Edward T. Vieira, Jr., a professor of marketing and statistics at Simmons University's School of Management for over two decades, brings a wealth of experience as both an academic and a former marketing and business consultant to multinational corporations across North America and Europe. His extensive body of work includes numerous books, book chapters, and articles published in peer-reviewed journals and presented at international conferences.

• PART I: GETTING STARTED 1. An Introduction to Applied Statistics 2. Statistics: Descriptive, Inferential, and Correlational 3. Data and Types of Variables 4. SPSS 29 Statistics Data Management Basics – Preparing Data for Analysis; PART II: SAMPLING CONSIDERATIONS 5. Sampling Strategies 6. Sample Size 7. Sources and Types of Statistical Error 8. Missing Data; PART III: DATA SCREENING, DESCRIBING, AND PROBABILITIES 9. Describing Categorical Variables 10. Basic Probabilities for Categorical Variables 11. The Concepts of Data Distribution, Probability Values, and Significance Testing 12. Numeric Variables: Data Screening and Removing Outliers; PART IV: STATISTICAL ANALYSIS Categorical Variables 13. Chi-Square Goodness of Fit Test: Comparing Counts in a Single Variable With Two or More Categories 14. Chi-Square Test of Independence: Comparing Counts Between Two Variables Each With Two or More Categories 15. Chi-Square Test of the Same Sample: Comparing Counts of the Same Sample Measured Twice Using a Categorical Variable Numeric Variables 16. t-Test: Comparing a Single-Sample Mean to a Specific Value 17. t-Test: Comparing Two Independent Samples’ Variable Means 18. Analysis of Variance (ANOVA): Comparing More Than Two Independent Samples’ Means to Test for Differences Among Them 19. Paired t-Test: Comparing the Means of the Same Sample Measured Twice Using a Numeric Variable Association and Regression 20. General Linear Model Repeated Measures: Comparing Means of the Same Sample Measured More Than Twice Using a Numeric Variable 21. Bivariate Correlation: The Association Between Two Variables 22. Linear Regression 23. Logistic Regression Data Reduction 24. Factor Analysis: From Data Reduction to Latent Variables 25. Classification Using Cluster Analysis; APPENDICES Appendix A: Glossary Appendix B: Chapter Statistical Exercise Solutions Appendix C: Statistics Flow Chart

“Dr. Vieira’s book is comprehensive, clear, and has great examples to illustrate the concepts.” -- Clayton W. Barrows, Professor Emeritus of Hospitality Management, University of New Hampshire, USA“This is a textbook that attempts to bridge areas of content that are traditionally addressed independently: a. the conceptual knowledge of technical information; b. the real-world application of such technical knowledge; and c. a leading software tool that assists a researcher in applying the conceptual knowledge of technical information to a real-world context. Vieira seems to have used the feedback he received from his students over several decades to successfully build a bridge across these three content areas. This textbook and its approach are a welcome addition for making the process of learning statistics in the social sciences a lot smoother and a lot more relevant to students.” -- Michael G. Elasmar, Associate Professor and Director of the Marketing Communication Research graduate program, Boston University, USA“Professor Vieira combines a straightforward approach to applied statistics with the most accessible statistical package, SPSS. His non-technical, clear, and concise writing style makes An Introduction to Applied Statistics a valuable handbook and reference for students and practitioners as well as an effective text for introductory statistics courses.” -- John Lowe, Associate Dean Emeritus for Undergraduate Programs, Simmons University, USA“This book serves students being introduced to quantitative research as well as research professionals seeking to add to their statistical analysis and quantitative reasoning skills. Its emphasis on providing the reasons and prerequisites for using a statistical procedure make it valuable as a book-shelf reference as well as a textbook. Integration of relevant exercises to be carried out with SPSS enhances understanding of general statistical concepts with a body of hands-on experience, and that combination results in a very valuable skill set that will serve the reader well for years.” -- James H. Watt, Professor Emeritus, University of Connecticut, USA

Edward T. Vieira, Jr., a professor of marketing and statistics at Simmons University's School of Management for over two decades, brings a wealth of experience as both an academic and a former marketing and business consultant to multinational corporations across North America and Europe. His extensive body of work includes numerous books, book chapters, and articles published in peer-reviewed journals and presented at international conferences.

• PART I: GETTING STARTED 1. An Introduction to Applied Statistics 2. Statistics: Descriptive, Inferential, and Correlational 3. Data and Types of Variables 4. SPSS 29 Statistics Data Management Basics – Preparing Data for Analysis; PART II: SAMPLING CONSIDERATIONS 5. Sampling Strategies 6. Sample Size 7. Sources and Types of Statistical Error 8. Missing Data; PART III: DATA SCREENING, DESCRIBING, AND PROBABILITIES 9. Describing Categorical Variables 10. Basic Probabilities for Categorical Variables 11. The Concepts of Data Distribution, Probability Values, and Significance Testing 12. Numeric Variables: Data Screening and Removing Outliers; PART IV: STATISTICAL ANALYSIS Categorical Variables 13. Chi-Square Goodness of Fit Test: Comparing Counts in a Single Variable With Two or More Categories 14. Chi-Square Test of Independence: Comparing Counts Between Two Variables Each With Two or More Categories 15. Chi-Square Test of the Same Sample: Comparing Counts of the Same Sample Measured Twice Using a Categorical Variable Numeric Variables 16. t-Test: Comparing a Single-Sample Mean to a Specific Value 17. t-Test: Comparing Two Independent Samples’ Variable Means 18. Analysis of Variance (ANOVA): Comparing More Than Two Independent Samples’ Means to Test for Differences Among Them 19. Paired t-Test: Comparing the Means of the Same Sample Measured Twice Using a Numeric Variable Association and Regression 20. General Linear Model Repeated Measures: Comparing Means of the Same Sample Measured More Than Twice Using a Numeric Variable 21. Bivariate Correlation: The Association Between Two Variables 22. Linear Regression 23. Logistic Regression Data Reduction 24. Factor Analysis: From Data Reduction to Latent Variables 25. Classification Using Cluster Analysis; APPENDICES Appendix A: Glossary Appendix B: Chapter Statistical Exercise Solutions Appendix C: Statistics Flow Chart

“Dr. Vieira’s book is comprehensive, clear, and has great examples to illustrate the concepts.” -- Clayton W. Barrows, Professor Emeritus of Hospitality Management, University of New Hampshire, USA“This is a textbook that attempts to bridge areas of content that are traditionally addressed independently: a. the conceptual knowledge of technical information; b. the real-world application of such technical knowledge; and c. a leading software tool that assists a researcher in applying the conceptual knowledge of technical information to a real-world context. Vieira seems to have used the feedback he received from his students over several decades to successfully build a bridge across these three content areas. This textbook and its approach are a welcome addition for making the process of learning statistics in the social sciences a lot smoother and a lot more relevant to students.” -- Michael G. Elasmar, Associate Professor and Director of the Marketing Communication Research graduate program, Boston University, USA“Professor Vieira combines a straightforward approach to applied statistics with the most accessible statistical package, SPSS. His non-technical, clear, and concise writing style makes An Introduction to Applied Statistics a valuable handbook and reference for students and practitioners as well as an effective text for introductory statistics courses.” -- John Lowe, Associate Dean Emeritus for Undergraduate Programs, Simmons University, USA“This book serves students being introduced to quantitative research as well as research professionals seeking to add to their statistical analysis and quantitative reasoning skills. Its emphasis on providing the reasons and prerequisites for using a statistical procedure make it valuable as a book-shelf reference as well as a textbook. Integration of relevant exercises to be carried out with SPSS enhances understanding of general statistical concepts with a body of hands-on experience, and that combination results in a very valuable skill set that will serve the reader well for years.” -- James H. Watt, Professor Emeritus, University of Connecticut, USA

Edward T. Vieira, Jr., a professor of marketing and statistics at Simmons University's School of Management for over two decades, brings a wealth of experience as both an academic and a former marketing and business consultant to multinational corporations across North America and Europe. His extensive body of work includes numerous books, book chapters, and articles published in peer-reviewed journals and presented at international conferences.

• PART I: GETTING STARTED 1. An Introduction to Applied Statistics 2. Statistics: Descriptive, Inferential, and Correlational 3. Data and Types of Variables 4. SPSS 29 Statistics Data Management Basics – Preparing Data for Analysis; PART II: SAMPLING CONSIDERATIONS 5. Sampling Strategies 6. Sample Size 7. Sources and Types of Statistical Error 8. Missing Data; PART III: DATA SCREENING, DESCRIBING, AND PROBABILITIES 9. Describing Categorical Variables 10. Basic Probabilities for Categorical Variables 11. The Concepts of Data Distribution, Probability Values, and Significance Testing 12. Numeric Variables: Data Screening and Removing Outliers; PART IV: STATISTICAL ANALYSIS Categorical Variables 13. Chi-Square Goodness of Fit Test: Comparing Counts in a Single Variable With Two or More Categories 14. Chi-Square Test of Independence: Comparing Counts Between Two Variables Each With Two or More Categories 15. Chi-Square Test of the Same Sample: Comparing Counts of the Same Sample Measured Twice Using a Categorical Variable Numeric Variables 16. t-Test: Comparing a Single-Sample Mean to a Specific Value 17. t-Test: Comparing Two Independent Samples’ Variable Means 18. Analysis of Variance (ANOVA): Comparing More Than Two Independent Samples’ Means to Test for Differences Among Them 19. Paired t-Test: Comparing the Means of the Same Sample Measured Twice Using a Numeric Variable Association and Regression 20. General Linear Model Repeated Measures: Comparing Means of the Same Sample Measured More Than Twice Using a Numeric Variable 21. Bivariate Correlation: The Association Between Two Variables 22. Linear Regression 23. Logistic Regression Data Reduction 24. Factor Analysis: From Data Reduction to Latent Variables 25. Classification Using Cluster Analysis; APPENDICES Appendix A: Glossary Appendix B: Chapter Statistical Exercise Solutions Appendix C: Statistics Flow Chart

“Dr. Vieira’s book is comprehensive, clear, and has great examples to illustrate the concepts.” -- Clayton W. Barrows, Professor Emeritus of Hospitality Management, University of New Hampshire, USA“This is a textbook that attempts to bridge areas of content that are traditionally addressed independently: a. the conceptual knowledge of technical information; b. the real-world application of such technical knowledge; and c. a leading software tool that assists a researcher in applying the conceptual knowledge of technical information to a real-world context. Vieira seems to have used the feedback he received from his students over several decades to successfully build a bridge across these three content areas. This textbook and its approach are a welcome addition for making the process of learning statistics in the social sciences a lot smoother and a lot more relevant to students.” -- Michael G. Elasmar, Associate Professor and Director of the Marketing Communication Research graduate program, Boston University, USA“Professor Vieira combines a straightforward approach to applied statistics with the most accessible statistical package, SPSS. His non-technical, clear, and concise writing style makes An Introduction to Applied Statistics a valuable handbook and reference for students and practitioners as well as an effective text for introductory statistics courses.” -- John Lowe, Associate Dean Emeritus for Undergraduate Programs, Simmons University, USA“This book serves students being introduced to quantitative research as well as research professionals seeking to add to their statistical analysis and quantitative reasoning skills. Its emphasis on providing the reasons and prerequisites for using a statistical procedure make it valuable as a book-shelf reference as well as a textbook. Integration of relevant exercises to be carried out with SPSS enhances understanding of general statistical concepts with a body of hands-on experience, and that combination results in a very valuable skill set that will serve the reader well for years.” -- James H. Watt, Professor Emeritus, University of Connecticut, USA