This crystal clear, applied text teaches readers how to plan, conduct, and write a research project and select and interpret data through its integrated approach to research methods. Although not a statistics book, readers learn to master which technique to use when and how to analyze results, making them better consumers of research. Organized around the steps of conducting a research project, independent sections and chapters can be read in any order for utmost learning flexibility. A consistent framework increases comprehension. Numerous examples, diagrams, tables, key terms, summaries,…mehr
This crystal clear, applied text teaches readers how to plan, conduct, and write a research project and select and interpret data through its integrated approach to research methods. Although not a statistics book, readers learn to master which technique to use when and how to analyze results, making them better consumers of research. Organized around the steps of conducting a research project, independent sections and chapters can be read in any order for utmost learning flexibility. A consistent framework increases comprehension. Numerous examples, diagrams, tables, key terms, summaries, applied problems and questions, and suggested readings further promote understanding.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jeffrey A. Gliner is Professor Emeritus of Occupational Therapy at Colorado State University. He taught graduate level research methods, statistics, and program evaluation courses to masters and doctoral level students for more than 20 years. George A. Morgan is a Professor Emeritus of Education and Human Development at Colorado State University. He taught research methods and applied statistics to graduate students in several departments at Colorado State. In addition to writing textbooks on SPSS and research methods, he currently advises students on their dissertation research. Nancy L. Leech is a Professor at the University of Colorado Denver. Dr. Leech is currently teaching graduate level courses in research, statistics, and measurement.
Inhaltsangabe
Part I. Introductory Chapters 1. Definitions, Purposes, and Dimensions of Research 2. Planning a Quantitative Research Project Part II: Quantitative Research Approaches, Questions, and Designs 3. Variables, Research Questions, and Hypotheses 4. Research Approaches 5. Randomized Experimental and Quasi-Experimental Designs 6. Single-Subject Designs 7. Non-experimental Approaches/Designs 8. Internal Validity Part III: Sampling, Measurement and Data Collection 9. Sampling and Introduction to External Validity 10. Measurement and Descriptive Statistics 11. Measurement Reliability 12. Measurement Validity 13. Types of Data Collection Techniques 14. Ethical Issues in Conducting the Study 15. Practical Issues in Data Collection and Coding Part IV: Data Analysis and Interpretation 16. Making Inferences from Sample Data I: The Null Hypothesis Significance Testing Approach 17. Making Inferences From Sample Data II: The Evidence-Based Approach 18. General Design Classifications for Selection of Difference Statistical Methods 19. Selection of Appropriate Statistical Methods: Integration of Design and Analysis 20. Data Analysis and Interpretation - Basic Difference Questions 21. Analysis and Interpretation of Basic Associational Research Questions 22. Analysis and Interpretation of Complex Research Questions Part V: Evaluating and Writing Research Reports 23. Evaluating Research Validity: Part I 24. Evaluating Research Validity: Part II 25. Evaluating Research for Evidence-Based Practice 26. Writing the Research Report
Part I. Introductory Chapters 1. Definitions, Purposes, and Dimensions of Research 2. Planning a Quantitative Research Project Part II: Quantitative Research Approaches, Questions, and Designs 3. Variables, Research Questions, and Hypotheses 4. Research Approaches 5. Randomized Experimental and Quasi-Experimental Designs 6. Single-Subject Designs 7. Non-experimental Approaches/Designs 8. Internal Validity Part III: Sampling, Measurement and Data Collection 9. Sampling and Introduction to External Validity 10. Measurement and Descriptive Statistics 11. Measurement Reliability 12. Measurement Validity 13. Types of Data Collection Techniques 14. Ethical Issues in Conducting the Study 15. Practical Issues in Data Collection and Coding Part IV: Data Analysis and Interpretation 16. Making Inferences from Sample Data I: The Null Hypothesis Significance Testing Approach 17. Making Inferences From Sample Data II: The Evidence-Based Approach 18. General Design Classifications for Selection of Difference Statistical Methods 19. Selection of Appropriate Statistical Methods: Integration of Design and Analysis 20. Data Analysis and Interpretation - Basic Difference Questions 21. Analysis and Interpretation of Basic Associational Research Questions 22. Analysis and Interpretation of Complex Research Questions Part V: Evaluating and Writing Research Reports 23. Evaluating Research Validity: Part I 24. Evaluating Research Validity: Part II 25. Evaluating Research for Evidence-Based Practice 26. Writing the Research Report
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