50 REAL TIME STATIC ANALYSIS of GROUPS Interview Questions and Answers

STATIC ANALYSIS of GROUPS Interview Questions and Answers :-

 

1. Typical defects discovered during static analysis are?
► Referencing a variable with undefined value
► Variables declared but used nowhere
► Dead code
► Programming standards and syntax violation
► Security vulnerabilities

2. Who leads a walk through?
Author presents the document to audience where a goal can be knowledge sharing or communication purpose.

3. What is not important goal of a walk through?
Finding defects.

4. Who leads a formal review process?
Moderator

5. What is a formal review technique?
Inspection.
Walk through and peer to peer are informal review techniques.

6. During review meeting, defects are logged by?
During review meeting, author or scribe logs a defect.

7. Different roles in review are assigned during:
Planning.
Different roles in review are assigned during planning phase so same defects are not found by reviewer.

8. Entry criteria is determined during which phase?
Planning.
Entry criteria is determined during planning phase where document under review is checked to see whether it fulfills certain standards to ensure that whole review process will not be waste of time if document has too many small mistakes.

9. What are the benefits of static testing?
► Early feedback of a quality
► Less rework cost
► Increased developmental productivity

10. Which can be found using static testing techniques?
Defect.
Static testing is method of reviewing a product without executing it so it will find defects. If, we execute the product with defect we encounter a failure.

11. What is static analysis tools?
It gives quality information about code without executing it.

12. Most of the time compilers can be used as static analysis tools.
True.
Static analysis tools are an extension of compiler technology so mostly compiler offers static analysis functionalities.

13. Who generally uses static analysis tools?
Developer.
Static analysis tools are generally used by developer during development and unit testing.

14. The defects found in static testing and dynamic testing are same.
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.

15. Static analysis is not useful & cost effective way of testing.
False.
Static analysis helps to find defects in documents by reviewing them so defects does not transmit to next phase.

16. Look at the output on p. 470. Which of the following statements is true of the effect of age and group differences?
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.

17. Look at the output on p. 470. What is the overall effect of the grouping?
1. 0.391
2. 0.882
3. 0.14265
4. 0.609

0.609

18. Look at the output on p. 470. What is the overall effect of age?
* 0.882
* 0.118
* 0.96933
* <.001

0.882

19. The adjusted group mean for the DV has been adjusted to standardize it with the other groups based on the grand mean for the covariate.
* True
* False

TRUE

20. Using the example in the text book (which begins on p. 471), which variable is the covariate in this study?
1. Errors on a driving simulator.
2. Driving experience.
3. Alcohol condition.
4. Both alcohol level and driving experience.

Driving experience

21. What would the degrees of freedom be if you were reporting the results of the effect of Age from the output on p. 470?
1. 2, 30
2. 2, 26
3. 1, 29
4. 3, 26

2, 26

22. Consider the output displayed on p. 470. What is the F-Value associated with the effect of age?
1. 23.091
2. 40.509
3. 71.187
4. 7.133

40.509

23. In the example in question 12 there are 3 groups to consider. If you found that the groups differed significantly on reading ability, what might you use to further explore these group differences?
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.

24. You are conducting a study looking at the group differences in reading ability between three groups of children, all receiving different remedial assistance. You decide that ages will co vary with reading ability, so you do ANCOVA.

There are 40 children in each of the three groups. The mean reading score for the whole sample is 42.69. The mean for group 1 is 46.92, for group 2 the mean is 41.89, and for group 3 the mean is 41.05.

What is the grand mean?
1. 3.25
2. 2) 43.29
3. 3) 129.86
4. 4) 42.69

43.29

25. Refer to the example from question 2 again. If you were conducting your analysis in SPSS, what would the fixed factor be?
1. Relationship satisfaction.
2. Depression.
3. Relationship satisfaction and depression are both fixed factors.
4. Attachment style.

Attachment style

26. Consider the hypothetical study presented in question 2. If you were conducting this analysis, what variable would you put into the covariate box?
1. Depression.
2. Relationship satisfaction.
3. Secure attachment.
4. Attachment style.

Depression

27. 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

28. You are conducting a study. The IV is attachment style. There are three groups of individuals with different attachment styles; these are secure, dismissing, and fearful. You want to explore whether these differ on their scores of relationship satisfaction. The DV is relationship satisfaction. You are aware, however, that relationship satisfaction is known to co vary with depression.

You conduct an ANCOVA with this data. The formula will remove the variance due to the association between which two variables?
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

29. 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?

30. 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.

31. 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

32. 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.

33. 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.

34. 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

35. 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.