Final SPSS Dataset: Psych200_Lab_Extra_Credit_NameInitial.sav(your final SPSS data file - do not send your output!)
- Completed worksheet, including graphs (Word-compatible, formatted text document: .doc, .docx, .rtf)
Worksheet completed and clean output file!
Lab Activity 2: Entering Data, Manipulating Data, Descriptive Statistics Names: . You may need to reference the instructions in the SPSS guides 1-2-3 available on Canvas (see Section 2 resources). This assignment also includes skills you have previously developed and demonstrated for Lab1. When you have completed this worksheet, do not forget to upload your files to Canvas: Psych200_Lab2_NameInitial.sav (your final SPSS data file, after saving your changes) Psych200_Lab2_NameInitial.doc (this worksheet, with your responses) No other documents or file formats are expected or accepted – no need for an output or syntax, or any sort of screenshot! __________________________________________________________________________ The SPSS Lab 2 dataset contains various information collected on a group of seniors. In Variable view, explore the Label column to learn more about each variable. · Demographic and medical data: ID, gender, age, marital, education, conditions, meds · Lifestyle: ex_overall, Weekly Activities Count (WAC1-12) · Quality of Life (QOL): 25 responses to the WHOQOL, which measures four domains: · WHOQOL_D1 (D1.1 - 1.7): Physical quality of life (7 items) · WHOQOL_D2 (D2.1 - 2.6): Psychological quality of life (6 items) · WHOQOL_D3 (D3.1 - 2.6): Social quality of life (3 items) · WHOQOL_D4 (D4.1 - 4.8): Environmental quality of life (8 items) · WHOQOL_Gen1, _Gen2: Overall self-reported quality of life and health satisfaction · Health-related Quality of Life (HRQOL): 16 responses to the SF-36, including: · SF36_1 (1.1 - 1.4): Physical health-related role limitations · SF36_2 (2.1 - 2.3): Emotional problem-related role limitations · SF36_3 (3.1 - 3.9): Frequency of physical/emotional feelings · SF36_Gen: Self-reported general health In Variable view, explore the Values column. Notice that WHOQOL scale, higher numbers are associated with more quality of life (Variable view: Values). On the SF36, high numbers don’t always have a positive meaning. That’s confusing. I started crunching the data: · I added up the number of times people did different kinds of activities in their Weekly Activities Count: · Solitary leisure (7,8,12)Structured socialization (4,5,11) · Self-care (1,3,9)Unstructured socialization (2,6,10) · I also added up the number of times people experienced a limitation in their roles because of their physical health (SF36_3.1-3.4 became SF36_PhysLimit) · I calculated the mean response for each of the four domains to get an average quality of life index for each (e.g., WHOQOL_D1_avg, WHOQOL_D2_avg., etc.) · I realized that the SF36_3 section contained a mix of negative (5 items) and positive (3 items) feelings, so I “flipped” the response scale for some negative feelings so I could average them with the positive feelings. · E.g., SF36_3.2 became SF36_3.2R 1 became 5, 2 became 4, etc. so that now 1 means feeling nervous none of the time) … but then I got bored. You need to finish my work for me. Step 1: Dataset Cleanup Your two weapon (Guide 2) TRANSFORM Compute TRANSFORM Recode into different variables A) Look at the possible Values for marital. A review of the literature indicates that for this population, quality of life is not related to marital status so much as whether a person has a living companion (married, living as married) or not (single, separated, divorced, widowed). · Use a TRANSFORM function to create a new variable called alone that classifies people based on whether they live with someone (0 = with someone) or not (1 = alone). · Everyone is now a 0 or a 1 in a new “alone” column, right? Good. B) I want to know the proportions for each of the four types of weekly activities. · First, calculate a total number of all 12 weekly activities, WAC_Total · Check that you have a new column and that it looks like the right total. · Then, calculate the proportion of solitary leisure (WAC_Solo_Total) out of the grand total activities you just created, and call that WAC_Solo_Prop. · This new variable should contain decimal fractions between 0 and 1. · Calculate the proportions for the three other kinds of activity (WAC_Self, WAC_Unstruc, WAC_Struc) out of the grand total. · You have four WAC proportions now. C) I want to know the total number of times people experienced role limitations because of emotional problems. I already did this for physical health problems (SF36_PhysLimit) · Use TRANSFORM function to create a new variable called SF36_EmotLimit that contains a total of the three SF36 items (SF36_2.1-2.3). · You just added up three items that consist of 0 and 1: check your total to make sure it makes sense. D) I didn’t finish “flipping” the 1 to 5 response scale for negative feelings in the SF36. Two negative feelings remain before I can average together positive and negative feelings. · Use a TRANSFORM function to create new variables similar to mine (e.g., SF36_3.2R) for each of the two negative feelings that were left. · Make sure you have flipped all the original values (1 to 5; Leave 0 as 0). There should be no blank spaces in your two new variables. · You should now have five modified variables about negative feelings on the SF-36 ending with “R” (three I created, two you just created in Question 3). · Notice there are 4 physical feelings, and 5 emotional feelings. Use a TRANSFORM function to calculate average physical feelings, SF36_PhysFeel_avg, and average emotional feelings, SF36_EmotFeel_avg. · Make sure you are using the new “flipped” negative feelings to calculate your averages! Step 2: Reporting on the data Answer the following questions about your specific use of the TRANSFORM commands. How did you create the new variable called alone? Name the SPSS command and explain the process. How did you create the new variables called WAC_Total and WAC _Solo_Prop? Name the SPSS command(s) and explain the process. How did you create the new variable called SF36_EmotLimit? Name the SPSS command and explain the process. How did you create the new variable called SF36_EmotFeel_avg? Name the SPSS command and explain the process (just the last step where you averaged various feelings). Your weapons to isolate subsets of participants (Guide 2) DATA Split file DATA Select cases Save standardized values (Remember to retrace your steps to undo these operations when you want to return to the whole dataset). Your weapons to report various statistics and graphs (Guide 3) ANALYZE: Descriptives Frequencies (almost everything you need) ANALYZE: Descriptives Descriptives Save standardized values GRAPHS: Legacy dialogs etc. Let’s look at gender. Did more men or women live alone? What SPSS commands did you use to find out? Across the entire sample: what is the mode for SF36_EmotLimit? What is the median of participants’ WAC_Unstruc_Prop? What score marks the 66th percentile for hours of exercise (Ex_Overall)? Let’s focus on the people who reported one or more role limitation for emotional reasons (SF36_Emot_Limit) Produce a graph that shows how many use medication or not (meds) and paste it here. Produce another graph that shows their age distribution and paste it here. For the whole sample, standardize the average level of physical and emotional feelings (SF36_PhysFeel_avg, SF36_EmotFeel_avg) -- this will create two new variables starting with the letter “z”. A positive z-score is bad news. What are the IDs of the participants with z-scores greater than 1.5 for their PhysFeel_avg? Do these participants have similar levels of negative emotions? Report their z-scores for EmotFeel_avg. What is the mean z-score for EmotFeel_avg For the whole sample, create a single bar graph that represents the mean overall quality of life (WHOQOL_Gen1) as a function of participants’ level of education (educ), and paste it here. 3