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The Resource Selecting the right analyses for your data : quantitative, qualitative, and mixed methods, W. Paul Vogt [and others]

Selecting the right analyses for your data : quantitative, qualitative, and mixed methods, W. Paul Vogt [and others]

Label
Selecting the right analyses for your data : quantitative, qualitative, and mixed methods
Title
Selecting the right analyses for your data
Title remainder
quantitative, qualitative, and mixed methods
Statement of responsibility
W. Paul Vogt [and others]
Contributor
Author
Subject
Language
eng
Summary
"What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"--
Assigning source
Provided by publisher
Cataloging source
EBLCP
Dewey number
001.4/2
Index
index present
Language note
Text in English
LC call number
H62
LC item number
.V6228 2014eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Vogt, W. Paul
http://library.link/vocab/subjectName
  • Social sciences
  • Quantitative research
  • Qualitative research
  • REFERENCE
  • Qualitative research
  • Quantitative research
  • Social sciences
Label
Selecting the right analyses for your data : quantitative, qualitative, and mixed methods, W. Paul Vogt [and others]
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Part I. Coding Data-by Design -- Part II. Analysis and Interpretation of Quantitative Data -- Part III. Analysis and Interpretation of Qualitative and Combined/Mixed Data --
  • Examples of Coding and Analysis
  • Conclusion: A Research Agenda
  • Part III.
  • Analysis and Interpretation of Qualitative and Combined/Mixed Data
  • Introduction to Part III
  • 11.
  • Inductive Analysis of Qualitative Data: Ethnographic Approaches and Grounded Theory
  • The Foundations of Inductive Social Research in Ethnographic Fieldwork
  • Grounded Theory: An Inductive Approach to Theory Building
  • Conclusion
  • 12.
  • Looking Ahead
  • Deductive Analyses of Qualitative Data: Comparative Case Studies and Qualitative Comparative Analysis
  • Case Studies and Deductive Analyses
  • When to Do a Single-Case Analysis: Discovering, Describing, and Explaining Causal Links
  • When to Conduct Small-N Comparative Case Studies
  • When to Conduct Analyses with an Intermediate N of Cases
  • Conclusions
  • 13.
  • Coding and Analyzing Data from Combined and Mixed Designs
  • Coding and Analysis Considerations for Deductive and Inductive Designs
  • Coding Considerations for Sequential Analysis Approaches
  • Part I.
  • Data Transformation/Data Merging in Combined Designs
  • Conclusions
  • 14.
  • Conclusion: Common Themes and Diverse Choices
  • Common Themes
  • The Choice Problem
  • Strategies and Tactics
  • Coding Data-by Design
  • Introduction to Part I
  • 1.
  • Coding Survey Data
  • An Example: Pitfalls When Constructing a Survey
  • What Methods to Use to Construct an Effective Questionnaire
  • Coding and Measuring Respondents' Answers to the Questions
  • General Introduction
  • Conclusion: Where to Find Analysis Guidelines for Surveys in This Book
  • 2.
  • Coding Interview Data
  • Goals: What Do You Seek When Asking Questions?
  • Your Role: What Should Your Part Be in the Dialogue?
  • Samples: How Many Interviews and with Whom?
  • Questions: When Do You Ask What Kinds of Questions?
  • Modes: How Do You Communicate with Interviewees?
  • Observations: What Is Important That Isn't Said?
  • Records: What Methods Do You Use to Preserve the Dialogue?
  • What Are Data?
  • Tools: When Should You Use Computers to Code Your Data?
  • Getting Help: When to Use Member Checks and Multiple Coders
  • Conclusion
  • 3.
  • Coding Experimental Data
  • Coding and Measurement Issues for All Experimental Designs
  • Coding and Measurement Issues That Vary by Type of Experimental Design
  • Conclusion: Where in This Book to Find Guidelines for Analyzing Experimental Data
  • 4.
  • Coding Data from Naturalistic and Participant Observations
  • Two Basic Organizing Questions
  • Introduction to Observational Research
  • Phase 1: Observing
  • Phase 2: Recording
  • Phase 3: Coding
  • Recommendations
  • Conclusions and Tips for Completing an Observational Study
  • Appendix 4.1. Example of a Site Visit Protocol
  • 5.
  • Coding Archival Data: Literature Reviews, Big Data, and New Media
  • Reviews of the Research Literature
  • Ranks or Ordered Coding (When to Use Ordinal Data)
  • Big Data
  • Coding Data from the Web, Including New Media
  • Conclusion: Coding Data from Archival, Web,and New Media Sources
  • Part II.
  • Analysis and Interpretation of Quantitative Data
  • Introduction to Part II
  • Conceptual and Terminological Housekeeping: Theory, Model, Hypothesis, Concept, Variable
  • and a Note on Software
  • 6.
  • Describing, Exploring, and Visualizing Your Data
  • Visual/Graphic Data, Coding, and Analyses
  • What Is Meant by Descriptive Statistics?
  • Overview of the Main Types of Descriptive Statistics and Their Uses
  • What Descriptive Statistics to Use to Prepare for Further Analyses
  • When to Use Correlations as Descriptive Statistics
  • When and Why to Make the Normal Curve Your Point of Reference
  • When Can You Use Descriptive Statistics Substantively?
  • When to Use Descriptive Statistics Preparatory to Applying Missing Data Procedures
  • Conclusion
  • 7.
  • What Methods of Statistical Inference to Use When
  • At What Point Does Coding Occur in the Course of Your Research Project?
  • Null Hypothesis Significance Testing
  • Which Statistical Tests to Use for What
  • When to Use Confidence Intervals
  • When to Report Power and Precision of Your Estimates
  • When Should You Use Distribution-Free, Nonparametric Significance Tests?
  • When to Use the Bootstrap and Other Resampling Methods
  • When to Use Bayesian Methods
  • Which Approach to Statistical Inference Should You Take?
  • The "Silent Killer" of Valid Inferences: Missing Data
  • Conclusion
  • Codes and the Phenomena We Study
  • Appendix 7.1. Examples of Output of Significance Tests
  • 8.
  • What Associational Statistics to Use When
  • When to Use Correlations to Analyze Data
  • When to Use Regression Analysis
  • What to Do When Your Dependent Variables Are Categorical
  • Summary: Which Associational Methods Work Best for What Sorts of Data and Problems?
  • The Most Important Question: When to Include Which Variables
  • Conclusion: Relations among Variables to Investigate Using Regression Analysis
  • 9.
  • A Graphic Depiction of the Relation of Coding to Analysis
  • Advanced Associational Methods
  • Multilevel Modeling
  • Path Analysis
  • Factor Analysis-Exploratory and Confirmatory
  • Structural Equation Modeling
  • Conclusion
  • 10.
  • Model Building and Selection
  • When Can You Benefit from Building a Model or Constructing a Theory?
  • When to Use a Multimodel Approach
Control code
879074384
Dimensions
unknown
Extent
1 online resource (522 pages)
File format
unknown
Form of item
online
Isbn
9781462516032
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)879074384
Label
Selecting the right analyses for your data : quantitative, qualitative, and mixed methods, W. Paul Vogt [and others]
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Part I. Coding Data-by Design -- Part II. Analysis and Interpretation of Quantitative Data -- Part III. Analysis and Interpretation of Qualitative and Combined/Mixed Data --
  • Examples of Coding and Analysis
  • Conclusion: A Research Agenda
  • Part III.
  • Analysis and Interpretation of Qualitative and Combined/Mixed Data
  • Introduction to Part III
  • 11.
  • Inductive Analysis of Qualitative Data: Ethnographic Approaches and Grounded Theory
  • The Foundations of Inductive Social Research in Ethnographic Fieldwork
  • Grounded Theory: An Inductive Approach to Theory Building
  • Conclusion
  • 12.
  • Looking Ahead
  • Deductive Analyses of Qualitative Data: Comparative Case Studies and Qualitative Comparative Analysis
  • Case Studies and Deductive Analyses
  • When to Do a Single-Case Analysis: Discovering, Describing, and Explaining Causal Links
  • When to Conduct Small-N Comparative Case Studies
  • When to Conduct Analyses with an Intermediate N of Cases
  • Conclusions
  • 13.
  • Coding and Analyzing Data from Combined and Mixed Designs
  • Coding and Analysis Considerations for Deductive and Inductive Designs
  • Coding Considerations for Sequential Analysis Approaches
  • Part I.
  • Data Transformation/Data Merging in Combined Designs
  • Conclusions
  • 14.
  • Conclusion: Common Themes and Diverse Choices
  • Common Themes
  • The Choice Problem
  • Strategies and Tactics
  • Coding Data-by Design
  • Introduction to Part I
  • 1.
  • Coding Survey Data
  • An Example: Pitfalls When Constructing a Survey
  • What Methods to Use to Construct an Effective Questionnaire
  • Coding and Measuring Respondents' Answers to the Questions
  • General Introduction
  • Conclusion: Where to Find Analysis Guidelines for Surveys in This Book
  • 2.
  • Coding Interview Data
  • Goals: What Do You Seek When Asking Questions?
  • Your Role: What Should Your Part Be in the Dialogue?
  • Samples: How Many Interviews and with Whom?
  • Questions: When Do You Ask What Kinds of Questions?
  • Modes: How Do You Communicate with Interviewees?
  • Observations: What Is Important That Isn't Said?
  • Records: What Methods Do You Use to Preserve the Dialogue?
  • What Are Data?
  • Tools: When Should You Use Computers to Code Your Data?
  • Getting Help: When to Use Member Checks and Multiple Coders
  • Conclusion
  • 3.
  • Coding Experimental Data
  • Coding and Measurement Issues for All Experimental Designs
  • Coding and Measurement Issues That Vary by Type of Experimental Design
  • Conclusion: Where in This Book to Find Guidelines for Analyzing Experimental Data
  • 4.
  • Coding Data from Naturalistic and Participant Observations
  • Two Basic Organizing Questions
  • Introduction to Observational Research
  • Phase 1: Observing
  • Phase 2: Recording
  • Phase 3: Coding
  • Recommendations
  • Conclusions and Tips for Completing an Observational Study
  • Appendix 4.1. Example of a Site Visit Protocol
  • 5.
  • Coding Archival Data: Literature Reviews, Big Data, and New Media
  • Reviews of the Research Literature
  • Ranks or Ordered Coding (When to Use Ordinal Data)
  • Big Data
  • Coding Data from the Web, Including New Media
  • Conclusion: Coding Data from Archival, Web,and New Media Sources
  • Part II.
  • Analysis and Interpretation of Quantitative Data
  • Introduction to Part II
  • Conceptual and Terminological Housekeeping: Theory, Model, Hypothesis, Concept, Variable
  • and a Note on Software
  • 6.
  • Describing, Exploring, and Visualizing Your Data
  • Visual/Graphic Data, Coding, and Analyses
  • What Is Meant by Descriptive Statistics?
  • Overview of the Main Types of Descriptive Statistics and Their Uses
  • What Descriptive Statistics to Use to Prepare for Further Analyses
  • When to Use Correlations as Descriptive Statistics
  • When and Why to Make the Normal Curve Your Point of Reference
  • When Can You Use Descriptive Statistics Substantively?
  • When to Use Descriptive Statistics Preparatory to Applying Missing Data Procedures
  • Conclusion
  • 7.
  • What Methods of Statistical Inference to Use When
  • At What Point Does Coding Occur in the Course of Your Research Project?
  • Null Hypothesis Significance Testing
  • Which Statistical Tests to Use for What
  • When to Use Confidence Intervals
  • When to Report Power and Precision of Your Estimates
  • When Should You Use Distribution-Free, Nonparametric Significance Tests?
  • When to Use the Bootstrap and Other Resampling Methods
  • When to Use Bayesian Methods
  • Which Approach to Statistical Inference Should You Take?
  • The "Silent Killer" of Valid Inferences: Missing Data
  • Conclusion
  • Codes and the Phenomena We Study
  • Appendix 7.1. Examples of Output of Significance Tests
  • 8.
  • What Associational Statistics to Use When
  • When to Use Correlations to Analyze Data
  • When to Use Regression Analysis
  • What to Do When Your Dependent Variables Are Categorical
  • Summary: Which Associational Methods Work Best for What Sorts of Data and Problems?
  • The Most Important Question: When to Include Which Variables
  • Conclusion: Relations among Variables to Investigate Using Regression Analysis
  • 9.
  • A Graphic Depiction of the Relation of Coding to Analysis
  • Advanced Associational Methods
  • Multilevel Modeling
  • Path Analysis
  • Factor Analysis-Exploratory and Confirmatory
  • Structural Equation Modeling
  • Conclusion
  • 10.
  • Model Building and Selection
  • When Can You Benefit from Building a Model or Constructing a Theory?
  • When to Use a Multimodel Approach
Control code
879074384
Dimensions
unknown
Extent
1 online resource (522 pages)
File format
unknown
Form of item
online
Isbn
9781462516032
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)879074384

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