Two dimensions

The two-dimensional structure makes it possible to reduce the complexity even further, though at a considerable cost in lost, meaningful information. Each conceptually opposed pair of higher-order values can be combined to form a dimension score. A good way to do this is to subtract the conservation score from the openness score to obtain a score for an openness vs. conservation dimension. Similarly, subtract the self-transcendence score from the self-enhancement score to get a score for a self-transcendence vs. enhancement dimension. Plotting individuals’ value scores on these two bipolar dimensions yields a simple picture of the differences between their value priorities.

  1. Create one variable for each of the two bipolar dimensions. SPSS
    *Create two dimensions based on the bipolar HO values.
    *Logical maximum 5 Logical minimum -5.
    COMPUTE opendim = otc-con.
    COMPUTE selfdim = self_tr-self_en.
    opendim 'Max Conservation = -5, Max Openness to change = 5'
    selfdim 'Max Self-enhancement = -5, Max Self-trancendence = 5'.
  2. Create a scattergram with the two dimensions. How would you describe the pattern? SPSS

    If you choose to use the menu: Graphs - scatter - define simple - opendim and selfdim on the x and y axis.

    /SCATTERPLOT(BIVAR)=opendim WITH selfdim
  3. Find the mean for the two dimensions in each country. What differences between the countries stand out? SPSS

    *If you use the menu: Analyze – compare means – means – set country as independent and the dimensions as dependent.

    TABLES=opendim selfdim BY cntry

Figure 5.1 plots the location of each country according to the mean score of its citizens on the two dimensions. It uses the data obtained in the last step above. Please note that the figure is based on data from ESS 1

The vertical axis in Figure 5.1 runs from .60 to 1.8, all above the neutral zero point on the self-transcendence vs. self-enhancement dimension. This indicates that self-transcendence is more important than self-enhancement in all the countries. The horizontal axis runs from -1 to .4. This indicates that conservation is more important than openness to change in most countries, but there are some countries that reverse this pattern.

The profile of the citizens of Scandinavian countries on the two value dimensions is relatively high on both self-transcendence and openness to change. Does this fit your stereotype of these countries? Why do you think they have such a value profile? How do the citizens of countries with predominantly Catholic or Orthodox populations differ from those with predominantly Protestant populations? Why do you think that is? Is the level of wealth of the countries related in any way to the value profiles of their citizens?

Israel looks like an outlier on the self-transcendence vs. enhancement dimension. Note that the Israeli sample consists of two very different cultural groups, Jews and Muslims. Do you think they have the same value profiles? How about the Belgian sample: Do you think the Flemish and Walloon citizens have the same value profiles? If you wanted to answer these questions, you could compute value scores for these sub-cultural groups by splitting the files for these countries.

  1. Create a new data file with country as the new unit. Please include the mean of the higher-order values and the two dimensions. SPSS

    If you use the menu: Data – aggregate – specify country as break variable and the variables to aggregate.

    *Please remember to specify where the file should be saved.
    /BREAK=Country cntry
    /Cpow = MEAN(Cpow) /Cach = MEAN(Cach) /CHED = MEAN(CHED) /Csti = MEAN(Csti) /Cself = MEAN(Cself) /Cuni = MEAN(Cuni) /Cben = MEAN(Cben) /Ctra = MEAN(Ctra) /Ccon = MEAN(Ccon) /Csec = MEAN(Csec) /otc = MEAN(otc) /con = MEAN(con) /self_tr = MEAN(self_tr) /self_en = MEAN(self_en) /opendim = MEAN(opendim) /selfdim = MEAN(selfdim).


  2. Use the aggregated file you just made and create a scatter similar to the one in figure 5.1. SPSS
    /SCATTERPLOT(BIVAR)=opendim WITH selfdim BY cntry (NAME)
  3. Please go to the following dataset: Country level data. Investigate whether you can find any variables that correlate with either of the two dimensions (opendim, selfdim). Develop a model that aims at explaining some of the variation on the two dimensions.