Deck 20: Discriminant, Factor and Cluster Analysis

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سؤال
The statistical explanation for discriminant analysis is that of maximizing the between-group variance relative to the within-group variance.
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سؤال
A factor score is a measurement of how closely related each input variable is to a derived factor.
سؤال
One function of factor analysis is to identify underlying constructs in the data.
سؤال
The cutoff score is the criterion score) against which each individual's discriminant score is judged to determine into which group the individual should be classified
سؤال
Factor analysis is usefully employed when it is desirable to combine several questions, thereby creating a new variable.
سؤال
Each respondent has a factor score on each factor in addition to the respondent's rating on the original variables.
سؤال
One rule of thumb in deciding on the number of factors to retain is to include all factors that explain at least 50 percent of the variance.
سؤال
In discriminant analysis, with 'm' groups and 'p' predictor variables, min p,m-1) gives the number of discriminant functions.
سؤال
Discriminant analysis can only be used for description and not for prediction purposes.
سؤال
Larger values of Wilks lambda indicate that the group means appear to be different.
سؤال
Discriminant analysis techniques are used to classify into one of two or more alternate groups based on a set of measurements.
سؤال
The objective of a discriminant analysis is to predict the value of the dependent variable based on the values of the fixed independent variables.
سؤال
Factor analysis is usefully employed when there is a need to determine the direction of causality between two or three variables.
سؤال
Discriminant analysis involves the maximization of the between-group variance relative to the within-group variance
سؤال
The underlying assumption in a discriminant analysis is that the independent variables are assumed to be normally distributed.
سؤال
Regression and Discriminant analyses are computationally similar
سؤال
Factor loadings are a measurement of the correlations between the factors and the original variables.
سؤال
A factor is a variable or construct that is not directly observable but needs to be inferred from the input variables.
سؤال
In discriminant analysis, predictors with a large coefficient contribute more to the discriminating power of the function.
سؤال
The group mean, in a discriminant analysis, is known as the centroid.
سؤال
A nonhierarchical clustering program is one in which objects are allowed to leave one cluster to join another as clusters are being formed if the clustering criterion will be improved by doing so.
سؤال
Common factor analysis focuses on shared variance, hence communalities are used in the diagonal of the matrix
سؤال
The first factor accounts for more of the variation in the data than the second factor.
سؤال
An attractive feature of principal components analysis is the easy interpretability of the factors.
سؤال
An attractive feature of varimax rotation is that it may retain the variance explained, while reducing the number of factors in the solution as compared to principal components analysis).
سؤال
In the hierarchical approach, the commonly used methods are single linkage, complete linkage, average linkage, Ward's method, and the centroid method.
سؤال
In both principal components analysis and varimax rotation, the factors are constrained to be uncorrelated or geometrically perpendicular.
سؤال
Rotation of factors changes the interpretation of the factors while retaining the principal component patterns of loadings.
سؤال
After performing a principal components analysis, a researcher finds that the cumulative variance explained by the solution is 0.56.He can increase the explained variance by performing a varimax rotation.
سؤال
Communality is the percent of a variable's variance which contributes to the correlation with other variables or is common to other variables.
سؤال
Simple Euclidean distance is a common measurement of similarity on a perceptual map.
سؤال
The percent of variance explained is a summary measurement indicating how much of the total original variance of all the respondents is represented by the factor.
سؤال
The basic task in cluster analysis is to uncover competing explanations for a causal phenomenon.
سؤال
If a clustering procedure starts with one cluster and subdivides until all objects are in their own single-object cluster, the procedure is termed top-down hierarchical clustering.
سؤال
The ABC Company is involved in trying to segment its market so that it can better design specific marketing programs directed at each segment.One method of segmenting that it might use is cluster analysis.
سؤال
A major advantage of cluster analysis is the availability of standard statistical tests to ensure that the output does not represent pure randomness.
سؤال
While analyzing and interpreting consumer perception data using factor analysis, a researcher found the factor loading on Factor 1 to be high.However, he could not interpret the factor meaningfully.A probable cause for this situation is computation error or shortsightedness in his interpretation, since a high loading ensures meaningfulness.
سؤال
All factor analysis methods constrain the factors to be uncorrelated.
سؤال
The variation in variable 3 is shown to be completely explained by the two-factor solution.
سؤال
Factor loadings and correlations are identical if each variable has its mean subtracted and is divided by its standard deviation.
سؤال
A plot of eigenvalues against the number of factors is called a) factor loading b) scree c) factor score d) communality
سؤال
In discriminant analysis, with M groups and p predictor variables, the number of discriminant functions is given by

A)m-1, p-1)
B)m-1, p)
C)m, p-1)
D)m, p)
سؤال
The coefficients that link the factors to the variables are called

A)factor loadings
B)screes
C)factor scores
D)eigenvalues
سؤال
The analysis technique used to identify variables that contribute to differences in the a prior defined groups is

A)regression.
B)discriminant analysis.
C)conjoint analysis.
D)factor analysis.
سؤال
Given multivariate data, cluster analysis techniques seek to identify natural groupings of objects.
سؤال
Initial starting points in nonhierarchical clustering is represented by a) cluster membership b) cluster seeds c) cluster centurions d) none of the above
سؤال
Factor is observable that is why it is a variable.
سؤال
The amount of variance a variable shares with other variables is called a) communality b) factor loading c) factor score d) none of the above
سؤال
Which of the following statements is not true of Wilks' Lamba? a) it is the ratio of within-group variance to total variance b) it takes values between 0 and 1 c) larger values indicate that group means do not appear to be different d) none of the above
سؤال
Which one of the following is not an objective of discriminant analysis?

A)Determining linear combinations of the predictor variables to separate groups
B)Developing procedures for assigning new objects
C)Determining the variables that explain the intergroup differences
D)Predicting the level of the dependent variable when the independent variable is changed.
سؤال
In factor analysis each subsequent factor accounts for a) increasing amount of variance in data b) decreasing amount of variance in data c) same amount of variance in data d) none of the above
سؤال
All of the following are true about factor analysis except

A)it is a technique that serves to combine questions, thereby creating new variables.
B)it is an analysis of interdependence technique that analyzes the interdependence between questions, variables, or objects.
C)it can help the analyst determine which questions, variables, or objects are redundant and what they are measuring.
D)all of these are true
سؤال
The simple correlation between the independent variable and the discriminant function is represented by a) discriminant loading b) structure correlation c) total correlation matrix d) centroid
سؤال
Nonhierarchical clustering will produce tighter clusters due to the fact that an object will be admitted into a cluster only if it improves the clustering criterion.
سؤال
Which of the following is not true about cluster analysis?

A)it is a technique for grouping individuals or objects into unknown groups.
B)there are two approaches to clustering- hierarchical and nonhierarchical.
C)the centroid - the average value of the objects in a cluster on each of the variables making up each object's profile - is used to describe the clusters.
D)there is a single approach to determining the appropriate number of clusters
سؤال
The amount of variance in the original variables that is associated with a factor is represented by

A)factor loading
B)scree
C)factor score
D)eigenvalue
سؤال
For discrimination to be based on all predictors the most appropriate function estimation method is a) sequential b) direct c) pooled d) stepwise
سؤال
If the primary purpose is data reduction one would use a) cluster analysis b) factor analysis c) discriminant analysis d) conjoint analysis
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ملء الشاشة (f)
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Deck 20: Discriminant, Factor and Cluster Analysis
1
The statistical explanation for discriminant analysis is that of maximizing the between-group variance relative to the within-group variance.
True
2
A factor score is a measurement of how closely related each input variable is to a derived factor.
False
3
One function of factor analysis is to identify underlying constructs in the data.
True
4
The cutoff score is the criterion score) against which each individual's discriminant score is judged to determine into which group the individual should be classified
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5
Factor analysis is usefully employed when it is desirable to combine several questions, thereby creating a new variable.
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6
Each respondent has a factor score on each factor in addition to the respondent's rating on the original variables.
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7
One rule of thumb in deciding on the number of factors to retain is to include all factors that explain at least 50 percent of the variance.
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8
In discriminant analysis, with 'm' groups and 'p' predictor variables, min p,m-1) gives the number of discriminant functions.
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9
Discriminant analysis can only be used for description and not for prediction purposes.
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10
Larger values of Wilks lambda indicate that the group means appear to be different.
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11
Discriminant analysis techniques are used to classify into one of two or more alternate groups based on a set of measurements.
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12
The objective of a discriminant analysis is to predict the value of the dependent variable based on the values of the fixed independent variables.
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13
Factor analysis is usefully employed when there is a need to determine the direction of causality between two or three variables.
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14
Discriminant analysis involves the maximization of the between-group variance relative to the within-group variance
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15
The underlying assumption in a discriminant analysis is that the independent variables are assumed to be normally distributed.
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16
Regression and Discriminant analyses are computationally similar
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17
Factor loadings are a measurement of the correlations between the factors and the original variables.
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18
A factor is a variable or construct that is not directly observable but needs to be inferred from the input variables.
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19
In discriminant analysis, predictors with a large coefficient contribute more to the discriminating power of the function.
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20
The group mean, in a discriminant analysis, is known as the centroid.
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21
A nonhierarchical clustering program is one in which objects are allowed to leave one cluster to join another as clusters are being formed if the clustering criterion will be improved by doing so.
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22
Common factor analysis focuses on shared variance, hence communalities are used in the diagonal of the matrix
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23
The first factor accounts for more of the variation in the data than the second factor.
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24
An attractive feature of principal components analysis is the easy interpretability of the factors.
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25
An attractive feature of varimax rotation is that it may retain the variance explained, while reducing the number of factors in the solution as compared to principal components analysis).
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26
In the hierarchical approach, the commonly used methods are single linkage, complete linkage, average linkage, Ward's method, and the centroid method.
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27
In both principal components analysis and varimax rotation, the factors are constrained to be uncorrelated or geometrically perpendicular.
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28
Rotation of factors changes the interpretation of the factors while retaining the principal component patterns of loadings.
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29
After performing a principal components analysis, a researcher finds that the cumulative variance explained by the solution is 0.56.He can increase the explained variance by performing a varimax rotation.
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30
Communality is the percent of a variable's variance which contributes to the correlation with other variables or is common to other variables.
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31
Simple Euclidean distance is a common measurement of similarity on a perceptual map.
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32
The percent of variance explained is a summary measurement indicating how much of the total original variance of all the respondents is represented by the factor.
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33
The basic task in cluster analysis is to uncover competing explanations for a causal phenomenon.
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34
If a clustering procedure starts with one cluster and subdivides until all objects are in their own single-object cluster, the procedure is termed top-down hierarchical clustering.
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35
The ABC Company is involved in trying to segment its market so that it can better design specific marketing programs directed at each segment.One method of segmenting that it might use is cluster analysis.
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36
A major advantage of cluster analysis is the availability of standard statistical tests to ensure that the output does not represent pure randomness.
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37
While analyzing and interpreting consumer perception data using factor analysis, a researcher found the factor loading on Factor 1 to be high.However, he could not interpret the factor meaningfully.A probable cause for this situation is computation error or shortsightedness in his interpretation, since a high loading ensures meaningfulness.
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38
All factor analysis methods constrain the factors to be uncorrelated.
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39
The variation in variable 3 is shown to be completely explained by the two-factor solution.
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40
Factor loadings and correlations are identical if each variable has its mean subtracted and is divided by its standard deviation.
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41
A plot of eigenvalues against the number of factors is called a) factor loading b) scree c) factor score d) communality
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42
In discriminant analysis, with M groups and p predictor variables, the number of discriminant functions is given by

A)m-1, p-1)
B)m-1, p)
C)m, p-1)
D)m, p)
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43
The coefficients that link the factors to the variables are called

A)factor loadings
B)screes
C)factor scores
D)eigenvalues
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44
The analysis technique used to identify variables that contribute to differences in the a prior defined groups is

A)regression.
B)discriminant analysis.
C)conjoint analysis.
D)factor analysis.
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45
Given multivariate data, cluster analysis techniques seek to identify natural groupings of objects.
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46
Initial starting points in nonhierarchical clustering is represented by a) cluster membership b) cluster seeds c) cluster centurions d) none of the above
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47
Factor is observable that is why it is a variable.
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48
The amount of variance a variable shares with other variables is called a) communality b) factor loading c) factor score d) none of the above
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49
Which of the following statements is not true of Wilks' Lamba? a) it is the ratio of within-group variance to total variance b) it takes values between 0 and 1 c) larger values indicate that group means do not appear to be different d) none of the above
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50
Which one of the following is not an objective of discriminant analysis?

A)Determining linear combinations of the predictor variables to separate groups
B)Developing procedures for assigning new objects
C)Determining the variables that explain the intergroup differences
D)Predicting the level of the dependent variable when the independent variable is changed.
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51
In factor analysis each subsequent factor accounts for a) increasing amount of variance in data b) decreasing amount of variance in data c) same amount of variance in data d) none of the above
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52
All of the following are true about factor analysis except

A)it is a technique that serves to combine questions, thereby creating new variables.
B)it is an analysis of interdependence technique that analyzes the interdependence between questions, variables, or objects.
C)it can help the analyst determine which questions, variables, or objects are redundant and what they are measuring.
D)all of these are true
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53
The simple correlation between the independent variable and the discriminant function is represented by a) discriminant loading b) structure correlation c) total correlation matrix d) centroid
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54
Nonhierarchical clustering will produce tighter clusters due to the fact that an object will be admitted into a cluster only if it improves the clustering criterion.
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55
Which of the following is not true about cluster analysis?

A)it is a technique for grouping individuals or objects into unknown groups.
B)there are two approaches to clustering- hierarchical and nonhierarchical.
C)the centroid - the average value of the objects in a cluster on each of the variables making up each object's profile - is used to describe the clusters.
D)there is a single approach to determining the appropriate number of clusters
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56
The amount of variance in the original variables that is associated with a factor is represented by

A)factor loading
B)scree
C)factor score
D)eigenvalue
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57
For discrimination to be based on all predictors the most appropriate function estimation method is a) sequential b) direct c) pooled d) stepwise
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58
If the primary purpose is data reduction one would use a) cluster analysis b) factor analysis c) discriminant analysis d) conjoint analysis
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