5. The following table shows the relevant questions and their variable names in SPSS

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1 Download and Open the Syntax File; Create the Data File 1. Download the SPSS syntax file from the link on the class website 2. Open the file in the SPSS Syntax Editor 3. Click Run All 4. The SPSS Data Editor should open in a new window The Variables 5. The following table shows the relevant questions and their variable names in SPSS Question On average, how frequently do you take a shower or bath? When you take a shower, do you prefer the water to be very hot? On average, what water temperature do you prefer while taking a shower or bath? Typically, about how much time do you spend in the bath or shower? What do you think is the perfect temperature to keep your house (apartment, room, etc.)? If you could paint your bedroom any of these colors, which would it be? On a cold, wintery day, do you typically wear If all else was equal, would you prefer to live in a Please go to the restroom and adjust the temperature of the water in the sink until it is the temperature that you would like to take a shower in. How often do you feel that you are "in tune" with the people around you? * How often do you feel that you lack companionship? How often do you feel that there is no one you can turn to? How often do you feel alone? How often do you feel part of a group of friends? * How often do you feel that you have a lot in common with the people around you? * How often do you feel that you are no longer close to anyone? How often do you feel that your interests and ideas are not shared by those around you? How often do you feel outgoing and friendly? * How often do you feel close to people? * How often do you feel left out? How often do you feel that your relationships with others are not meaningful? How often do you feel that no one really knows you well? How often do you feel isolated from others? How often do you feel you can find companionship when you want it? * How often do you feel that there are people who really understand you? * How often do you feel shy? Variable Name ShwFreq ShwVHot ShwTemp ShwTime RoomTemp RoomColor Clothing LocaleTemp SnkTemp Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17

2 How often do you feel that people are around you but not with you? How often do you feel that there are people you can talk to? * How often do you feel that there are people you can turn to? * * Reverse scaled questions Q18 Q19 Q20 Create the Loneliness Variable 6. The UCLA Loneliness Scales (questions Q1-Q20) is scored by summing the values of the 20 question after reversing the response on the indicated questions 7. To compute the loneliness score in SPSS, click on Transform Compute Variable 8. In the Target Variable box, type the name of the new variable, loneliness 9. In the Numeric Expression box, type (or copy and paste) the following: (5 - Q1) + Q2 + Q3 + Q4 + (5 - Q5) + (5 - Q6) + Q7 +Q8 + (5 - Q9) + (5 - Q10) + Q11 + Q12 + Q13 + Q14 + (5 - Q15) + (5 - Q16) + Q17 + Q18 + (5 - Q19) + (5 - Q20) This will create a new variable that is the sum of the responses to the 20 questions while reversing the scale on the appropriate questions (Q1, Q5, Q6, Q9, Q10, Q15, Q16, Q19 and Q20). There are shorter expressions that will work, but this one clearly shows the reverse scaling and the summation.

3 10. Click OK. The new variable, loneliness, should appear at the right-hand edge of the previously existing variables in the SPSS Data Editor. Higher values of loneliness indicate that the person is more lonely. Create the Physical Warmth Extraction Variable 11. Bargh and Shalev (2012) created a summary variable that measured how much warmth each participant, on average, extracts when taking a shower or bath. They do this by converting their three main measures, shower frequency (ShwFreq), shower temperature (ShwTemp) and shower time (ShwTime), into z-scores (so they can be compared and have the same mean and standard deviation) and then averaging the three z-scores. 12. First, we will create the z-scores. In SPSS, click Analyze Descriptive Statistics Descriptives. 13. Move the three variables of interest (ShwFreq, ShwTemp and ShwTime) into the Variable(s) box on the right

4 14. Check the box next to Save standardized values as variables 15. Click OK 16. In the Data Editor, on the right edge of the existing data there should be three new variables called ZShwFreq, ZShwTemp and ZShwTime. These are the z-scores of the three variables. 17. Second, we will average the three newly created z-scores. Click Transform Compute Variable 18. Click the Reset button to clear the dialog box 19. In the Target Variable box type the name of the new variable, pwarmth 20. In the Numeric Expression box type (or copy and paste) the following: (ZShwFreq + ZShwTemp + ZShwTime) / 3

5 21. Click OK. The physical warmth extraction variable should appear at the right edge of the data set. Scatterplot / Scattergram 22. A scatterplot (some people call them scattergrams) is often used with correlational data to visually show the relation between the variables 23. Click Graphs Chart Builder 24. If the following warning appears, click OK

6 25. In the lower half of the Chart Builder dialog box, click on Scatter / Dot

7 26. Of the eight different types of scatterplots, we want the simplest the one in the upper left of the matrix. If you hover your mouse pointer over it, it will say Simple Scatter. Drag the simple scatter chart into the white box in the upper part of the dialog box.

8 27. Drag the variable you are predicting (in this example loneliness) to the Y-Axis? box 28. Drag the variable from which you are predicting (in this example pwarmth) to the X-Axis? box

9 29. Click OK

10 30. The scatterplot appears in the output viewer Descriptive Statistics and Correlations 31. Based on previous research, loneliness should be positively correlated with physical warmth extraction, shower frequency, shower duration, and shower temperature. H 0 : ρ Loneliness, Physical Warmth 0 H 0 : ρ Loneliness, Freq. 0 H 0 : ρ Loneliness, Time. 0 H 0 : ρ Loneliness, Temp 0 H 1 : ρ Loneliness, Physical Warmth > 0 H 1 : ρ Loneliness, Freq. > 0 H 1 : ρ Loneliness, Time > 0 H 1 : ρ Loneliness, Temp > 0 You can create additional hypotheses based on the other relevant questions from the questionnaire 32. We typically set α, the probability of making a Type I error (incorrectly conclude that the variables are positively related to each), to Click Analyze Correlate Bivariate 34. Move the two variables that you want to correlate into the Variables box. For our first hypothesis, move loneliness and pwarmth into the Variables box

11 (You may want to put all of the variables into the Variables box so you can create the correlation matrix talked about at the end of this document.) 35. Because both variables are approximately interval scale, most psychologists would use Pearson s product moment correlation coefficient as the correlation coefficient. 36. The hypotheses are one-tailed, so set the Test of Significance to One-tailed. One-tailed tests are also called directional tests. The alternative hypotheses specify the direction of the predicted relation. 37. Click the Options button. 38. In the Statistics section of the dialog box, check Means and standard deviations. 39. Click Continue 40. Click OK 41. The Descriptive Statistics section of the output gives the mean, standard deviation and sample size (N) for each variable. For the output below, the mean loneliness score is and it has a standard deviation of

12 42. The Correlations section of the output gives the value of the correlation coefficient (Pearson Correlation), the p value associated with the correlation (Sig. (1-tailed)) and sample size (N). For the output below, r =.101, p =.115 and the sample size is Because the p value is greater than the α level, we fail to reject H 0. There is insufficient evidence in this data to suggest that loneliness and physical warmth extraction are positively related to each other. 44. Repeat for any other hypotheses that you have Regression 45. If two variables are reliably related to each other, we can determine the equation that allows you to predict the value of one variable given the value of the other variable. Regression does that. Because Pearson s r assumes that the relation between the two variables is linear, regression will give us an equation of a line. To know the equation of the line, we must know the slope and the intercept of the line. Because the correlation in the previous example is not statistically significant, we should not do regression. The following regression is only done so you can see how to do it in case you need to do regression for any of the other hypotheses. 46. Click on Analyze Regression Linear 47. Move the variable that you want to predict (loneliness in this example) into the Dependent box 48. Move the variable from which you are predicting (pwarmth in this example) into the Independent(s) box (move only one variable into the Independent(s) box otherwise you will perform multiple regression which is beyond the scope of this class.)

13 49. Click OK 50. The Coefficients section of the output gives the slope (on the line labeled with the independent variable you specified above) and intercept (on the line labeled with (Constant). Both values are in the B column. In the example output given below, the slope is and the intercept is : 51. Thus, the regression equation for predicting loneliness from the physical warmth extraction variable is given by 52. We can add the regression line to the scatterplot. Find the scatterplot in the output viewer and double click on it. The Chart Editor should open with the scatterplot in it. 53. Click on the Add Fit Line at Total Button ( -- when you hover the mouse pointer over it, it will say Add Fit Line at Total). The regression line should appear on the scatterplot.

14 54. Close the Chart Editor. The scatterplot in the output should update to include the regression line: Reporting Several Correlation Coefficients 55. When you need to report several correlations in a Results section, one frequently does so by using a correlation matrix. A correlation matrix is a table that lists all the variables across the columns, and all but the first variable in the first row. Then, the correlation of the variable in a given column and the variable in a given row is placed in the cell at the intersection of the column and row. You might want to add two additional rows at the bottom of the table to give descriptive statistics (e.g. the mean and standard deviation of each variable.) See the table on the next page.

15 RELATION BETWEEN PHYSICAL WARMTH AND LONELINESS 15 Table 1 Correlation Matrix for Physical Warmth and Loneliness Loneliness Physical Warmth Extraction Shower Frequency Shower Duration Shower Temperature Loneliness **.013 Physical Warmth Extraction.477 ***.605 ***.718 *** Shower Frequency *.031 Shower Duration.261 ** M s Note. N = 143. * p <.05, ** p <.01, *** p <.001. All tests are one-tailed.