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Statistics for Marketing & Consumer Research (M. Mazzocchi)
SAGE Publications
Multiple choice questions
Chapter 1
1. The difference between random and systematic errors is:
A) Systematic errors are always larger than random errors
B) Random errors are subject to probability laws, while systematic errors are not
C) Systematic errors can be eliminated by taking repeated measurements
2. If measurement error follows a Normal curve centred at zero, then:
A) Errors in excess compensate error in defect
B) Errors in excess are more likely than error in defect
C) The average error is negative
3. Which of the following is a qualitative ordinal variable?
A) Monthly income
B) Quantity of coffee drank in a week
C) Customer satisfaction measured on a Likert scale from one to seven
4. High reliability for a set of questionnaire items measuring a latent construct means that:
A) The items are bipolar
B) The items are not internally consistent
C) The items are internally consistent
5. What is the difference between a Likert and a Semantic Differential (SD) scale?
A) The Likert scale requires two bipolar attributes, the SD scale one
B) The SD scale requires two bipolar attributes, the Likert scale one
C) There is no difference
Chapter 2
6. Primary data are:
A) Data already collected by others for different purposes
B) Data collected before secondary data
C) Data explicitly collected for the specific purpose of the research
7. Secondary data are always preferable to primary data when:
A) Secondary data are more expensive than primary data
B) The target population and sampling fit with the research objective and quality is acceptable
C) The sample size of secondary data is larger than the one of primary data
8. What is the difference between household budget surveys and panel household surveys?
A) Household budget surveys include the same households over time
B) Panel household surveys include the same households over time
C) Panel household surveys do not record budgets
9. What is the COICOP classification?
A) A classification of households according to the economic status of the reference person
B) A classification of surveys according to the type of sampling procedure
C) A classification of expenditure items according to their purpose
10. What is scan data?
A) Purchase data collected through a bar-code scanner
B) Old data transcribed into electronic files through scanning facilities
C) Machine-readable questionnaires
Chapter 3
11. Which of the following is not a non-response error?
A) Sampling frame error
B) Not-at-home error
C) Refusal
12. What is the difference between sampling and non-sampling error?
A) Sampling error can be quantified with probabilistic sampling
B) Non-sampling error can be quantified with large sample sizes
C) Sampling error is always larger than non-sampling error
13. What is the reference population?
A) A population used for comparison with the selected sample
B) The list of subjects included in the sample
C) The complete set of subjects relevant to the research
14. Which survey method has the highest non-response rate?
A) Telephone
B) Mall intercept
C) Mail surveys
15. What is the duration of a telephone interview to ensure adequate quality of responses?
A) 15-20 minutes
B) 30-40 minutes
C) 45-60 minutes
16. What is the duration of a telephone interview to ensure adequate quality of responses?
A) 15-20 minutes
B) 30-40 minutes
C) 45-60 minutes
17. Sensitive questions
A) Should never be at the beginning of a questionnaire
B) Should never be asked through multiple choice questions
C) Are better recorded through face-to-face interview
Chapter 4
18. What is a dummy variable?
A) A metric variable with no decimals
B) A binary variable
C) A useless variable which should be discarded
19. Which of the following is not the definition of an outlier?
A) A value with a distance from the mean higher than 2.5 times the standard deviation
B) A value with a distance from the mean higher than 1.5 times the interquartile range
C) A value with distance from the mean higher than the median
20. Listwise or casewise deletion of missing data implies:
A) That observations (cases) with missing data are omitted from the analysis
B) That observations (cases) enter the analysis only with their non-missing values
C) That variables with missing data are omitted from the analysis
21. In statistical analysis with pairwise deletion of missing data:
A) Observations (cases) with one or more missing data are omitted from the analysis
B) In each estimation step all valid cases are exploited
C) If a variable is omitted in an estimation step because of missing data, then it does not enter
estimation any more
22. Missing response should be treated as non-random when:
A) They are more than 5% of total responses
B) The mean of relevant variables is very different between respondents and non-respondents
C) The mean of relevant variables for non-respondents is equal to the one for respondents
23. Which of the following is not a good strategy for imputing missing data?
A) Substituting missing values with the sample mean for that variable
B) Substituting missing values with the standard deviation for that variable
C) Imputing missing values with several approaches, then taking the average
24. Which of the following is a measure of central tendency?
A) Standard error
B) Coefficient of variation
C) Mode
25. What is the median?
A) The value which splits the observations in a data-set in two halves, those below and those
above the median value
B) The most likely value
C) The average value
Chapter 5
26. What is a statistic sample?
A) A measure of variability
B) A group of variables measured on all the subjects object of interest
C) A subset of the target population expected to represent the whole population
27. What is a sampling frame?
A) A list of all subjects included in the sample
B) A list of statistics to be computed in the sample
C) A list of all subjects included in the reference population
28. Which of the following is a form of probability sampling?
A) Convenience sampling
B) Quota sampling
C) Stratified sampling
29. Which of the following statistics is a measure of sample precision?
A) Standard error of the mean
B) Mode
C) Mean
30. Which of the following parameters influence the choice of the sample size?
A) Variability of the target variable in the population
B) Size of the population
C) Both
31. What is the advantage of the sampling error over the non-sampling error?
A) Sampling error can be quantified according to probability laws
B) Sampling error is much smaller than non-sampling error
C) Sampling error can be eliminated by training interviewers
32. The rationale behind stratified sampling is:
A) to maximise heterogeneity within each stratum
B) to minimise heterogeneity between different strata
C) to maximise homogeneity within each stratum
33. A population parameter is estimated in a sample through:.
A) A sample statistic
B) The precision level
C) The sampling frame
34. Which of the following statistics is an estimate of population variability
A) Sample average
B) Population average
C) Sample standard deviation
35. Which of the following situations does not favor the use of a census?
A) There is high variance in the characteristic to be measured.
B) The cost of nonsampling errors is low.
C) The population is large.
36. All of the following statements are limitations of simple random sampling except:
A) It is often difficult to construct a sampling frame that will permit a simple random sample to be
drawn.
B) Simple random sampling often results in lower precision with larger standard errors than other
probability sampling techniques.
C) The sample results may be projected to the target population.
37. Which probability sampling technique selects a random starting point and picks up every ith
element in succession from the sampling frame.
A) Simple random sampling
B) Systematic sampling
C) Cluster sampling
38. In which one of the following ways do cluster sampling and stratified sampling differ:
A) the former is probabilistic and the latter is non probabilistic
B) there is no difference
C) with respect to homogeneity and heterogeneity within/across subgroups
39. All of the factors listed below favor the use of probability sampling except:
A) Nonsampling errors are likely to be an important factor
B) The nature of the research is conclusive
C) The population is heterogeneous with respect to variables of interest
40. The difference between the mean value for the sample and the true mean value of the population is
a measure of :
A) Precision
B) Accuracy
C) Randomness
41. The probability sampling technique where each element in the population has exactly the same
probability of extraction is:
A) Stratified sampling
B) Simple random sampling
C) Quota sampling
42. The sampling technique which divides the population into sub-populations which are expected to
be similar among them is:
A) Stratified sampling
B) Simple random sampling
C) Cluster sampling
43. Which of the following is an estimate of the variability of estimates of the mean in different
samples?
A) Standard error of the mean
B) Variance
C) Standard deviation
Chapter 6
44. What is the probability distribution for sample means extracted through simple random sampling?
A) The uniform distribution
B) The Normal distribution
C) The F distribution
45. The 95% confidence interval for a mean from a large sample has
A) A width of about twice the standard error of the mean
B) A width of about four times the standard error of the mean
C) A width of about ten times the standard error of the mean
46. The critical values for hypothesis testing are:
A) A measure of variability of the target variable
B) The values which separate the acceptance region from the rejection region
C) The area under the probability distribution
47. What is the level of significance  of a test?
A) The probability of rejecting the null hypothesis when it is actually true
B) The probability of non-rejecting the null hypothesis when it is actually true
C) The probability of non-rejecting the null hypothesis when it is actually false
48. What is the relation between level of confidence and the level of significance ()?
 The level of confidence is 1+
B) The level of confidence is 1– 
C) There is no difference
49. What is the power of a test?
A) The probability of rejecting the null hypothesis when it is actually true
B) The probability of non-rejecting the null hypothesis when it is actually true
C) The probability of rejecting the null hypothesis when it is actually false
50. What is the difference between parametric and non-parametric tests?
A) Parametric tests do not require assumption on the probability distribution of the variables being
tested
B) Non-parametric tests do not require assumption on the probability distribution of the variables
being tested
C) Parametric tests are more powerful
51. If the same sample of individuals is interviewed before and after an advertising campaign, a mean
comparison test about the purchasing habits before and after the campaign is a test for:
A) Independent samples
B) Related samples
C) Paired samples
52. Which test is more appropriate for mean comparison with related samples?
A) The t-test
B) The F-test
C) The Wilcoxon test
53. What is the distribution of the ratio of two variances under the null hypothesis of variance equality?
A) The t-distribution
B) The Normal distribution
C) The F-distribution
54. A t-test in two means returns a t-statistic with a p-value of 0.03. Which of the following is correct?
A) The means of the two populations are equal at 97% confidence level
B) The means of the two populations are different between each other at 95% confidence level but
not at a 99% confidence level
C) The mean of the two populations show a 3% difference
Chapter 7
55. Which of the following is a consequence of running multiple mean comparison tests?
A) It is more likely to reject the null hypothesis when it is true
B) It is impossible to compute the F-statistic
C) It is less likely to reject the null hypothesis when it is true
56. When does one-way ANOVA rejects the hypothesis of mean equality?
A) When all means are different
B) When at least two means are different
C) When all means are equal
57. What is the main difference between planned comparisons and post-hoc tests?
A) Planned comparison take into account the familywise error, post-hoc tests don’t
B) Post-hoc tests take into account the familywise error, planned comparison don’t
C) Planned comparison are decided prior to the analysis, post-hoc test afterwards
58. What is three-way ANOVA?
A) An ANOVA with three variables and one factor
B) An ANOVA with one variable and three factors
C) An ANOVA with both categorical and scale variables
59. What is the effect size in ANOVA?
A) It is the number of factors being considered
B) It corresponds to the F-value
C) Is a measure of the relative weight of the variability imputable to the factor
60. What is the difference between random and fixed effects?
A) Fixed effects are measured with no error, random effects are the outcome of a random variable
B) Fixed effects are larger than random effects
C) Random effects are under the control of researchers, fixed effects are not
61. Which of the following is not an assumptions of one-way ANOVA?
A) Independence between the units sampled in different treatments
B) Absence of large discrepancy in variance across different treatments
C) Different treatments are measured on the same units
62. What is the difference between multi-way (factorial) ANOVA and multivariate ANOVA?
A) Multivariate ANOVA has more than one target variable, factorial ANOVA only one
B) Factorial ANOVA has more than one target variable, multivariate ANOVA only one
C) There is no difference
63. What is the General Linear Model (GLM)?
A) A regression model with the target variable(s) on the left-hand side and the factors on the righthand side
B) A general model which includes ANOVA, factorial ANOVA, MANOVA and ANCOVA as
special cases
C) Both of the above
64. Which of the following statement on ANCOVA is true?
A) ANCOVA is a GLM where all the right-hand side variables are categorical
B) ANCOVA is a GLM where the dependent variable is categorical
C) ANCOVA is a model which can contain both metric and non-metric variables on the righthand side
Chapter 8
65. If the F-test in a multiple regression equations returns a probability of 0.22, it means that:
A) None of the coefficient is significantly different from 0
B) All of the coefficients are significantly different from 0
C) At least one coefficient is significantly different from 0
66. The t-test on regression coefficients tests the null hypothesis that:
A) There is no collinearity
B) The residuals are normally distributed
C) The coefficient is significantly different from 0
67. Which of the following statements on the relationship between the bivariate correlation coefficient
r and bivariate regression is true?
A) The regression coefficient is equal to the bivariate correlation coefficient
B) The regression R2 is the squared correlation coefficient
C) The F-test on the regression is equal to the bivariate correlation coefficient
68. What is the difference between covariance and correlation?
A) Covariance depends on the measurement unit, correlation is standardized to be between zero
and one
B) Correlation depends on the measurement unit, covariance is standardized to be between zero
and one
C) Covariance depends on the measurement unit, correlation is standardized to be between minus
one and one
69. A bivariate correlation coefficient equal to minus one means that:
A) There is no correlation between the two variables
B) The two variables are perfectly correlated, as one increases by n%, the other also increases by
n%
C) The two variables are perfectly correlated, as one increases by n%, the other decreases by n%
70. Which of the following is an appropriate equation for multiple regression?
A) yi  a  bxi  ei
B) yi  bx1i  bx2i
C) y = Xβ + ε
71. The R-square indicator measures the goodness of fit of a regression model. What does an R-square
value of zero mean?
A) There is no relationship between the dependent and explanatory variables
B) The model performance is very poor
C) The model fits perfectly the data
72. What does the partial correlation coefficient measure?
A) The correlation between two variables after controlling for the effects of one or more
additional variables
B) The correlation among three variables
C) The percentage of correlation between two variables which depends upon a third variable
73. What is the difference between stepwise and forward model selection methods?
A) The stepwise method allows the removal of variables after they have been included, the forward
method does not
B) The stepwise method goes backward (removing variables), the forward method goes forward
(adding variables)
C) There is no difference
Chapter 9
74. When are two categorical variables said to be associated?
A) When frequencies in the contingency table are equally distributed across cells
B) When a relationship exists between frequencies in each category of the first variable and
frequencies in categories of the second variable
C) When they have the same number of categories
75. What is the null hypothesis of the Pearson chi-square test?
A) That two categorical variables are independent
B) That two categorical variables are associated
C) That two categorical variables have the same distribution
76. Which of the following test works with strictly nominal variable?
A) Goodman and Kruskal's Lambda
B) Somer’s d statistic
C) Tau statistic
77. What is the dependent variable in a log-linear model?
A) The exponent of the cell frequencies in a contingency table
B) The logarithm of the cell frequencies in a contingency table
C) The ratio of the cell frequencies in a contingency table
78. What is the relation between the Pearson chi-square test and log-linear analysis in a 2×2 table?
A) The Pearson chi-square test is more powerful
B) Log-linear analysis does not work with 2×2 tables
C) The Pearson chi-square test corresponds to the test on 2nd order interaction in log-linear anaysis
79. What is the model selection process in hierarchical log-linear analysis?
A) Higher order interaction are tested first, the process stops when deletion of a term makes the
frequencies predicted by the model significantly different from frequencies of the saturated
model
B) Lower order interaction are tested first, the process stops when deletion of a term makes the
frequencies predicted by the model significantly different from frequencies of the saturated
model
C) Higher order interaction are tested first, the process stops if the frequencies predicted by the
model are not significantly different from frequencies of the saturated model
80. Which of the following is used in log-linear analysis as a distributional assumption for the
frequencies of the contingency table?
A) Normal distribution
B) Student-t distribution
C) Poisson distribution
81. Which elements are related in canonical correlation analysis?
A) Two categorical variable
B) Two sets of variables
C) Two frequency tables
82. What are canonical cross-loadings?
A) The correlations between a variable in one set and the opposite canonical variate
B) The correlations between two canonical variates
C) The correlations between a variable in one set and the corresponding canonical variate
83. What is redundancy analysis?
A) An evaluation of which variables are redundant to canonical correlation analysis
B) A test of significance on the canonical correlations
C) An evaluation of the proportion of variance explained by canonical variates
Chapter 10
84. In which of the following aspects is there no difference between factor analysis and PCA (Principle
Component Analysis)?
A) The way scores are determined
B) The indeterminacy of solutions
C) The use of rotation methods
85. Which of the following is an implication of running PCA or factor analysis on the correlation rather
than covariance matrix?
A) It eliminates the influence of different measurement units
B) It increases the number of factors (components) extracted
C) All communalities are equal to 1
86. Which of the following is the Kaiser rule for deciding the number of components?
A) Extracted components should account for at least 70% of the variability
B) The eigenvalues of the extracted component should be larger than the mean eigenvalue
C) The scree diagram shows an elbow
87. What does a communality measure?
A) The proportion of original variability of the entire data-set retained by the extracted factors
(components)
B) The proportion of original variability for each variable retained by the extracted factors
(components)
C) The proportion of original variability discarded in the analysis
88. What does a VARIMAX rotation do?
A) It provides an orthogonal rotation of the original solution which maximizes the variance of
loadings in each factor
B) It provides an orthogonal rotation of the original solution which maximizes the variance of
loadings for each variable
C) An average of the two rotations above
89. Which of the following is a definition of factor loading?
A) The correlation between the factor scores and the original variables
B) The correlation between two factors
C) The ratio between two communalities
Chapter 11
90. Which of the following is a requirement for discriminant analysis as a classification technique?
A) There must be at least three groups
B) The number of variables must be larger than the number of groups
C) The classification of units into groups must be known
91. What is the discriminant function?
A) A function of the classification variable which tests the relevance of predictors
B) A function of predictors which allows classification into groups
C) A unique value which discriminates between two groups
92. What is the cut-off point in binary discriminant analysis?
A) A unique value which defines classification for each unit according to the corresponding value
of the discriminant function
B) The stopping rule in stepwise discriminant analysis
C) A test on the significance of the predictors
93. To which purpose is the Wilks Lambda used in discriminant analysis?
A) To evaluate the discriminant power of each predictor
B) To test for significantly different means of a discriminant function across groups
C) Both of the above
94. What is the purpose of the Box’s M statistic?
A) To evaluate the rate of correct predictions
B) To test for significant differences in the discriminant functions across groups
C) To test the null hypothesis of equality of covariance matrices across groups
95. How should be the covariance matrices of predictors be across the groups?
A) Linearly related
B) Significantly different
C) Equal
96. What is cross-validation?
A) A method to compute correlations between groups
B) A method to evaluate the predictive power of the discriminant functions
C) A method to evaluate the discriminating power of the predictors
Chapter 12
97. The objective of cluster analysis when extracting cluster is:
A) Maximising homogeneity within clusters and heterogeneity between them
B) Maximising heterogeneity within clusters and homogeneity between them
C) Maximising homogeneity within and between clusters
98. Given N statistical units, an agglomerative hierarchical method
A) Starts with N clusters and proceeds with merges until one cluster is obtained
B) Starts with one cluster and proceeds with extractions until N clusters are obtained
C) Extracts immediately the desired number of clusters
99. The Ward algorithm is
A) A non hierarchical method
B) A hierarchical agglomerative method
C) A hierarchical divisive method
100.
A)
B)
C)
K-means clustering is a method of:
Non-hierarchical clustering
Divisive clustering
Agglomerative clustering
101.
A)
B)
C)
The CCC, Pseudo F and Pseudo t2 are statistics for:
Identifying the optimal number of cluster
Determining a stopping rule in hierarchical algorithms
Testing whether mean differ across clusters
102.
A)
B)
C)
Which of the following is a characteristic of the k-mean clustering:
It provides output for a range of numbers of clusters
It allows reallocation of units at each iteration
It allocates the same observation to more than one cluster
103. What is the difference between the centroid and average linkage methods?
A) Centroid computes all potential distances between two clusters, then takes the average, while
the average linkage computes the mean values within each cluster, then computes the distance
between cluster means
B) Average linkage computes all potential distances between two clusters, then takes the average,
while the centroid method computes the mean values within each cluster, then computes the
distance between cluster means
C) There is no difference
104. What is the difference between single linkage method and the complete linkage method?
A) The single linkage considers the minimum distance between observations belonging to different
clusters, the complete linkage the maximum distance
B) The complete linkage considers the minimum distance between observations belonging to
different clusters, the single linkage the maximum distance
C) The single linkage considers one distance between two clusters only (taken randomly), the
complete distance considers all distances between two clusters
Chapter 13
105.
A)
B)
C)
What is a proximity matrix
A symmetric matrix containing measure of similarity or dissimilarity between objects
A symmetric matrix containing two-dimensional co-ordinates of objects
A correlation matrix
106.
A)
B)
C)
Which of the following is not a characteristic of non-metric scaling?
Deals with ordinal data and guarantees monotonicity
Does not work with metric data
Has no analytical solution and works through computational algorithms
107. What is the difference between the compositional and decompositional approach
A) The compositional approach uses ordinal variables, the decompositional approach uses metric
variables
B) The compositional approach requires evaluations of the product attributes, the decompositional
approach requires evaluation of the product in its integrity
C) Both of the above
108.
A)
B)
C)
What are the ideal points?
The co-ordinates of the best-selling products
The average product evaluations for each subject
The points in the perceptual mapping representing the ideal product for each subject
109. What is the difference between internal and external preference mapping?
A) Internal preference mapping always precedes external preference mapping
B) Internal preference mapping bases attribute evaluations on ordinal preference variables,
external preference mapping on metric perceptual or objective variables
C) Internal preference mapping is decompositional, external preference mapping is compositional
110.
A)
B)
C)
How are perceptions measured?
As the ordered ranking of different objects as stated by each subject
As the evaluation of different attributes of the objects as stated by each subject
As the paired comparison between pair of objects as stated by each subject
111.
A)
B)
C)
How are preferences measured?
As the ordered ranking of different objects as stated by each subject
As the evaluation of different attributes of the objects as stated by each subject
As the objective measurement of product attributes
112.
A)
B)
C)
Which value of the STRESS function indicate a good fit?
A value close to one
A negative value
Below 0.05
Chapter 14
113. How are distances between categorical variables defined?
A) By considering the nominal variables as scale variables
B) By computing the distance between the actual frequencies and the expected frequencies
assuming no association (like in chi-square statistics)
C) By considering the differences between the cell frequency and the total frequency
114.
A)
B)
C)
What is total inertia?
A measure of association between the two variable in correspondence analysis
The chi-square statistic divided by the number of observations
Both of the above
115.
A)
B)
C)
What does the percentage of inertia for a given dimension measure?
The percentage of total variability explained by that dimension
The percentage of the existing association explained by that dimension
The percentage of variability not explained by that dimension
116.
A)
B)
C)
What is a row mass?
The row totals of the contingency table
The inertia divided by the number of observations
The chi-square statistic for each category
117.
A)
B)
C)
What are the implications of a row principal normalization for extracting the dimensions?
That distances between categories of the row variable are assumed to be more meaningful
That distances between categories of the row and column variables are ignored
That distances between categories of the column variables are assumed to be more meaningful
118. What is the bi-plot?
A) A plot showing all the categories of several categorical variables in the same space
B) A double graph, one showing the categories of the row variable, one those of the column
variable
C) A plot showing one category for each variable
119. What is multiple correspondence analysis?
A) A correspondence analysis between two variables with more than two categories each
B) A technique which allows summarization of more than two non-metric variable with a small
number of metric co-ordinates
C) A sequence of simple correspondence analyses using different models
Chapter 15
120.
A)
B)
C)
What is the difference between an explanatory and a confirmatory factor analysis?
Exploratory analysis imposes constraints on which variable loads on which factor
Confirmatory analysis imposes constraints on which variable loads on which factor
It is merely a philosophical distinction
121.
A)
B)
C)
What are latent variables?
Variables measured with a large degree of error
Unmeasurable variables which can be proxied by a set of other manifest variables
Unmeasurable variables which can be estimated through regression analysis
122. What is the difference between the structural and the measurement equation in a SEM
(Structural Equation Modeling)?
A) The measurement equation relates latent construct to manifest variables, the structural equation
describes the overall relationships among endogenous and exogenous variables, including latent
variables
B) The structural equation relates latent construct to manifest variables, the measurement equation
describes the overall relationships among endogenous and exogenous variables, including latent
variables
C) There is no difference
123.
A)
B)
C)
What is the difference between path analysis and structural equation model?
Path analysis has no latent variables
Path analysis does not allow for correlation between explanatory variable
Path analysis has no dependent variable
124. What is the distinction between over-identification, just-identification and under-identification?
A) Over-identified models have more observations than parameters and allow model testing, justidentification provide a unique estimate, and in under-identification there are more parameters
to be estimated than available observation and the model cannot be estimated
B) Under-identified models have more observations than parameters and allow model testing,
just-identification provide a unique estimate, and in over-identification there are more
parameters to be estimated than available observation and the model cannot be estimated
C) . Over-identified models have more latent variables than manifest ones, under-identified the
opposite and in just-identified models the number of manifest variables equals the number of
latent variables
125. What is the purpose of goodness-of-fit statistics?
A) An evaluation of the performance of the estimated model in reproducing the original
observations
B) An evaluation of the performance of the estimated model in reproducing the original data
covariance matrix
C) An evaluation of the amount of variability explained by the structural model compared to the
measurement model
126.
A)
B)
C)
What does a chi-square test with a probability of 0.10 mean in a SEM framework?
That the theoretical model is not rejected by the data at the 5% significance level
That the theoretical model is rejected by the data at the 5% significance level
That difference between the original and estimated covariance matrices is 10%
127.
A)
B)
C)
What is the meaning of box and circles in path diagrams?
Boxes refer to exogenous variables, circles to endogenous variables
Boxes refer to manifest variables, circles to latent variables
Boxes refer to variables, circles to errors
Chapter 16
128.
A)
B)
C)
Which of the following is a problem of ordinary regression with a binary dependent variable?
Model predictions are not bounded between the two binary values
The distribution of residuals is not normal
Both of the above
129. Which of the following is the model for a nominal dependent variable with metric explanatory
variables?
A) The regression model
B) Multinomial logistic regression
C) Logit model
130.
A)
B)
C)
Which of the following is the link function for logistic regression?
The logistic transformation
The logit transformation
The exponential transformation
131. What is the difference between logistic regression and the probit model?
A) In logistic regression residuals follow the logistic distribution, in probit models they follow the
normal distribution
B) In logistic regression residuals follow the logit distribution, in probit models they follow the
logistic distribution
C) Logistic regression deals with metric explanatory variables, in probit model the explanatory
variables are categorical
132. What is the difference between revealed and stated preferences?
A) Revealed preferences are based on the observation of actual choices, stated preferences are
based on responses to a questionnaire
B) Revealed preferences are measured with metric scales, stated preferences are categorical
C) There is no difference
133.
A)
B)
C)
What is conjoint analysis?
A statistical method for estimating non-linear relationships
A statistical approach to creating choice sets for a stated preference analysis
A statistical method for relating metric variables to categorical variables
134.
A)
B)
C)
Which of the following is not a characteristic of conjoint analysis?
It is a compositional approach
It can be modelled through multinomial logit models
It is based on non-probabilistic samples
135. What is the difference between the GLM and the generalized linear model?
A) The generalized linear model has a categorical dependent variable, the GLM has a metric
dependent variable
B) The generalized linear model has only categorical explanatory variables, the GLM has only
metric dependent variables
C) Both of the above
136. What is the difference between the ordered logit model and the multinomial logit model?
A) The ordered probit model has a categorical ordered dependent variable, the multinomial logit a
metric continuous dependent variable
B) The ordered probit model has a categorical ordered dependent variable, the multinomial logit a
categorical nominal dependent variable
C) The ordered probit model has a categorical ordered dependent variable, the multinomial logit
has more than one categorical dependent variable