Atjaunināt sīkdatņu piekrišanu

E-grāmata: Qualitative Comparative Analysis in Mixed Methods Research and Evaluation

  • Formāts: EPUB+DRM
  • Sērija : Mixed Methods Research Series
  • Izdošanas datums: 21-Dec-2018
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781506390208
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 49,96 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: EPUB+DRM
  • Sērija : Mixed Methods Research Series
  • Izdošanas datums: 21-Dec-2018
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781506390208
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Qualitative Comparative Analysis in Mixed Methods Research and Evaluation provides a user-friendly introduction for using Qualitative Comparative Analysis (QCA) as part of a mixed methods approach to research and evaluation. Offering practical, in-depth, and applied guidance for this unique analytic technique that is not provided in any current mixed methods textbook, the chapters of this guide skillfully build upon one another to walk researchers through the steps of QCA in logical order. To enhance and further reinforce learning, authors Leila C. Kahwati and Heather L. Kane provide supportive learning objectives, summaries, and exercises, as well as author-created datasets for use in R via the companion site.  

Qualitative Comparative Analysis in Mixed Methods Research and Evaluation is Volume 6 in SAGE’s Mixed Methods Research Series. To learn more about each text in the series, please visit sagepub.com/mmrs.



Recenzijas

"This book is written in a way that is easy to follow and should expand the range of fields in which QCA is used. Also, there are quite a few principles and practice tips articulated, especially in later chapters, which are applicable more broadly across social sciences and evaluation work. Novice researchers will find those suggestions especially helpful, even if QCA does not become a major tool in their practice." -- Elizabeth H. McEneaney "The practical, how-to, nature of the text is very appealing to me as an instructor. I like the examples and appreciate the numerous figures used to illustrate processes and arguments for visual learners." -- James R. Martin "The text introduces an important, specific approach to research." -- Tamar Ginossar "I think the key strengths of this text are its organization and breadth. From an organization perspective, the wealth of resources and focus is essential for guiding the reader/learner toward practical keywords, i.e. language, and skills necessary to implement." -- Raymond Blanton

Editors' Introduction xii
Preface xiv
Acknowledgments xvi
About the Authors xviii
Chapter 1 Qualitative Comparative Analysis as Part of a Mixed Methods Approach
1(18)
Learning Objectives
1(1)
Overview of Mixed Methods Study Designs
2(3)
How QCA Compares to Other Quantitative and Qualitative Methods
5(5)
Underlying Assumptions of Causal Complexity
10(2)
QCA in Mixed Methods Studies
12(4)
Overview of the Rest of the Book and Guiding QCA Heuristic
16(2)
Summary and Key Points
18(1)
Chapter 2 Overview of QCA Concepts and Terminology
19(24)
Learning Objectives
19(1)
Configural Research Questions
20(3)
The Concept of Sets
23(10)
Relationships of Necessity and Sufficiency
25(8)
Set Operators and Symbolic Notation
33(5)
From Concepts to Real-World Application
38(3)
Conclusion
41(1)
Summary and Key Points
41(1)
Supplementary Digital Content
42(1)
Chapter 3 Selecting Cases and Choosing Conditions and Outcome
43(22)
Learning Objectives
43(2)
Overview of Case, Condition, and Outcome Selection
45(1)
Apply Theoretical, Empirical, and Practical Considerations for Selecting Cases
45(8)
Theoretical Considerations
47(1)
Empirical Considerations
48(1)
Practical Considerations
49(1)
Number of Cases to Select
50(3)
Apply Theoretical, Empirical, and Practical Considerations for Selecting Conditions and Outcomes
53(7)
Theoretical Considerations for Selecting Conditions and Outcomes
53(3)
Empirical Consideration for Selecting Conditions
56(2)
Empirical Considerations for Selecting Outcomes
58(1)
Practical Considerations
59(1)
Identify Strategies for Optimizing the Number of Cases and Conditions
60(1)
School Health Features and Academic Performance Example
61(2)
Conclusion
63(1)
Summary and Key Points
64(1)
Supplementary Digital Content
64(1)
Chapter 4 Calibrating Sets and Managing Data
65(36)
Learning Objectives
65(1)
Data Types and Sources
65(4)
Calibration Versus Measurement
69(2)
Types of Calibration and Processes Used
71(5)
Calibration Points
71(5)
Calibration Examples
76(11)
Examples of Crisp Set Calibration
78(2)
Example of Fixed-Value Fuzzy Set Calibration
80(1)
Example of Continuous Fuzzy Set Calibration
80(4)
Example Using All Approaches to Calibration in the Same Study
84(3)
Good Calibration Practices and Data Management Strategies
87(12)
Good Calibration Practices
87(1)
Data Management Strategies
88(11)
Conclusion
99(1)
Summary and Key Points
99(1)
Supplementary Digital Content
100(1)
Chapter 5 Analyzing the Data--Initial Analyses
101(34)
Learning Objectives
101(1)
Overview of Analysis
102(1)
Transform a Data Matrix Into a Truth Table
103(9)
Step 1: Creating a Truth Table Shell
103(2)
Step 2: Assign Cases From the Data Matrix to a Truth Table Row
105(4)
Step 3 Assign an Outcome Value to Each Truth Table Row
109(3)
Strategies for Managing Contradictory Truth Table Rows
112(3)
Including Only Rows With High Consistency
112(2)
Including All Contradictory Rows
114(1)
Excluding All Contradictory Rows
114(1)
Revisiting the Data to Manage Contradictory Truth Table Rows
115(2)
Adding a Condition
115(1)
Revisiting Case Selection
115(1)
Revisiting the Definition and Calibration of the Conditions and Outcomes
116(1)
Inspect the Truth Table for Potential Issues
117(2)
Conduct an Analysis of Necessary Conditions and Combinations of Conditions
119(7)
Conduct Analysis of Sufficient Conditions and Combinations of Conditions
126(6)
Conclusions
132(1)
Summary and Key Points
133(1)
Supplementary Digital Content
134(1)
Chapter 6 Analyzing the Data--Model Analytics
135(31)
Learning Objectives
135(1)
Overview of Model Analytics
135(2)
Interpret Solution Parameters of Fit
137(8)
Consistency
137(4)
Coverage
141(4)
Evaluating Assumptions
145(4)
Implausible Assumptions
146(1)
Incoherent Assumptions
147(1)
Tenable Assumptions: Easy vs. Difficult Simplifying Assumptions
148(1)
Tenable Assumptions: Nonsimplifying Assumptions
149(1)
Evaluating Assumptions: An Applied Example
149(2)
Model Ambiguity
151(1)
Evaluating Model Ambiguity: An Applied Example
152(2)
Assessing Robustness
154(2)
Adding or Excluding Cases
155(1)
Calibration Points and Functions
155(1)
Consistency Threshold
156(1)
Evaluating Robustness: An Applied Example
156(5)
Iterative Respecification
161(1)
Conclusion
162(2)
Summary and Key Points
164(1)
Supplementary Digital Content
165(1)
Chapter 7 Interpreting Results: Within- and Cross-case Analysis
166(20)
Learning Objectives
166(1)
Overview of Interpretation
166(2)
Considerations for Conducting Within- and Cross-case Analysis
168(1)
Study Aims and Case Selection for Within - and Cross-case Analysis
169(9)
Types of Cases With Fuzzy Set Analysis for Sufficiency
171(3)
Methods for Within Case and Cross-case Analysis
174(1)
Process Tracing
175(1)
Pattern Matching
176(1)
Data Matrices and Displays
177(1)
Example: School Health and Wellness Policies and Academic Performance
178(7)
Initial Interpretation
180(1)
Explore Deviant Cases That Decrease Coverage to Identify Missing Conditions
181(1)
Explore Deviant Cases That Decrease Consistency to Identify Missing Conditions
182(1)
Explore Causal Mechanisms
182(1)
Explore Unique Cases to Identify New Underlying Causal Mechanisms
182(1)
Explore Surprising Findings in a Solution
183(2)
Conclusion
185(1)
Summary and Key Points
185(1)
Chapter 8 Advanced Topics in QCA
186(16)
Learning Objectives
186(1)
Multi-value QCA
187(2)
Incorporating Time in QCA
189(6)
Informal Techniques
189(2)
Formal Techniques
191(4)
Critiques of QCA
195(5)
Critique Related to Calibration
195(1)
Critique Related to the Robustness of Results
196(2)
Critique Related to Causality
198(1)
Critique Related to QCA Practice
199(1)
Conclusion
200(1)
Summary and Key Points
201(1)
Chapter 9 Preparing Proposals, Reports, Manuscripts, and Presentations
202(33)
Learning Objectives
202(1)
Overview
203(1)
Reporting QCA Methods
204(6)
Case, Condition, and Outcome Selection
204(2)
Calibration Approach
206(1)
Analytic Steps
206(4)
Summarizing Findings and Limitations
210(15)
Summarizing Findings
210(9)
Summarizing Limitations
219(6)
Improving Accessibility to Readers
225(2)
Managing Jargon
225(1)
Generate Several Research Products
226(1)
Responding to Peer Review Critiques
227(6)
Research Funding Proposals
227(1)
Manuscripts
228(5)
Conclusion
233(1)
Summary and Key Points
233(1)
Supplementary Digital Content
234(1)
Chapter 10 Examples of Mixed Methods Approaches Using QCA
235(1)
Learning Objectives
235(1)
QCA Within a Mixed Methods Approach
236(1)
Example of Convergent Design: Evaluation of the Jobs to Careers Program
237(12)
Qualitative Data Collection and Analysis
237(2)
Quantitative Data Collection and Analysis
239(1)
Integration Using QCA
239(8)
Synthesizing the Results
247(2)
Example of Sequential Design: A Configurational Approach to Understanding Project Delays
249(6)
Quantitative Data Collection and Analysis
250(1)
Qualitative Data Collection and Analysis
250(2)
Integration Using QCA
252(2)
Synthesizing the Results
254(1)
Conclusion
255(1)
Summary and Key Points
255(2)
Appendix: Recommended QCA Resources 257(33)
Glossary 260(7)
References 267(11)
Index 278
Leila C. Kahwati, MD MPH, is a senior research scientist in RTI Internationals Social and Health Organizational Research and Evaluation Program. She has over 20 years of experience in government, community, and academic health care practice and research settings with a focus on clinical program and policy development and evaluation. Dr. Kahwati is board certified in family medicine and in general preventive medicine/public health and has training in epidemiology and health services research through a Health Resources and Services Administration National Research Service Award in Primary Care Research through the Cecil G. Sheps Center for Health Services Research and the Department of Family Medicine at the University of North Carolina at Chapel Hill. With her colleagues, she has pioneered the use of qualitative comparative analysis in mixed methods health services research and in systematic reviews of complex interventions. Before joining RTI she was the Deputy Chief Consultant for Preventive Medicine in the Veterans Health Administration. She holds an adjunct faculty appointment in the School of Medicine at the University of North Carolina at Chapel Hill. Heather L. Kane, PhD is a senior public health analyst and director of the Child and Adolescent Research and Evaluation program at RTI International, specializing in qualitative research methodologies, qualitative comparative analysis (QCA), and evaluation. Dr. Kane has more than 15 years of experience in planning and implementing mixed methods evaluations. Her work focuses on the lives of vulnerable populations, such as children and adults experiencing food insecurity, persons living with multiple sclerosis (MS), and low-income persons lacking access to health care. Before joining RTI, she was an Agency for Healthcare Research and Quality National Research Service Award postdoctoral research fellow at the Cecil G. Sheps Center for Health Services Research at the University of North Carolina at Chapel Hill and recipient of the Jacob K. Javits Fellowship from the U.S. Department of Education.  She holds an adjunct faculty appointment with Tulane University, School of Professional Advancement.