# Voluntary organisation theory

In the following exercises, you will be asked to perform operations that are not possible to do online. This is why you should download the dataset "Download: Trust" to your own computer. You could download the data in any of the available formats, but you must be aware that all syntax examples will be given with reference to SPSS.

From Table 1.1 we saw that the voluntary organisation theory has been measured using membership of different associations/organisations. The ESS survey asked about membership of twelve different types of organisations. (Please note that the voluntary organisation questions were not asked in Switzerland and in the Czech Republic.) By adding the information from these twelve variables into one variable, we get a more robust measure of the involvement in the civic society.

Figure 2.3. Operationalisation of the voluntary organisation theory

1. Create a variable that summarises the twelve original variables with information about membership of different organisations. SPSS

You could create a simple additive index by running the syntax below. The index will vary from 0 (not a member in any voluntary organisation) to 12 (member of all types of organisations).

Compute vol_org =SUM(sptcmmb, cltommb, trummb, prfommb, cnsommb, hmnommb, epaommb, rlgommb, prtymmb, setommb, sclcmmb, othvmmb).
VARIABLE LABELS vol_org "Additive index: Membership in 12 types of voluntary organisations".
FORMATS vol_org (F2.0).
EXECUTE.
1. Examine the distribution of the computed variable - i.e. how many people score 0, 1, 2, 3 …up to 12. What is the shape of the distribution? Does the shape make sense to you given what you know about the likely membership patterns of the people you know? Does the shape of the distribution have any implications for the regression analysis that follows? SPSS

To examine the distribution, you could run a frequency analysis and draw a bar graph of the variable vol_org.

FREQUENCIES
VARIABLES=vol_org
/ORDER= ANALYSIS.
GRAPH
/BAR(SIMPLE)=COUNT BY vol_org.

The distribution is highly skewed. Approximately 40% of respondents are not members of any organisation, and fewer than 25% are members of three or more organisations. Because there are so few respondents who are members of several organisations, these respondents might have a large impact in the following regression (outliers). In short, this variable is not particularly suited for regression.

There are at least two reasons for being somewhat sceptical about using the variable computed in the exercise above as a measure for civic involvement.

Firstly, it is a weakness that the level of personal engagement in the organisation is not accounted for. A passive member is assigned the same value as a more committed member.

Secondly, the assumption that the individual's involvement increases in a linear way through the addition of each new membership is doubtful. Is it likely that a person playing in a brass band, singing in a religious choir and playing in a chess club is three times as involved as a person who is only a member of a political party?

The first problem could be met by expanding the index to include variables that could say something about the level of involvement, for example if the respondent has done voluntary work for the organisation. The second problem could be met by emphasising the first couple of memberships more than later ones. If a respondent has three memberships, it is reasonable to expect that a new membership will have less impact on his civic involvement than the first and the second membership had. Practically, this can be done by transforming the additive scale into a logarithmic one, to reduce the effect of large values on the index.

1. Create a variable called "Voluntary organisations: Logarithm of membership and involvement index" according to the description above. For each of the twelve types of organisations you should use three variables: "member", "participated" and "voluntary work". In total this makes 36 variables. The first step is to add these together, and we get an additive index varying from 0 to 36. The second step is to take the logarithm of this index. SPSS

The first step is to make the additive index:

COMPUTE org=SUM(sptcmmb, sptcptp, sptcvw, cltommb, cltoptp, cltovw, trummb, truptp, truvw, prfommb, prfoptp, prfovw, cnsommb, cnsoptp, cnsovw, hmnommb, hmnoptp, hmnovw, epaommb, epaoptp, epaovw, rlgommb, rlgoptp, rlgovw, prtymmb, prtyptp, prtyvw, setommb, setoptp, setovw, sclcmmb, sclcptp, sclcvw, othvmmb, othvptp, othvvw).
EXECUTE.

The second step is to take the logarithm of this index. Please note that it is necessary to add 1 to the additive index before computing the logarithm. 0 is a valid value indicating that the respondent is not in any way involved in organisational work. But the logarithm of 0 is nonexistent, and the logarithm of 1 is 0, and that is what we need:

COMPUTE ln_org = LN((org+1)).
VARIABLE LABELS ln_org "Voluntary organisations: Logarithm of membership and involvement index".
EXECUTE.
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