Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion
The first wave of the survey contained 72 questions, and the second wave contained 100 questions. These were distributed between the three sections in each wave as shown in Table 8, and many of them were filtered questions. This means that they were only asked if particular answers to previous questions had been chosen by respondents. In some cases this meant that no question was asked and in others it meant that only one version of multiple alternative questions was asked. Further, some of the filtered questions were only asked of respondents if their answers had not already been recorded in previous surveys. As such, it would be time consuming to establish how many questions respondents received on average but it is the case that no respondent would have received all 172. It is also the case that the 1501 respondents in the sample provided answers to all of the questions that they received because progression through the survey was conditional on providing valid answers. This means that the combined first and second wave answers provide a rich picture of the respondents.
Table 8 – Number of Questions and Filtered Questions
in the Sections of Each Wave of the Survey
As outlined in the introduction there are four elements of the full model analysed below. These are background characteristics, the components of the Civic Voluntarism Model, further elements of economic, cultural, and social capital, and perceptions of privilege. Before the analysis of those elements is presented it is useful to outline the variables that constitute each of them. As will become clear, some of the variables are simply single survey items whilst others are based on multiple items. A number of these variables are likely to be respecified in the course of further analysis. As such, the below variables reflect the interim nature of this analysis.
Age (years) – As the name suggests, this is a simple interval variable indicating respondent age in years.
Female (binary) – Again, as the name suggests, this is a binary recoding of the sex variable with ‘Female’ coded as 1 and ‘Male’ as 0.
White British ethnicity (binary) – This is a recoded version of the ethnicity variable included in the data with ‘White British’ coded as 1 and all other ethnic groups as 0. The latter includes the ‘White Other’ group because it is considered that such respondents are likely to be European migrants who, as such, should be considered distinct from the majority population.
Religious (binary) – This is a recoded version of the religious denomination variable included in the data with all of the religious options combined into the 1 category and the remaining non-religious categories combined into the 0 category.
Heterosexual (binary) – This is a recoded version of the sexuality variable included in the data with ‘Heterosexual’ recoded as 1 and all other sexualities as 0. The ‘Prefer not to say’ answers were recoded into 9 and excluded from the analysis.
Disability limitations (three categories) – This is an inverted version of the original three-category variable in the data so that 2 represents significant limitations resulting from disability, 1 equates to some limitations, and 0 indicates no limitations. The categories have been split into binary variables in the model with the no disability limitation category acting as the reference.
Health Conditions (count) – This is a summary variable inverting the count of positive (with a value of 1) selections of each category of health condition, meaning that a higher value in this (with a maximum possible of 19) represents having fewer of the listed health conditions.
Components of the Civic Voluntarism Model:
Educational qualification (highest) – This is an ordinal variable included in the data, with higher numerical values (there are 18 categories) associated with higher educational qualifications. The categories have been split into binary variables (with the ‘No qualifications’ acting as the reference category) and the ‘Don’t know’ and ‘Prefer not to say’ answers are excluded from the analysis.
Gross household income (fifteen categories) – This is an ordinal variable included in the data, with higher values (as the name suggests, there are 15 categories) indicating a higher income bracket. The categories have been recoded into binaries for the analysis (with the lowest income acting as the reference category).
Free time (hours per weekday) – This is an interval variable included in the data indicating the average number of free hours per weekday reported by the respondent (after sleep and a range of responsibilities such as work and childcare are accounted for).
Work-based civic skills – This is a calculated summative variable indicating the frequency of undertaking four key skills in current or past work. The highest possible value (28) indicates that all four of the skills (writing a formal email or letter, taking part in a decision-making meeting, chairing or planning a meeting, and giving a presentation) are used every day. The original answers were inverted before summing so that the ‘Daily’ answer had the highest value.
Political interest – This is a calculated summative variable indicating the level of attention paid to local and national politics, and the frequency with which each is discussed. The component variables were inverted so that high interest and frequent discussion had the highest values before they were summed. This means that the highest possible value in the summative variable (24) indicates ‘A great deal’ of interest in both local and national politics, and that both topics are discussed ‘Every day or almost every day’.
Political efficacy (internal and external) – This is a calculated summative variable indicating both the belief that citizens have influence over the political system, and the respondent’s self-assessed level of influence relative to others, as well as their ability to understand politics. The latter variable was inverted before summing so the maximum score (36) on the summative variable indicates that respondent strongly disagrees that it is often difficult to understand what is going on in government and politics, that they believe members of the public have ‘A great deal’ of influence at local, regional, and national level, and that they believe they have ‘Much more’ influence than most people at all three of those levels.
Political knowledge – This is a calculated summative variable indicating locally relevant political knowledge and national government knowledge. The variable is coded so that the maximum value (9) indicates correct identification of the local MP (from a list), correct answering of a question relating to whether MPs have to answer letters, correct identification of five senior politicians (of varying renown), and certainty of identifying a local group meeting space.
Identifies with a party (binary) – This is a recoding of the nominal party identification variable included in the data so that identification with any party is assigned a value of 1 whilst lack of identification is coded as 0.
Group recruitment – This is a calculated summative variable indicating the frequency with which invitations to get involved in voluntary groups are received via a range of means. Each of the constituent variables was inverted before summing, meaning that the maximum possible score (44) indicates requests being received ‘Once a month or more often’ from each of eleven possible sources (mass communication, family member, friend, neighbour, colleague, member of a religious congregation, political party member (if respondent is a member of a party), trade union or professional association member (again, if respondent is a member themselves), campaigning organisation member (if respondent is a member), charity member (if respondent is a member), or campaigner from an organisation that the respondent is not a member of).
Bourdieusian Elements of Economic, Cultural, and Social Capital:
Count of cultural activities – This is a calculated summative variable totalling coded binary variables indicating whether a number of cultural activities are undertaken outside the home, in the home, and when on holiday. The variable is itself a sum of three summative variables relating to each of those areas. Because the three constituent scales are of different lengths (there are more external activities to choose from than there are home-based activities or holiday activities) they are divided by the maximum score possible in each case so that none of the three scales can contribute more than 1 to the overall summative scale, which therefore has a (non-integer) numerical value of between 0 and 3.
Count of cultural tastes – This is a calculated summative variable totalling a sequence of binary variables indicating tastes in different areas. These are the venues visited when eating out, the cuisines consumed when eating out, the radio stations listened to, the types of magazines read, the television channels watched, the music genres selected, and the film genres selected. As with the previous variable, the overall summative variable in this case is a sum of five constituent summative variables, each of which has been divided by the maximum score possible in each case so that they contribute equally. As such, the overall summative variable has a (non-integer) score of between 0 and 5.
Frequency of cultural activities – This is a calculated summative variable totalling the frequency of a range of cultural activities taking place outside the home (from playing bingo to attending the opera), inside the home, and of holidays. The constituent variables were inverted so that frequent attendance was coded with a higher score. This is a simple overall frequency variable so greater frequency of any cultural activity will contribute to a higher overall score. As such, the maximum score (160) on the summative scale indicates that all seventeen external activities are done ‘A couple of times a month or more often’, all eight home-based activities are done ‘Daily’, and holidays are taken ‘Twice a year or more often’ with all four possible groups of people.
Legitimate status of activities – This is a calculated summative average variable. The frequency of each of the external, internal, and holiday cultural activity was multiplied by a score of between 1 and 3 (e.g. bingo and opera, respectively, in the case of external activities), those scores were summed and then divided by the count of cultural activities participated in to give an average legitimate status of activities taking into account their frequency.
Legitimate status of tastes – As above, this is a calculated summative average variable. The eat out venues, radio stations listened to, magazine types read, television stations watched, music genres selected, and film genres selected were assigned a score of between 1 and 4 (e.g. fast food outlet and fine dining restaurant, respectively, in the case of eating out venue), those scores were summed and divided by the count of those selected to give an average legitimate status of tastes.
Friends and acquaintances – This is a calculated summative variable totalling the numbers of friends seen daily, weekly, and monthly with the number of close relatives living nearby and further away, and the number of neighbours known. The maximum score (24) indicates that the highest option was opted for in each case, with no inversion of the original variables needed to achieve this.
Intensity of Relationships – This is a calculated summative variable totalling the frequency of seeing friends and acquaintances as well as the level of support received from various groups. The option selected for number of friends seen daily was multiplied by three (allowing a score of up to 12), for number of friends seen weekly by two (allowing a score of up to 8), and monthly by one (meaning a maximum score of 4). To these variables was added the inverted frequency of talking to neighbours (maximum score of 4), the inverted frequency of going out with colleagues (maximum score of 7), and the number of thirteen different types of help received from five groups (giving a maximum score of 65). This meant that of the total maximum summative score (100) considerably more than half could come from the help received by respondents. This was an intentional weighting in the summative variable towards the measurement of what can be relied on in relationships rather than just the frequency of engaging with acquaintances.
Count of acquaintances status categories – This is a simple calculated summative variable totalling the number of binary selections indicating acquaintance with people holding a range of job statuses. The maximum possible score is 14.
Diversity of acquaintances – This is a calculated summative variable reflecting the proportion of friends from the same gender, ethnicity, and religion as the respondent. In the case of gender, fifty was set as the most diverse point on the percentage scale, meaning that a new variable was calculated with fifty set as zero (with zero becoming minus fifty and one hundred becoming fifty) so that the numbers could be squared to give a curve with its lowest point at zero (reset from fifty). Thus, a score higher than zero indicated that more of one gender was known than of the other. This score was then divided by two thousand to give a maximum possible value of 1.25, which was then added to the proportion of friends from the same ethnicity and from the same religion, each divided by eighty (again giving a maximum possible value of 1.25 in each case). The variables relating to ethnicity and religion, each of which (unlike gender) has notably more than two possible groups, were not recoded (except for being divided by eighty) because it is considered that a lower proportion of friends from the same ethnicity or religion increases the likelihood of a diverse friendship group. This is a flawed assumption (the diversity is likely to depend on whether the respondent is in the majority group in society) but is considered acceptable for the interim analysis. As such, the maximum possible score on the summative variable is 3.75. This value was opted for as the maximum so that, if combined with the previous variable it would contribute less of the overall variation (because of the flawed assumptions involved in calculating this variable).
Average status of acquaintances – The fourteen different categories of acquaintances’ jobs were recombined as necessary to give the nine categories reflecting the NS-SEC and then assigned a weight on the basis of their status. The weighted values were then summed and divided by the count of categories selected by respondents to give an average of the status of acquaintances, with the maximum possible score being 8.
Property ownership – The five binary variables indicating ownership of a range of forms of property were weighted between 1 and 3 (depending on the asset type (e.g. ownership of commercial premises was weighted with a 3 whereas holding a mortgage on a property to live in was weighted with a 1)) and the scores summed to give this variable with a maximum score of 10.
Assets (fourteen categories) – This is an ordinal variable included in the data, with higher values (as the name suggests, there are 14 categories) indicating a higher income bracket. The categories were recoded into binaries for the analysis (with the lowest asset value acting as the reference category).
State benefits received – This is a simple constructed summative variable totalling the binary variables indicating receipt of a range of state benefits. There are twelve binary variables so the summary variable has a maximum possible score of 12 (indicating receipt of all listed state benefits).
Perception of Privilege:
Perceived role of privilege in society – This is a calculated summative variable based on the binary selection and inverted rank score of privilege-orientated explanations of status differences in society. One point was assigned if either ‘Because of their backgrounds’ or ‘ Because of inequality based on things like sex, race, and religion’ was selected from a list of possible explanations for status differences in society. A further three points were assigned if either was subsequently ranked as the top explanation, two points if ranked second, or one point if ranked third. This allows a maximum possible score on the summative variable of 7 (both privilege-orientated explanation selected, one ranked first, and the other second).
Perceived role of privilege in own life – This is a summative variable that was calculated in a similar manner to the previous variable. However, because there was no initial multiple-choice question in this case the summary score is based only on the ranking of the background or structural inequality explanations as they apply to the respondent’s life. In addition, a subsequent filter question went to those who did not rank either option asking them whether they thought background played any part in their status. If they answered yes to this prompt then they were assigned half a point. As such, the maximum possible score on the summative variable is 5 (ranking background first and structural inequality second, or vice versa, as explanations for own status, in which case the subsequent prompt question would be skipped).
Perceived status in society and own group – This is a simple calculated summative variable totalling the respondent’s self-placement on two ten-point hierarchical scales (respectively relating to their status in society at large and within the group of people they know). The two component variables were inverted so that high values reflect high self-perceived status, and the summative variable has a maximum score of 20.
Frequency of political acts – This is a summary variable of the frequency of undertaking eleven different political acts (from displaying materials to going on a protest). The original answer categories have been inverted so that higher frequency has a higher value. As such, the maximum value of 55 indicates that all eleven activities are done ‘Once a month or more often’.
Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion
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