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Summary >100 Theoretical Practice Questions IRM
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>100 Theoretical Practice Questions IRM, including theoretical Exam questions from this year's exam.
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Some examples from this set of practice questions
1.
What are the assumptions of ANOVA
Answer: (1) Check if the observations are independent (2) Check if homoscedasticity is met. This is done by checking if the Levene\'s test is > 0.0* (3) Check if the dependent variable is normally distributed** * if Homoscedasticity is not met check (1) if the treatment groups are similar or (2) transform the DV with a logarithm (3) adjust the cut off ** If normalitaty is not met, check (1) if the sample size if big enough (30 ) or transform the DV so it more symmetric.
2.
What the assumptions of Factor Analysis
Answer: For factor analysis, multicollinearity is required. This can be checked by three ways: (1) Check the pearson R collinearity matrix and check if sufficient number has a higher correlation then 0.3 (2) Bartletts\'s test of spericity; Check if the significance level is < 0.05 (3) KMO/MSA: test if the MSA value is above 0.5* *Method 2 and 3 results can be find in the same table
3.
What are the assumptions of Cluster Analysis?
Answer: No hard statistical assumptions, but to subjective ones to keep in mind: (1) Representativeness (2) Multicollinearity: An overlap between variables is not per se an issue. Keep in mind that if you put both variables in the analysis, these variables will get more magnitude with them.
4.
What are the assumptions of Logistic Regression
Answer: (1) Dependent / outcome variable has only two groups (2) Robust to deviations from multivariate normality and homoscedasticity (2) No high correlations between independent variables (3) The outcome is a probability and must lie between zero and one
5.
What are the assumptions of Conjoint Analysis
Answer: (1) Check overfitting: Respondents only give a few answers per case and therefore it\'s hard to get real parameters (2) Garbage in Garbage out (GIGO) If data are badly collected, the results won\'t matter to.
6.
What are the assumptions of MDS
Answer: (1) Metric vs Non-metric MDS (2) Homogeneity: dimensionality, time, importance?* * If you make map at one point of time, it may be very different some time later.
Content preview
Theoretical practice questionsIntroduction to research in marketing – Fall 2018-2019
* Exam October 2018 Q&A
** Practice exam 2018 Q&A
What is multicollinearity?
High intercorrelations among independent variables in a multiple regression model. It can lead to
skewed/misleading results.
What kind of data belongs to Non-metric data?
Nominal, Ordinal
What kind of data belongs to metric data?
Interval (scales) and Ratio (measurable and countable)
What are bivariate visualizations?
Relationships between two or more variables
What is the assumption of homoscedasticity and when is it satisfied?
Homoscedasticity means we want to find no differences in the variances across conditions. When
Levene's test finds a significance below .05, it is NOT satisfied. When the significance is above .05, it IS
satisfied. H0 is rejected when it is not satisfied.
What does ANOVA test?
It tests if there are differences in the mean of a metric dependent variable across different levels of one
or more non-metric IVs
What does cluster analysis do?
Combines objects or persons into groups based on a predefined set of characteristics.
What does factor analysis do?
Combine highly correlated variables together.
What does logistic regression do?
Predict the probability that a non-metric variable is "A"
What does Conjoint analysis do?
Instead of rating attributes, customers rate the whole product. The product rating can be decomposed
into values attached to each feature; part worths
What is MDS?
Multidimensional scaling is an exploratory technique to identify dimensions by which objects are
perceived
Eline van de Ven
, Why is it important to use all but 1 of the part worths? (Conjoint) *
Multicollinearity. Part worths have to sum to zero. So only j-1 is independent.
What does the angle between vectors mean (MDS)?*
The smaller the angle, the closer they are, and the more correlated
What does the length of a vector mean (MDS)?*
How much information the attribute adds to the map. A long vector means a lot of information
What does the distance from the origin (0,0) to the point at which the perpendicular crosses the vector
mean?*
How well the object scores on that attribute
A vector =
Indicator of magnitude and direction in which the attribute is increasing in the Euclidian space
What are the axes of the map?
Special set of vectors suggesting the underlying dimensions that best characterize how consumers
differentiate among alternatives.
Does this belong to MDS?
Similar based approach is most appropriate for functional products*
No
When multicollinearity is an assumption to be met instead of a violation, what do we use?*
Factor analysis
What does a scree plot explain?**
How much variation is explained by the factors
exp(x)/1 exp(x)
Does the function get closer to -1 when x decreases?*
No
What is power?
The probability of correctly rejecting the null when it's false
When there is a large effect, do we need a small or large sample to get good/excellent power?
Small
How big is good and excellent power respectively?
0.80 and 0.95
When your sample is too small, which of the following is correct?
a) the effect is probably insignificant
b) the effect is probably significant
c) when the effect is significant, it is probably overstated
Eline van de Ven
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