Test bank herman aguinis – performance management ch24
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Chapter 24—Multivariate Statistical Analysis
TRUE/FALSE
1. Multivariate statistical analysis permit the researcher to consider the effects of three or more variables
at the same time.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 581
2. The variate is a mathematical way in which a set of variables can be represented with one equation.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 581
3. The basic types of multivariate techniques are metric methods and nonmetric methods.
ANS: F
The two basic types of mulitvariate techniques are dependence methods and interdependence methods.
PTS: 1
REF: p. 582
NAT: AACSB: Reflective Thinking
4. Multidimensional scaling is a type of interdependence method.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
5. The type of measurement scales used will determine which multivariate statistical techniques are
appropriate for the data.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
6. Nominal and ordinal scales are referred to as metric scales.
ANS: F
These are nonmetric scales.
PTS: 1
REF: p. 583
NAT: AACSB: Reflective Thinking
7. In multiple regression, the dependent variable must be continuous and interval-scaled.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
8. Multiple regression analysis includes a single independent variable but several dependent variables.
ANS: F
Multiple regression analysis is an extension of simple regression analysis allowing a metric dependent
variable to be predicted by multiple independent variables.
PTS: 1
REF: p. 584
NAT: AACSB: Reflective Thinking
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
9. Mulitvariate dependence techniques are variants of the general linear model (GLM).
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
10. Several dummy variables can be included in a regression model.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 585
11. In multiple regression, dummy variables are those that have no effect on the dependent variable.
ANS: F
A dummy variable uses 0 and 1 to code the different levels of a dichotomous variable.
PTS: 1
REF: p. 585
NAT: AACSB: Reflective Thinking
12. In a regression equation, the beta coefficients indicate the effect on the dependent variable of a 1-unit
increase in any of the independent variables.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 585
13. Partial correlations measure the variance inflation among independent variables.
ANS: F
Partial correlation is the correlation between two variables after taking into account the fact that they
are correlated with other variables too.
PTS: 1
REF: p. 586
NAT: AACSB: Reflective Thinking
14. In multiple regression, the coefficient of multiple determination indicates the percentage of the
variation in Y that can be explained by all independent variables.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 586
15. Multicollinearity in regression analysis refers to how strongly interrelated the independent variables in
a model are.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 589
16. MANOVA predicts multiple continuous dependent variables with multiple continuous independent
variables.
ANS: F
The independent variables are categorical.
PTS: 1
REF: p. 589
NAT: AACSB: Reflective Thinking
17. Discriminant analysis predicts a categorical dependent variable based on a linear combination of
independent variables.
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 590
18. To determine whether the discriminant analysis can be used as a good predictor, information provided
in the “confusion matrix” is used.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 592
19. The purpose of factor analysis is to summarize the information contained in a large number of
variables into as large a number of factors as possible.
ANS: F
Factor analysis is a multivariate interdependence technique that statistically identifies a reduced
number of factors from a larger number of measured variables.
PTS: 1
REF: p. 593
NAT: AACSB: Reflective Thinking
20. A factor loading indicates how strongly a measured variable is correlated with a factor.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 594
21. The most common rule for extracting factors in factor analysis is to base the number of factors on the
number of eigenvalues greater than 5.0
ANS: F
The rule is an eigenvalue greater than 1.0.
PTS: 1
REF: p. 594
NAT: AACSB: Reflective Thinking
22. Factor rotation is a mathematical way of simplifying factor results.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 594
23. In factor analysis, "communality" is a measure of the percentage of a variable's variation that can be
explained by the factors.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 596
24. In cluster analysis, each cluster should have low internal homogeneity and high external heterogeneity.
ANS: F
The cluster should have high internal (within-cluster) homogeneity and external (between-cluster)
heterogeneity.
PTS: 1
REF: p. 597
NAT: AACSB: Reflective Thinking
25. Multidimensional scaling provides a means for placing objects in multidimensional space on the basis
of respondents’ judgments of the similarity of objects.
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
ANS: T
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 599
MULTIPLE CHOICE
1. Which type of analysis involves three or more variables?
a. univariate statistical analysis
b. bivariate statistical analysis
c. multivariate statistical analysis
d. all of the above
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 581
2. Which of the following is a mathematical way in which a set of variables can be represented with one
equation?
a. structuralism
b. variate
c. ANOVA
d. synergy
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 581
3. The two basic groups of multivariate techniques are:
a. dependence methods and interdependence methods
b. primary methods and secondary methods
c. simple methods and complex methods
d. partial methods and complete methods
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 582
4. When a multivariate statistical technique is used to predict a dependent variable from several
independent variables, the researcher is studying:
a. dependence
b. independence
c. interdependence
d. segments
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
5. Which of the following is a dependence method of analysis?
a. structural equations modeling
b. multiple regression analysis
c. multiple discriminant analysis
d. all of the above
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
6. All of the following are examples of dependence methods of analysis EXCEPT:
a. multiple regression analysis
b. multiple discriminant analysis
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
c. cluster analysis
d. multivariate analysis of variance
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
7. Which of the following is an example of an interdependence analysis method?
a. multidimensional scaling
b. multiple regression analysis
c. conjoint analysis
d. all of the above
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
8. All of the following are examples of interdependence methods of analysis EXCEPT:
a. factor analysis
b. cluster analysis
c. multidimensional scaling
d. conjoint analysis
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
9. Nominal and ordinal scales are examples of _____ scales, while interval and ratio scales are examples
of _____scales.
a. metric; co-metric
b. nonmetric; metric
c. nonmetric; advanced
d. metric; continuous
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 583
10. If the analysis contains only one dependent variable and that variable is metric, the appropriate
statistical analysis is:
a. multiple discriminant analysis
b. conjoint analysis
c. multivariate ANOVA
d. multiple regression
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
11. Which of the following is an appropriate technique when the inputs are metric?
a. cluster analysis
b. metric multidimensional scaling
c. factor analysis
d. all of the above
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
12. Mulitvariate dependence techniques are variants of the _____, which is a way of modeling some
process based on how different variables cause fluctuations from the average dependent variable.
a. ordinary linear model (OLM)
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
b. weighted average model (WAM)
c. general linear model (GLM)
d. metric scaling model (MSM)
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
13. When a researcher is attempting to predict sales volume by using building permits, amount of
advertising, and the income levels of residents, the researcher is using:
a. univariate analysis
b. a chi-square analysis
c. multiple regression analysis
d. factor analysis
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
14. Which analysis is portrayed by the equation: Y = bο + b1X1 + b2X2 + b3X3... + bnXn?
a. simple regression
b. multiple regression
c. chi-square
d. factor analysis
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 584
15. A variable that is coded as either zero or one and that has two distinct levels is called a(n):
a. regression variable
b. dummy variable
c. MANOVA variable
d. ANOVA variable
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 585
16. The correlation between two variables after taking into account the fact that they are correlated with
other variables too is called:
a. partial correlation
b. standardized correlation
c. raw correlation
d. variant correlation
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 586
17. If the regression equation is: Y = 98.3 +.35X1 + 22.3X2, the predicted value for Y when X1 = 3 and X2
= 5 is:
a. 118.45
b. 210.85
c. 67.23
d. 98.3
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 586
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
18. A value of R2 = 0.40 means that _____ percent of the variance in the dependent variable is explained
by the independent variables.
a. 80
b. 64
c. 40
d. 16
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 586
19. In the following formula, k stands for:
a.
b.
c.
d.
the number of observations
the degrees of freedom of the denominator
the number of independent variables
the sample size
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 588
20. In the formula for the F-test in multiple regression, n - k - 1 stands for:
a.
b.
c.
d.
the degrees of freedom of the numerator
the number of observations
the degrees of freedom of the denominator
the number of independent variables
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 588
21. Jeff is analyzing data and is concerned over how strongly interrelated the independent variables in his
model are. Jeff is concerned about:
a. multicollinearity
b. MANOVA
c. degrees of freedom
d. convergence
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 588
22. Which of the following is computed by most regression programs and provide an indication of how
much multicollinearity exists among a set of independent variables?
a. χ2
b. β
c. collinear coefficient
d. variance inflation factor (VIF)
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 588
23. Which of the following suggests problems with multicollinearity?
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
a.
b.
c.
d.
VIF > 5.0
β < 3.0
Power > 0.8
α > 0.8
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 588
24. If the analysis predicts several continuous dependent variables with several categorical independent
variables, the appropriate statistical technique is:
a. multiple regression
b. multiple discriminant analysis
c. conjoint analysis
d. MANOVA
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 589
25. Which type of analysis attempts to predict a categorical dependent variable?
a. factor analysis
b. discriminant analysis
c. regression analysis
d. linear analysis
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 590
26. If a bank wants to differentiate between successful and unsuccessful credit risks for home mortgage
loans, it should use:
a. factor analysis
b. multidimensional scaling
c. MANOVA
d. discriminant analysis
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 590
27. In discriminant analysis, a linear combination of independent variables that explains group
memberships is known as a(n):
a. regression equation
b. discriminant function
c. discriminant factor
d. n-way ANOVA
ANS: B
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 590
28. Which multivariate analysis statistically identifies a reduced number of factors from a larger number of
measured variables?
a. factor analysis
b. regression
c. discriminant analysis
d. logit analysis
ANS: A
PTS: 1
REF: p. 593
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
NAT: AACSB: Reflective Thinking
29. Which of the following indicates how strongly a measured variable is correlated with a factor?
a. factor β
b. discriminator
c. factor link
d. factor loading
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 593
30. A researcher has 57 variables in a large dataset and wishes to summarize the information from them
into a reduced set of variables. Which multivariate technique should be used?
a. factor analysis
b. multidimensional scaling
c. logit analysis
d. regression analysis
ANS: A
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 593
31. A mathematical way of simplifying factor analysis results is:
a. factor loading
b. factor reduction
c. factor rotation
d. factor analysis
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 594
32. In cluster analysis, the researcher wants clusters to have high ______ within-clusters and high
between-cluster ______.
a. independence; dependence
b. significance; insignificance
c. heterogeneity; homogeneity
d. homogeneity; heterogeneity
ANS: D
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 597
33. General Mills would like to “see” a picture of how its brands are perceived by consumers compared to
competitive brands. Which statistical technique can measure brands in multidimensional space on the
basis of respondents’ judgements of the similarity of the brands?
a. structural equations modeling
b. factor analysis
c. multidimensional scaling
d. partial positioning
ANS: C
PTS: 1
NAT: AACSB: Reflective Thinking
REF: p. 599
COMPLETION
1. Statistical methods that permit the study of three or more variables at the same time are called
____________________ statistical analysis.
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
ANS: multivariate
PTS: 1
REF: p. 581
NAT: AACSB: Reflective Thinking
2. The two types of multivariate techniques are ____________________ methods and
____________________ methods.
ANS:
dependence, interdependence
interdependance, dependance
PTS: 1
REF: p. 582
NAT: AACSB: Reflective Thinking
3. Multivariate techniques that try to group things together are known as ____________________
methods.
ANS: interdependence
PTS: 1
REF: p. 583
NAT: AACSB: Reflective Thinking
4. When an analysis studies the effect of several independent variables on a single dependent variable
that is interval-scaled, this is called ____________________ analysis.
ANS: multiple regression
PTS: 1
REF: p. 584
NAT: AACSB: Reflective Thinking
5. Multivariate dependence techniques are variants of the ____________________.
ANS:
general linear model
GLM
PTS: 1
REF: p. 584
NAT: AACSB: Reflective Thinking
6. A ____________________ variable has two distinct levels that are coded as 0 and 1.
ANS: dummy
PTS: 1
REF: p. 585
NAT: AACSB: Reflective Thinking
7. The test used to test statistical significance by comparing variation explained by the regression
equation to the residual error variation is the ____________________.
ANS: F-test
PTS: 1
REF: p. 586
NAT: AACSB: Reflective Thinking
8. ____________________ in regression analysis refers to how strongly interrelated the independent
variables in a model are.
ANS: Multicollinearity
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
PTS: 1
REF: p. 588
NAT: AACSB: Reflective Thinking
9. ____________________ predicts several dependent variables by using several independent variables.
ANS:
Multivariate analysis of variance
MANOVA
PTS: 1
REF: p. 589
NAT: AACSB: Reflective Thinking
10. If the researcher wants to classify objects into two mutually exclusive categories, the researcher should
use ____________________ analysis.
ANS: discriminant
PTS: 1
REF: p. 590
NAT: AACSB: Reflective Thinking
11. The purpose of ____________________ analysis is to summarize information in a large number of
variables into a smaller number of factors.
ANS: factor
PTS: 1
REF: p. 593
NAT: AACSB: Reflective Thinking
12. An indication of how strongly a measured variable is correlated with a factor is given by the
____________________.
ANS: factor loading
PTS: 1
REF: p. 594
NAT: AACSB: Reflective Thinking
13. A mathematical way of simplifying factor results is ____________________.
ANS: factor rotation
PTS: 1
REF: p. 594
NAT: AACSB: Reflective Thinking
14. A statistical technique that measures objects in multidimensional space on the basis of respondents’
judgments of the similarity of objects is ____________________.
ANS: multidimensional scaling
PTS: 1
REF: p. 599
NAT: AACSB: Reflective Thinking
15. A multivariate interdependence technique that classifies individuals or objects into a small number of
mutually exclusive and exhaustive groups is ____________________.
ANS: cluster analysis
PTS: 1
REF: p. 597
NAT: AACSB: Reflective Thinking
ESSAY
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
1. Compare and contrast dependence and interdependence techniques. List the statistical techniques for
both.
ANS:
When hypotheses involve distinction between independent and dependent variables, dependence
techniques are needed. Multiple regression analysis, multiple discriminant analysis, multivariate
analysis of variance, conjoint analysis, and structural equations modeling are all dependence
techniques. When researchers examine questions that do not distinguish between independent and
dependent variables, interdependence techniques are used. No one variable or variable subset is to
be predicted from or explained by the others. The most common interdependence methods are factor
analysis, cluster analysis, and multidimensional scaling.
PTS: 1
REF: pp. 582-583
NAT: AACSB: Reflective Thinking| AACSB: Communication
2. List the steps in interpreting a multiple regression model.
ANS:
Multiple regression models can be interpreted using these steps:
(1) Examine the model F-test for significance.
(2) Examine the individual statistical tests for each parameter estimate.
(3) Examine the model R2.
(4) Examine collinearity diagnostics, such as variance inflation factors (VIF) for each variable to
detect multicollinearity.
PTS: 1
REF: p. 588
NAT: AACSB: Reflective Thinking| AACSB: Communication
3. Explain how MANOVA models differ from ANOVA models.
ANS:
An ANOVA or MANOVA model represent a form of the general linear model (GLM). ANOVA can be
extended beyond one-way ANOVA to predict a dependent variable with multiple categorical
independent variables. Multivariate analysis of variance (MANOVA) is a multivariate technique that
predicts multiple continuous dependent variables with multiple independent variables. The
independent variables are categorical, although a continuous control variable can be included in the
form of a covariate.
PTS: 1
REF: p. 589
NAT: AACSB: Reflective Thinking| AACSB: Communication
4. Explain why and how a business researcher uses factor analysis.
ANS:
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
Factor analysis is a prototypical multivariate, interdependence technique. It is a technique of
statistically identifying a reduced number of factors from a larger number of measured variables. The
factors themselves are not measured, but instead, they are identified by forming a variate using the
measured variables. Factors are usually latent constructs like attitude or satisfaction or an index like
social class. A researcher need not distinguish between independent and dependent variables to
conduct factor analysis. Factor analysis can be divided into two types: exploratory factor analysis
(EFA) and confirmatory factor analysis (CFA). Exploratory factor analysis reveals how many
factors exist among a set of variables and what variables match up or “load on” which factors. A factor
loading indicates how strongly correlated a factor is with a measured variable. Factor analysis is
considered a data reduction technique that allows a researcher to summarize information from many
variables into a reduced set of variates or composite variables.
PTS: 1
REF: p. 593
NAT: AACSB: Reflective Thinking| AACSB: Communication
5. Explain how cluster analysis can identify market segments.
ANS:
Cluster analysis is a multivariate approach for identifying objects or individuals that are similar to one
another in some respect. It classifies individuals or objects into a small number of mutually exclusive
and exhaustive groups. Objects or individuals are assigned to groups so that there is great similarity
within groups and much less similarity between groups. The cluster should have high internal (withincluster) homogeneity and external (between-cluster) heterogeneity. Cluster analysis facilitates market
segmentation by identifying subjects or individuals who have similar needs, lifestyles, or responses to
marketing mixes.
PTS: 1
REF: p. 597
NAT: AACSB: Reflective Thinking| AACSB: Communication
© 2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in
whole or in part.
