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 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. 2 / 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) 3 / 24 In a factor analysis, what do the factor scores represent? Strength of correlations Original variables Number of factors extracted Values per person on the latent factors 4 / 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 1: Checking data suitability Step 4: Assessing factor scores 5 / 24 What are factor loadings in factor analysis? Assessment values per person The factors themselves The strength of the correlations between original variables and the factors The number of factors extracted 6 / 24 What is the primary purpose of determining factor scores in factor analysis? To calculate the eigenvalues of factors To understand how objects score on the factors To reduce the number of factors To determine the factor loadings 7 / 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. 8 / 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 9 / 24 What does the fundamental theorem of factor analysis state? It relates observed data to unobserved factors. It explains how to standardize data. It calculates the variance of factors It defines the correlation matrix. 10 / 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. 11 / 24 What is the primary objective of factor analysis compared to principal component analysis (PCA)? To eliminate unique variance in observed variables To uncover the common causes (factors) of observed variables and their correlations To reduce the dimensionality of data To maximize the explained variance by the extracted factors 12 / 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 sum of the squared factor loadings for the variable The eigenvalue of the variable 13 / 24 What is the first step in conducting a factor analysis? Determining the factor scores Interpreting the factors Evaluating the suitability of the data Extracting the factors 14 / 24 What does a negative factor score represent in factor analysis? An object is rated above average on the factor. An object is rated below average on the factor. An object has the highest rating on the factor. An object has an average rating on the factor. 15 / 24 In factor analysis, what does the procedure primarily analyze? The correlation matrix The covariance matrix The regression coefficients The mean of the data 16 / 24 What is one of the main objectives of factor analysis? To increase the number of correlating variables 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 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. MSA assesses the suitability of a single variable. KMO assesses the suitability of the correlation matrix. 18 / 24 What is the H0 of the Bartlett test of sphericity? The variables in the sample are correlated. The variables in the population are uncorrelated. The variables in the population are correlated. The variables in the sample are uncorrelated. 19 / 24 How are summated scales calculated for factor scores in factor analysis? By multiplying factor loadings by the number of variables By using regression analysis on factor loadings By calculating the mean of the high-loading variables for each factor By taking the sum of the highest factor loadings for each factor 20 / 24 How is the suitability of data for factor analysis typically evaluated? By conducting a regression analysis By assessing inter-item reliability By examining means and standard deviations By calculating the correlation matrix 21 / 24 What does CFA refer to when it mentions "reflective measurement models"? Models that reflect the data structure without assumptions. Models that are highly complex and difficult to interpret. Models that involve exploratory data analysis. Models that reflect hypothetical constructs via measurement variables. 22 / 24 What is the difference between communality and eigenvalue? Check 23 / 24 What does a high correlation between two variables indicate in factor analysis? They can be combined into a common factor. They are weakly correlated with other variables. They are unsuitable for factor analysis. They should be eliminated from the analysis. 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 an average rating on the factor. An object is rated above average on the factor. An object has the highest rating on the factor. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
