SPSS Result Analysis Sample

Question 1 [15 marks]

Dr. Ian Stagram was interested to see how well happiness (Y) could be predicted by amount of time spent on social media (X). Happiness was measured using the HAP-I questionnaire, with high scores representing higher levels of happiness. Amount of time spent using a smartphone was measured in total minutes in an average day.

Bivariate analysis in SPSS produced the following output:

1a. Describe the relationship between happiness and amount of time spent on social media? [2 marks]

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Simple regression analysis in SPSS produced the following output

1b. Interpret the regression coefficient [4 marks]

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1c. Write out the equation for the regression. Use the equation to predict a happiness score for person who uses social media for 120 minutes a day? Show your calculations [5 marks]            

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1d. What would the happiness score theoretically be if someone never used social media?          Show your calculations using the same regression equation as before [2 marks]

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1e. Dr. Stagram concluded that time spent on social media caused decreased happiness. Explain why this conclusion may, or may not, be supported by the data [2 marks]

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Q.2 FOLLOWS ON NEXT PAGE

Question 2 [37 marks]

Professor Anne Neesia wanted to see how well performance on a memory task could be predicted by three independent variables (X1 to X3) listed below. The number of words correctly recalled from a word list was used as the memory task performance measure. The sample size was 80.

DV                      = WORDS number of words recalled

X1                       = AGE in years

X2                       = LIFESTYLE (0=unhealthy lifestyle; 1=healthy lifestyle)

X3                       = ANXIETY trait anxiety measured on a scale from 0 (no anxiety) to 10 (extreme anxiety)

Professor Neesia performed a standard multiple regression, with the SPSS output shown below.

2a. Data screening revealed an outlier, with one participant reporting an anxiety score of 11. What action should Professor Neesia take and why? [2 marks]

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2b. What is multicollinearity? [2 marks]

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2c. Does the SPSS output indicate any multicollinearity problems? [2 marks]

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2d. Which predictor variable has been dummy-coded? [1 mark]

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2e. Explain how the values produced for regression, residual and total df in the ANOVA table were calculated [3 marks]

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2f. Explain the R in terms of the relationship between the predictor and outcome variables [5 marks]

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2g. Explain the R² in terms of the relationship between the predictor and outcome variables [3 marks]

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2h. What does the adjusted R² represent [2 marks]

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2i. The ANOVA table shows the model to be significant. Does this mean that each IV is a significant predictor of words recalled? Justify your answer [2 marks]

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2j. With reference to the SPSS output, write down the regression equation you would use if you wanted to predict number of words recalled (Ŷ) from scores on each of the three IVs. [4 marks]

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2k. Interpret all three regression coefficients [7 marks]

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2l. Which of the independent variables is the best predictor of the number of words recalled and why? [2 marks]

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2m. Are there any of the three IVs that Professor Neesia might consider dropping from future studies – explain your decision? [2 mark]

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Q.3 FOLLOWS ON NEXT PAGE

Question 3 [12 marks]

Dr Spooner was interested in speech production and wanted to design a new questionnaire to help identify potential factors that affect speech performance. Participants (n=457) were asked to complete a six-item questionnaire designed to measure speech performance. Item responses were subjected to a factor analysis.

1: I carefully consider what I want to say before I say it

2: I often start talking without knowing what I want to say 

3: I rarely think before I open my mouth

4: I enjoy talking with friends

5: I find it difficult to concentrate when I am talking

6: I find it difficult to pay attention to what I am saying

Factor analysis was performed on the data with factor loadings after varimax rotation given below:

3a. What is the advantage of performing a (varimax) rotation of the factors? [3 marks]

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3b. List the tests that load on to the first and second factors [2 marks]

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Factor 1           =

Factor 2           =

3c. Give names that best describe the two factors [2 marks]

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Factor 1           =

Factor 2           =

3d. Was an adequate sample size used? Justify your answer [3 marks]

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3e. Which item could be removed from the questionnaire? Explain the statistical reason for your answer [2 marks]

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Q.4 FOLLOWS ON NEXT PAGE

Question 4 [9 marks]

Professor de Sade was interested in the effect of resilience (Group: high resilience; low resilience) on pain. In a laboratory-based experiment, participants were asked to immerse their hand in a receptacle filled with very cold water. Pain was assessed by measuring the time participants could tolerate hold their hand in the cold water (pain tolerance), as well as a self-report measure of the intensity of pain experienced (pain intensity).

4a. Why is MANOVA a suitable test? [2 marks]

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4b. Pain tolerance and pain intensity were significantly correlated (r = .467, p = .004). Is this a problem for MANOVA or not, and why?    [2 marks]

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A one-way MANOVA was conducted on the data:

4c. What is your conclusion based on the SPSS output above? [2 marks]

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4d. Looking at the results for each of the dependent variables in the table below, what conclusions would you draw? [3 marks]

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