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 the communality of a variable in factor analysis measure? The sum of the squared factor loadings for the variable The proportion of variance in the variable explained by the factors The eigenvalue of the variable The uniqueness of the variable 2 / 24 What is the primary assumption about highly correlated variables in factor analysis? They have no correlation. They are not suitable for analysis. They should be reduced to a single variable. They are the result of common causes (factors). 3 / 24 When is a correlation matrix considered suitable for factor analysis according to the Bartlett test of sphericity? When the variables in the sample are uncorrelated When the degrees of freedom are low When the correlation matrix is equal to the inversed covariance matrix When the correlation matrix is not an identity matrix 4 / 24 What are factor loadings in factor analysis? The strength of the correlations between original variables and the factors The factors themselves Assessment values per person The number of factors extracted 5 / 24 In factor analysis, what do factor loadings (ajq) represent? The number of observations (cases) The correlation between an observed variable and the extracted factor The variance of the observed variables The sum of the squared factor loadings 6 / 24 What does a negative factor score represent in factor analysis? 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. An object is rated below average on the factor. 7 / 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 increase the number of correlating variables To decrease the number of factors 8 / 24 What is the difference between communality and eigenvalue? Check 9 / 24 What does CFA refer to when it mentions "reflective measurement models"? Models that involve exploratory data analysis. Models that reflect the data structure without assumptions. Models that are highly complex and difficult to interpret. Models that reflect hypothetical constructs via measurement variables. 10 / 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. 11 / 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 2: Extracting the factors Step 4: Assessing factor scores Step 1: Checking data suitability 12 / 24 In a factor analysis, what do the factor scores represent? Original variables Number of factors extracted Strength of correlations Values per person on the latent factors 13 / 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. 14 / 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. 15 / 24 What is the first step in conducting a factor analysis? Evaluating the suitability of the data Interpreting the factors Extracting the factors Determining the factor scores 16 / 24 How is the suitability of data for factor analysis typically evaluated? By examining means and standard deviations By assessing inter-item reliability By conducting a regression analysis By calculating the correlation matrix 17 / 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. 18 / 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 19 / 24 In factor analysis, what does the procedure primarily analyze? The regression coefficients The mean of the data The covariance matrix The correlation matrix 20 / 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 21 / 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 are unsuitable for factor analysis. They should be eliminated from the analysis. 22 / 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 23 / 24 What does the fundamental theorem of factor analysis state? It explains how to standardize data. It relates observed data to unobserved factors. It defines the correlation matrix. It calculates the variance of factors 24 / 24 In the context of factor scores, what does a factor score of 0 signify? An object is rated above average on the factor. 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. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
