48 Questions
Which of the following best describes factorial validity?
It determines if test scores match the theoretical structure of the test
What does a test's internal structure refer to?
The way the parts of a test are related to each other
How does factorial validity help specify what a test measures?
By determining the number and definition of dimensions underlying the construct of interest
Factorial analysis is a data analytic technique that helps us...
Clarify the number of factors within a set of items
What is the purpose of determining the definitions of the dimensions in factorial validity?
To determine which items load onto which dimension
How does factorial validity help with the interpretation of factors?
All of the above
Which of the following is considered a compromised score?
A composite score that contains items measuring more than one dimension
What do we want composite scores to be relatively?
Factor pure
Which of the following is an example of a unidimensional test?
A multiple choice final exam where students receive one score concerning their knowledge of content covered over the semester
What is the key characteristic of a unidimensional test?
Items that reflect only a single attribute that are unaffected by other attributes
Which of the following best describes a unidimensional test?
Each item is linked to only one attribute and all items contribute variance to that attribute
Which of the following best describes correlated multidimensional tests?
Tests that measure two or more dimensions that are correlated with each other
What is the relationship between items and dimensions in correlated multidimensional tests?
Each item is linked to only one dimension and the dimensions are correlated with each other
Which of the following best describes uncorrelated multidimensional tests?
Consists of items which measure two or more dimensions which are unrelated to each other
What is the key characteristic of uncorrelated multidimensional tests?
Each item is linked to one attribute only and there is no link between the two dimensions
How are scores calculated in uncorrelated multidimensional tests?
A separate score is calculated for each dimension
What is the key characteristic of higher-order models?
Dimensions are correlated with an overall global factor
Which of the following is a theoretical difference between factor analysis and component analysis?
In factor analysis, extracted factors are considered to be correlated with each other, while the components are not in component analysis
Which of the following statements is true about the loadings in factor analysis and component analysis?
Component loadings are typically larger than factor loadings
Which of the following is considered a more sophisticated approach to data reduction?
Factor analysis
Which of the following is a key empirical difference between factor analysis and component analysis?
Factor analysis includes error terms for each item, while component analysis does not.
What is the defining characteristic of factors in factor analysis?
They are defined by the same indicators.
Which of the following statements is true about the correlation between factors and components?
The correlation between factors is typically larger than the correlation between components.
Which type of test score is based on the items of a single dimension?
Subtest score
Which type of test score combines several common subtests, but not all of them?
Area score
What does the dimensionality of a test impact?
The use of test scores
Which of the following best describes a total score?
The sum of all subtest scores within an inventory
Which of the following tests is an example of a test with uncorrelated dimensions?
NEO-PI R
How is the psychological meaning of each test dimension determined in factorial analysis?
By evaluating which items load onto each respective dimension through inter-item correlations
What is the purpose of conducting a PCA twice?
To determine the number of components to extract and then extract them
Which of the following best describes a scree plot?
A plot of eigenvalues in a scatter plot ordered from smallest to largest
What does an eigenvalue represent in a scree plot?
The percentage of variance accounted for by a dimension
What is the purpose of examining a scree plot?
To determine the number of worthwhile components in the data
What is the purpose of communality in factor analysis?
To represent the percentage of variance associated with a variable that was included in the analysis
What is the expected communality for items in factor analysis?
.04 or greater
What does uniqueness represent in factor analysis?
The percentage of variance associated with a variable that was not included in the analysis
Which of the following is the expected minimum communality for subscales in factor analysis?
.09
Which of the following statements is true about the reliability of items and subscales in factor analysis?
Subscales are more reliable than items.
Which of the following are considered useful component loadings for items in factor analysis?
.20 or greater
What is communality in factor analysis?
The sum of the squared component loadings
Which of the following is considered a useful component loading for subscales in factor analysis?
.30
What is simple structure in factor analysis?
The degree to which an item is associated with only one substantial loading on a single dimension and negligible loadings on the remaining dimensions.
When can you achieve simple structure in factor analysis?
When you extract two or more components
What is the key to achieving simple structure in factor analysis?
Rotating the solution
What are the two main factors that affect the sample size required for principal component analysis?
The amount of communality associated with the variables and the number of variables per factor
What is the minimum number of variables per factor required for a sample size of about 150 with component loadings of .20 to be sufficient in principal component analysis?
5
What is the relationship between the number of variables per factor and the sample size required for principal component analysis?
The higher the number of variables per factor, the less sample size required
What is the relationship between communality and required sample size for principal component analysis?
Higher communality means less sample size is required
Test your knowledge on Factorial Validity and learn about its importance in determining the internal structure of test scores. This quiz will cover the basics of Factorial Validity and help you understand its relevance in measuring the construct of interest.
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