Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Factor Analysis /24 59 Quiz | Factor Analysis 1 / 24 What is the difference between orthogonal and oblique rotation methods? Orthogonal rotation methods assume uncorrelated factors, while oblique methods allow for correlation between factors. There is no difference. Oblique rotation methods assume uncorrelated factors, while orthogonal methods allow for correlation between factors. 2 / 24 What statement(s) related to the KMO and MSA criterion are correct? KMO assesses the suitability of the correlation matrix. MSA assesses the suitability of a single variable. There is no difference between the two criteria. Both criteria measure the same. 3 / 24 What does a high correlation between two variables indicate in factor analysis? They are unsuitable for factor analysis. They are weakly correlated with other variables. They can be combined into a common factor. They should be eliminated from the analysis. 4 / 24 What are factor loadings in factor analysis? The factors themselves The strength of the correlations between original variables and the factors The number of factors extracted Assessment values per person 5 / 24 What is the first step in conducting a factor analysis? Evaluating the suitability of the data Determining the factor scores Extracting the factors Interpreting the factors 6 / 24 What is the primary assumption about highly correlated variables in factor analysis? They are the result of common causes (factors). They are not suitable for analysis. They should be reduced to a single variable. They have no correlation. 7 / 24 When is a correlation matrix considered suitable for factor analysis according to the Bartlett test of sphericity? When the correlation matrix is equal to the inversed covariance matrix When the correlation matrix is not an identity matrix When the degrees of freedom are low When the variables in the sample are uncorrelated 8 / 24 In factor analysis, what does the procedure primarily analyze? The mean of the data The covariance matrix The regression coefficients The correlation matrix 9 / 24 What is the primary purpose of determining factor scores in factor analysis? To calculate the eigenvalues of factors To reduce the number of factors To understand how objects score on the factors To determine the factor loadings 10 / 24 What is the H0 of the Bartlett test of sphericity? The variables in the sample are uncorrelated. The variables in the population are uncorrelated. The variables in the sample are correlated. The variables in the population are correlated. 11 / 24 What does CFA refer to when it mentions "reflective measurement models"? Models that reflect hypothetical constructs via measurement variables. Models that reflect the data structure without assumptions. Models that are highly complex and difficult to interpret. Models that involve exploratory data analysis. 12 / 24 How are summated scales calculated for factor scores in factor analysis? By multiplying factor loadings by the number of variables By taking the sum of the highest factor loadings for each factor By calculating the mean of the high-loading variables for each factor By using regression analysis on factor loadings 13 / 24 In what step of factor analysis is the user required to decide how many factors are to be extracted? Step 3: Interpreting the factors Step 4: Assessing factor scores Step 1: Checking data suitability Step 2: Extracting the factors 14 / 24 What does the fundamental theorem of factor analysis state? It calculates the variance of factors It explains how to standardize data. It relates observed data to unobserved factors. It defines the correlation matrix. 15 / 24 How is the suitability of data for factor analysis typically evaluated? By examining means and standard deviations By calculating the correlation matrix By assessing inter-item reliability By conducting a regression analysis 16 / 24 What is the difference between communality and eigenvalue? Check 17 / 24 What is the primary objective of factor analysis compared to principal component analysis (PCA)? To uncover the common causes (factors) of observed variables and their correlations To maximize the explained variance by the extracted factors To eliminate unique variance in observed variables To reduce the dimensionality of data 18 / 24 In factor analysis, what do factor loadings (ajq) represent? The variance of the observed variables The sum of the squared factor loadings The correlation between an observed variable and the extracted factor The number of observations (cases) 19 / 24 What is one of the main objectives of factor analysis? To reduce a large number of correlating variables to a fewer number of factors To increase the variance-covariance matrix To decrease the number of factors To increase the number of correlating variables 20 / 24 What does a negative factor score represent in factor analysis? An object is rated below average on the factor. An object is rated above average on the factor. An object has an average rating on the factor. An object has the highest rating on the factor. 21 / 24 Explain why the variable "I have difficulty in imagining Calvé in my mind” has a negative factor loading for factor 1. This must be a mistake. Factor loadings need to be positive. The item is a reverse coded item, and has, thus, a negative factor loading. 22 / 24 In a factor analysis, what do the factor scores represent? Original variables Values per person on the latent factors Strength of correlations Number of factors extracted 23 / 24 What does the communality of a variable in factor analysis measure? The proportion of variance in the variable explained by the factors The uniqueness of the variable The sum of the squared factor loadings for the variable The eigenvalue of the variable 24 / 24 In the context of factor scores, what does a factor score of 0 signify? An object is rated below average on the factor. An object has the highest rating on the factor. An object is rated above average on the factor. An object has an average rating on the factor. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
