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Chapter 5: Higher-order values

The relationships among the 21 value items and among the ten motivationally distinct types of values form a circular structure, see Figure 1.1. This structure can be summarized in terms of two bipolar, conceptual dimensions that underlie it. Each pole of these dimensions can be treated as a higher-order value type that combines two or three values. If the fine distinctions among the ten values are not needed for an analysis, it is possible to compute an importance score for each of the four higher-order types of values. This simplifies analyses, but if you relate other variables only to the higher-order value types you may lose substantial, meaningful information.

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Defining the Higher-order Values

Each of the four higher-order values expresses a broad motivational goal shared by the basic values that compose it. Of course, these motivational goals are more general than those defining the ten basic values. Here is the set of higher-order values, each with its motivational goal:

These higher-order values form two basic dimensions:

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Computing Scores for the Higher-order (HO) Values

Table 5.1 lists the items and variable names included in each higher-order value. The numbers are those of the items as ordered from 1-21. Because the four higher-order value types are composed of 4-6 items, it is reasonable to expect their alphas to be somewhat higher than the alphas of the ten basic values. However, the wider range of concepts included in each higher-order value may limit their internal consistency to some extent.

Table 5.1. The Higher-order Value, item number and variable name*
Higher-order Value Items in Index Variable Name
Openness to Change 1,11,6,15 (10,21) ipcrtiv, impfree, ipmdiff, ipadvnt,(ipgdtim, impfun)
Conservation 5,14,7,16,9,20 impsafe, ipstrgv, ipfrule, ipbhprp, ipmodst, imptrad
Self-enhancement 2,17,4,13 imprich, iprspot, ipshabt, ipsuces
Self-trancendence 3,8,19,12,18 ipeqopt, ipudrst, impenv, iphlppl, iplyfr

*The two hedonism items are placed in parentheses because hedonism shares elements of both Openness to change and Self-enhancement, but is closer to openness in most cases.

Use the data you downloaded in chapter four. If you have not done the exercises in chapter four, you must go back and solve the exercises 1, 2, 3 and 4.

  1.  Find Cronbach’s alpha for each of the higher-order values. SPSS

    Please note that the recoded items are used in the following syntax:

    *RELIABLITY ANALYSIS – HIGHER-ORDER VALUES.
    *Openness to change (one analysis for each HO value, replace the variables).
    RELIABILITY
    /VARIABLES=nipcrtiv nimpfree nimpdiff nipadvnt nipgdtim nimpfun
    /FORMAT=NOLABELS
    /SCALE(ALPHA)=ALL/MODEL=ALPHA.
  2. Compute the indexes for the higher-order values. Use the recoded value items. SPSS
    *Compute higher-order values, hedonism is put together with openness.
    *Logical maximum = 6, max is obtained when a respondent has answered 6 on all the items indexing the HO value.
    *Logical minimum = 1, min is obtained when a respondent has answered 1 on all items indexing the HO value.
    *Create higher-order values.
    Compute OTC = Mean(nimpdiff, nipadvnt, nipcrtiv, nimpfree, nimpfun, nipgdtim).
    Compute CON = Mean(nipmodst, nimptrad, nipbhprp, nipfrule, nimpsafe, nipstrgv).
    Compute Self_tr = Mean(niphlppl, niplylfr, nipeqopt, nipudrst, nimpenv).
    Compute Self_en = Mean(nimprich, niprspot, nipshabt, nipsuces).
    execute.
    VARIABLE LABELS
    OTC 'Openness to change'
    CON 'Conservation'
    Self_tr 'Self-transcendence'
    Self_en 'Self-enhancement'.
    EXECUTE.
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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.
    VARIABLE LABELS
    opendim 'Max Conservation = -5, Max Openness to change = 5'
    selfdim 'Max Self-enhancement = -5, Max Self-trancendence = 5'.
    execute.
  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.

    GRAPH
    /SCATTERPLOT(BIVAR)=opendim WITH selfdim
    /MISSING=LISTWISE.
  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.

    MEANS
    TABLES=opendim selfdim BY cntry
    /CELLS MEAN.

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.
    AGGREGATE
    /OUTFILE='C:\TEMP\AGGREGATE.sav'
    /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
    *SCATTER, THE TWO DIMENSIONS.
    GRAPH
    /SCATTERPLOT(BIVAR)=opendim WITH selfdim BY cntry (NAME)
    /MISSING=LISTWISE.
  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.
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