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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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  • January 6, 2019
  • 9
  • 2018/2019
  • Summary

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Available practice questions

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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.

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