Static Analysis of Groups Interview Questions And Answers

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.

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

General Linear Model

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.

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.

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

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.

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?

1. Secure attachment and relationship satisfaction.
2. Depression and attachment style.
3. Depression and relationship satisfaction.
4. Attachment style and relationship satisfaction.

Depression and relationship satisfaction

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

1. Depression.
2. Relationship satisfaction.
3. Secure attachment.
4. Attachment style.

Depression

1. Relationship satisfaction.
2. Depression.
3. Relationship satisfaction and depression are both fixed factors.
4. Attachment style.

Attachment style

1. 3.25
2. 2) 43.29
3. 3) 129.86
4. 4) 42.69

43.29

1. You would have to examine partial eta squared to see which of the groups the difference was between.
2. You would have to examine Pearson's correlations to see which of the groups the difference was between.
3. You would have to examine pair wise correlations to see which of the groups the difference was between.
4. You would have to examine pair wise comparisons to see which of the groups the difference was between.

You would have to examine pair wise comparisons to see which of the groups the difference was between.

1. 23.091
2. 40.509
3. 71.187
4. 7.133

40.509

1. 2, 30
2. 2, 26
3. 1, 29
4. 3, 26

2, 26

1. Errors on a driving simulator.
2. Driving experience.
3. Alcohol condition.
4. Both alcohol level and driving experience.

Driving experience

* True
* False

TRUE

* 0.882
* 0.118
* 0.96933
* <.001

0.882

1. 0.391
2. 0.882
3. 0.14265
4. 0.609

0.609

1. The effect of group differences and the effect of age cannot be compared as they are measured differently and represent different variables.
2. The effect of group differences is larger than the effect of age.
3. The effect of group differences and the effect of age are roughly the same.
4. The effect of group differences is smaller than the effect of age.

The effect of group differences is larger than the effect of age.

False.
Static analysis helps to find defects in documents by reviewing them so defects does not transmit to next phase.

False.
During static analysis, program is not executed yet so defects such as missing requirements,programming standard violation etc. can be found while during dynamic testing, program is actually executed so failures can be found.

Developer.
Static analysis tools are generally used by developer during development and unit testing.

True.
Static analysis tools are an extension of compiler technology so mostly compiler offers static analysis functionalities.

It gives quality information about code without executing it.