Exploratory Factor Analysis in Statistics MCQs
Exploratory Factor Analysis in Statistics MCQs
Welcome to MCQss.com, your go-to resource for MCQs on exploratory factor analysis. This page is dedicated to helping you test and expand your knowledge of this important statistical technique.
By engaging with these MCQs, you can assess your understanding of exploratory factor analysis concepts, techniques, and applications. Each MCQ presents a question with multiple answer options, and you can select the most appropriate response based on your knowledge and analysis of the given scenario. Detailed explanations are provided for both correct and incorrect answers, allowing you to learn from your mistakes and deepen your understanding.
Take advantage of the MCQs available on this page to test your knowledge and enhance your proficiency in exploratory factor analysis. Engage with the interactive questions, learn from the explanations, and deepen your understanding of this powerful statistical technique.
Expand your statistical repertoire and become a more proficient data analyst or researcher by exploring the MCQs on exploratory factor analysis now. Unlock the potential of this technique and gain confidence in your ability to analyze and interpret complex datasets effectively.
1: _______________ is an “imaginary” variable used to understand why observed X’s are correlated. In factor analysis, it is assumed that measured X scores are correlated because they measure the same underlying construct or latent variable.
A. Latent Variable
B. Factor
C. Z scores
D. Standard Scores
2: A factor is a latent or an imaginary variable that can be used to reconstruct the observed correlations among measured X variables.
A. True
B. False
3: The distance of an individual score from the mean of a distribution expressed in unit-free terms is called _______________ .
A. Latent Variable
B. Factor
C. Z scores
D. Standard Scores
4: The formula to calculate a standard score or z score is z = (X – M)/SD. A distribution of z scores has M = 0 and SD = 1. This formula is known as:
A. Latent Variable
B. Factor
C. Z scores
D. Standard Scores
5: This is a computation that involves estimating a set of factor or component loadings; loadings are correlations between actual measured variables and latent factors or components is known as:
A. Factor Score
B. Extraction
C. Diagonal Matrix
D. Exploratory Factor Analysis
6: A score for each individual participant that is calculated by applying the factor score coefficients for each factor to z scores on all items is called _______________ .
A. Factor Score
B. Extraction
C. Diagonal Matrix
D. Exploratory Factor Analysis
7: _____________ is an exploratory factor analysis that initially estimates k loadings for k measured variables on k factors.
A. Factor Score
B. Extraction
C. Diagonal Matrix
D. Exploratory Factor Analysis
8: Diagonal Matrix is a matrix in which all the off-diagonal elements are 0.
A. True
B. False
9: For each factor, the sum of squared loadings is obtained by squaring and summing the loadings of all variables with that factor is known as:
A. Communality
B. Iteration
C. Sum of Squared Loadings (SSL)
D. Reproduced Correlation Matrix
10: A communality, sometimes denoted b2, is an estimate of the proportion of variance in each of the original p variables that is reproduced by a set of retained components or factors is known as:
A. Communality
B. Iteration
C. Sum of Squared Loadings (SSL)
D. Reproduced Correlation Matrix
11: Squaring the factor loading matrix A (i.e., finding the matrix product A´A) reproduces the correlation matrix R is known as:
A. Communality
B. Iteration
C. Sum of Squared Loadings (SSL)
D. Reproduced Correlation Matrix
12: In this process, model parameters are estimated multiple times until some goodness-of-fit criterion is reached is called ___________ .
A. Varimax Rotation
B. Initial Solution
C. Scree Plot
D. Iteration
13: A plot of the eigenvalues (on the Y axis) by factor number 1, 2, . . ., p (on the X axis). “Scree” refers to the rubble at the foot of a hill is known as:
A. Varimax Rotation
B. Initial Solution
C. Scree Plot
D. Iteration
14: Varimax Rotation is the most popular method of factor rotation. The goal is to maximize the variance of the absolute values of loadings of variables within each factor, that is, to relocate the factors in such a way that, for each variable, the loading on the relocated factors is as close to 0, or as close to +1 or –1, as possible.
A. True
B. False
15: When a set of p variables is factor analyzed, the initial solution consists of p factors. This full solution (same number of factors as variables) is called the initial solution is called ______________ .
A. Varimax Rotation
B. Initial Solution
C. Scree Plot
D. Iteration
16: ____________ is a factor rotation in which the factors are constrained to remain uncorrelated or orthogonal to one another.
A. Dimensionality
B. Orthogonal Rotation
C. Reverse-Worded Questions
D. Rotated Factor Loadings
17: Factor loadings (or correlations between X variables and factors) that have been re-estimated relative to rotated or relocated factor axes. The goal of factor rotation is to improve the interpretability of results is known as:
A. Dimensionality
B. Orthogonal Rotation
C. Reverse-Worded Questions
D. Rotated Factor Loadings
18: These are questions that are worded in such a way that a strong level of disagreement with the statement indicates more of the trait or attitude that the test is supposed to measure is known as:
A. Dimensionality
B. Orthogonal Rotation
C. Reverse-Worded Questions
D. Rotated Factor Loadings
19: How many factors or components are needed to reproduce the correlation matrix R adequately is known as:
A. Dimensionality
B. Orthogonal Rotation
C. Reverse-Worded Questions
D. Rotated Factor Loadings
20: A rotation in which the factors are allowed to become correlated to some degree is called ____________ .
A. Oblique Rotation
B. Orthogonal Rotation
C. Reverse-Worded Questions
D. Rotated Factor Loadings
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Exploratory Factor Analysis in Statistics MCQs
