Exercises

  1. Make a summated scale that measures degree of religiosity by adding together these variables
    • How religious are you?
    • How often do you attend religious services apart from special occasions?
    • How often do you pray apart from at religious services?

    The values of the first of these variables increases with the respondents’ subjectively perceived religiosity, whereas the values for the latter two decrease with the frequency of their religious activities. Therefore, either the first one or the last two must be recoded to make the value of all three either increase or decrease with intensity of religiosity. In this case, we get the most sensible ordering of scale values if we change the value ordering of the last two. Recoding can be done in many different ways. In this case, the easiest way is to use the ‘Automatic Recode’ feature in the ‘Transform’ menu. Open the dialogue box. Find the variables on the list. Give names to the two recoded variables, tick the ‘Highest value’ option, and click ‘OK’. (See figure below.)

    Compute the scale by taking the average of the two recoded variables and the ‘How religious are you’ variable. Select the British sample and use the scale as the dependent variable in a regression analysis, with gender, two-digit year of birth and years of full-time education as independent variables. You can also compute a squared years of education variable and add this as another term on the independent variables list to see whether education length has a non-linear effect on religiosity.

     

    Figure 18. Automatic Recode in SPSS

     

  2. Verify that the ‘explained’ proportion of the dependent variable’s variance (i.e. the R2) becomes (at least somewhat) smaller if you replace the dependent summated scale with any of the individual indicators that have been used to construct it. (You can also try the same with the variables in the example in the main text. In addition, you will also find that the coefficients of summated scales used as independent variables tend to be higher than those of such scales’ component indicator variables.) These results support the idea that the summated scale is less distorted by random measurement errors than any of its components.
  3. Use the syntax below to compute Schwartz value scales. Read about these scales and their applications on the ESS Edunet pages on Human values. Develop your own regression models in which you use these scales as dependent or independent variables.
Schwartz value scales

*This syntax makes SPSS compute 10 Schwartz value scales. *In addition, you could consider multiplying the resulting scales by -1 to make their values increase rather than decrease with the respondents’ adherence to the values they measure.

USE ALL.
COMPUTE mrat = MEAN(ipcrtiv, ipcrtiv, ipeqopt, ipshabt, impsafe, impdiff, ipfrule, ipudrst, ipmodst, ipgdtim, impfree, iphlppl, ipsuces, ipstrgv, ipadvnt, ipbhprp, iprspot, iplylfr, impenv, imptrad, impfun).
EXECUTE.
COMPUTE SEcenter = MEAN(impsafe, ipstrgv) - mrat.
EXECUTE.
VARIABLE LABELS SEcenter 'Security'.
EXECUTE.
COMPUTE COcenter = MEAN(ipfrule, ipbhprp) - mrat.
EXECUTE.
VARIABLE LABELS COcenter 'Conformity'.
EXECUTE.
COMPUTE TRcenter = MEAN(ipmodst, imptrad) - mrat.
EXECUTE.
VARIABLE LABELS TRcenter 'Tradition'.
EXECUTE.
COMPUTE BEcenter = MEAN(iphlppl, iplylfr) - mrat.
EXECUTE.
VARIABLE LABELS BEcenter 'Benevolence'.
EXECUTE.
COMPUTE UNcenter = MEAN(ipeqopt, ipudrst , impenv) - mrat.
EXECUTE.
VARIABLE LABELS UNcenter 'Universialism'.
EXECUTE.
COMPUTE SDcenter = MEAN(ipcrtiv, impfree) - mrat.
EXECUTE.
VARIABLE LABELS SDcenter 'Self-Direction'.
EXECUTE.
COMPUTE STcenter = MEAN(impdiff, ipadvnt) - mrat.
EXECUTE.
VARIABLE LABELS STcenter 'Stimulation'.
EXECUTE.
COMPUTE HEcenter = MEAN(ipgdtim, impfun) - mrat.
EXECUTE.
VARIABLE LABELS HEcenter 'Hedonism'.
EXECUTE.
COMPUTE ACcenter = MEAN(ipshabt, ipsuces) - mrat.
EXECUTE.
VARIABLE LABELS ACcenter 'Achievement'.
EXECUTE.
COMPUTE POcenter = MEAN(imprich, iprspot) - mrat.
EXECUTE.
VARIABLE LABELS POcenter 'Power'.
EXECUTE.