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30 Cards in this Set
- Front
- Back
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What is the purpose of CFA?
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To test hypotheses about the factor structure that underlies a set of observed variables
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What is the difference between EFA and CFA?
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EFA is data-driven, and factor structure is interpreted post hoc from the results
CFA is theory-driven, where factor structure is specified a priori, the program only calculates specified loadings (all else = 0) |
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When should you use CFA?
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When you want to test a priori hypotheses based on theory and/or previous research with these variables
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What is the range for factor loadings?
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0 to +/- 1
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In a CFA diagram, what shape represents latent factors?
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Circles/ Ovals
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What is a latent factor?
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Unmeasured constructs that are made up of common variance among items
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What two bits of information do you need to provide to the software package to run a CFA?
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1. Variables to be included in the analysis
2. Specified model: factor loadings and correlations |
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What is a FIXED parameter?
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A relationship between factors which is constrained to be zero.
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What does CFA compare in order to find fit of the model?
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Estimated Variance-Covariance Matrix
and Actual Variance-Covariance Matrix |
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What are the 4 steps in CFA?
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1. prepare for analysis
2. evaluate model fit 3. evaluate factor loadings and factor correlations 4. evaluate alternative models |
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What scale should data be measured on in CFA?
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Interval scale
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Why do you need to have at least one constrained parameter?
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If no parameters are constrained, the observed and estimated variance-covariance matrices will be equal, and you will be unable to assess the fit of your hypothesised model
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What is the ideal number of participants in a CFA or EFA?
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More than 10 participants per estimated parameter
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What does a good model fit mean?
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The hypothesised model provides an accurate account of observed relationships in the dataset
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Which type of parameter can never contribute to bad fit?
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Estimated Parameters
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Why does good fit NOT mean that the model is correct?
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Because it does not rule out alternatives
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Why does good fit NOT mean that the factors explain a lot of variance?
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Because a model that explains very little variance can still fit well if the researcher has correctly identified constraints
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What does the chi-squared test do?
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Tests the degree of similarity between estimated and actual variance-covariance matrices
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What does a non-significant chi-squared test indicate?
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That the matrices are not significantly different, meaning that the model fits well
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What are the limitations of the chi-squared test?
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1. It is sensitive to sample size
2. Assumption of multivariate normality is often violated |
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What are the two options you have if the chi-squared test is significant?
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1. calculate the chi-squared/df ratio
2. if other indices suggest good fit, downplay chi-squared test |
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What do absolute and incremental fit indices show?
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How much of the observed variance-covariance matrix has been accurately accounted for by the model
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What do residual fit indices show?
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The discrepancies between the estimated and observed variance-covariance matrices
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What is the threshold for good fit in absolute and incremental fit indices?
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>.95 (the higher the better)
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What is the threshold for good fit in residual fit indices?
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SRMR: <.80
RMSEA: <.60 (the lower the better) |
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How many absolute and incremental fit indices should be reported?
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Two
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What does a bad fit mean?
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the model does not account for all relationships in data. One or more fixed parameters needs to be freed.
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When comparing the fit of two models, what should you do?
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Nest the models. The model with fewer parameters is nested within the model with more parameters
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What test should you perform when comparing the fit of nested models?
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The chi-squared difference text
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If the Chi-squared difference test is significant, what does this mean?
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The nested model fits worse than the larger model, thus the larger model is preferred because it provides a better account of the observed relationships
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