Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Factor Analysis /24 59 Quiz | Factor Analysis 1 / 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. 2 / 24 In the context of factor scores, what does a factor score of 0 signify? An object has an average rating on the factor. An object is rated above average on the factor. An object has the highest rating on the factor. An object is rated below average on the factor. 3 / 24 What is the difference between communality and eigenvalue? Check 4 / 24 In a factor analysis, what do the factor scores represent? Strength of correlations Number of factors extracted Original variables Values per person on the latent factors 5 / 24 What are factor loadings in factor analysis? The number of factors extracted The strength of the correlations between original variables and the factors The factors themselves Assessment values per person 6 / 24 When is a correlation matrix considered suitable for factor analysis according to the Bartlett test of sphericity? When the correlation matrix is not an identity matrix When the degrees of freedom are low When the correlation matrix is equal to the inversed covariance matrix When the variables in the sample are uncorrelated 7 / 24 In factor analysis, what does the procedure primarily analyze? The regression coefficients The covariance matrix The mean of the data The correlation matrix 8 / 24 What is the primary objective of factor analysis compared to principal component analysis (PCA)? To reduce the dimensionality of data 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 9 / 24 What does a high correlation between two variables indicate in factor analysis? They are weakly correlated with other variables. They can be combined into a common factor. They should be eliminated from the analysis. They are unsuitable for factor analysis. 10 / 24 What is the first step in conducting a factor analysis? Interpreting the factors Extracting the factors Determining the factor scores Evaluating the suitability of the data 11 / 24 In factor analysis, what do factor loadings (ajq) represent? The variance of the observed variables The correlation between an observed variable and the extracted factor The number of observations (cases) The sum of the squared factor loadings 12 / 24 What does a negative factor score represent in factor analysis? An object has the highest rating on the factor. An object has an average rating on the factor. An object is rated below average on the factor. An object is rated above average on the factor. 13 / 24 What is the primary purpose of determining factor scores in factor analysis? To understand how objects score on the factors To determine the factor loadings To reduce the number of factors To calculate the eigenvalues of factors 14 / 24 What is one of the main objectives of factor analysis? To increase the number of correlating variables To decrease the number of factors To reduce a large number of correlating variables to a fewer number of factors To increase the variance-covariance matrix 15 / 24 How is the suitability of data for factor analysis typically evaluated? By conducting a regression analysis By examining means and standard deviations By assessing inter-item reliability By calculating the correlation matrix 16 / 24 What does the communality of a variable in factor analysis measure? The uniqueness of the variable The proportion of variance in the variable explained by the factors The eigenvalue of the variable The sum of the squared factor loadings for the variable 17 / 24 Explain why the variable "I have difficulty in imagining Calvé in my mind” has a negative factor loading for factor 1. The item is a reverse coded item, and has, thus, a negative factor loading. This must be a mistake. Factor loadings need to be positive. 18 / 24 What is the H0 of the Bartlett test of sphericity? The variables in the population are uncorrelated. The variables in the sample are uncorrelated. The variables in the sample are correlated. The variables in the population are correlated. 19 / 24 What is the difference between orthogonal and oblique rotation methods? Oblique rotation methods assume uncorrelated factors, while orthogonal methods allow for correlation between factors. There is no difference. Orthogonal rotation methods assume uncorrelated factors, while oblique methods allow for correlation between factors. 20 / 24 What does the fundamental theorem of factor analysis state? It explains how to standardize data. It defines the correlation matrix. It calculates the variance of factors It relates observed data to unobserved factors. 21 / 24 In what step of factor analysis is the user required to decide how many factors are to be extracted? Step 4: Assessing factor scores Step 3: Interpreting the factors Step 1: Checking data suitability Step 2: Extracting the factors 22 / 24 How are summated scales calculated for factor scores in factor analysis? By calculating the mean of the high-loading variables for each factor By using regression analysis on factor loadings By multiplying factor loadings by the number of variables By taking the sum of the highest factor loadings for each factor 23 / 24 What is the primary assumption about highly correlated variables in factor analysis? They are the result of common causes (factors). They should be reduced to a single variable. They are not suitable for analysis. They have no correlation. 24 / 24 What statement(s) related to the KMO and MSA criterion are correct? There is no difference between the two criteria. Both criteria measure the same. KMO assesses the suitability of the correlation matrix. MSA assesses the suitability of a single variable. 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