This worksheet is designed to cover all material covered in AQM 2022 – 2023. Each part of this worksheets covers the material of each worksheet (i.e., Part A reflects material covered in Worksheet 1 and so on). While there are 200 marks available in the worksheet, the final grade will be out of 100. The weighting of each Part reflects the weighting of the respective worksheet (10% for Worksheet 1; 25% for Worksheet 2; 30% for Worksheet 3, and 35% for worksheet 4). This is already reflected in the marks for each question (i.e., 20 / 200 for Worksheet 1 is 10% of the overall mark).
PART A (20 marks)
Section 1 (10 marks)
The file “GeneralWorksheet_PartA_Mood.sav” contains scores of 71 subjects. The subjects completed a task and before the task mood ratings of positive as well as negative affect were taken.
1. Use SPSS (or similar) to calculate the descriptive statistics (including mean, standard deviation, skewness, and kurtosis) for mood ratings (positive mood and negative mood) separated by Gender and present them in table format (using APA). Comment on the pattern you see. How does mood ratings differ according to Gender? [4 marks]
2.
a) Perform an analysis to see if there are any differences between positive and negative mood (separately) in men and in women. Perform an appropriate parametric statistical test to test this hypothesis and report the results in APA format. Report the type of test conducted. [4 marks]
b) If you were to instead test for gender differences for each of the positive and negative mood variables, what type of test would you have to conduct (you are not expected to run this test)? What does it mean if Levene’s test is significant and what would you need to do in this situation? [2 marks]
Section 2 (10 marks)
You are interested in examining the relationship between time spent on Instagram and depression in teenagers and adults. The file GeneralWorksheet_PartA_Instagram.sav includes data for daily minutes on Instagram and level of depression reported by adults and teenagers.
1. Draw a scatterplot to reflect the relationship between time spent on Instagram and depression in teenagers and adults. What pattern of relationship do you see? Does this relationship differ according to group? Comment. [3 marks]
2. Perform a regression analysis to assess the predictive utility of time spent on Instagram in explaining depression levels for each group separately. How good a predictor is ‘time spent on Instagram’ of depression for each group? Compare the results. [3 marks]
3. How would you determine if there is good external validity on your regression? Describe the different patterns that need to be consulted. [2 marks]
4. If someone in this sample had spent 40 minutes a day on Instagram, what would your model predict for his depression levels for teenagers? [2 marks]
PART B (50 marks)
Section 1 (25 marks)
The data file GeneralWorksheet_PartB_Gambling.sav contains data from a study looking at factors predicting gambling. Here, the roles of Neuroticism, Depression, Impulsiveness and Attentional Control were examined. Gambling was measured with the number of gambling incidents during a given week. Participants were selected from various gambling centre email lists and completed the above questionnaires.
1. How do the variables correlate with Gambling and also amongst each other? Comment on the coefficients. Comment on whether you are concerned about multicolinearity. [5 marks]
2. Run a standard regression using the ENTER method and comment on the model and coefficients. Comment on the explanatory power of the variables. [10 marks]
3. Repeat Question 2 using a hierarchical regression, with Neuroticism in Step 1, Impulsivity in Step 2, and Depression and Attentional Control in Step 3. Explain your findings and compare them to the results of Question 2. [10 marks]
Section 2 (25 marks)
You are interested in examining the factors that can predict a successful facial lift outcome. In the data file GeneralWorksheet_PartB_FLoutcome.sav a few of the variables that you have measured to predict the likelihood of facial lift success are presented: these are ‘Believe’ which measures the extent to which patient believes and has faith in the outcome; ‘Age of Surgeon’ which classifies the surgeon as young or old, and the final variable measures the extent of atrophy to face before surgery.
Perform a standard logistic regression analysis to assess the predictive utility of the variables under investigation in predicting the likelihood of success of facial lift. Please do not include the interaction of the two independent variables in the model.
1. Comment on the classification table(s) of interest. What percentage of the cases is correctly and incorrectly classified by the model’s attempt at each stage of the analysis? [8 marks]
2. Comment on the significance of the model(s) tested, the amount of variation explained by the model(s), and the -2LLs of interest. [10 marks]
3. Which variable(s) predict the likelihood of facial lift significantly? Provide an interpretation of the significance of Exp (B) of the significant variable(s). [7 marks]
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