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Statistical methods for questionnaire development: Questionnaire reliability analysis, Factor Analysis, and Principal Component Analysis
The document discusses statistical methods for questionnaire development, particularly focusing on reliability, validity, and accuracy, with an emphasis on Cronbach's alpha as a measure of reliability. It explains the purpose and requirements of Principal Component Analysis (PCA) for reducing variables and ensuring that measurement scales accurately represent the constructs being studied. Additionally, the document describes testing sample adequacy through the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity for assessing correlation within the data.
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Statistical methods for questionnaire development: Questionnaire reliability analysis, Factor Analysis, and Principal Component Analysis
- 1. Statistical Methods in QuestionnaireDevelopment Ahmed Said Negida School of Medicine, Zagazig University, Egypt
- 2. About the lecturer AHMEDNEGIDA • Final Year Medical Student (Dec 2018) • Early career researcher • Founder of MRGE (national research team) • Published ˃ 40 research papers • Co-author of 2 international book chapters • Trained 750 researchers in Egypt since 2014
- 3. Learning objectives • Reliabilityvs. Validity vs. Accuracy • Reliability • Validity
- 4. Reliability Cronbach’s Alpha The mostwidely used measure for reliability
- 5. Cronbach’s Alpha How itis calculated? • n = number of questions • Vi = variance of scores on each question • Vtest = total variance of overall scores on the entire test Vtest Vi n n 1 1
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- 17. Writing Results ofReliability Analysis
- 18. Writing Results ofReliability Analysis
- 19. Principal Component Analysis Principalcomponents analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of variables into a smaller set of 'articifial' variables, called 'principal components', which account for most of the variance in the original variables.
- 20. Uses of thePrincipal Component Analysis (a) you have measured many variables (e.g., 7-8 variables, represented as 7-8 questions/statements in a questionnaire) and you believe that some of the variables are measuring the same underlying construct (e.g., depression). If these variables are highly correlated, you might want to include only those variables in your measurement scale (e.g., your questionnaire) that you feel most closely represent the construct, removing the others (b) you want to create a new measurement scale (e.g., a questionnaire), but are unsure whether all the variables you have included measure the construct you are interested in (e.g., depression). Therefore, you test whether the construct you are measuring 'loads' onto all (or just some) of your variables. This helps you understand whether some of the variables you have chosen are not sufficiently representative of the construct you are interested in, and should be removed from your new measurement scale (c) you want to test whether an existing measurement scale (e.g., a questionnaire) can be shortened to include fewer items (e.g., questions/statements), perhaps because such items may be superfluous (i.e., more than one item may be measuring the same construct) and/or there may be the desire to create a measurement scale that is more likely to be completed (i.e., response rates tend to be higher in shorter questionnaires).
- 21. Requirements of thePCA 1. Sample Adequacy 2. Type of Data (Continuous) 3. No outliers 4. All in the same direction 5. Linear correlation between the items
- 22. Requirements of thePCA Are items correlated?
- 23. Requirements of thePCA Is the sample size adequate? Kaiser-Meyer-Olkin Measure of Sampling Adequacy
- 24. Requirements of thePCA Is the sample size adequate? Kaiser-Meyer-Olkin Measure of Sampling Adequacy • KMO values between 0.8 and 1 indicate the sampling is adequate. • KMO values less than 0.6 indicate the sampling is not adequate and that remedial action should be taken. Some authors put this value at 0.5, so use your own judgment for values between 0.5 and 0.6. • KMO Values close to zero means a large problem for factor analysis.
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- 27. Kaiser-Meyer-Olkin (KMO) Test •KMO values between 0.8 and 1 indicate the sampling is adequate. • KMO values less than 0.6 indicate the sampling is not adequate and that remedial action should be taken. Some authors put this value at 0.5, so use your own judgment for values between 0.5 and 0.6. • KMO Values close to zero means a large problem for factor analysis. 1. Bartlett's Test of Sphericity P<0.001 (Interpretation: the correlation matrix is not an identity matrix; the correlation matrix shows unique correlations)
- 28. Bartlett's Test ofSphericity Bartlett's Test of Sphericity P<0.001 Interpretation: the correlation matrix is not an identity matrix; the correlation matrix shows unique correlations
- 29. Factor Extraction The mostcommon method eigenvalue >1 approach
- 30. Requirements of thePCA Is the sample size adequate? Kaiser-Meyer-Olkin Measure of Sampling Adequacy • KMO values between 0.8 and 1 indicate the sampling is adequate. • KMO values less than 0.6 indicate the sampling is not adequate and that remedial action should be taken. Some authors put this value at 0.5, so use your own judgment for values between 0.5 and 0.6. • KMO Values close to zero means a large problem for factor analysis.
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- 32. Total variance explained Totalvariance explained this indicates how much of the variability in the data has been modelled by the extracted factors.
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