Static Analysis of Groups Interview Preparation Guide

Static Analysis of Groups Interview Questions and Answers will guide us now that Static analysis, static projection, and static scoring are pejorative terms for statistical analysis for which existing trends are projected into the future simplistically, or beyond what is possible to predict in any manner, producing results often wildly unrealistic. So learn more about Static Analysis of Groups with the help of this Static Analysis of Groups Interview Questions with Answers guide

35 Static Analysis Questions and Answers:

1 :: If the assumptions for conducting an ANCOVA are not met, what could you do?

1. Use ANOVA.
2. Use MANOVA.
3. You could repeat your study and control for the covariate experimentally.
4. Use regression.

You could repeat your study and control for the covariate experimentally.

2 :: When conducting an ANCOVA in SPSS, which function would you select from the analyze drop down list?

1. General Linear Model.
2. Classify.
3. ANCOVA.
4. Time Series.

General Linear Model

3 :: Which of the below designs would be best suited to ANCOVA?

1. Participants were placed in four treatment groups for eating disorders. Their cognitive distortions regarding eating and food were measured before treatment, and again after 6 months of intensive treatment.
2. Participants were placed in four treatment groups for eating disorders. Their cognitive distortions regarding eating and food were measured before treatment, and this is used to allocate them to groups. You are exploring whether participants were allocated appropriately.
3. Participants were placed in four treatment groups for eating disorders. You are examining the relationship between cognitive distortions regarding eating and their therapists rating of improvement over a 6 month treatment period.
4. Participants were placed in four treatment groups for eating disorders. Their cognitive distortions regarding eating and food were compared after 6 months of intensive treatment.

Participants were placed in four treatment groups for eating disorders. Their cognitive distortions regarding eating and food were measured before treatment, and again after 6 months of intensive treatment.

4 :: What problems do you foresee with the study described in question 2?

1. It is likely that the regression lines will be parallel.
2. It is likely that there will be a linear association between depression and relationship satisfaction.
3. We don't know how reliably we can measure depression.
4. There could be more than three groups.

We don't know how reliably we can measure depression.

5 :: Which of the below assumptions must be met in order to conduct ANCOVA?

1. The covariate should be linearly related to the dependent variable.
2. The regression lines for the different groups must be parallel to each other.
3. The covariate should be measured without error (reliable).
4. All of the above.

All of the above

6 :: What is a grand mean?

1. It is the mean of all group means.
2. It is the population mean.
3. It is the total sample mean, controlling for error.
4. It is the total sample mean.

It is the mean of all group means.

7 :: Consider the study in question 2. Which of the below questions would be pertinent to this analysis?

1. Does relationship satisfaction have a significant effect on the relationship between attachment and depression?
2. What would the mean depression score be for the three groups of attachment styles if their levels of relationship satisfaction were constant?
3. What would the mean relationship satisfaction be if levels of depression were constant?
4. What would the means of the groups be on relationship satisfaction if their levels of depression were constant?

What would the means of the groups be on relationship satisfaction if their levels of depression were constant?

9 :: What are the two main reasons for using ANCOVA?

1. To increase error variance AND to adjust the means on the covariate so that the mean covariate score is the same for all participants.
2. To reduce error variance AND to explore patterns of correlations.
3. To reduce error variance AND to correct the means on the covariate.
4. To reduce error variance AND to adjust the means on the covariate so that the mean covariate score is the same for all groups.

To reduce error variance AND to adjust the means on the covariate so that the mean covariate score is the same for all groups