The Survey Questionnaire

In this research, choice exercises were used to look at what role the cultural offer of an area plays in determining people’s residential preferences. In other words, how important a neighbourhood’s cultural and leisure offer is compared to other characteristics, such as good transport connections, environmental/scenic value or access to retail facilities. This was done through a specifically designed questionnaire in which participants were asked to ‘create’ their ideal residential location.

In order to do this, one of the survey questions provided them with a list of five main attributes qualifying the location and 100 tokens to spend. The exercise consisted of using the tokens to ‘buy’ more or less of each attribute depending on how important it was to the participant. This process was used to reveal which locational characteristics people were prepared to ‘trade-in’ for others, and to what extent. The result was a ranking of locational attributes and their ‘relative value’ obtained through the analysis of the average distribution of tokens across the five choice bundles.

A second exercise involved the grouping of the five main attributes of residential location into couples. In order to pair up each attribute with all the others, 10 couples were created. Participants then had to choose one attribute over the other within each couple. Once again, statistical analysis, more specifically a Wilcoxon Signed Ranks Test, provided a relative ranking of the five attributes. This was particularly useful, as it provided results on the same specific question of the first exercise, but through a different tool, thus allowing checking the significance and reliability of both questions.

The third survey question presented a similar task to the first one, with five attributes to score and 100 tokens to “spend” across them. However, in this case it was the Culture & Leisure bundle to be divided up into its main components, and the result of the exercise was a ranking and scoring of the various components of culture in the two cities. This was particularly important and meaningful given the wide and inclusive definition adopted for “culture”. Knowing how highly cultural resources are considered would be of relative significance when these include diverse components such as museum and galleries and pubs and restaurants. Therefore, being able to look “into” the bundle and determine the importance attached to the

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individual components becomes paramount. Once again, analysing the average distribution of tokens across the various types of cultural resources allowed for a preliminary ranking and an idea of their relative valuation to be formed.

The results from the three main parts of the questionnaire were then utilised for a deeper statistical analysis aimed at highlighting relationships between higher or lower valuations given to the cultural sector as a whole or to its individual components, and demographic and socio- economic characteristics of respondents. This was made possible by the section of the questionnaire that collected information relative to gender, age, household composition, employment, qualifications and income of all participants.

As described in Chapter 2 (section 2.4.2.i), varying levels of cultural engagement have been linked to a number of demographic and socio-economic variables (DCMS, 2015), in particular employment, income levels, age and gender. Data relative to these characteristics was therefore gathered in the questionnaire, with the two-fold aim to verify whether there was an element of valuation independent from engagement and consumption, and to investigate the possibility of certain sections of the population having a different notion of what culture actually is, and therefore being “differently engaged”, rather than unengaged. Levels of educational attainment were also included in the survey questionnaire, as they have been highlighted in the cultural valuation literature as one of the main factors influencing the likelihood of cultural participation (Reeves, 2015). Finally, the presence and age of children in the household was considered an important factor in determining participants’ cultural preferences, with some cultural facilities and resources – such as libraries and youth sports clubs – being specifically targeted to children. The collection of this background information on the survey participants contributed to the study’s main aim, as it helped in the investigation of the links between geographical and socio- economic differences, diverging notions of what constitutes culture and different patterns of cultural valuation.

All statistical calculations and analyses were performed using the SPSS package. Logistic regression analyses were conducted to explore patterns of correlation between each background variable and the results in the scoring exercises. Specifically, negative binomial regression was chosen as the preferred statistical technique for this analysis. This choicewas dictated by the non-normal distribution of the data relative to the allocation of tokens, which constituted the dependent variable in the model. In particular, as the conditional variance was

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consistently greater than the conditional mean, Poisson regression was excluded and negative binomial regression was identified as a preferable option.

In terms of the goodness of fit of the chosen models, a number of statistical tests were selected and implemented. The Omnibus test indicates the degree to which the selected model represents a statistically significant improvement in fit compared with the null hypothesis model. The results of this test are reported at the bottom of each table as “chi-square likelihood ratio”, with the relative level of statistical significance. Other indications of the overall goodness of fit of the model are the level of deviance, and the Bayesian Information Criterion (BIC). A deviance level approaching the value of 1 indicates an overall goodness of fit of the model. Lower BIC values also contribute to determine the goodness of fit. Throughout this thesis, the values for both deviance and BIC were compared between the previously run Poisson regression models and the negative binomial, and the improvements in model fit are reported at the bottom of each table.

Initially, the intention was to also conduct a detailed statistical analysis of the possible relationships between area of residence within the city and patterns of cultural valuation. To this end, data on respondents’ postcode of residence was collected. However, many participants decided not to include this information, and the majority of those who provided it did so incompletely. Therefore, the dataset relative to the geographical distribution of respondents was too limited and too “dirty” to conduct any meaningful and reliable statistical test.

Finally, information relative to the frequency of use of the various components of Culture & Leisure was gathered in the last question of the survey. Frequency of use was coded in an ordinal scale of six, descending from “at least once a week” to “never”. This information was linked to the token exercise in question 3 of the questionnaire through its inclusion in the negative binomial regression model, as one of the independent variables. This way, it was possible to explore the relationship between the frequency with which a given resource is used and the importance attributed to it.