Understand what mindfulness is and why 21st century psychology is so interested in this centuries old Buddhist concept
Be able to describe the trait and state MAAS including its purpose, administration, scoring and some important psychometric properties.
Be able to do simple statistical analyses of questionnaire data using SPSS including – descriptive statistics, reliability analysis, dependent samples t-tests and factor analysis.
Q.1 Briefly describe, in your own words, the general purpose and the response format of the MAAS trait and state scales. Your answer should include a comprehensive definition of mindfulness (4 marks).
Q.2 Explain in your own words how both the trait and state MASS scales are scored (2 marks).
Q.3 Describe the demographic characteristics (age, gender) of your sample (1 marks).
Q.4 Using the MAAS_PA_566604 dataset calculate the following (3 marks):
Means and standard deviations for the total score of both State and Trait MAAS scales at each of the 3 time points of administration.
Present your data in an APA6 table.
Q. 5 (2 marks)
Plot the distribution of total state and trait MAAS scores at time 1 for the class in two histograms and briefly describe each of them. You can copy and paste the histograms from the SPSS output.
Are there differences in the distribution of scores between state and trait MAAS, and if so, how do you explain them?
Q.6 Compare the state MAAS mean scores between time1, time 2 and time 3 using three paired sample t-tests: report and interpret your results appropriately (4 marks).
Is there a significant mean difference between each pair of times?
Would you expect a significant state mean difference across times?
Provide a plausible explanation for your results by considering situational factors.
Q.7 What are the test-retest reliability coefficients for the total state and trait MAAS scores at a one week interval (between time 1 and time 2)? Interpret the reliability coefficients obtained for the state and for the trait MAAS scales. Is this acceptable reliability? (2 marks)
Q.8 Using MAAS_PA_566604_Univariate_dataset examine the factor structure of the MAAS state and trait item pool using principal component analysis (scree plot) and Varimax rotation (6 marks).
Is the MAAS state and trait item pool best characterised by a 1, 2, or 3 factor solution? Explain your choice?
Label these factors and present your solution with relevant factor loadings per item in an APA6 table.
Discuss advantages and limitations of your solution (i.e. factor loadings below .40, cross loadings).
If there are any limitations/problems: how could you best explain them.