Outgroup prejudice among secondary pupils in northern England: are the predictors at the individual, school or neighbourhood level?
The aim of the present article is to examine what factors best predict levels of outgroup prejudice among Christian, Muslim and secular youth. This was done by administering the Outgroup Prejudice Index (OPI) among separate samples of the three groups in secondary schools in areas of England where these three groups are clearly visible.
The OPI is a scale of outgroup prejudice based on notions of proximity developed among Christian, Muslim and secular secondary school pupils (Brockett, Village and Francis, 2010). Previous study of a different sample of pupils in these areas had shown that notions of physical and social distance could be used to create scales for White attitudes towards Muslims (Brockett, Village and Francis, 2009). Built on this earlier work, the OPI is a scale that operates in a comparable way for both Asian/Muslim groups and for Whites who are Christian or of no religious affiliation.
We envisaged that the factors that best predict levels of outgroup prejudice among Christian, Muslim and secular youth might be related to individual pupils (their sex, age, friendships and religion), to where they live (social deprivation and ethnic make-up) and to their school (type of school, size, ethnic make-up, levels of social deprivation, academic achievement and the neighbourhood in which the school is located).
It must be said that attitudes towards outgroups in the samples were generally found to be positive or neutral rather than negative. Although outgroup prejudice was a minority position, it remains important to examine what factors predict it so that any perceived change in the future can be measured and monitored.
Secondary schools in three areas of northern England (Blackburn, Kirklees and York) were asked to participate in the study, and twenty-three agreed to do so. Of these, twenty provided at least forty pupil responses, and these schools were chosen for this analysis. Of the twenty schools, twelve were state comprehensive schools, four were voluntary aided church schools (two Roman Catholic and two Church of England), and four were independent schools. All but one were mixed-sex schools. Schools were asked to provide data on their roll number (RN), percentage of ethnically White pupils (%White), percentage of pupils eligible for free school meals (%FSM) and the percentage of eligible children that passed five or more GCSEs at grade C or above (%5GCSE). The latter is a standard measure of attainment for sixteen year olds. In a few cases data were not available for 2007 and were estimated from other years.
Statistics on levels of social deprivation and ethnic make-up were used to relate pupils and schools to their neighbourhoods. We used Lower Super Output Areas (LSOAs) data from the 2001 National Census (CASWEB, 2003) for all the school catchment areas. LSOAs are geographic areas that cover approximately 1500 residents (with a minimum of 400 households and a range of 1000 to 1800 residents). Data available at LSOA level included several key statistics, such as the proportion of various ethnic or religious groups. The percentage of Whites in the relevant LSOA was used as a proxy measure of ethnic and religious diversity within a pupil's home neighbourhood. Also linked to LSOAs was an index of multiple deprivation (IMD). The IMD is based on a range of social indicators including income, employment, health and education (Communities and Local Government, 2008). It is available through the website of the Office of National Statistics (ONS, 2011). Thus, for each pupil, the home neighbourhood data used in this analysis consisted of the IMD and percentage Whites. A similar procedure was used for the twenty schools in the sample, allowing assessment of the IMD and ethnic religious diversity of the neighbourhood in which each school was set (see neighbourhood data in Table 1).
The use of percentage Whites as a proxy for ethnic and religious diversity requires explanation. Across the three areas in which the study was based, ethnicity and religious affiliation are strongly correlated, reflecting the fact that most of the region is ethnically White and religiously Christian, and by far the largest ethnic minority in the region were Muslims of Asian origin. The catchment areas of the schools in the study comprised a possible 19, 975 LSOAs, and the religious and ethnic make-up of these are presented in Table 2a. The average ethnicity was 94.1 per cent White and 4.0 per cent Asian, though figures for individual neighbourhoods varied from virtually 0 per cent to 100 per cent in each case. A small proportion of neighbourhoods (0.7 per cent) had over 80 per cent Asians but over half (53.8 per cent) recorded none at all. Given this ethnic and religious mix in the region, there were predictably very high correlations between ethnic and religious make-up of LSOAs (see Table 2b). Given the nature of the data and the high sample size, all correlations were highly statistically significant; however, high levels of White ethnicity were most strongly predictive of low levels of Asian ethnicity (r = -0.97) and Muslim religion (r = -0.95). The correlation with Christian religion was lower (r = 0.83) because Whites were more likely than Asians to be religiously unaffiliated. Overall, these data suggest that the proportion of ethnic Whites in a neighbourhood was a strong negative predictor of the levels of Asian ethnicity and Muslim religion, and using a single indicator in this way is therefore justified.
Class teachers gave questionnaires to pupils during normal school activities between 2007 and 2008. All pupils were assured of anonymity and confidentiality and given the opportunity to opt out of the survey. Response rates were high, with nearly all pupils agreeing to complete the questionnaire. Over 95 per cent of participants indicated their religion as either 'no religion', 'Christian' or 'Muslim', and 2502 of these whose responses had no relevant missing data comprised the study sample, details of which are given in Table 1 above.
The OPI (Brockett, Village and Francis, 2010) is a six-item scale with a high score indicating greater levels of outgroup prejudice. As a measure of reliability, its Cronbach's alpha coefficiant is 0.85, and its validity has been tested against independent measures of religious and racial stereotyping. The items in the scale use social distance as an indication of prejudice against those of a different race or religion. In the first four items pupils were asked to rate their attitude towards the idea of families moving in next door who were Asian, Muslim, Black or Sikh (for White pupils) or White, Christian, Black or Sikh (for Asian pupils). Responses on a five-point scale ranged from 'I would love it' (scored 1) to 'I would hate it' (scored 5). In the remaining two items pupils were asked to respond to the following statements: 'People of a different race/colour should not hang out together' and 'People of a different religion should not hang out together'. In each case possible responses ranged from 'strongly disagree' (scored 1) to 'strongly agree' (scored 5).
The Astley-Francis Scale of Attitude Toward Theistic Faith (or TBS--Theistic Belief Scale--for short) was used to measure religious affect related to generalised theistic belief (Astley et al., in press). It consists of seven Likert items ('I know that God is very close to me'; 'God means a lot to me'; 'God helps me'; 'God helps me to lead a better life'; 'I know that God is very close to me'; 'Prayer helps me a lot'; 'I find it hard to believe in God'; and 'I think going to a place of worship is a waste of my time') with a five-level response scale that is reverse coded for the last two items. This scale was considered to be a better measure of religious salience than simple religious affiliation. The scale had a high internal consistency among pupils in this study (Cronbach's alpha = 0.96), and was positively correlated with items measuring frequency of attendance at services (r = 0.52, df = 2756, p < 0.001) and religious salience (r = 0.69, df = 2746, p < 0.001).
Other key variables were measured using various other instruments. Outgroup contact was assessed by a single question asking respondents how many friends they had who were of a different race. Answers were recoded into an ordinal scale such that 0 = none, 1 = 1, 2 = 2-5, and 3 = > 5. Sex was considered an important predictor variable to include in the model because it has been shown to be related to religious belief (Argyle and Beit-Hallahmi, 1975; Beit-Hallahmi and Argyle, 1997; Francis, 1997; Walter and Davie, 1998) and to prejudice (Hoxter and Lester, 1994; Altemeyer, 1998; Ekehammar, Akrami and Araya, 2003). It was coded as 0 = male and 1 = female. Age group (1 = 10-13 years, 2 = 14-16 years, 3 = 17-19 years) was also included because of the possibility that both prejudice and religiosity might vary during adolescence and to control for variations in age distributions between schools.
Linking neighbourhood, pupil and school data
Participants were asked to give the full postcode of their home address, and this was used to calculate demographic variables for pupils' home neighbourhoods. Some pupils were excluded from the analysis because they gave no postcodes, incomplete postcodes or postcodes that could not be found in the national dataset. Neighbourhood statistics for individual LSOAs were then matched to postcodes using data generated from the UKBorders website (Edina, 2011) and postcodes in use in 2007. This procedure was repeated for schools using school postcodes.
The data thus included variables measured at individual, school and neighbourhood level. The standard way of assessing the effects of community characteristics on individual attitudes or behaviours is to use multilevel regression models (Snijders and Bosker, 1999; Hox, 2002; Bickel, 2007), especially if subjects are clustered into particular contexts (such as pupils in the same school). Multilevel regression models not only adjust for lack of independence between sample members who reside in the same community, but also allow the effects of contextual-level variables (such as school characteristics) to be correctly specified.
In this study, the individual-level explanatory variables were those associated with pupils, such as their sex, age, religious affiliation and religious attitude. Pupils were potentially grouped in two independent ways: by living in the same neighbourhood and by going to the same school. Although in theory there could be an element of grouping at neighbourhood level (if lots of pupils came from the same neighbourhoods), in practice very few pupils came from the same LSOA, so effects related to home neighbourhood were treated as level 1 fixed effects. Pupils were, however, strongly grouped in schools, and so the most appropriate statistical model was a linear mixed model, using school as the subject (grouping) variable.
The initial analyses were therefore based on a mixed linear model with school as the grouping variable and fixed variables related to pupils, namely sex, age, friends of a different race, religious affiliation and TBS score. This analysis indicated a marked difference between White pupils (Christians or those of no religion) and Muslim pupils, and so it was decided to treat these two groups separately. All continuous variables were grand mean centred to reduce the effects of multicollinearity (Bickel, 2007). For Muslims, variance in the OPI associated with school contextual variables was very low, and a generalised linear model fitted to pupil data only was more appropriate than a mixed level model.
Basic metrics for the variables in the analysis are shown in Table 3. Mean scores on the TBS were highest for Muslims (32.9, SD = 2.7, n = 459), intermediate for Christians (21.9, SD = 6.6, n = 1216) and lowest for those of no religion (13.6, SD = 5.5, n = 781). The TBS was also significantly higher in female (22.2, SD = 8.6, n = 1380) than male (20.3, SD = 8.9, n = 1076) pupils, and in pupils aged 10-13 (22.9, SD = 8.2, n = 1195) compared with those aged 14-16 (20.1, SD = 9.1, n = 1026) or 17-19 (18.9, SD = 8.4, n = 235). The most religious pupils thus appeared to be young female Muslims, and the least religious appeared to be older males of no religion.
Relationships between the OPI and the various variables were first tested using a multilevel model on the overall data. The first step was to fit a null model (that is, one without any predictor variables, but which allows for the fact that observations were grouped by school) to assess how much of the variation in the OPI was associated with factors measured at the school contextual level (Table 4). The Intraclass Correlation Coefficient (ICC) is a standard measure of the proportion of variation in the dependent variable that can be explained by the grouping variable, and for these data it indicated that around 6 per cent of the variation in outgroup prejudice among pupils was related to variations in their schools. Although this seems small, the level indicated that multilevel modelling was an appropriate method for these data because there was significant variation in the OPI related to differences between schools rather than between pupils. As expected, adding the pupil-level data did little to reduce the ICC (because this is related to school-level variation), but it did significantly improve the fit of the model by reducing the overall amount of unexplained (residual) variance.
The results of these tests on the overall data are as follows. The OPI, and thus levels of outgroup prejudice, was higher among males than females and among younger pupils (especially 14-16 years olds), but lower among those who had friends of a different race compared with those who had none. In terms of religion, the results were somewhat surprising. In the overall data, mean OPI scores were lower for Muslims (14.4, SD = 4.0) than for either Christians (15.7, SD = 4.5) or those of no religion (15.9, SD = 4.1). However, after controlling for religious salience with the TBS, Christians appeared to be significantly more prejudiced than those of no religion. The OPI was strongly negatively correlated with attitude towards theistic belief, implying that religious salience reduced prejudice. However, among White (non-Muslim) pupils, affiliating with Christianity was associated with slightly higher levels of outgroup prejudice at any given level of theistic belief compared with those of no religion (Figure 1). Muslims had generally high levels of religious salience and correspondingly low levels of prejudice. As a result of the marked differences between Muslims and others in this initial analysis, the two groups were treated separately thereafter.
[FIGURE 1 OMITTED]
Individual, neighbourhood and school effects among White pupils
Multilevel model analysis for White pupils only is shown in Table 5, starting with the null model, then successively adding individual variables, neighbourhood variables and school-level (level 2) variables. The results for individual variables were as reported for the overall dataset, with significant positive effect on the OPI for males, 14-16 year olds and Christians, and significant negative effect for having friends of a different race and positive attitudes to theistic belief. When neighbourhood variables were added, prejudice was significantly increased by both higher levels of deprivation and higher levels of ethnic Whites in the neighbourhood (0.025 and 0.024 respectively). The effects were small, however, and the significance of the latter moved to the 10 per cent level in the final model. When school level variables were added they successfully explained the variance in OPI between schools: outgroup prejudice was slightly (but significantly) higher in church schools (compared to state schools) and slightly (but significantly) lower in schools with higher proportions of pupils with either eligibility for free school meals or GCSE passes (0.843 and -0.107 and -0.105 respectively).
Individual and neighbourhood effects among Muslim pupils
Variation in the OPI between schools was not significant for Muslim pupils, and so fitting a multilevel model of school contextual variables was inappropriate. Instead a generalised linear model was fitted to individual and neighbourhood variables, using a comparable method to the analysis for White pupils, as shown in Table 6. For Muslim pupils, the main predictors of variation in the OPI were sex (higher among males) and friends of another race. The latter significantly reduced prejudice in this group (-2.099). Neighbourhood effects were negligible, although there was a small but statistically significant effect of %Whites in the neighbourhood (-0.015), suggesting that, after allowing for individual factors, Muslims living with a higher percentage of Whites were less prejudiced than those living in areas with fewer Whites. The effect of religion in this group was difficult to assess because the scores for the TBS were uniformly high and showed little variation. The generally low levels of prejudice among Muslims compared with other pupils could be linked to the high salience of their religion. However, without a higher proportion of non-religious Asians in the sample this is difficult to test. What is true is that Muslim belief/affiliation was not associated with above average levels of outgroup prejudice.
So are the predictors of outgroup prejudice at the individual, school or neighbourhood level? Six main findings emerge from this study, some predictable from other work on prejudice, but others apparently new. The evidence to support the findings is laid out in the various tables below. The new findings implied by the results suggest that outgroup prejudice is predicted by somewhat different factors among White pupils in the region than among Muslims.
1 Girls are less prejudiced than boys. There was strong evidence for this for both Whites and Muslims.
2 Having friends of a different race reduces prejudice. There was strong evidence for this also for both Whites and Muslims.
3 Mid-teens are more prejudiced than other adolescents. Prejudice peaked among mid-teens for Whites, but not Muslims.
4 Christian affiliation is associated with increased prejudice, although greater religious salience reduces prejudice.
5 Home neighbourhood has a slight effect on Muslim pupils' prejudice.
6 School effects are only evident for White pupils, and school neighbourhood has rather little effect.
1. Sex. That females are less prejudiced than males is a well-known trend documented in much sociological research on prejudice (Ekehammar and Sidanius, 1982; Bierly, 1985; Qualls, Cox and Schehr, 1992; Altemeyer, 1998), including some specifically on adolescents (Moore, Hauck and Denne, 1984; Hoover and Fishbein, 1999). It is true that this is not a universal trend: research suggests, for example, that females have more implicit prejudice than males (e.g. Ekehammar, Akrami and Araya, 2003) and that there is no difference in terms of closer social relations (e.g. Hoxter and Lester, 1994). In our sample, the individual male score was significantly higher for males (1.553) than for females (see Table 4). It is interesting to note that Bevelander and Otterbeck (2010: 10-12) found no difference between the sexes as a whole in attitudes towards Muslims among 15-19 year olds in Sweden, although the individual year groups did show some differences between boys and girls.
2. Friendships. That having friends of a different race reduces outgroup prejudice is also an expected and well-known trend (e.g. Allport, 1958; Jackman and Crane, 1986; Hamberger and Hewstone, 1997; Pettigrew, 1997, 1998; Levin, van Laar and Sidanius, 2003; Pettigrew and Tropp, 2006). This trend is also present in studies with children and adolescents specifically (Aboud, Mendelson and Purdy, 2003; McGlothlin, Killen and Edmonds, 2005; McGlothlin and Killen, 2006; White et al., 2009). There is also a correlation between having friendships with people of a different religion and reduced prejudiced towards people of different religions (e.g. Paolini et al., 2004; Pettigrew and Tropp, 2006; Pettigrew et al., 2007; Lee et al., 2009).
3. Age. The literature on outgroup prejudice suggests that racial and ethnic bias is present from about the age of four and begins to decline from around the ages of five to seven (e.g. Aboud, 1988; Doyle, Beaudet and Aboud, 1988; Powlishta et al., 1994). For outgroup prejudice during the adolescent stage, however, the findings vary. In our sample, prejudice peaked among mid-teens for Whites (although not Muslims), but that mid-teens in particular are more prejudiced than other adolescents is not evidenced in literature. There is research that suggests a pattern of lower prejudice in earlier than in later teenage years (White et al., 2009), and in particular increasing between the ages of ten and fourteen to sixteen (Black-Gutman and Hickson, 1996; Rutland, 1999), remaining stable between fourteen and eighteen and then increasing again between eighteen and twenty-three (Hoover and Fishbein, 1999). Conversely, Robinson, Witenberg and Sanson (2001: 86; Bevelander and Otterbeck, 2010: 3) claim that research shows that as adolescents get older they become more tolerant of people with different beliefs. And there is also research that finds no age-related differences between eleven and sixteen (Moore, Hauck and Denne, 1984), and research that shows explicit prejudice declining from age ten while implicit prejudice remains stable from age six to adulthood (Baron and Banaji, 2006).
4. Religion, specifically Christian affiliation. This is the most interesting finding--and the most complicated. Our data shows that Christian affiliation is associated with increased prejudice, although greater religious salience--a more positive attitude towards theistic religion--reduces the prejudice. The three previous individual factors (sex, friendship and age) are well evidenced by other research, but this finding is relatively new. The analysis was complicated by the fact that Muslim pupils are very different from non-Muslim pupils with respect to religious salience and so had to be treated separately for some analyses.
A curvilinear relationship between Christianity and prejudice, with Christians being generally more prejudiced than non-religious people but prejudice declining with increased salience, is a pattern to emerge from other studies (e.g. Gorsuch and Aleshire, 1974; Batson, Schoenrade and Ventis, 1993; Altemeyer, 1996). A more limited number of studies have also reported this pattern in Europe (Bagley, 1970; Scheepers, Gijsberts and Hello, 2002) and our research has added significant new evidence to support these interesting findings. However, there is evidence from elsewhere that seems to challenge this pattern. There have been two recent studies on prejudice towards Muslim practices among European Christians that found that while a majority of Christians are no more prejudiced than non-Christians (Fetzer and Soper, 2003; Saroglou et al., 2009), those with 'orthodox' beliefs are more prejudiced (Saroglou et al., 2009). Additionally, research seeking to explain such findings has concentrated on the factors within religiosity that encourage or discourage prejudice and has identified religious fundamentalism as a factor that increases prejudice (Altemeyer and Hunsberger, 1992; Wylie and Forrest, 1992; Kirkpatrick, 1993; Laythe, Finkel and Kirkpatrick, 2001). Such findings suggest a complex relationship between religiosity and prejudice and provide impetus for further research.
5. Home neighbourhood (social deprivation and ethnic make-up). These findings were found to have a slight but not very strong effect. There seems be consensus in the literature that greater neighbourhood deprivation equates with less social cohesion (e.g. Tolsma, van der Meer and Gesthuizen, 2009; Laurence, 2011) but findings from research on the effects of ethnic diversity are more mixed. Some argue that more diversity equates with less prejudice (e.g. Oliver and Wong, 2003; Semyonov and Glikman, 2009), while others argue that it equates with less cohesion (e.g. Alesina and Ferrara, 2002; Putnam, 2007).
The first finding from our sample was that for White pupils higher prejudice was associated with living in more socially deprived areas. Other research has found that Swedish children in higher socio-economic homes had more positive attitudes towards Muslims (Bevelander and Otterbeck, 2010: 15). This accords with the 'power-threat hypothesis' whereby negative attitudes arise towards groups seen as economic, social and/or political competitors (Bevelander and Otterbeck, 2010: 4, citing Blalock, 1967). The second finding from our sample was that for Muslim pupils there was a slight indication that those living in areas with more White residents were less prejudiced than those who lived in areas with more Asians and/or Muslims. This accords with Allport's (1958) contact hypothesis, which suggested that outgroup attitudes become more positive with increased contact between groups of similar socio-economic background.
6. School (type of school, size, ethnic make-up, levels of social deprivation, academic achievement and the neighbourhood where it is located). School effects are only evident for White pupils and may be associated with going to church schools (higher prejudice), having a bigger mix of disadvantaged children (lower prejudice) and better academic standards (lower prejudice). School neighbourhood has rather little effect.
That the four church schools showed slightly increased prejudice despite higher Christian affiliation and salience may be explainable by the increasing sense of religious identity they foster. Little empirical research has been published on prejudice in faith schools, although some research into Catholic schools published in the 1990s suggests that they do not encourage students to be more prejudiced than others and are not necessarily divisive (Grace, 2003). However, this issue needs further investigation. That attending a school with more socially disadvantaged children (as evidenced by numbers of free school meals) reduced levels of prejudice for White pupils while living in more socially deprived areas increased it seems to fit with Allport's (1958) hypothesis that contact engenders tolerance. The reduction in prejudice associated with better academic standards was also found by Bevelander and Otterbeck in Sweden where higher grades at school correlated with higher socio-economic background and with more positive attitudes towards Muslims (2010: 13).
This study set out to examine the Outgroup Prejudice Index and assess what factors best predict levels of outgroup prejudice among eleven- to sixteen-year-old adolescents living in northern England. We envisaged that these factors would be related to individual pupils (their sex, age, friendships and religion), to where they live (social deprivation and ethnic make-up) and to their school (type of school, size, ethnic make-up, levels of social deprivation, academic achievement and the neighbourhood where it is located).
Although the effects were small the results were statistically significant and six main conclusions emerged from the data.
First, in terms of individual factors, it is clear that sex is significant in shaping levels of outgroup prejudice among young people in the present study: girls were less prejudiced than boys.
Second, also in terms of individual factors, it is clear that having friends of a different race reduced young people's outgroup prejudice. There was strong evidence for this for both Whites and Muslims.
Third, again in terms of individual factors, it is clear that age played a significant role in shaping levels of young people's outgroup prejudice. Those in their mid-teens had less positive attitudes towards outgroups. Both before and after this period their outgroup prejudice was significantly less.
Fourth, again in terms of individual factors and perhaps most interestingly, it is clear that being Christian plays a more significant role in raising levels of young people's outgroup prejudice than being Muslim or secular. Overall, Muslim and secular youth growing up in northern England show lower levels of outgroup prejudice than is the case among young Christians. When religious salience is taken into consideration, however, Christian prejudice is significantly reduced. This finding deserves further research.
Fifth, the data shows that after taking these more significant individual factors into account, home neighbourhood factors (social deprivation and ethnic make-up) have some slight effect in shaping outgroup prejudice within the overall model. In this limited sense home neighbourhood is important.
Sixth, the effects of school factors (type of school, size, ethnic make-up, levels of social deprivation and academic achievement) are only evident for White pupils and may be associated with going to church schools (higher prejudice), having a bigger mix of disadvantaged children (lower prejudice) and better academic standards (lower prejudice). School neighbourhood has rather little effect.
Taken together these findings demonstrate that individual differences in outgroup prejudice among young people are a complex function primarily of individual factors (their sex, age, friendships and religion), and secondarily of aspects of residence and school type. The residential aspects were social deprivation and ethnic make-up and the school aspects were type of school, size, ethnic make-up, levels of social deprivation, academic achievement and the neighbourhood where it is located. Building on previous research, the present study has contributed to knowledge by succeeding in measuring outgroup prejudice among secular, Christian and Muslim and young people within the same study and thereby confirming the usefulness of the Outgroup Prejudice Index in extending the scope of studies in the psychology of prejudice within a multi-faith context.
Help with the data from Dr Andrew Village, York St John University, is very gratefully acknowledged.
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Adrian Brockett and Kate Wicker York St John University
Address for correspondence
Adrian Brockett, Faculty of Education and Theology, York St John University, Lord Mayor's Walk, York, YO31 7EX, United Kingdom. E-mail firstname.lastname@example.org
Table 1 Data for the twenty schools in the sample Mean Range School roll 897.6 114-1733 % Ethnically White (school roll) 75.8 0-98 % Eligible for free school meals 12.4 0-39 % With 5+ GCSE passes 62.3 36-98 School neighbourhood: Index of Multiple Deprivation (IMD) 20.4 8-60 % Ethnically White (school neighbourhood) 91.1 21-100 Table 2 Ethnic and religious 2001 Census Data for the 19,975 Lower Super Output Areas within the catchment areas of all the schools in the study (a) Mean percentages Percentage of Mean SD Minimum Maximum area population: White 94.1 13.3 0.5 100.0 Asian 4.0 12.0 0.0 97.9 Other ethnicity 1.9 3.4 0.0 82.3 Christian 74.4 13.6 0.0 97.7 Muslim 3.4 11.1 0.0 93.8 Other religion 0.9 2.4 0.0 72.6 No religion 13.3 6.4 0.0 50.5 (b) Correlation matrix Asian Other Christian Muslim ethnicity White -0.97 -0.47 0.83 -0.95 Asian 0.24 -0.80 0.98 Other ethnicity -0.42 0.25 Christian -0.79 Muslim Other religion Other No religion religion White -0.21 0.09 Asian 0.16 -0.17 Other ethnicity 0.22 0.25 Christian -0.30 -0.38 Muslim 0.08 -0.17 Other religion 0.07 Table 3 Summary of individual-level variables Nominal Value Coded Number % variables Sex Male 0 1076 43.8 Female 1 1380 56.2 Age group 10-13 1 1195 48.7 14-16 2 1026 41.8 17-19 3 235 9.6 Friends of a Some 0 1419 57.8 different race None 1 1037 42.2 Religion Christian 1 1216 49.5 Muslim 2 459 18.7 None 3 781 31.8 Continuous variables Mean SD Minimum Maximum Theistic Belief Scale 21.3 8.8 7 35.0 Neighbourhood IMD 22.0 16.3 1.6 75.3 Neighbourhood %White 87.1 22.5 4.6 100.0 Table 4 Multilevel linear model fitted to all data for individual variables Variable Value Null Individual Intercept 15.375 *** 14.590 *** Sex (Female) Male 1.553 *** Age (17-19) 10-13 0.800 * 14-16 1.027 ** Friends of Some -1.259 ** different race (none) Religion (No Christian 0.739 ** religion) Muslim 0.411 TBS -0.141 *** -2loglikelihood 14028.564 13711.997 Deviance 316.567 *** Parameters 3 10 Residual 17.406 *** 15.274 *** Intercept 1.154 ** 1.295 ** ICC 6.2% 7.8% Note For nominal variables, reference value is given in parentheses. ICC = Intraclass Correlation Coefficient. * p < 0.05; ** p < 0.01; *** p < 0.001. Table 5 Multilevel linear models fitted to data for White pupils only Variable Value Null Individual Intercept 15.676 *** 14.301 *** Individual: Sex (Female) Male 1.573 *** Age (17-19) 10-13 0.708 14-16 1.151 ** Friends of Some -1.129 *** different race (none) Religion (No religion) Christian 0.815 *** TBS -0.146 *** Neighbourhood: IMD %White School: Type (State) Church Independent Number on roll %White pupils on roll % Eligible FSM % 5+ GCSEs School neighbourhood: IMD %White -2loglikelihood 11395.7 11146.218 Deviance 249.457 *** Parameters 3 9 Residual 17.239 *** 15.202 *** Intercept 1.698 ** 1.6622 ** ICC 9.0% 9.9% Variable Neighbourhood School Intercept 14.292 *** 13.902 *** Individual: Sex (Female) 1.593 *** 1.592 *** Age (17-19) 0.681 0.609 1.118 ** 1.160 ** Friends of -1.120 *** -1.087 *** different race (none) Religion (No religion) 0.842 *** 0.860 *** TBS -0.145 *** -0.147 *** Neighbourhood: IMD 0.025 ** 0.025 ** %White 0.024 * 0.021 School: Type (State) 0.843 * 1.017 Number on roll 0.000 %White pupils on roll 0.009 % Eligible FSM -0.107 * % 5+ GCSEs -0.105 *** School neighbourhood: IMD 0.018 %White 0.005 -2loglikelihood 11135.394 11104.295 Deviance 10.824 ** 31.099 *** Parameters 11 19 Residual 15.134 *** 15.166 *** Intercept 1.468 * 0.070 ICC 8.8% 0.5% Table 6 General linear models fitted to data for Muslim pupils only Variable Value Null Individual Neighbourhood Intercept 14.41 *** 14.626 *** 14.464 *** Individual: Sex (Female) Male 1.51 *** 1.47 *** Age (17-19) 10-13 0.356 0.35 14-16 -0.239 -0.26 Friends of Some -2.099 *** -2.031 *** different race (none) TBS 0.036 0.026 Neighbourhood: IMD -0.017 %White -0.015 *
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|Author:||Brockett, Adrian; Wicker, Kate|
|Publication:||Research in Education|
|Date:||Nov 1, 2012|
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