Social capital and social exclusion: development of a ...

Social Capital/Social Exclusion condensed module National Centre for Social Research

Social capital and social exclusion: development of a condensed module for the Health Survey for England Madhavi Bajekal, Susan Purdon

Report commissioned by Department of Health March 2001 National Centre for Social Research, 35, Northampton Square London EC1V 0AX Tel: 0207 250 1866 Email: [email protected] Website: www.natcen.ac.uk

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Contents

1

INTRODUCTION ..............................................................................................3

2

THE QUESTIONNAIRE ...................................................................................3

3

METHODS...........................................................................................................4

1.1 2.1 3.1 3.2 3.3 3.4 3.5

Background...............................................................................................3 Question recoding.....................................................................................4 Overview...................................................................................................4 Stage 1: Factor analysis of the original data to identify the underlying constructs of social capital/exclusion.......................................................5 Stage 2: Multiple linear regression to identify a sub-set of questions......6 Stage 3: Measure the association between the individual factors and health.........................................................................................................6 Stage 4: Check that the associations are approximately the same for the reconstructed factors.................................................................................7

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AREA LEVEL MEASURES...............................................................................7

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INDIVIDUAL LEVEL MEASURES................................................................9

4.1 4.2

Neighbourhood.........................................................................................7 Access to services......................................................................................9

5.1 5.2

Trust and reciprocity ................................................................................9 Informal social networks ........................................................................10

5.3

Participation in organisations.................................................................11

5.2.1

Relationship with perceived social support................................................ 10

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SUMMARY........................................................................................................12

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RECOMMENDATIONS .................................................................................13

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SELECTED REFERENCES .............................................................................14

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TABLES ..............................................................................................................15

6.1 6.2

Summary of the factor analysis...............................................................12 Interview Mode: Face to face vs. Self- Completion.................................12

A: Area Social Capital Factors ............................................................................... 15 B: Access to services factors ................................................................................. 19 C: Trust and Reciprocity factors ............................................................................. 21 D: Informal social network factors (received social support) ..................................... 23 E: Organisational membership and participation...................................................... 28 APPENDIX A

QUESTIONNAIRE MODULE (HSE 2000)……………………………………...29

APPENDIX B

OVERALL DISTRIBUTION BY RESPONSE CATEGORIES………………...33

APPENDIX C

RECOMMENDED CONDENSED SELF-COMPLETION SOCIAL CAPITAL QUESTIONNAIRE…………………………………………………………………35


1

INTRODUCTION

1.1

Background

The social capital and social exclusion module for the Health Survey (2000) was developed to capture key elements of social capital, new to the survey, and aspects of social exclusion not covered by the existing suite of questions on socio-economic status asked every year in the Health Survey. To measure the key concepts of social capital, a set of questions were identified on the basis of a comprehensive review of existing instruments across a range of surveys in the UK and the US and a literature review of the link between social capital and health. The selected battery of questions was put through a process of cognitive piloting to ensure that questions are unambiguous and easily understood and to test alternative formats of questions that tapped into similar concepts (Cognitive pilot report, National Centre, 1999). The final set of recommended questions were then introduced as a social capital/exclusion module on the Health Survey as part of the main face to face interview conducted with all adults aged 16 and over resident in private households. For this analysis, we have used data collected over the first three-quarters of the 2000 survey (n=5945). The module was designed to cover a number of domains: neighbourhood, access, trust, social networks, and participation. In total there are 33 questions (excluding sub-questions) and the module takes about 10 minutes of interview time in total. The aim of the analysis reported here is to reduce these 33 questions to a significantly smaller number that interviewers can administer in about three minutes rather than 10. Ideally, this smaller sub-set of questions should capture as much as possible of the range covered by the current set.

2 THE QUESTIONNAIRE The questionnaire module is included in Appendix A. Of the 33 separate questions included in the module, the first asks how long the informant has lived in the area. This question was thought to be essential both in terms of good questionnaire design in that it sets the context for the questions that follow on area characteristics, as well as a key variable in analysis. The length of time an individual has lived in the area is thought to be related to their involvement in informal social networks and can be considered a proxy indicator of social integration (Cooper et al). For the purpose of this analysis, the remaining 32 questions have been regrouped into five domains (as below) and the percentage distribution of the responses to each question by domain is presented in Appendix B. In terms of the key underlying concepts, we have categorised the questions asked into 5 groups: 1.

Area or neighbourhood characteristics: this domain includes 12 questions relating to the informants’ perception of both positive and negative aspects of their neighbourhood, including the fear of crime. These questions together provide a measure of area-level social capital, albeit as perceived by the respondent.

2.

Access to services: This set of questions measures how easy or difficult informants find getting to essential services, using their normal mode of transport. Although geographical access can be conceived of as a property of neighbourhoods, and therefore linked to the previous area domain, it is conceptually related to deprivation and social exclusion rather than social capital. It is argued that restricted access to services – such as to shops providing affordable and healthy food, a post office and health services –impact on health

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Social Capital/Social Exclusion condensed module National Centre for Social Research indirectly, such as by limiting the opportunities for people to lead a healthy lifestyle; and directly, by contributing to the stress of daily life. 3.

Trust and reciprocity: Central to the concept of social capital is trust of others. Trust is developed through reciprocity and through engagement in formal and informal social networks. The three questions in this domain have been adapted from the questions included in the US General Social Survey used by Putnam as the measure of social trust in the US (Putnam, 1995).

4.

Informal social networks: The social connectedness of individuals was measured by asking respondents whether or not they had met with or telephoned their friends, family or neighbours in the past fortnight (10 questions). Respondents record whether or not such contacts had been made, but not the frequency of contact with network members. Because the questions do not measure network content or density, they can be thought of more as proxy measures of received social support rather than a true measure of social integration, an important element in the measurement of individual social capital. In terms of the relationship of social support to health, studies have shown that levels of perceived social support, rather than received support, have the most effect on health, particularly mental health (Cooper et al, p.13). Perceived social support is measured on the Health Survey as a series of 7 questions included in a self-completion questionnaire. The relationship to health of both sets of measures – perceived and received social support – will be assessed to identify their combined and independent effects.

5.

Participation in organisations: This domain includes two sets of questions; the first asks about regular participation in the activities of a list of organisations and the second about membership of the same organisations. In Putnam’s definition of social capital, areas with high social capital are characterised by high levels of civic engagement, born out of social trust and developed through reciprocity, which facilitate co-operation for mutual benefit. Recent studies in the US by Kawachi, Kennedy and colleagues have suggested that social cohesion, as measured by levels of trust and community participation, is undermined by increasing income inequality and that the “withering of social capital” mediates the relationship between relative poverty and health. (Kawachi, Kennedy 1997)

2.1

Question recoding

Some of the categorical responses on the original variables included in this module were reordered into ordinal categories (e.g. cphelp: “1=yes, 2=no, 3=neither” into rthelp: “1=yes, 2=neither and 3=no”) before factor analysis. The original variable names in the social capital/exclusion module are all prefixed “cp -”. Where variable responses have been reordered, the prefix has been changed to “r-“ in the tables presented. For example, the variables in the neighbourhood or area domain are prefixed “rn-“ and trust are “rt-“ with the rest of the variable name the same as the original to allow ease of reference between them.

3 METHODS 3.1

Overview

As noted in Section 1.1, the aim of the analysis reported here is to reduce the original 33 questions to a significantly smaller number that interviewers can administer in about three minutes rather than 10. The analysis approach is described in detail in Sections 3.2 to 3.5, but an overview of the method, and the assumptions made, are given here. A key assumption of the analysis is that the questions on each domain are variables that all attempt to measure, albeit imperfectly, an underlying concept or construct. For instance, the

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Social Capital/Social Exclusion condensed module National Centre for Social Research questions about the local area are attempts to measure an underlying factor, which might be labelled something like ‘quality of life in the area’. The reason for collecting the ‘imperfect’ or ‘indirect’ data is that the underlying factor cannot be asked about or measured directly. We have assumed that, as long as these underlying concepts are related to health, they should still be measured. However, we also assume that the number of variables needed to ‘capture’ each of these factors is smaller than the number of questions currently asked. If this assumption is correct, the implication is that the number of questions in the module can be reduced without too much information about social capital/exclusion being lost. The approach taken has four main stages: Stage 1 Factor analysis is carried out on the data collected using the current range of variables to identify and ‘measure’ the underlying constructs of social capital/exclusion. Implicit in this analysis is the assumption that the current range of questions is sufficient to identify these factors. Stage 2 In Stage 2 we identify the smallest sub-set of variables that will still allow for the factors identified at Stage 1 to be recreated with ‘reasonable’ reliability. The definition of ‘reasonable’ we have adopted is that the reliability score for the reconstructed factor should be 80% or more (or, equivalently, the reconstructed factor explains at least 80% of the variance of the original factor). The approach taken is to use multiple linear regression to identify a small set of variables that, in combination, predict the original factor to the accuracy noted above. In two final stages, the association between the original factors and health is measured (Stage 3). Assuming an association is found, the factor is assumed important enough to retain, and, for each of these, a check is carried out that the new ‘reconstructed’ factor has approximately the same association with health as the original (Stage 4). More details on the approach taken in stages 1-4 is given below, taking each stage in turn. The results of the analysis for each ‘domain’ (see below) are described in Sections 4 and 5.

3.1

Stage 1: Factor analysis of the original data to identify the underlying constructs of social capital/exclusion.

At Stage 1 of the analysis, factor analysis is used to identify the underlying constructs of social capital/exclusion. Given that the original module was grouped into separate ‘domains’, which were designed to cover discrete elements of social capital and exclusion, these domains were retained in the analysis and separate factor analyses carried out within each domain. Thus four factor analyses were undertaken, covering: the neighbourhood; access to services; trust; and informal social networks. The data from the domain, ‘participation in organisations’, is not suitable for factor analysis, so the analysis for this domain is dealt with separately in Section 5.2. In principle a single factor analysis could have been carried out using all of the questions in the original module, rather than doing separate factor analyses within domains. This could however have resulted in some domains being unrepresented in the reduced module, which would limit the options for analysis of the data. By maintaining the original domain structure we are allowing for analysis by domain, yet at the same time, not removing the opportunity for cross-domain scales to be constructed if appropriate. The factor analysis was carried out using SPSS. The technical details are as follows. For each domain, the initial factors were extracted using principal component analysis. Factors with an eigenvalue of greater than 1 were used in the final factor model. These factors were then rotated using an oblique rotation (oblimin), and the factor structure matrix (i.e. the correlation between the original variables and the factors) recorded. Finally, the factors can

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Social Capital/Social Exclusion condensed module National Centre for Social Research be estimated as a weighted sum of the original variables (after standardisation). These weights (known as factor scores) were estimated and recorded.

3.2

Stage 2: Multiple linear regression to identify a sub-set of questions

The factors identified in Stage 1 were based on the data from all the questions in each domain. For the purposes of this analysis we worked with the assumption that these original questions are, in some sense, a complete set, and that the derived factors are the gold standard measures of social capital/exclusion. Obviously this assumption is unrealistic – in practice, the questions included in the original module may be far from complete, and the factors are model based and have their own underlying assumptions which may or may not be valid. Nevertheless, given that the object of the current exercise is to produce a sub-set of questions from the total set that will capture the same range of social capital/exclusion as the original set, treating the original questions and factors as the gold standard seems to be an appropriate way forward 1 . With these assumptions, the aim of the second stage of the analysis is to identify a sub-set of the original questions that allow for the factors identified in Stage 1 to be reconstructed with reasonable reliability. ‘Reasonable’ reliability was interpreted, somewhat arbitrarily, as a reliability coefficient of 80% or higher (the reliability coefficient being the square of the correlation between the original and reconstructed factor). The analysis method used was ordinary least squares (OLS) multiple linear regression. Each factor identified at Stage 1 was entered as the dependent variable in a regression model. The variables from the same domain were then entered into the model forward stepwise. The first variables entered that, in combination, explained at least 80% of the variance in the factor were accepted for the subset, the rest were rejected. At the end of this process we have a sub-set of questions and an alternative ‘reconstructed factor’ for each of the original factors. The reconstructed factor is simply the predicted factor, regressed on the sub-set of selected variables, as generated by the regression model.

3.3

Stage 3: Measure the association between the individual factors and health.

Although Stage 2 of the analysis identifies the sub-set of questions needed to measure all the factors identified at Stage 1, before this sub-set is accepted it is valid to ask whether all of the factors are actually associated with health, on the grounds that factors that are not associated with health should not be included in a health survey, especially if interview time constraints mean that their inclusion means other questions have to be excluded. To test the association of the factors to health, a total of 20 logistic regression models were run: 5 different binary health measures by the 4 original domains. The health measures were self-assessed general health status, functional limitation, psychosocial health, health-related behaviour and an objective measure of a health risk factor defined as below (variable name): • Self-assessed fair, bad or very bad health (poorhlth) • Having a limiting longstanding illness (llsi) • GHQ12 score of 4 or more (ghq) • Current smoker (smoker) • BMI 30 or over (obese) In practice the assumption that the factors derived are the ‘gold standards’ is very difficult to test. The number of questions used to identify each factor is typically small and consequently the Cronbach’s alpha for each factor (which is a measure of internal reliability, the value of which increases as the number of questions increases) are typically small. It is quite plausible that with more questions each factor would be better identified. Nevertheless, since the object of the current exercise is to reduce the set of questions from the original set without losing too much information, rather than to validate the original set of questions, we do not consider the reliability or otherwise of the factors to be of very great importance here. The aim of the current research is to be able to approximate these factors using fewer questions, however unreliable these factors may be. 1

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Social Capital/Social Exclusion condensed module National Centre for Social Research For each domain, all of the factors identified at Stage 1 of the analysis were entered per model. All of the models included other variables available on the dataset that are known from previous work to be associated with health: age, sex, equivalised household income, economic activity, and educational attainment. 2 Including these allows for an assessment of the independent effect of social capital/exclusion factors on health to be made after controlling for confounding socio-demographic variables. Two indicators of association are used. Firstly, because all of the factors calculated at Stage 1 are standardised (that is they have a mean of zero and a standard deviation of 1) the coefficients of regression models for different factors can be directly compared, each coefficient being interpretable as the increase in the odds of ‘ill-health’ (however measured) associated with a one unit increase in the factor. Factors with a ‘large’ coefficient can be considered strongly associated with health, although it is not automatically clear how large is ‘large’. The other indicator of association is the p-value or significance level. Given that the sample size of the Health Survey is large, we would expect most important associates of health to be significant (i.e. have a p-value of less than 0.05). Taking these two measures together we have judged individual factors to be associated with health if they either have a p-value of less than 0.05 across a range of health outcomes or the regression coefficient is greater than 2 or less than 0.5. The latter criteria means that nonsignificant factors do not get set aside if the size of the coefficient is sufficiently far from 1 to suggest a genuine association.

3.4

Stage 4: Check that the associations are approximately the same for the reconstructed factors

To check that the reconstructed factors have the same association with health as the original factors, the 20 models of Stage 3 were re-run with the reconstructed factors in place of the original factors. The earlier models were then compared with these models to check that the significance levels and the regression coefficients had not changed greatly. This is, to some extent, a formality – the reconstructed factors were designed so as to correlate very highly with the original factors, so it is extremely unlikely the coefficients and significance levels would differ much. (Since, in theory, the reconstructed factors can be thought of as the original factors but measured with some error, measurement error theory would lead us to expect the regression coefficients to reduce by a small amount.) In Sections 4 and 5 the results from the analysis steps described in this section are discussed for each social capital/exclusion domain in turn. Section 4 deals with the two area level domains: the neighbourhood; and access to services. Section 5 deals with the individual level domains: trust; informal social networks; and participation in organisations.

4 AREA LEVEL MEASURES

4.1

Neighbourhood

As described in Section 2, the ‘neighbourhood domain’ covers 12 questions. Table 1.1 shows the correlation between these 11 variables in matrix format. The factor analysis (Stage 1) of the data from these questions identified three factors (Table 1.2 shows that there are three eigenvalues greater than 1). The structure matrix (which shows the correlation between the original variables and the three factors) is given in the left hand 2

Social class coding of records is done at the end of fieldwork and is therefore not yet available on the three-quarters survey dataset.

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Social Capital/Social Exclusion condensed module National Centre for Social Research side of Table 1.3, and the factor scores or weights (i.e. the weight given to each variable to create the factor as a weighted sum) are given in the right hand side of Table 1.3. In interpreting the factors the structure matrix is perhaps the most informative of these tables. In this instance it shows that the first factor (Factor 1) is heavily loaded on vandalism, ‘teenagers hanging around’, rubbish, drunks, and noise. The second factor is heavily loaded on leisure facilities, facilities for children, and good transport. The third is heavily loaded on ‘neighbours look after each other’, ‘enjoy living in area’ and ‘safety after dark’. Although we have not attempted to put labels on the factors systematically, it is clear that the first factor is a measure of area problems, the second reflects facilities, and the third perception of community cohesion. Tables 1.4, 1.5 and 1.6 show the multiple linear regression models fitted in Stage 2 of the analysis. In the model of Table 1.4 the dependent variable is the first factor, and so on. The first model suggests that over 80% of the variance in the first factor can be captured with the data from just two questions: vandalism and teenagers. The second model (Table 1.5) suggests that almost 80% (79.3%) of the variance of Factor 2 can be explained with data from, again, two questions: leisure facilities and transport. The third model (Table 1.6) suggests that over 80% of the variance in the third factor can be explained with the two questions, ‘neighbours look after each other’ and ‘enjoy living in area’. So, for the ‘neighbourhood domain’ it would appear that we can reduce the number of questions down from 11 to 6. Table 1.7 shows the percentage of variance explained in each factor when the six variables are used as predictors in the models rather than just the two selected per model. In each case the percentage of variance explained is over 80%. Nevertheless, in all the analysis reported on here, the reconstructed factors used are created using the initial (selected subset variables) models rather than these ‘larger’ models. Stage 3 of the analysis is to test whether the original three factors are related to health. If they are then they should be included in the Health Survey; if not then there is an argument for dropping them. Stage 4 is a check whether the relationships are similar if the original factors are replaced with the new reconstructed factors based on the sub-set of questions. Table 1.8 shows the results of five sets of logistic regression models where the dependent variable in each case is a different binary health or risk measure. Each model includes a range of potential confounders (age, sex, socio-economic status) as well as the factors themselves. Table 1.8 presents a range of figures per model: • the regression coefficient per factor, labelled the ‘odds ratio’ • the 95% confidence interval around this coefficient • the significance level for the factor 2 • a measure of the overall model goodness-of-fit (R ). The models are presented in pairs. The first model is the model with the original three factors; the second is the model with the three reconstructed factors. Based on the criteria set out in Section 3.4, where we judge individual factors to be associated with health if they either have a p-value of less than 0.05 across a range of health outcomes or the regression coefficient is greater than 2 or less than 0.5, the figures from Table 6 suggest that all three factors are indeed associated with health and so should be represented in the social capital/exclusion module. For instance, all factors are significantly associated with limiting long-standing illness. Comparing the second model of each pair with the first, the model coefficients stay virtually the same with the reconstructed factors, although as expected they tend to be slightly lower, and the p-values are broadly the same. Only in a very few instances would the conclusions about the relationship between social capital and health be changed if the reconstructed factor were used in place of the original factor. Furthermore, although the goodness-of-fit of 2 the model (i.e. R ) tends to reduce slightly, the reduction is small. Overall, these findings suggest that reducing the number of questions on the ‘neighbourhood domain’ to six will not seriously obscure the relationship between social capital and health on this domain.

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4.2

Access to services

The ‘access domain’ covers five questions in total. The correlations between the responses to these questions are shown in Table 2.1. The factor analysis of these data from these questions identified just one factor (Table 2.2). Using this factor as the dependent variable in a linear regression model (Stage 2 of the analysis) we found that 85% of the variance in the factor could be explained with just two of the original five questions: access to post office and supermarket (Table 2.4). Table 2.5 shows the results of five logistic regression models where the single factor is used as a predictor of health. With the exception of obesity, there is a highly significant relationship between the factor and the dependent health variable, the p-value being less than 0.001 in each case. Replacing the original factor with the reconstructed factor based on just two variables changes the regression coefficients and p-values only marginally, and similarly, reduces the goodness-of-fit of the models by only a small amount. Thus, the reconstructed factor would appear to be an adequate proxy for the original factor.

5 INDIVIDUAL LEVEL MEASURES

5.1

Trust and reciprocity

The ‘trust domain’ covers just three questions. The factor analysis of the responses reveals just one factor (Table 3.2). Almost 85% of the variance in this single factor can be explained by two of the original three variables: ‘advantage’ and ‘help’ (Table 3.4). Table 3.5 shows the original factor to be related to health (the p-values across the five models of health are consistently small). Replacing the original factor with the reconstructed factor based on just the two variables only changes the regression coefficients by a small amount, and the p-values stay about the same, as do the goodness-of-fit statistics. So, again, the reconstructed factor would appear to be an adequate proxy for the original factor. However, given that the body of research on the operational definition of concepts such as “trust” is in its early stages, it is important to assess whether the two questions used in the reconstructed factor are satisfactory from a wider perspective. Put another way, we need to ask whether the question specifically about social trust can be dropped. While there is considerable debate on how best to measure social capital, or even if it is measurable at all, the consensus view is that the question relating to social trust is the simplest, tried and tested measure that has reliably been used across nations and studies. Of the three questions included in the trust and reciprocity domain, the question which specifically asks about social trust was found not to perform as well as the other two questions. While all three questions are highly correlated, the factor analysis indicated that 85% of the variance was captured by two of the three questions, thereby satisfying our decision rules of statistical adequacy. However, if it is considered important that the HSE data is used for cross-national analyses or to build up regional, small area or trend data on social capital per se, then it would be important to include all three of the original questions on trust and reciprocity. This would increase interview length marginally but would allow for more consistent analysis across studies and countries.

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5.2

Informal social networks

The social support domain is covered by 10 questions, the correlations between which are shown in Table 4.1. Factor analysis of the data identifies three factors (Table 4.2). The first of these three factors is heavily loaded on four of the questions: ‘went to visit friends’, ‘went out with friends’, had friends visit me’, and ‘spoke to friends on the phone’. The second factor is heavily loaded on three of the questions: ‘went out with relatives’, ‘had relatives visit me’, and ‘went to visit relatives’. The third factor is heavily loaded on the remaining three questions: ‘no contact with friends or family in last fortnight’, ‘spoke to neighbours’ and ‘spoke to relatives on the phone’. Multiple linear regression of the first factor suggests that: • 80% of the variance in the first factor can be explained by the two questions ‘went to visit friends’ and ‘went out with friends’; • 81% of the variance in the second factor can be explained by the two questions ‘went out with relatives’ and ‘had relatives visit me’; and • Just under 80% (79.5%) of the variance in the third factor can be explained by the two questions ‘no contact with friends or family in last fortnight’ and ‘spoke to neighbours’. Thus the original three factors can be ‘captured’ reasonably well by just six questions.

5.2.1

Relationship with perceived social support

Although the analysis described above suggests that the social support questions can be reduced from 10 questions to six, the question as to whether or not to include these six questions (or a variation on them) is complicated by the fact that the HSE also includes, in some years, a question on perceived social support. The questions we thus need to address are: • • •

Is perceived social support a better indicator of social support than can be derived from the 10 questions in the social capital module? Is perceived social support more closely associated with health than the measures captured in the social capital module? And if the answer to this second question is yes, can the social capital module questions be dropped entirely?

The first of these three questions is not possible to answer with the data we have, because we have no external measures against which to make a comparison. So we have taken this no further. This is arguably not particularly damaging because in a health survey context the more important question is the second. To address the question of which measure of social support is more closely related to health, two sets of five logistic regression models of health were fitted. The first set used perceived social support as a predictor of health; the second set used the three factors of social support derived from the social capital module as predictors of health. The results are shown in Table 4.8, where the models being presented in pairs (the first model in a pair being the model with perceived social support, the second being the model with the factors). Comparing the 2 goodness-of-fit of the models (i.e. R ) the three factors are slightly more correlated with health than perceived social support for self-assessed health and limiting long-standing illness, but the difference is very small. Perceived social support is very clearly a better predictor of GHQ 2 score than the three factors (R for the model with perceived social support being 0.054 2 compared with an R of 0.033 for the model with the three factors). For smoking and obesity perceived social support performs moderately better than the three factors. On these grounds, if a decision is to be made between including perceived social support or the social capital modules question (but not both) we judge that the perceived social support question is the better of the two. If it is accepted that perceived social support remains, then the next question to be addressed is the third one noted above, namely, is it now appropriate to drop the received social support questions in the social capital module. To address this question we ran a separate set of five

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Social Capital/Social Exclusion condensed module National Centre for Social Research logistic regression models (one for each health measure), with both perceived social support and the three factors entered as predictors in the model. The models are shown in Table 4.9. The significance levels associated with the three factors in each model are typically very close to or greater than 0.05, the single exception being the first factor (‘friends’) in the model of self-assessed health which has a p-value of 0.008. In addition the regression coefficients associated with each factor are typically close to 1. This suggests that, once perceived social support is controlled for, the social capital module factors tend to add very little explanatory power. The sole exception is the ‘friends’ factor in the model of self-assessed health. Even with this one exception, looked at in aggregate we conclude that relatively little would be lost if the social capital module of the HSE excluded all of the questions on social support, as long as the perceived social support questions are retained. This is especially the case because to exclude all of the social support questions from the social capital/exclusion module will significantly reduce interview time. The implication is that in any years of the HSE when the condensed social capital/exclusion module is included, the perceived social support questions should also be included in the self-completion questionnaire.

5.3

Participation in organisations

The two questions covering participation are very similar to each other in that the first asks about regular participation in the activities of a list of organisations (Card R, see Appendix A) and the second asks about membership of the same organisations. To reduce the questions we can either drop one or both. The other alternative, of reducing the number of organisations on the list, is unlikely to save much interview time (unless it was reduced considerably) and we have not pursued this. To check which of the questions to retain (if either) we have reduced both questions to a single score, the score being calculated as the number of organisations positively identified each time. As might be expected, the average number of organisations people participate in is slightly larger (at 0.93) than the number that they are members of (0.75), but the correlation between the two scores is very high at 0.82 (Table 5.1). This very high correlation between the two scores means that they perform almost identically as predictors of health. This is demonstrated in Table 5.2, which gives the output from pairs of logistic regression models of health measures. Comparing models with participation as the predictor with models with membership as the predictor, the two models tend to have very 2 similar regression coefficients (i.e. odds ratios) and the goodness-of-fit as measured by R is 3 almost identical each time. This suggests that either of the two questions might be selected. On the grounds that participation is more relevant for some of the organisations in the list than simple ‘membership’ we suggest that the participation question be kept rather than the membership question. Over and above this, a case needs also to be made for having even one of the questions. The models of Table 5.2 only demonstrate a clear relationship between participation and health for self-assessed health and smoking. There appears to be no relationship between participation and limiting long-standing illness, GHQ score and obesity. Thus participation appears to have a weaker association with health than the other measures of social capital and exclusion under consideration. From the review of literature, it is clear that the level of community participation is a central measure of area social capital. The measure as it stands is based only on reported community activity in the past two weeks; it provides no indication of the time devoted in this activity or the individual’s role within the group. From the data available, it is unclear whether the lack of association with health is because the questions do not measure the underlying concept adequately or because participation has little, if any, effect on health after controlling for material deprivation factors. In light of this, our recommendation is that the participation 3

The models were re-run with the addition of the length of residence in the area variable. Both membership and participation remained significant and do not change the reported findings for this domain .

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Social Capital/Social Exclusion condensed module National Centre for Social Research question is kept, but if time constraints mean that the social capital module should be reduced further then this question may need to be reviewed – either amended or dropped entirely.

6 SUMMARY

6.1

Summary of the factor analysis

On the basis of the empirical analysis reported here, the condensed social capital/exclusion module would include the following sets of questions for each domain (cf. full question wording in Appendix A): Social Capital/ Exclusion Questions 1. Neighbourhood: • Length of residence in the area (cparea) • ..enjoy living in the area (cpenjy) • ..where neighbours look after each other (cpaft) • ..good local transport or not (cptran) • ..good leisure things for people like yourself (cpleis) • ..(problem of) .. teenagers hanging around (cpten) • .. (problem of) ..vandalism, graffiti and deliberate damage (cpvand) 2. Access to services From here, how easy is it for you to get to a .. • .. medium or large supermarket (cpmrkt) • .. a post office (cppo) 3.

Trust and reciprocity • .. most of the time people try to be helpful (cphelp) • ..most people would take advantage of you (cpadvn) • .. (generally speaking) most people can be trusted (cptrust)

4.

Informal social networks: replaced by existing questions on perceived social support.

5. Participation in organisations (amended) .. regular participant in any organisations listed on this card? (cpsjorg) The selected set of questions is about a third of the original module (from 33 to 13 questions) and correspondingly, administering the questionnaire should be achievable in about 3 rather than 10 minutes. The condensed module would therefore satisfy the principal aims of the analysis – namely, to cut down questionnaire length and to ensure that the questions retained are related to a range of measures of health and wellbeing.

6.2

Interview Mode: Face to face vs. Self- Completion

Having reduced the number of questions, there remains the issue of where the questions should be placed. At present the social capital module is part of the face-to-face interview. This creates particular problems with concurrent interviewing (where the interviewer reads the question out and all those being interviewed answer in turn). There is considerable evidence from the data collected to date that this generates consensus views of social capital within households rather than individual views. Since the purpose of the module is to capture the views of individuals rather than a kind of ‘household average’ it seems appropriate to move the questions to the self-completion questionnaire. This would have the added benefit of reducing the face-to-face interview length. There is, however, a small trade-off because not all respondents complete the self-completion questionnaire. In the 1998 HSE for example, 3% of respondents interviewed did not fill in the self-completion booklet. This possibly introduces

12

Social Capital/Social Exclusion condensed module National Centre for Social Research a small non-response bias, and slightly reduces the sample size for analysis. On balance however we believe the advantage of getting genuine ‘individual’ views far outweigh the disadvantages of potential bias and slightly reduced sample sizes. The evidence for the consensus in concurrent interviewing comes from comparing the level of agreement between household members who are interviewed concurrently with the level of agreement when interviewed separately. For the analysis we have restricted the data to married or cohabiting couples of opposite sex. Furthermore, since couples interviewed concurrently tend to be older than couples interviewed separately, we have compared couples of similar ages (the age used being that of the man). The table below shows the level of agreement by age-group for just one variable, ‘vandalism’. The measure of agreement used is weighted kappa. Weighted kappa varies between 0 and 1, complete agreement being 1; and ‘no more agreement than would happen purely by chance’ being 0. The weights reflect the ordinal nature of the responses: couples who disagree by just one category are considered to be more in agreement, and so get a larger ‘agreement weight’, than those who differ by two or more categories. 4 As can be seen very clearly, weighted kappa is consistently higher for couples interviewed concurrently than it is for couples interviewed separately. A similar pattern arose for all of the social capital variables tested in this way (the data is not shown) which, in aggregate, suggests very strongly that the social capital module should be moved to the self-completion questionnaire if the views of individuals are to be captured. Table 6: Values of weighted kappa amongst couples for responses to the question on vandalism. Age of man Couple interviewed separately Couple interviewed concurrently 20-34 0.32 0.78 35-44 0.31 0.84 45-54 0.46 0.85 55-64 0.32 0.84 65 and over 0.49 0.79

7 RECOMMENDATIONS We recommend that the condensed set of 13 questions identified in this report (see Appendix C), together with the questions on perceived social support, be included in the condensed social capital/exclusion module in future rounds of the Health Survey. The module would be ideally suited to self-completion, both in terms of the quality of responses and in achieving a reduction in the face to face component of the first stage HSE interview. In view of the recent vintage of the social capital concept, there is continuing discussion and debate on the definition and measurement of social capital. Our longer term recommendation, therefore, is to review the coverage of the shortened questionnaire at a future date in the light of findings from a growing body of research in the UK and internationally.

4

The weights used were: 1.0 when the respondents gave an identical answer, 0.5 where the answers shifted by one point up or down on the ordinal scale, and 0 if they differed by more than 2 points on the scale.

13


8 SELECTED REFERENCES 1. 2. 3. 4. 5. 6.

Cooper H, Arber S, Fee L, Ginn J. The influence of social support and social capital on health. A review and analysis of British data. HEA, London 1999. Putnam RD. Bowling Alone: America’s declining social capital. Journal of Democracy, 6:1, Jan 1995, 65-78. Kawachi I, Kennedy BP, Lochner K, Prothrow Smith D. Social capital, income inequality and mortality. American Journal of Public Health, vol. 87, no.9, 1997 Lochner K, Kawachi I, Kennedy BP. Social Capital: a guide to it’s measurement (abstract). Health Place 1999 Dec: 5 (4): 259-70. Halpern D: Social Capital: the new golden goose? (provisional draft: 15.2.99) Winter I: Towards a theorised understanding of family life and social capital. Australian Institute of Family Studies, Working Paper 21, April 2000.

14


9 TABLES

A: Area Social Capital Factors Table 1.1 Correlation Matrix N=5367

RNVAND RNTEEN

RNRUB RNDRUNK

RNNOI

RALEIS

RAPLAY

RNTRAN

RNAFT RNENJY

RNDARK

RNVAND

1

RNTEEN

0.57

1

RNRUB

0.57

0.46

1

RNDRUNK

0.40

0.42

0.38

1

RNNOI

0.32

0.35

0.29

0.29

1

RALEIS

-0.03

-0.03

-0.02

0.01

-0.03

1

RAPLAY

-0.02

-0.03

-0.04

-0.03

-0.03

0.26

1

RNTRAN

0.08

0.09

0.09

0.09

0.06

0.18

0.13

1

RNAFT

-0.14

-0.13

-0.19

-0.09

-0.17

0.09

0.08

0.00

1

RNENJY

-0.26

-0.21

-0.25

-0.17

-0.24

0.11

0.13

0.00

0.24

1

RNDARK

-0.21

-0.20

-0.21

-0.17

-0.13

0.04

0.10

-0.05

0.15

0.18

1

Table 1.2 Total Variance Explained Initial Eigenvalues Component

Total

% of Variance

Cumulative %

1

3.00

27.28

27.28

2

1.47

13.33

40.61

3

1.01

9.22

49.83

4

0.91

8.24

58.06

5

0.83

7.58

65.64

6

0.76

6.94

72.58

7

0.74

6.69

79.28

8

0.73

6.62

85.90

9

0.64

5.85

91.75

10

0.52

4.75

96.49

11

0.39

3.51

100.00

Extraction Method: Principal Component Analysis. Table 1.3: Factor Loadings and Score Correlation Matrix Structure Matrix

a

Component Score Coefficient Matrix Component

Component 3

1

2

3

RNVAND

0.82

1

2

-0.27

0.311

-0.007

0.023

RNTEEN

0.79

-0.23

0.308

-0.011

0.050

RNRUB

0.75

-0.33

0.274

0.010

-0.043

RNDRUNK

0.69

0.273

0.004

0.075

RNNOI

0.54

-0.37

RALEIS

0.73

RAPLAY

0.66

RNTRAN

0.63

RNAFT

0.23

0.173

0.023

-0.144

-0.012

0.525

0.022

0.004

0.464

0.102

0.013

0.476

-0.162

0.77

0.067

-0.048

0.586

RNENJY

-0.33

0.66

-0.036

0.065

0.430

RNDARK

-0.29

0.52

-0.039

-0.025

0.338

a

Coefficients less than 0.2 not shown

a

3 factors (components) extracted

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.

15

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Area social capital factors) Table 1.4 OLS Model Summary: Dependent Variable: Area factor_1 R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

F Change

Sig. F Change

1

0.815

0.664

0.664

0.579

R Change 0.664

10624.08

0.000

2

0.909

0.826

0.826

0.417

0.162

4990.61

0.000

3

0.958

0.917

0.917

0.288

0.091

5891.16

0.000

4

0.985

0.969

0.969

0.175

0.052

9088.94

0.000

5

0.997

0.994

0.994

0.078

0.025

21800.34

0.000

6

0.998

0.997

0.997

0.055

0.003

5234.75

0.000

7

0.999

0.999

0.999

0.037

0.002

6623.71

0.000

8

1.000

1.000

1.000

0.016

0.001

22947.10

0.000

9

1.000

1.000

1.000

0.011

0.000

5574.34

0.000

10

1.000

1.000

1.000

0.004

0.000

42953.91

0.000

11

1

1

1

0.000

0.000

2.0632E+13

0.000

a Predictors: (Constant), RNVAND b Predictors: (Constant), RNVAND, RNTEEN c Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK d Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB e Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI f Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT g Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT, RNDARK h Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT, RNDARK, RNENJY I Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT, RNDARK, RNENJY, RNTRAN j Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT, RNDARK, RNENJY, RNTRAN, RALEIS k Predictors: (Constant), RNVAND, RNTEEN, RNDRUNK, RNRUB, RNNOI, RNAFT, RNDARK, RNENJY, RNTRAN, RALEIS, RAPLAY

Table 1.5 OLS Model Summary: Dependent Variable: Area factor_2 R

R

2

Adj R

2

SE of Est. 2

Model

Change Statistics F Change 6143.21

1

0.731

0.534

0.534

0.683

R Change 0.534

2

0.891

0.793

0.793

0.455

0.259

6734.07

0.000

3

0.997

0.994

0.994

0.077

0.201

183566.06

0.000

4

0.998

0.996

0.996

0.062

0.002

2906.25

0.000

5

0.999

0.999

0.999

0.034

0.003

11945.98

0.000

6

1.000

0.999

0.999

0.024

0.001

5925.44

0.000

7

1.000

1.000

1.000

0.013

0.000

13031.25

0.000

8

1.000

1.000

1.000

0.010

0.000

4270.19

0.000

9

1.000

1.000

1.000

0.006

0.000

6837.20

0.000

10

1.000

1.000

1.000

0.004

0.000

9567.99

0.000

11

1

1

1

0.000

0.000 .

a b c d e f g h i j k

Sig. F Change 0.000

Predictors: (Constant), RALEIS Predictors: (Constant), RALEIS, RNTRAN Predictors: (Constant), RALEIS, RNTRAN, RAPLAY Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK, RNNOI Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK, RNNOI, RNTEEN Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK, RNNOI, RNTEEN, RNRUB Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK, RNNOI, RNTEEN, RNRUB, RNVAND Predictors: (Constant), RALEIS, RNTRAN, RAPLAY, RNENJY, RNAFT, RNDARK, RNNOI, RNTEEN, RNRUB, RNVAND, RNDRUNK

16

Social Capital/Social Exclusion condensed module National Centre for Social Research ,,.cont (Area social capital factors)

Table 1.6 OLS Model Summary: Dependent Variable: Area factor_3 R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model 1

0.766

0.587

0.587

0.642

R Change 0.587

2

0.911

0.830

0.830

0.413

0.242

7627.63

0.000

3

0.973

0.946

0.946

0.232

0.117

11661.75

0.000

4

0.984

0.967

0.967

0.181

0.021

3445.34

0.000

5

0.990

0.979

0.979

0.144

0.012

3112.55

0.000

6

0.995

0.990

0.990

0.099

0.011

6065.95

0.000

7

0.998

0.997

0.997

0.056

0.007

11110.84

0.000

8

0.999

0.998

0.998

0.040

0.002

5208.63

0.000

9

1.000

0.999

0.999

0.027

0.001

6786.75

0.000

10

1.000

1.000

1.000

0.017

0.000

8436.11

0.000

11

1

1

1

0.000

0.000

3.888E+14

0.000

a b c d e f g h I j k

F Change 7638.06

Predictors: (Constant), RNAFT Predictors: (Constant), RNAFT, RNENJY Predictors: (Constant), RNAFT, RNENJY, RNDARK Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY, RNDRUNK Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY, RNDRUNK, RNTEEN Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY, RNDRUNK, RNTEEN, RNRUB Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY, RNDRUNK, RNTEEN, RNRUB, RALEIS Predictors: (Constant), RNAFT, RNENJY, RNDARK, RNTRAN, RNNOI, RAPLAY, RNDRUNK, RNTEEN, RNRUB, RALEIS, RNVAND

Table 1.7 Percent variation explained by each of 3 Area factors and selected variables Dependent

Sig. F Change 0.000

R

R

2

Adj R

2

SE of Est.

Factor_1

0.914

0.836

0.836

0.405

Factor_2

0.896

0.803

0.803

0.444

Factor_3

0.927

0.860

0.859

0.375

Predictors: (Constant), RNENJY, RNTRAN, RALEIS, RNTEEN, RNAFT, RNVAND

17

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Area social capital factors)

Table 1.8: Multivariate analysis of relationship of health to area social capital: factors and reconstructed factors NB: Prefix RED denotes factor reconstructed from the sub-set of variables

Health status measure

Self assessed fair, bad or very bad health

Limiting longstanding illness

GHQ12 score of 4+

Current smoker

Obese (BMI 30+)

Model

Factor

Odds ratio

Lower CI (95%)

Upper CI (95%)

Sig.

1 FAC1_1

0.87

0.77

1.00

0.047

FAC2_1

1.13

0.99

1.29

0.062

FAC3_1

1.36

1.19

1.55

0.000

2 REDF1_1

0.83

0.72

0.95

0.008

REDF2_1

1.13

0.98

1.31

0.092

REDF3_1

1.27

1.11

1.46

0.001

3 FAC1_1

0.87

0.80

0.94

0.000

FAC2_1

1.11

1.03

1.19

0.007

FAC3_1

1.11

1.03

1.20

0.010

4 REDF1_1

0.87

0.80

0.94

0.001

REDF2_1

1.09

1.01

1.19

0.030

REDF3_1

1.08

1.00

1.18

0.055

5 FAC1_1

0.84

0.77

0.92

0.000

FAC2_1

1.09

0.99

1.19

0.075

FAC3_1

1.22

1.11

1.34

0.000

6 REDF1_1

0.83

0.75

0.91

0.000

REDF2_1

1.06

0.96

1.17

0.245

REDF3_1

1.22

1.11

1.34

0.000

7 FAC1_1

0.97

0.90

1.04

0.366

FAC2_1

1.12

1.04

1.20

0.002

FAC3_1

1.14

1.05

1.22

0.001

8 REDF1_1

0.95

0.88

1.03

0.239

REDF2_1

1.07

0.99

1.15

0.088

REDF3_1

1.18

1.09

1.27

0.000

9 FAC1_1

0.89

0.82

0.96

0.005

FAC2_1

1.07

0.99

1.15

0.080

FAC3_1

1.03

0.95

1.12

0.472

10 REDF1_1

0.87

0.80

0.95

0.002

REDF2_1

1.08

1.00

1.18

0.063

REDF3_1

1.07

0.98

1.16

0.126

Variables entered in each model: age (10-year age bands), sex, equivalised household income (quintile), education qualification (none, GCSE/CSE, A level or higher), economic activity (un/employed, inactive, retired)

18

2

R (Cox & Snell) 0.077

0.075

0.150

0.149

0.043

0.041

0.086

0.084

0.028

0.029


B: Access to services factors Table 2.1 Correlation Matrix CPPO Correlation

CPPO

CPMRKT

CPSHOP

CPDOC

CPHOSP

1

CPMRKT

0.56

1

CPSHOP

0.65

0.50

1

CPDOC

0.55

0.52

0.46

1

CPHOSP

0.32

0.41

0.29

0.39

1

Table 2.2 Total Variance Explained by Factor Analysis Initial Eigenvalues Component

Total

% of Variance

Cumulative %

1

2.88

57.64

57.64

2

0.79

15.83

73.47

3

0.52

10.46

83.93

4

0.47

9.43

93.36

5

0.33

6.64

100.00

Extraction Method: Principal Component Analysis.

Table 2.3: Factor Loadings and Score Correlation Matrix Structure Matrix

a

Component


1

1

CPPO

0.83

0.289

CPMRKT

0.79

0.276

CPSHOP

0.78

0.270

CPDOC

0.77

0.269

CPHOSP

0.60

0.207

a

1 Factor extracted.


19


..cont (Access to services factors) Table 2.4: OLS Model summary: Dependent variable Access factor_1. R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

R Change

F Change Sig. F Change

1

0.832

0.692

0.692

0.555

0.692

12618.45

0.000

2

0.923

0.851

0.851

0.386

0.159

5989.03

0.000

3

0.962

0.925

0.925

0.275

0.073

5455.50

0.000

4

0.983

0.967

0.966

0.183

0.042

7007.65

0.000

5

1

1

1

0

0.033 .

1

1 Predictors: (Constant), CPPO 2 Predictors: (Constant), CPPO, CPMRKT 3 Predictors: (Constant), CPPO, CPMRKT, CPDOC 4 Predictors: (Constant), CPPO, CPMRKT, CPDOC, CPSHOP 5 Predictors: (Constant), CPPO, CPMRKT, CPDOC, CPSHOP, CPHOSP

Table 2.5: Multivariate analysis of relationship of health to access to services: factors and reconstructed factors NB: Prefix RED denotes factor reconstructed from the sub-set of variables




GHQ12 score of 4+

Current smoker

Obese (BMI 30+)

Model

Factor

2

Odds ratio

Lower CI (95%)

Upper CI (95%)

Sig.

R (Cox & Snell)

1 FAC1_2

1.51

1.37

1.68

0.000

0.079

2 REDF1_2

1.57

1.41

1.75

0.000

0.078

3 FAC1_2

1.28

1.20

1.38

0.000

0.148

4 REDF1_2

1.30

1.21

1.40

0.000

0.145

5 FAC1_2

1.22

1.12

1.33

0.000

0.037

6 REDF1_2

1.25

1.15

1.37

0.000

0.036

7 FAC1_2

1.14

1.06

1.22

0.000

0.079

8 REDF1_2

1.12

1.04

1.21

0.002

0.077

9 FAC1_2

0.95

0.88

1.03

0.261

0.024

10 REDF1_2

0.95

0.87

1.03

0.205

0.024


20


C: Trust and Reciprocity factors Table 3.1 Correlation Matrix RTADVN Correlation

RTHELP

RTADVN

1

RTHELP

0.39

1

RTTRUST

0.35

0.34

RTTRUST

1

Table 3.2 Total Variance Explained Initial Eigenvalues Component

Total

% of Variance Cumulative %

1

1.72

57.26

2

0.67

22.34

57.26 79.60

3

0.61

20.40

100.00



a

Component


1

1

RTADVN

0.77

0.449

RTHELP

0.76

0.444

RTTRUST

0.74

0.428

a

1 Factor extracted.


Table 3.4: OLS Model summary: Dependent variable Trust factor_1. R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

R Change

F Change Sig. F Change

1

0.771

0.594

0.594

0.637

0.594

8598.207

2

0.921

0.848

0.848

0.390

0.253

9771.808

3

1

1

1

0

a

Predictors: (Constant), RTADVN

b

Predictors: (Constant), RTADVN, RTHELP

c

Predictors: (Constant), RTADVN, RTHELP, RTTRUST

0.152 .

0.000 0.000 .

21

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Trust and reciprocity factors) Table 3.5: Multivariate analysis of relationship of health to trust & reciprocity: factor and reconstructed factor NB: Prefix RED denotes factor reconstructed from the sub-set of variables


Model

Factor

Self assessed fair, bad or

1 2

very bad health


GHQ12 score of 4+

Current smoker

Obese (BMI 30+)

2

Odds ratio

Lower CI (95%)

Upper CI (95%)

Sig.

FAC1_3

1.35

1.18

1.54

0.000

0.078

REDF1_3

1.34

1.17

1.54

0.000

0.078

3

FAC1_3

1.13

1.05

1.22

0.001

0.147

4

REDF1_3

1.11

1.03

1.20

0.008

0.147

5

FAC1_3

1.29

1.18

1.41

0.000

0.038

6

REDF1_1

1.31

1.19

1.44

0.000

0.038

7

FAC1_3

1.11

1.03

1.18

0.003

0.077

8

REDF1_3

1.10

1.02

1.18

0.010

0.077

9

FAC1_3

1.24

1.15

1.34

0.000

0.032

10

REDF1_3

1.27

1.17

1.37

0.000

0.031


22

R (Cox & Snell)


D: Informal social network factors (received social support) Table 4.1: Correlation matrix CPSNET05

CPSNET07

CPSNET06

CPSNET08

CPSNET03

CPSNET02

CPSNET01

CPSNET10

CPSNET09

CPSNET05

1

CPSNET07

0.42

1.00

CPSNET06

0.41

0.34

1.00

CPSNET08

0.35

0.31

0.32

1.00

CPSNET03

0.18

0.19

0.15

0.12

1.00

CPSNET02

0.04

0.02

0.13

0.05

0.35

CPSNET01

0.13

0.08

0.06

0.10

0.34

0.27

1.00

CPSNET10

-0.08

-0.07

-0.07

-0.13

-0.05

-0.08

-0.09

1.00

CPSNET09

0.12

0.08

0.12

0.17

0.14

0.12

0.15

-0.12

1.00

CPSNET04

0.07

0.07

0.09

0.17

0.23

0.20

0.24

-0.17

0.19

CPSNET04

1.00

1.00

Table 4.2: Total variance explained Initial Eigenvalues Component

Total

% of Variance

Cumulative %

1

2.53

25.29

25.29

2

1.55

15.46

40.75

3

1.08

10.75

51.51

4

0.87

8.68

60.19

5

0.80

7.96

68.14

6

0.76

7.63

75.78

7

0.67

6.71

82.49

8

0.65

6.48

88.97

9

0.57

5.69

94.65

10

0.53

5.35

100.00



a


Component 1

2

3

CPSNET05

0.78

1

2

3

0.377

0.003

0.047

CPSNET07

0.74

0.364

-0.005

0.081

CPSNET06

0.71

0.342

0.015

0.030

CPSNET08

0.64

0.289

-0.066

-0.213

CPSNET03

0.26

0.096

-0.37 0.76

0.063

0.442

CPSNET02

0.72

-0.047

0.432

0.026

CPSNET01

0.69

-0.23

-0.020

0.392

-0.051

0.73

0.018

0.113

0.593

CPSNET09

0.20

-0.60

0.018

0.024

-0.440

CPSNET04

0.43

-0.59

-0.037

0.179

-0.399

CPSNET10

a

Coefficients less than 0.2 not shown

a

3 factors (components) extracted


23

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Informal social network factors) Table 4.4 OLS Model Summary: Dependent Variable: Support factor_1 R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

R Change

F Change

1

0.776

0.602

0.602

0.631

0.602

8972.52

0.000

2

0.898

0.806

0.806

0.441

0.204

6208.69

0.000

3

0.963

0.928

0.928

0.268

0.122

10077.38

0.000

4

0.997

0.994

0.994

0.077

0.066

65320.22

0.000

5

0.998

0.995

0.995

0.068

0.001

1676.16

0.000

6

0.999

0.997

0.997

0.054

0.002

3380.80

0.000

7

1.000

0.999

0.999

0.030

0.002

13993.69

0.000

8

1.000

0.999

0.999

0.024

0.000

3019.84

0.000

9

1.000

1.000

1.000

0.018

0.000

5132.43

10

1

1

1

0

0.000 .

Sig. F Change

0.000 .

a

Predictors: (Constant), CPSNET05

b

Predictors: (Constant), CPSNET05, CPSNET07

c

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06

d

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08

e

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04

f

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04, CPSNET03

g

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04, CPSNET03, CPSNET02

h

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04, CPSNET03, CPSNET02, CPSNET01

I

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04, CPSNET03, CPSNET02, CPSNET01, CPSNET09

j

Predictors: (Constant), CPSNET05, CPSNET07, CPSNET06, CPSNET08, CPSNET04, CPSNET03, CPSNET02, CPSNET01, CPSNET09, CPSNET10

Table 4.5 OLS Model Summary: Dependent Variable: Support factor_2 R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

R Change

F Change

Sig. F Change

1

0.760

0.577

0.577

0.650

0.577

8087.97

0.000

2

0.902

0.813

0.813

0.432

0.236

7477.98

0.000

3

0.980

0.960

0.960

0.199

0.147

21900.86

0.000

4

0.991

0.983

0.983

0.132

0.022

7571.29

0.000

5

0.998

0.996

0.996

0.064

0.013

19058.45

0.000

6

1.000

0.999

0.999

0.028

0.003

24589.74

0.000

7

1.000

1.000

1.000

0.014

0.001

16666.47

0.000

8

1.000

1.000

1.000

0.004

0.000

61698.42

0.000

9

1.000

1.000

1.000

0.003

0.000

10234.87

10

1

1

1

0

0.000 .

0.000 .

a

Predictors: (Constant), CPSNET03

b

Predictors: (Constant), CPSNET03, CPSNET02

c

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01

d

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04

e

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04, CPSNET10

f


g

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04, CPSNET10, CPSNET08, CPSNET09

h

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04, CPSNET10, CPSNET08, CPSNET09, CPSNET06

i

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04, CPSNET10, CPSNET08, CPSNET09, CPSNET06, CPSNET07

j

Predictors: (Constant), CPSNET03, CPSNET02, CPSNET01, CPSNET04, CPSNET10, CPSNET08, CPSNET09, CPSNET06, CPSNET07, CPSNET05

24

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (informal social network factors) Table 4.6 OLS Model Summary: Dependent Variable: Support factor_3 R

R

2

Adj R

2

SE of Est.

Change Statistics 2

Model

R Change

F Change

Sig. F Change

1

0.728

0.530

0.530

0.685

0.530

6684.89

0.000

2

0.891

0.795

0.795

0.453

0.264

7625.04

0.000

3

0.975

0.950

0.950

0.224

0.155

18368.55

0.000

4

0.987

0.974

0.974

0.162

0.024

5324.78

0.000

5

0.993

0.986

0.986

0.117

0.013

5448.32

0.000

6

0.997

0.995

0.995

0.073

0.008

9385.27

0.000

7

0.998

0.997

0.997

0.058

0.002

3534.52

0.000

8

0.999

0.999

0.999

0.037

0.002

8449.15

0.000

9

1.000

0.999

0.999

0.023

0.001

8798.77

10

1

1

1

0

0.001 .

0.000 .

a Predictors: (Constant), CPSNET10 b Predictors: (Constant), CPSNET10, CPSNET09 c Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04 d Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08 e Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07 f Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07, CPSNET03 g Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07, CPSNET03, CPSNET05 h Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07, CPSNET03, CPSNET05, CPSNET01 I Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07, CPSNET03, CPSNET05, CPSNET01, CPSNET06 j Predictors: (Constant), CPSNET10, CPSNET09, CPSNET04, CPSNET08, CPSNET07, CPSNET03, CPSNET05, CPSNET01, CPSNET06, CPSNET02

Table 4.7 Percent variation explained by each of 3 Support factors and selected variables

Dependent

R

R

2

2

Adj R SE of Est.

Factor_1

0.902

0.814

0.814

0.431

Factor_2

0.908

0.824

0.824

0.420

Factor_3

0.893

0.797

0.797

0.450


25

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Informal social support factors) Table 4.8: Percieved social support score vs. received social support (separate models) NB: For each health measure, two sets of models were run - one with PSSSCR2 and the other with the factors Health status measure


Model

Factor

Odds ratio

Lower CI (95%)

Upper CI (95%)

-no lack

0.50

0.36

0.70

0.000

-some lack

0.74

0.51

1.07

0.113

1 PSSCR2

-severe lack


0.073

0.81

0.71

0.93

0.002

FAC2_4 (family)

0.94

0.82

1.08

0.372

FAC3_4 (other)

1.04

0.95

1.14

0.374

-no lack

0.77

0.63

0.95

0.016

-some lack

0.92

0.73

1.16

0.467

0.146

1

4 FAC1_4 (friends)

0.92

0.86

1.00

0.040

FAC2_4 (family)

0.96

0.89

1.03

0.277

FAC3_4 (other)

0.99

0.93

1.06

0.776

-no lack

0.30

0.24

0.38

0.000

-some lack

0.45

0.35

0.57

0.000

0.147

0.000

5 PSSCR2

0.054

1

6 FAC1_4 (friends)

0.99

0.90

1.09

0.854

FAC2_4 (family)

0.92

0.85

1.01

0.085

FAC3_4 (other)

1.05

0.97

1.13

0.204

-no lack

0.71

0.58

0.87

0.001

-some lack

0.89

0.71

1.11

0.297

0.033

0.001

7 PSSCR2

0.084

1

8 FAC1_4 (friends)

1.06

0.98

1.14

0.145

FAC2_4 (family)

0.92

0.86

0.99

0.021

FAC3_4 (other)

1.03

0.97

1.10

0.344

-no lack

0.94

0.75

1.18

0.619

-some lack

0.90

0.70

1.16

0.424

0.078

0.723

9 PSSCR2

-severe lack 10 FAC1_4 (friends)

0.075

0.024

3 PSSCR2

-severe lack

Obese (BMI 30+)

0.000

2 FAC1_4 (friends)

-severe lack

Current smoker

R (Cox & Snell)

1

-severe lack

GHQ12 score of 4+

2

Sig.

0.027

1 1.01

0.93

1.09

0.787

FAC2_4 (family)

1.08

1.00

1.17

0.041

FAC3_4 (other)

0.96

0.89

1.05

0.401

0.026

Variables entered in each model: age (10-year age bands), sex, equivalised household income (quintile), education qualification (none, GCSE/CSE, A level or higher), economic activity (un/employed, inactive, retired) *PSSSCR2 is the percieved social support score grouped into 3 categories.

26

Social Capital/Social Exclusion condensed module National Centre for Social Research ..cont (Informal social support factors) Table 4.9: Percieved social support score vs. received social support (combined model) NB: For each health measure, a combined model was run including both PSSSCR2 and the factors Health status measure


Model

Variable/ Factor

Odds ratio

Lower CI (95%)

Upper CI (95%)

-no lack

0.53

0.37

0.75

0.000

-some lack

0.78

0.54

1.14

0.198

0.83

0.72

0.95

0.008

FAC2_4 (family)

0.99

0.86

1.15

0.937

FAC3_4 (other)

1.01

0.92

1.11

0.824

-no lack

0.80

0.65

1.00

0.046

-some lack

0.94

0.74

1.19

0.613

1

FAC1_4 (friends)

0.93

0.86

1.00

0.052

FAC2_4 (family)

0.96

0.89

1.03

0.254

FAC3_4 (other)

0.99

0.92

1.06

0.788

-no lack

0.31

0.24

0.39

0.000

-some lack

0.45

0.35

0.58

0.000

0.000

3 PSSCR2

1

FAC1_4 (friends)

1.01

0.92

1.12

0.782

FAC2_4 (family)

0.99

0.90

1.08

0.820

FAC3_4 (other)

1.02

0.94

1.11

0.589

-no lack

0.72

0.59

0.89

0.002

-some lack

0.90

0.72

1.13

0.366

0.001

4 PSSCR2

-severe lack

Obese (BMI 30+)

0.062

2 PSSCR2

-severe lack

Current smoker

1

FAC1_4 (friends)

-severe lack

GHQ12 score of 4+

0.001

1 PSSCR2

-severe lack


Sig.

1

FAC1_4 (friends)

1.05

0.97

1.13

0.223

FAC2_4 (family)

0.94

0.87

1.01

0.077

FAC3_4 (other)

1.02

0.95

1.09

0.659

-no lack

0.90

0.71

1.13

0.369

-some lack

0.87

0.67

1.12

0.286

0.556

5 PSSCR2

-severe lack

1

FAC1_4 (friends)

1.00

0.92

1.08

0.920

FAC2_4 (family)

1.09

1.00

1.18

0.046

FAC3_4 (other)

0.97

0.89

1.05

0.445

Variables entered in each model: age (10-year age bands), sex, equivalised household income (quintile), education qualification (none, GCSE/CSE, A level or higher), economic activity (un/employed, inactive, retired) *PSSSCR2 is the percieved social support score grouped into 3 categories.

27


E: Organisational membership and participation Table 5.1 Correlations of membership of organisations and regular participation in organisations

Names

Variables

Political parties

cporg01 / cpjorg01

Correlation Coefficients 0.75

Trade unions (incl students' union)

cporg02 / cpjorg02

0.74

Environmental group

cporg03 / cpjorg03

0.69

Parents'/School Association

cporg04 / cpjorg04

0.76

Education, arts, music group/evening class

cporg05 / cpjorg05

0.78

Tenants'/ Residents' group or Neighbourhood watch

cporg06 / cpjorg06

0.74

Religious group or church organisation

cporg07 / cpjorg07

0.86

Group for elderly people (eg lunch clubs)

cporg08 / cpjorg08

0.84

Youth group (e.g. scouts, guides, youth clubs etc)

cporg09 / cpjorg09

0.78

Sports Club

cporg10 / cpjorg10

0.89

Social club/working men's club

cporg11 / cpjorg11

0.89

Women's Institute/Townswomen's Guild

cporg12 / cpjorg12

0.92

Women's Group

cporg13 / cpjorg13

0.92

Other group or organisation

cporg14 / cpjorg14

0.82

Total count of membership/participation

cporg/ cpjorg

0.82

cpjorg : regular participation in listed organisations cporg : membership of listed organisations cpjorg: total count of regular participation in organisations cporg: total count of membership of organisations

28


APPENDIX A

Social Capital/ Exclusion questionnaire module (HSE 2000)

NB: Variable names in bold. {All aged 16 and over} CpArea "How many years ^havehas[PNo] ^youname[PNo] lived in this local area?" : (less "Less than 1 year", one "1 year but less than 2 years", two "2 years but less than 5 years", five "5 years but less than 10 years", ten "10 years or more") CpEnjy "I would like to ask you about your local area. Would you say this area ...READ OUT... @/@/ ...is a place you enjoy living in or not?" : (yes "Yes, a place I enjoy living in", no "No, not a place I enjoy living in", neither "(Neither)") CpAft "@/Is a place where neighbours look after each other or not?" : (yes "Yes, is a place where neighbours look after each other", no "No, is not a place where neighbours look after each other", neither "(Neither)") CpPly "@/Has things for young children - playgrounds and parks for example or not?" : (yes "Yes, has things for young children", no "No, does not have things for young children", neither "(Neither)") CpTran "@/Has good local transport or not?" : (yes "Yes, has good local transport", no "No, does not have good local transport", neither "(Neither)") CpLeis "@/Has good leisure things for people like yourself - leisure centres or community centres, for example, or not?" : (yes "Yes, has good leisure things", no "No, does not have good leisure things", neither "(Neither)") CpDark "How safe do you feel walking alone in this area after dark? Would you say you feel ...READ OUT... " :(safe "...very safe,", fsafe "...fairly safe,", unsaf "...a bit unsafe,", vunsaf "...or, very unsafe?", Never "(Never goes out alone)") {IF CPDark = Never}

29

Social Capital/Social Exclusion condensed module National Centre for Social Research CPWld "If you did go out, how safe @Iwould@I you feel walking alone in this area after dark? Would you say you would feel ...READ OUT... " : (safe "...very safe,", fsafe "...fairly safe", unsaf "...a bit unsafe,", vunsaf "...or, very unsafe?") CpNgh "SHOW CARD O. For the following things I read out, can you tell me how much of a problem they are in your local area.@/How much of a problem is ...READ OUT..@/@/...noisy neighbours or loud parties?@/" : YProb CpTen "SHOW CARD O.@/@/...teenagers hanging around on the streets?" : YProb CpTrp "SHOW CARD O.@/@/...drunks or tramps on the streets?" : YProb CpRub "SHOW CARD O.@/@/...rubbish and litter lying around?" : YProb CpVnd "SHOW CARD O. How much of a problem is ...(READ OUT)... @/@/...vandalism, graffiti and deliberate damage to property?" : YProb CpShop "SHOW CARD P. From here, how easy is it for you to get to the following using your usual type of transport?@/@/INTERVIEWER: INCLUDE WALKING IF THAT IS THE RESPONDENT'S USUAL TRANSPORT. @/@/...READ OUT... a corner shop?@/" : YeasyDiff CpMrkt "SHOW CARD P. @/@/...a medium to large supermarket?" : YEasyDiff CpPO "SHOW CARD P. @/@/...a post office?" : YEasyDiff CpDoc "SHOW CARD P. @/@/...a GP (i.e. a family doctor)?" : YEasyDiff CpHosp "SHOW CARD P. @/@/..an accident and emergency department (i.e. casualty)?" : YEasyDiff

30

Social Capital/Social Exclusion condensed module National Centre for Social Research CpSNet "SHOW CARD Q. From this card, could you tell me please which, if any, of these ^youname[PNo] ^havehas[PNo] done in the past fortnight? @/@/PROBE: What else? CODE ALL THAT APPLY." : SET [9] OF (cpsnet01 "Went to visit relatives", cpsnet02 "Had relatives visit me", cpsnet03 "Went out with relatives", cpsnet04 "Spoke to relatives on the phone", cpsnet05 "Went to visit friends", cpsnet06 "Had friends visit me", cpsnet07 "Went out with friends", cpsnet08 "Spoke to friends on the phone", cpsnet09 "Spoke to neighbours", cpsnet10 "(None of these)") CpJoin "SHOW CARD R. ^DoDoes[PNo] ^youname[PNo] join in the activities of any of the organisations listed on this card, on a regular basis?" : YesNo {IF CpJoin = Yes} CpJOrg "Which ones? PROBE: Any others? @/@/CODE ALL THAT APPLY.@/" : SET [14] OF Yorganise (cpjorg01 to cpjorg14)

CARD R Political parties Trade unions (including student unions) Environmental group Parents’/School Association Tenants’/ Residents’ group or Neighbourhood watch Education, arts or music group/evening class Religious group or church organisation Group for elderly people (eg lunch clubs) Youth group (eg scouts, guides, youth clubs etc) Women’s Institute/Townswomen’s Guild Women’s Group Social club/working men’s club Sports club Other group or organisation (GIVE DETAILS) None

{IF CpJOrg = othgrp} CpJOth "@/PLEASE SPECIFY." : STRING[60] CpMem "SHOW CARD R. ^AreIs[PNo] ^youname[PNo] currently a @Imember@I of any of the organisations on this card?" : YesNo {IF CpMem = Yes}

31

Social Capital/Social Exclusion condensed module National Centre for Social Research CpOrg "SHOW CARD R. Which ones? PROBE: Any others? @/@/CODE ALL THAT APPLY.@/" : SET [14] OF Yorganise (cporg01 to cporg14) {IF CpOrg = othgrp} CpOrgO "@/PLEASE SPECIFY.": STRING[60] CpHelp "Would you say that most of the time people ...READ OUT..." : (help "...try to be helpful,", lokout "or, just look out for themselves?", dep "(Depends)")

CpAdvn "Do you think most people ...READ OUT..." : (advn "...would take advantage of you if they got the chance,", fair "or, would try to be fair?", dep "(Depends)") CpTrst "Generally speaking, would you say that most people ...READ OUT..." : (trstd "...can be trusted,", care "or, you can't be too careful in dealing with people?", dep "(Depends)")

32


APPENDIX B

Overall distribution by response categories of questions in the social capital/exclusion module of the HSE.

Neighbourhood (positive aspects): Would you say this area is a place… variable 1.Yes 2. No ..you enjoy living in? cpenjy 84.4 11.7 ..where neighbours look after each other cpaft 65.2 28.0 ..has things for children? cpply 65.1 32.7 ..has good transport? cptrans 70.3 24.3 .. has good leisure things? cpleis 58.5 37.7

3. Neither 3.9 6.8 2.1 5.4 3.8

Neighbourhood (negative aspects): How much of a problem are the following in your area?… variable 1 Very big 2 Fairly 3 Not a 4 Not a problem big very big problem problem problem at all ..noisy neighbours or loud parties cpngh 2.2 4.4 21.2 71.9 ..teenagers hanging around cpten 6.0 14.6 30.0 48.6 ..drunks or tramps cptrp 1.2 3.4 16.3 78.2 ..rubbish or litter lying around cprub 7.2 14.8 32.1 45.6 ..vandalism/graffitti/deliberate damage cpvnd 6.8 14.9 31.9 45.8

5 (Don't know) 0.3 0.7 0.9 0.3 0.6

Neighbourhood: Fear/personal safety variable

1.very safe,

2.fairly safe,

cpdark

65.2

28.0

6.8

65.1

cpwld

2.1

70.3

24.3

5.4

29.5

40.2

18.5

11.7

Access to services: From here, how easy is it to get to the… variable 1 Very 2 Fairly 3 Fairly easy easy difficult ..corner shop cpshop 79.6 13.3 2.5 ..large supermarket cpmrkt 61.4 30.8 4.6 ..post office cppo 72.8 21.4 2.9 ..a GP (I.e. a family doctor) cpdoc 59.9 30.9 6.0 ..an A&E depatment cphosp 24.0 42.0 21.5

4 Very difficult 2.2 1.8 1.4 2.0 10.9

How safe do feel walking alone in this area after dark If you did go out, how safe would you feel? (combined cpdark+cpwld)

Trust and Reciprocity: Most of time people/most people.. 1. Yes .. try to be helpful cphelp 52.1 ..would take advantage if got the cpadvn 30.4 chance ..can be trusted cptrst 36.1

3.a bit 4.or, very 5 (Never unsafe, unsafe? go alone) 32.7

5 (Never go) 2.4 1.4 1.4 1.2 1.6

2. No 3. Depends 35.6 12.3 58.4 11.2 54.2

9.6

33

Social Capital/Social Exclusion condensed module National Centre for Social Research Informal Social Networks: Which, if any, have you done in the past fortnight? variable 1. Yes Went to visit relatives cpsnet01 64.3 Had relatives visit me cpsnet02 58.9 Went out with relatives cpsnet03 41.1 Spoke to relatives on the phone cpsnet04 87.2 Went to visit friends cpsnet05 59.6 Had friends visit me cpsnet06 54.1 Went out with friends cpsnet07 53.3 Spoke to friends on the phone cpsnet08 80.7 Spoke to neighbours cpsnet09 79.2 (None of these) cpsnet10 0.4 Frequency distribution of activities 0 0.4 1 2.1 2 4.5 3 8.3 4 13.3 5 16.4 6 16.0 7 13.3 8 12.6 9 13.0 100.0 Mean number of activities per person 5.78

Participation: Are you currently a member/ do you regularly participate in any of these organisations? Member Participant (cporg) (cpjorg) Political parties 4.1 3.1 Trade unions (incl students' union) 17.6 8.2 Environmental group 3.2 2.9 Parents'/School Association 6.3 10.5 Education, arts, music group/evening class 10.4 16.6 Tenants'/ Residents' group or Neighbourhood watch 10.8 10.7 Religious group or church organisation 18.1 22.9 Group for elderly people (eg lunch clubs) 4.5 6.3 Youth group (e.g. scouts, guides, youth clubs etc) 2.8 4.4 Sports Club 17.6 15.3 Social club/working men's club 34.4 35.6 Women's Institute/Townswomen's Guild 2.1 2.2 Women's Group 4.2 4.7 Other group or organisation 16.7 19.8 None of these 51.2 50.3 Number of organisations member of (max 14)

Mean number of organisations

0 1 2 3 4 5+

51.16 31.14 11.74 4.26 1.17 0.54 0.75

50.29 24.99 13.46 6.87 2.77 1.62 0.93

34

Social Capital/Social Exclusion condensed module National Centre for Social Research Appendix C

Recommended condensed self-completion Social Capital questionnaire YOUR LOCAL AREA

The following questions are about the local area in which you live. We are interested to find out about how life in your local area is related to health. 9.1.2 Q29 Q30

How long have you lived in this local area? Please say whether you agree or disagree with the following statements:

Write in

9.1.3

Years

Write in

Months Tick one box 555

Strongly agree

Agree

Disagree

Strongly disagree

2 3 Tick one box 556 Agree Disagree

4

a This area is a place I enjoy living in. 1 Strongly agree

b This area is a place where neighbours look after each other.

1 Strongly agree

Strongly disagree

2 3 Tick one box 557 Agree Disagree

4 Strongly disagree

c This area has good local transport. 1

2

Strongly agree

d This area has good leisure things for people like myself, leisure centres or community centres, for example.

1

Q31

From here, how easy is it for you to get to a medium to large supermarket using your usual type of transport?

1

Very easy Q32

From here, how easy is it for you to get to a post office using your usual type of transport?

1

Very big problem

Q33

In your local area how much of a problem are teenagers hanging around on the streets?

1

4

Tick one box 558 Agree Disagree

2

Very easy

3

Strongly disagree

3

4

Tick one box 559 Fairly easy Fairly

2

Very difficult

3

4

Tick one box 560 Fairly easy Fairly difficult 2

Very difficult

3

4

Tick one box 561 Fairly big Not a very problem big problem

2

3

Not a problem at all

4

35


Very big problem Q34

Q35

In your local area how much of a problem is vandalism, graffiti or deliberate damage to property?

Tick one box 562 Fairly big Not a very big Not a problem problem problem at all

1

Do you regularly join in the activities of any of these organisations?

2

3

Tick all that apply 01

Trade unions (including student unions)

Environmental groups

Parent-teacher association or school association

Tenants’ or residents’ group or neighbourhood watch

Education, arts, music or singing group (including evening classes)

Religious group or church organisation

Charity, voluntary or community group

Group for elderly or older people (eg lunch club)

Youth group (eg scouts, guides, youth club)

Women’s institute or Townwomen’s Guild or Women’s group

Social club (including working men’s club, Rotary club)

Sports club, gym, exercise or dance group

Other group or organisation

02

03

04

05

06

07

08

Go to Q36

09

10

11

12

13

14

OR No I don’t regularly join in any of the activities of these organisations 15

36

4


Can be trusted Q36

Generally speaking, would you say that most people can be trusted or you can’t be too careful in dealing with people?

1

Try to be helpful Q37

Would you say that most of the time people try to be helpful or just look out for themselves?

1

Take advantage Q38

Do you think most people would take advantage of you if they got the chance or would they try to be fair?

1

Tick one box 591 Can’t be too careful

2

Don’t know

8

Tick one box 592 Look out for themselves

2

Don’t know

8

Tick one box 593 Try to be fair

Don’t know

2

8

37

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