Making Sense of Factor Analysis : The Use of Factor Analysis for Instrument Development in Health Care Research

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  • Format: Hardcover
  • Copyright: 2003-04-05
  • Publisher: Sage Publications, Inc
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Many health care practitioners and researchers are aware of the need to employ factor analysis in order to develop more sensitive instruments for data collection. Unfortunately, factor analysis is not a unidimensional approach that is easily understood by even the most experienced of researchers.Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research presents a straightforward explanation of the complex statistical procedures involved in factor analysis. Authors Marjorie A Pett, Nancy M Lackey, and John J Sullivan provide a step-by-step approach to analyzing data using statistical computer packages like SPSS and SAS. Emphasizing the interrelationship between factor analysis and test construction, the authors examine numerous practical and theoretical decisions that must be made to efficiently run and accurately interpret the outcomes of these sophisticated computer programs.Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research offers a practical method for developing tests, validating instruments and reporting outcomes through the use of factor analysis. To facilitate learning, the authors provide concrete testing examples, three appendices of additional information, and a glossary of key terms. Ideal for graduate level nursing students, this book is also an invaluable resource for health care researchers.

Table of Contents

1. An Overview of Factor Analysis
Characteristics of Factor Analysis
Exploratory Vs. Confirmatory Factor Analysis
Assumptions of Exploratory Factor Analysis
Historical Developments of Factor Analysis
Uses of Factor Analysis in Health Care Research
Decision-Making Process in Exploratory Factor Analysis
2. Designing and Testing the Instrument
Types of Measurement and Frameworks
The Use of Latent Variables in Instrument Development
Identifying Empirical Indicators of Latent Variables
Using Qualitative Research Methods to Identify Empirical Indicators
Additional Qualitative Approaches to Identifying Empirical Indicators
Development of the Instrument
Scoring the Instrument
Pilot Testing the Instrument
Determining the Number of Subjects
3. Assessing the Characteristics of Matrices
Characteristics and Types of Matrices
Tests of Matrices
Review of the Process
4. Extracting the Initial Factors
Evaluating the Correlation Matrix
Sources of Variance in Factor Analysis Models
Determining the Factor Extraction Method
Selecting the Number of Factors to Retain
Comparing the Two-Factor Solution Using PCA and PAF
5. Rotating the Factors
Achieving a Simple Structure
Types of Rotations
Mapping Factors in Geometric Space
Orthogonal Rotations
Oblique Rotations
Comparing the Orthogonal and Oblique Solutions
Advantages and Disadvantages of the Oblique Solution
Choosing Between Orthogonal and Oblique Rotations
Summary of the Process of Rotations
6. Evaluating and Refining the Factors
Evaluating and Refining the Factors
Assessing the Reliability of an Instrument
Evaluating the Internal Consistency of an Instrument
Estimating the Effects on Reliability of Increasing or Decreasing Items
Cronbach's Coefficient Alpha
Assessing Reliability Using Cronbach's Alpha: A Computer Example
Two Additional Reliability Estimates: Temporal Stability and Equivalence
7. Interpreting Factors and Generating Factor Scores
Interpreting the Factors
Naming the Factors
Interpreting and Naming the Four Factors on the CGTS Scale
Determining Composite Factor Scores
8. Reporting and Replicating the Results
When to Report the Results
What to Include in the Report
An Exemplar of a Published Report
Replicating the Factors in Other Studies
Appendix A: Concerns About Genetic Testing Scale
Appendix B: SAS Commands and Generate Output
Appendix C: Output for 20-item CGTS Scale
Appendix D: Tables for the Chi-Square and Normal Distributions
Appendix E: Unraveling the Mystery of Principal Component Extraction
About the Authors

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