Unit 12: Multivariate Analysis - Practice Quiz

DEMGN832 — Research Methodology 60 Questions
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1 What is the main purpose of classification in multivariate analysis?

Classification Easy
A. To arrange observations by date
B. To assign cases to predefined groups
C. To remove variables from a dataset
D. To calculate averages for each variable

2 Which type of data is normally required to train a supervised classification model?

Classification Easy
A. Data with known class labels
B. Data with missing class labels
C. Data containing no variables
D. Data containing one observation

3 In a classification problem, the dependent variable is usually:

Classification Easy
A. A correlation coefficient
B. A random error term
C. A categorical variable
D. A continuous ratio

4 What is the basic objective of factor analysis?

Important methods of factor analysis Easy
A. To compare group percentages
B. To rank individual observations
C. To forecast a time series
D. To identify underlying factors

5 Principal component analysis primarily explains which type of variance?

Important methods of factor analysis Easy
A. Common variance only
B. Between-group variance only
C. Total variance
D. Error variance only

6 Principal axis factoring mainly focuses on:

Important methods of factor analysis Easy
A. Mean differences among groups
B. Common variance among variables
C. Total variance among variables
D. Distances among cluster centers

7 Which factor extraction method estimates parameters by maximizing a likelihood function?

Important methods of factor analysis Easy
A. Varimax rotation
B. Multidimensional scaling
C. Maximum likelihood
D. Ward's method

8 Which matrix is commonly examined at the beginning of factor analysis?

Factor analysis procedure Easy
A. Confusion matrix
B. Payoff matrix
C. Distance matrix
D. Correlation matrix

9 What does the Kaiser-Meyer-Olkin (KMO) measure assess?

Factor analysis procedure Easy
A. Preference strength
B. Classification accuracy
C. Sampling adequacy
D. Cluster separation

10 According to the Kaiser criterion, which factors are generally retained?

Factor analysis procedure Easy
A. Factors with loadings equal to
B. Factors with eigenvalues below
C. Factors with eigenvalues above
D. Factors with communalities below

11 Why is factor rotation performed?

Rotation in factor analysis Easy
A. To increase the sample size
B. To create new observations
C. To improve factor interpretability
D. To remove all correlations

12 Which of the following is an orthogonal rotation method?

Rotation in factor analysis Easy
A. Promax
B. Quartimin
C. Oblimin
D. Varimax

13 What does an oblique rotation allow?

Rotation in factor analysis Easy
A. Variables to become categorical
B. Factors to be correlated
C. Observations to form clusters
D. Factors to remain identical

14 What is the main objective of cluster analysis?

Overview of cluster analysis Easy
A. To measure consumer utility
B. To group similar observations
C. To predict a labeled category
D. To estimate factor loadings

15 Which diagram commonly displays the results of hierarchical clustering?

Overview of cluster analysis Easy
A. Scatter matrix
B. Dendrogram
C. Perceptual map
D. Scree plot

16 What must usually be specified before applying the -means clustering method?

Overview of cluster analysis Easy
A. The class labels
B. The number of clusters
C. The number of factors
D. The utility scores

17 What is discriminant analysis mainly used to predict?

Discriminant analysis Easy
A. Positions on a perceptual map
B. Utilities of product attributes
C. Membership in unknown clusters
D. Membership in predefined groups

18 A discriminant function is usually formed as:

Discriminant analysis Easy
A. A graph of factor eigenvalues
B. A linear combination of predictors
C. A list of cluster labels
D. A matrix of preference ranks

19 What does multidimensional scaling commonly produce?

Multidimensional scaling Easy
A. A table of factor loadings
B. A hierarchy of clusters
C. A spatial map of objects
D. A list of class predictions

20 What is conjoint analysis primarily used to study?

Conjoint analysis Easy
A. Differences between class labels
B. Correlations among latent factors
C. Preferences for product attributes
D. Distances between cluster centers

21 A researcher wants to predict customer loyalty from satisfaction, perceived value, and service quality. How should this multivariate problem be classified?

Classification Medium
A. A dependence technique with several criterion variables
B. An interdependence technique with no criterion variable
C. A dependence technique with one criterion variable
D. An interdependence technique based only on distances

22 A study examines 15 attitude variables without specifying any variable as an outcome and seeks a smaller set of underlying dimensions. Which classification is most appropriate?

Classification Medium
A. An interdependence method for data reduction
B. An interdependence method for case classification
C. A dependence method for group comparison
D. A dependence method for prediction

23 A researcher wants to summarize the total variance in 12 observed variables into a few weighted composites. Which method is most suitable?

Important methods of factor analysis Medium
A. Principal axis factoring
B. Maximum likelihood factoring
C. Canonical discriminant analysis
D. Principal component analysis

24 A researcher is interested in latent constructs that explain only the shared variance among measured variables. Which method best matches this objective?

Important methods of factor analysis Medium
A. Principal axis factoring
B. Hierarchical cluster analysis
C. Multidimensional scaling
D. Principal component analysis

25 A researcher wants to test the statistical fit of a factor model and construct confidence intervals, assuming approximate multivariate normality. Which extraction method is most appropriate?

Important methods of factor analysis Medium
A. Principal component extraction
B. Centroid factor extraction
C. Maximum likelihood extraction
D. Unweighted least-squares extraction

26 A dataset produces a Kaiser-Meyer-Olkin value of and a significant Bartlett's test with . What is the best conclusion?

Factor analysis procedure Medium
A. The sample must be divided into clusters
B. The factors require an oblique rotation
C. The variables have perfect multicollinearity
D. The data are suitable for factor analysis

27 After extraction, one variable has a communality of , while all other variables have communalities above . What does the value indicate?

Factor analysis procedure Medium
A. The retained factors explain little variance in that variable
B. The retained factors explain of that variable's variance
C. The variable has a loading of on every factor
D. The variable explains little variance in the retained factors

28 The first six eigenvalues from a correlation matrix are , , , , , and . Under the Kaiser criterion, how many factors should initially be retained?

Factor analysis procedure Medium
A. Four factors
B. Six factors
C. Three factors
D. Two factors

29 A theory states that anxiety and depression are related latent constructs. Which rotation is most appropriate when extracting these factors?

Rotation in factor analysis Medium
A. Oblimin rotation
B. Equamax rotation
C. Varimax rotation
D. Quartimax rotation

30 After varimax rotation, a variable has loadings of , , and on three factors. How should the variable usually be interpreted?

Rotation in factor analysis Medium
A. It primarily represents the third factor
B. It primarily represents the second factor
C. It represents all three factors equally
D. It primarily represents the first factor

31 A retailer knows in advance that customers must be divided into four market segments and has a large numerical dataset. Which method is most appropriate?

Overview of cluster analysis Medium
A. Single-linkage clustering with four cases
B. K-means clustering with
C. Discriminant analysis with four predictors
D. Factor analysis with four factors

32 Income is measured in dollars and age is measured in years. Before clustering customers using Euclidean distance, what should the researcher generally do?

Overview of cluster analysis Medium
A. Standardize the variables
B. Convert both variables to ranks
C. Reverse-score the variables
D. Remove the variable with less variance

33 A researcher uses single-linkage hierarchical clustering and obtains a long sequence of cases joined through close neighbors. Which known tendency does this illustrate?

Overview of cluster analysis Medium
A. The rotation effect
B. The scaling effect
C. The chaining effect
D. The suppression effect

34 A bank has historical data in which applicants are already labeled as low, medium, or high credit risk. It wants to classify new applicants using financial ratios. Which technique is appropriate?

Discriminant analysis Medium
A. Exploratory factor analysis
B. Multiple discriminant analysis
C. Metric multidimensional scaling
D. Hierarchical cluster analysis

35 A discriminant function for two groups is . If an observation has and , what is its discriminant score?

Discriminant analysis Medium
A.
B.
C.
D.

36 Box's M test is significant in a discriminant analysis involving several groups. Which assumption is most directly questioned?

Discriminant analysis Medium
A. Equality of covariance matrices
B. Linearity of factor loadings
C. Independence of group memberships
D. Equality of group sample sizes

37 Consumers rate the pairwise dissimilarity of six smartphone brands. A researcher wants a two-dimensional map in which similar brands appear close together. Which technique should be used?

Multidimensional scaling Medium
A. Multidimensional scaling
B. Multiple discriminant analysis
C. Two-stage cluster sampling
D. Principal axis factoring

38 A two-dimensional multidimensional scaling solution has Stress , while a one-dimensional solution has Stress . What is the most reasonable interpretation?

Multidimensional scaling Medium
A. The one-dimensional solution reproduces the distances better
B. Both solutions reproduce the distances equally well
C. Stress cannot be used to compare these solutions
D. The two-dimensional solution reproduces the distances better

39 In a conjoint study of laptops, a respondent's part-worths are for a low price, for a high-resolution screen, and for a light weight. Ignoring the intercept, what is the total utility of a laptop with all three features?

Conjoint analysis Medium
A.
B.
C.
D.

40 In a conjoint analysis, the utility range for price is , for brand is , and for warranty is . What is the relative importance of price?

Conjoint analysis Medium
A.
B.
C.
D.

41 A researcher has 12 continuous variables and wants to assign observations to one of four groups. The group labels are known for a training sample, but the group covariance matrices may differ. Which classification is most appropriate?

Classification Hard
A. Unsupervised classification using principal component analysis
B. Supervised classification using linear discriminant analysis
C. Unsupervised classification using Ward's clustering
D. Supervised classification using quadratic discriminant analysis

42 A binary classifier estimates . A false negative costs 8 units, a false positive costs 2 units, and correct decisions have zero cost. Under minimum expected loss, how should the observation be classified?

Classification Hard
A. Classify as 1 because
B. Classify as 1 because
C. Classify as 0 because
D. Classify as 0 because

43 Which statement correctly distinguishes principal component analysis from principal axis factoring when both are applied to the same correlation matrix?

Important methods of factor analysis Hard
A. PCA initially uses unit diagonal entries, while principal axis factoring uses communality estimates
B. PCA models only common variance, while principal axis factoring models total observed variance
C. PCA requires multivariate normality, while principal axis factoring always requires interval data
D. PCA estimates unique variances, while principal axis factoring fixes all unique variances at zero

44 A dataset contains strongly non-normal indicators, and the researcher wants latent common factors without relying on a multivariate-normal likelihood-ratio test. Which extraction method is most defensible?

Important methods of factor analysis Hard
A. Principal axis factoring
B. Maximum likelihood factoring
C. Unweighted least squares PCA
D. Canonical discriminant factoring

45 Under maximum likelihood factor analysis, which result can be formally evaluated when its assumptions are sufficiently satisfied?

Important methods of factor analysis Hard
A. Whether a specified number of common factors adequately fits the covariance matrix
B. Whether rotated factor scores are uniquely determined for every observation
C. Whether every retained factor explains more variance than every observed variable
D. Whether the observed variables form mutually exclusive classification groups

46 For three standardized variables, the inverse correlation matrix is

Using , what additional operation is required before computing the KMO values?

Factor analysis procedure Hard
A. Standardize each inverse-matrix row to unit variance
B. Replace inverse-matrix diagonals with estimated communalities
C. Convert inverse-matrix off-diagonals into partial correlations
D. Rotate the inverse matrix using an orthogonal criterion

47 A sample correlation matrix has eigenvalues . The corresponding mean eigenvalues from parallel random datasets are . How many factors should parallel analysis retain?

Factor analysis procedure Hard
A. Three factors
B. Four factors
C. One factor
D. Two factors

48 After extracting a two-factor model, one variable has loadings and . Assuming standardized variables and no additional factor contributions, what are its communality and uniqueness?

Factor analysis procedure Hard
A. Communality and uniqueness
B. Communality and uniqueness
C. Communality and uniqueness
D. Communality and uniqueness

49 An unrotated orthogonal loading matrix is transformed as , where . Which quantity is necessarily unchanged for every observed variable?

Rotation in factor analysis Hard
A. Its correlation with each individual factor
B. Its loading on the first retained factor
C. Its ranking by the largest absolute loading
D. Its communality across the retained factors

50 In an oblique factor solution, variable has pattern coefficients , and the factor correlation matrix is . What is the structure coefficient of with the second factor?

Rotation in factor analysis Hard
A.
B.
C.
D.

51 A researcher expects two substantive constructs to be correlated but applies varimax rotation. What is the most important interpretive risk?

Rotation in factor analysis Hard
A. The solution may redistribute shared construct variance into cross-loadings
B. The extraction will necessarily retain too many common factors
C. The resulting factors will no longer reproduce the correlation matrix
D. The rotation will necessarily change every variable's communality

52 A cluster analysis uses Euclidean distance on income measured in dollars and satisfaction measured on a seven-point scale. Income has far greater numerical variance. What is the most defensible preprocessing decision?

Overview of cluster analysis Hard
A. Retain raw scales because clustering automatically adjusts variable variance
B. Standardize observations within each cluster after estimating memberships
C. Convert Euclidean distances into squared correlations after clustering
D. Standardize variables using substantively justified scales before clustering

53 Under Ward's method with squared Euclidean distance, clusters and contain 4 and 6 cases, and their centroids are 5 units apart. What is the increase in within-cluster sum of squares if they are merged?

Overview of cluster analysis Hard
A.
B.
C.
D.

54 A hierarchical single-linkage solution joins two dense, well-separated groups through a sequence of sparse intermediate observations. Which diagnosis best explains the result?

Overview of cluster analysis Hard
A. A chaining effect caused by nearest-neighbor links
B. A rotation effect caused by correlated cluster axes
C. A centroid reversal caused by unequal cluster sizes
D. A suppression effect caused by negative communalities

55 Two groups have multivariate normal distributions with equal covariance matrices but unequal prior probabilities. Relative to equal priors, how does incorporating the unequal priors affect the linear discriminant boundary?

Discriminant analysis Hard
A. It leaves both the orientation and intercept unchanged
B. It converts the linear boundary into a quadratic boundary
C. It changes the orientation while retaining the same boundary intercept
D. It shifts the intercept while retaining the same boundary orientation

56 In a three-group discriminant analysis with five predictors, what is the maximum number of nonzero canonical discriminant functions?

Discriminant analysis Hard
A. Two functions
B. Five functions
C. Three functions
D. Four functions

57 A study applies quadratic discriminant analysis with 80 predictors but only 25 observations in each group. What is the central estimation problem?

Discriminant analysis Hard
A. Each group covariance matrix is singular or highly unstable
B. The group means become identical after predictor standardization
C. The discriminant functions cannot include group prior probabilities
D. The pooled covariance matrix must equal the identity matrix

58 A nonmetric multidimensional scaling solution has low stress, but its recovered distances are not linearly related to the original dissimilarities. Why can the solution still be valid?

Multidimensional scaling Hard
A. Nonmetric MDS primarily preserves the covariance of object coordinates
B. Nonmetric MDS primarily preserves the rank order of dissimilarities
C. Nonmetric MDS constrains every disparity to equal its original value
D. Nonmetric MDS requires only equal average distances among all objects

59 In classical MDS, double-centering the squared dissimilarity matrix produces a matrix with several substantial negative eigenvalues. What is the best interpretation?

Multidimensional scaling Hard
A. The original dissimilarities must have been measured on a ratio scale
B. The configuration is Euclidean but requires only one dimension
C. The dissimilarities are not exactly representable in Euclidean space
D. The object coordinates are unique up to translation but not rotation

60 An effects-coded conjoint model estimates level utilities for a three-level attribute as for level 1 and for level 2. What utility is implied for level 3?

Conjoint analysis Hard
A.
B.
C.
D.