The Concept of a Probability7.0 Learning Objectives7.1 Motivation7.2 Uncertainty, Chance, and Probabilit7.3 Selection Outcomes and Probabilities7.4 Events and Probabilities7.5 Describing a Probability Model for a Quantitative Property7.6 Summary7.7 ExercisesChapter 8. Coexisting Properties and Joint Probability Models8.0 Learning Objectives8.1 Motivation8.2 Probability Models Involving Coexisting Properties8.3 Models of Association, Conditional Probabilities, and Stochastic Independence8.4 Covariability in Two Quantitative Properties8.5 Importance of Stochastic Independence and Covariance in Statistical Inference8.6 Summary8.7 ExercisesChapter 9. Sampling and the Normal Probability Model9.0 Learning Objectives9.1 Motivation9.2 Samples and Sampling9.3 Bernoulli Trials and the Binomial Distribution9.4 Representing the Character of a Population9.5 Predicting Potential Samples from a Known Population9.6 The Normal Distribution9.7 The Central Limit Theorem9.8 Normal Sampling Variability and Statistical Significance9.9 Summary9.10 ExercisesPART IV. TOOLS FOR MAKING STATISTICAL INFERENCESEstimation StudiesAssociation StudiesChapter 10. Estimation Studies: Inferring the Parameters of a Population from the Statistics of a Sample10.0 Learning Objectives10.1 Motivation10.2 Estimating the Occurrence of a Qualitative Property for a Population10.3 Estimating the Occurrences of a Quantitative Property for a Population10.4 Some Notes on Sampling10.5 SPSS Tutorial10.6 Summary10.7 ExercisesChapter 11. Chi-Square Analysis: Investigating a Suspected Association Between Two Qualitative Properties11.0 Learning Objectives11.1 Motivation11.2 An Example11.3 An Extension: Testing the Statistical Significance of Population Proportions11.4 Summary11.5 SPSS Tutorial11.6 ExercisesChapter 12. The t-Test of Statistical Significance: Comparing a Quantitative Property Assessed for Two Different Groups12.0 Learning Objectives12.1 Motivation12.2 An Example12.3 Comparing Sample Means Using the Central Limit Theorem (Optional)12.4 Comparing Sample Means Using the t-Test12.5 Summary12.6 SPSS Tutorial12.7 ExercisesChapter 13. Analysis of Variance: Comparing a Quantitative Property Assessed for Several Different Groups13.0 Learning Objectives13.1 Motivation13.2 An Example13.3 The F-Test13.4 A Note on Sampling Distributions (Optional)13.5 Summary13.6 SPSS Tutorial13.7 ExercisesChapter 14. Correlation Analysis and Linear Regression: Assessing the Covariability of Two Quantitative Properties14.0 Learning Objectives14.1 Motivation14.2 An Example14.3 Visual Interpretation with a Scatter Plot (Optional)14.4 Assessing an Association as a Covariance14.5 Regression Analysis: Representing a Correlation as a Linear Mathematical Model14.6 Assessing the Explanatory Value of the Model14.7 Summary14.8 SPSS Tutorial14.9 ExercisesIndex

Review

"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
"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
"This text takes a refreshing approach to presenting statistical concepts in a methodologically rigorous yet meaningful way that students will intuitively grasp." -- Brian Frederick
"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
"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.
"This book provides a thorough introduction to statistics. End-of-chapter exercises and SPSS® tutorials will greatly enhance students′ abilities to transfer skills learned in the classroom to real-world problems." -- Christopher Larimer
"Functional and straightforward. A comprehensive introduction to statistics!" -- Derrick Bryan
"This is a remarkable book that integrates examples, SPSS® tutorials, and exercises. The chapters provide an in-depth analysis of the key concepts. This book is an essential resource for advanced-level undergraduate students and graduate students in the study of statistical analysis." -- Prachi Kene
"An enjoyable read. The book has the potential to promote numeracy among the general public, and serve as a resource in statistics education." -- Michael Raisinghani

Review Quote

"This is a remarkable book that integrates examples, SPSStutorials, and exercises. The chapters provide an in-depth analysis of the key concepts. This book is an essential resource for advanced-level undergraduate students and graduate students in the study of statistical analysis."

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