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Modelling Trajectories through the Educational System in North West England

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Title: Modelling Trajectories through the Educational System in North West England
Language: English
Authors: Penn, Roger, Berridge, Damon
Source: Education Economics. Dec 2008 16(4):411-431.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 21
Publication Date: 2008
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Foreign Countries, Young Adults, Educational Attainment, Influences, Models, Secondary Education, Exit Examinations, Scores, Gender Differences, Age Differences, Ethnic Groups, Social Class, Educational Environment
Geographic Terms: United Kingdom (England)
DOI: 10.1080/09645290802024744
ISSN: 0964-5292
Abstract: The main aim of this paper is to identify those school-level and locality-level factors that significantly affect each of the three stages in a young adult's educational trajectory in North West England: GCSE results, track taken at age 16 and "A"-level scores. By applying three-level models to data collected as part of the EFFNATIS project, we find no evidence of any locality-level effects. Overall, none of the explanatory variables conventionally considered to affect educational attainment had a consistent effect across "all" three stages. Rather, each explanatory variable had a contingent effect at specific points within the overall trajectory of educational outcomes. (Contains 7 tables and 9 notes.)
Abstractor: As Provided
Number of References: 75
Entry Date: 2008
Accession Number: EJ816573
Database: ERIC
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  Value: <anid>AN0035020633;ede01dec.08;2019Feb20.14:06;v2.2.500</anid> <title id="AN0035020633-1">Modelling trajectories through the educational system in North West England. </title> <p>The main aim of this paper is to identify those school‐level and locality‐level factors that significantly affect each of the three stages in a young adult's educational trajectory in North West England: GCSE results, track taken at age 16 and 'A'‐level scores. By applying three‐level models to data collected as part of the EFFNATIS project, we find no evidence of any locality‐level effects. Overall, none of the explanatory variables conventionally considered to affect educational attainment had a consistent effect across all three stages. Rather, each explanatory variable had a contingent effect at specific points within the overall trajectory of educational outcomes.</p> <p>Keywords: GCSE scores; track taken at age 16; 'A'‐level scores; multi‐level models; linear regression; multinomial logit</p> <hd id="AN0035020633-2">1. Introduction</hd> <p>The current paper presents an analysis of trajectories through the educational system in two localities in the North West of England. The data were collected as part of Effectiveness of National Integration Strategies for Children of International Migrants (EFFNATIS), a wider project funded by the European Union's Fourth Framework into the situation of children of international migrants in contemporary Europe (see Heckmann and Schnapper [<reflink idref="bib40" id="ref1">40</reflink>]). Part of the EFFNATIS project involved empirical research into the educational situation of such young people in Britain, France (Lambert and Peignard [<reflink idref="bib44" id="ref2">44</reflink>]) and Germany (see Worbs [<reflink idref="bib73" id="ref3">73</reflink>]). Information on a range of educational outcomes was obtained from a sample of 844 young adults aged between 16 and 25 years in Rochdale and Blackburn. The data presented here contains information on GCSE results with subsequent information on 'tracks' out of compulsory education at age 16 and on 'A'‐level scores. The structure of the data collected in Britain permitted the use of three‐level modelling of educational trajectories that incorporated both school‐level and locality‐level factors. The primary objective of this study is to identify those school‐level and locality‐level factors that significantly affect each of the three stages in a young adult's educational trajectory: GCSE results, track taken at age 16 and 'A'‐level scores using data collected as part of the EFFNATIS project.</p> <p>A detailed discussion of the factors that have been identified as determining differences in educational trajectories is presented in Section 3. The subset of EFFNATIS data analysed in this paper is described in Section 4. Various aspects of the modelling framework are discussed in Section 5: the need for a three‐level model to handle the hierarchical nature of the data structure is explained (Section 5.1); the types of model required to analyse the three responses are identified (Sections 5.2–5.4); and some general modelling issues are considered (Section 5.5). The results and conclusions are presented in Sections 6 and 7, respectively.</p> <hd id="AN0035020633-3">2 Sociological debate about educational attainment in Britain</hd> <p>Sociology of education in Britain can be seen in many ways as a longstanding debate since 1945 about the social factors that determine differences in educational trajectories and attainment. A series of factors have been identified as prominent including social class, gender, ethnicity and age as well as school‐level and community‐level variations.</p> <hd id="AN0035020633-4">2.1 Social class</hd> <p>In the immediate post‐war period social class was seen as the most powerful factor determining differences in educational results in Britain. Various authors (Floud, Halsey, and Martin [<reflink idref="bib28" id="ref4">28</reflink>]; Douglas [<reflink idref="bib22" id="ref5">22</reflink>]; Bernstein [<reflink idref="bib5" id="ref6">5</reflink>]; Willis [<reflink idref="bib72" id="ref7">72</reflink>]; Halsey, Heath, and Ridge [<reflink idref="bib39" id="ref8">39</reflink>]) emphasized the systematic disadvantages experienced by pupils from working‐class backgrounds within the British educational system. Despite the overall expansion of educational opportunities since the 1950s, the relationship between social class origins and educational outcomes is still seen by many commentators as a persistent feature of the British educational system (Heath and Clifford [<reflink idref="bib41" id="ref9">41</reflink>]; Marshall, Swift, and Roberts [<reflink idref="bib47" id="ref10">47</reflink>]). In recent years, issues of social and cultural 'capital' have been grafted onto this classical social class stem (Bourdieu [<reflink idref="bib7" id="ref11">7</reflink>]; Coleman [<reflink idref="bib14" id="ref12">14</reflink>]; Furstenberg and Hughes [<reflink idref="bib30" id="ref13">30</reflink>]; Sullivan [<reflink idref="bib68" id="ref14">68</reflink>]; Noguera [<reflink idref="bib54" id="ref15">54</reflink>]) to re‐emphasize the powerful link between an individual's socio‐economic background and his/her educational results. Notions of cultural 'capital' have also been used to suggest that parents with knowledge of how the educational system works are far more able to assist their children within the educational system itself. In 2005, the former Secretary of State for Education and Skills Ruth Kelly released evidence that social class differences in attainment had increased between 1998 and 2004 amongst pupils leaving primary schools in England (Department of Education and Skills [<reflink idref="bib18" id="ref16">18</reflink>]).</p> <hd id="AN0035020633-5">2.2 Gender</hd> <p>There has been a major transformation in the relationship between gender and educational attainment over the past 40 years. From a historic situation when girls performed worse in examinations at the end of compulsory education (Martin and Roberts [<reflink idref="bib48" id="ref17">48</reflink>]), the current pattern involves boys lagging well behind girls in levels of educational attainment in both GCSE and 'A'‐level examinations (Younger and Warrington [<reflink idref="bib75" id="ref18">75</reflink>]; Dolton et al. [<reflink idref="bib20" id="ref19">20</reflink>]; Gayle, Berridge, and Davies [<reflink idref="bib31" id="ref20">31</reflink>]). Various factors have been suggested to account for this development, including claims that there is a clash between the 'culture' of schools and that of adolescent boys (Mac an Ghaill [<reflink idref="bib46" id="ref21">46</reflink>]; Yates [<reflink idref="bib74" id="ref22">74</reflink>]; Epstein et al. [<reflink idref="bib26" id="ref23">26</reflink>]; Martino [<reflink idref="bib49" id="ref24">49</reflink>]), the gendered expectations of teachers themselves (Jones and Myhill [<reflink idref="bib43" id="ref25">43</reflink>]; Mendick [<reflink idref="bib50" id="ref26">50</reflink>]) and also that the curriculum itself penalizes boys over girls (Millard [<reflink idref="bib51" id="ref27">51</reflink>]; Francis [<reflink idref="bib29" id="ref28">29</reflink>]). Both Scott ([<reflink idref="bib65" id="ref29">65</reflink>]) and Bradley and Taylor ([<reflink idref="bib9" id="ref30">9</reflink>]) confirmed the continued centrality of gender for educational attainment in Britain with their recent multivariate analyses of British Household Panel Study data and Youth Cohort Study data, respectively.</p> <hd id="AN0035020633-6">2.3 Ethnicity</hd> <p>The importance of ethnic differences in educational attainment has been increasingly evident since the publication of the seminal Swann Report ([<reflink idref="bib69" id="ref31">69</reflink>]). Demack, Drew, and Grimsley ([<reflink idref="bib17" id="ref32">17</reflink>]) revealed an increasing polarization between ethnic majority and minority pupils in the period between 1988 and 1995 in their analysis of successive waves of the Youth Cohort Study. Modood and Berthond ([<reflink idref="bib52" id="ref33">52</reflink>]) have also shown that there are wide variations amongst ethnic groups in contemporary Britain in terms of educational performance. This theme has been reiterated in a succession of Official Reports into educational attainment (Church and Summerfield [<reflink idref="bib13" id="ref34">13</reflink>]; Pathak [<reflink idref="bib56" id="ref35">56</reflink>]; Owen et al. [<reflink idref="bib55" id="ref36">55</reflink>]; Cabinet Office 2002, 2003; Bhattacharyya, Ison, and Blair [<reflink idref="bib6" id="ref37">6</reflink>]). Bradley and Taylor ([<reflink idref="bib9" id="ref38">9</reflink>]) found that ethnic minority youths performed better on average than Whites, controlling for a range of socio‐economic and family background factors. However, they also argued for considerable ethnic heterogeneity in outcomes. Broadly speaking, Afro‐Caribbeans, Bangladeshis and Pakistanis achieved relatively poor results whilst Whites, Indians and Chinese performed relatively well. They also noted a complex set of interactions between ethnicity and gender within these overall patterns of attainment. A similar pattern was also highlighted earlier by Penn ([<reflink idref="bib58" id="ref39">58</reflink>]) in his analysis of the determinants of vocational training paths in Britain.</p> <hd id="AN0035020633-7">2.4 Language spoken at home</hd> <p>There is considerable evidence that speaking a language other than English is a powerful determinant of disadvantage in societies with large‐scale international migration (see Dustmann [<reflink idref="bib23" id="ref40">23</reflink>]; Shields and Price [<reflink idref="bib66" id="ref41">66</reflink>]; Dustmann and Fabbri [<reflink idref="bib24" id="ref42">24</reflink>]; Crul and Doomernik [<reflink idref="bib16" id="ref43">16</reflink>]; Esser [<reflink idref="bib27" id="ref44">27</reflink>]; Diefenbach [<reflink idref="bib19" id="ref45">19</reflink>]). The respondents analysed in this paper were all born in the United Kingdom and were educated in British schools where the sole medium of education is English (with the exception of certain areas of North and West Wales) (see Penn and Scattergood [<reflink idref="bib62" id="ref46">62</reflink>]; Penn, Perrett and Lambert [<reflink idref="bib60" id="ref47">60</reflink>]).</p> <p>In the current analysis we distinguished those respondents who spoke English at home with their parents from those who did not. As is clear from the later Table 3, this covered all the White respondents but varying proportions of ethnic minority respondents: 43.2% of Pakistanis, 26.9% of Indians and 59.0% of Bangladeshis reported speaking languages other than English at home with their parents. This variable introduced an element of stratification within the ethnic minority respondents interviewed for the project and facilitated the testing of the putative effects of language used at home upon educational attainment.</p> <hd id="AN0035020633-8">2.5 Age</hd> <p>There has been a marked improvement in GCSE results since their inception (Demack, Drew and Grimsley [<reflink idref="bib17" id="ref48">17</reflink>]). This means that <emph>pari passu</emph> younger respondents on average would have better GCSE scores than those who were older. This could be either the result of improved teaching methods or the consequence of the examinations themselves becoming easier or of both factors combined. Interpretations of this contentious issue tend to be marked less by empirical evidence than by political ideology!</p> <hd id="AN0035020633-9">2.6 School effects</hd> <p>Factors associated with schools themselves have become of increasing concern to social scientists over the past 20 years. Smith and Tomlinson's ([<reflink idref="bib67" id="ref49">67</reflink>]) study of The School Effect argued strongly that schools had a more powerful effect on educational results than standard sociological factors such as class, ethnicity and gender. Dolton and Vignoles ([<reflink idref="bib21" id="ref50">21</reflink>]) and Bradley et al. ([<reflink idref="bib8" id="ref51">8</reflink>]) both reported a significant relationship between pupil–teacher ratios (a proxy for class size) and examination results. However, in their review of evidence on school effects in the USA, Ehrenberg et al. ([<reflink idref="bib25" id="ref52">25</reflink>]) sounded a strong note of caution, arguing that reduction in class sizes had proved inconclusive in terms of educational performance and attainment. Penn and Scattergood ([<reflink idref="bib62" id="ref53">62</reflink>]) modelled school effects in combination with social class, ethnicity and gender in their analysis of routes beyond post‐compulsory education but found that social class was a much more powerful factor than school effects, albeit primarily amongst the White respondents. Almost all South Asians in their study of pupils in Rochdale preferred to continue within full‐time education irrespective of either their socio‐economic background or their GCSE results.</p> <hd id="AN0035020633-10">2.7 Locality effects</hd> <p>There has also been a persistent view that there are significant spatial variations in educational attainment associated with locality (see Butler and Hamnett [<reflink idref="bib10" id="ref54">10</reflink>]; Webber and Butler [<reflink idref="bib71" id="ref55">71</reflink>]). Official data have consistently revealed a marked disparity between average GCSE results in deprived, inner‐city areas and those in suburban and rural localities (see Hamnett, Ramsden, and Butler [<reflink idref="bib38" id="ref56">38</reflink>]; Gordon and Monastiriotis [<reflink idref="bib36" id="ref57">36</reflink>]). Recently, researchers (see Gibson and Asthana [<reflink idref="bib32" id="ref58">32</reflink>]) have suggested an increasing polarization between different types of schools within specific localities.</p> <p>Clearly there are a range of factors that determine educational outcomes. The central issue is, of course, their relative significance (Penn [<reflink idref="bib57" id="ref59">57</reflink>]). There has been relatively little research on this issue utilizing a statistical modelling approach. Gayle, Berridge, and Davies ([<reflink idref="bib31" id="ref60">31</reflink>]) are a notable exception. Furthermore, as Connor et al. indicate, 'there is no consensus about the relative significance of different factors on attainment' ([<reflink idref="bib15" id="ref61">15</reflink>], 14). The present paper examines a wide range of explanatory variables within a statistical modelling framework in order to ascertain such relative effects, using the data‐set collected as part of the EFFNATIS Project.</p> <hd id="AN0035020633-11">3 The EFFNATIS Project</hd> <p>The data were collected during the British fieldwork for the EFFNATIS Project. This project was funded by the European Union and ran from 1999 to 2002. Information was obtained on educational attainment from a random sample of young people in North West England aged between 16 and 25 years. The sample was drawn in two localities (Rochdale and Blackburn) with significant numbers of children of international migrants, using addresses selected at random from the electoral roll. The overall EFFNATIS project involved a comparison between England, France and Germany and required at least 100 respondents from two different ethnic minority groups in each of the three countries as well as a control group from the autochthonous population. The English sample comprised 844 respondents. One hundred and twenty‐one respondents had either a missing value on the continuous covariate age, or missing values on the school‐level explanatory variables (see the next section). These respondents were deleted prior to the analysis, leaving 723 respondents from four ethnic groupings (see Table 1).</p> <p>Table 1. The EFFNATIS sample.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Ethnic grouping</td><td><italic>n</italic></td></tr></thead><tbody><tr><td>White</td><td char=".">413</td></tr><tr><td>Pakistani</td><td char=".">164</td></tr><tr><td>Indian</td><td char=".">105</td></tr><tr><td>Bangladeshi</td><td char=".">41</td></tr><tr><td>Total</td><td char=".">723</td></tr></tbody></table> </ephtml> </p> <p>Most South Asian respondents in the sample were also Moslems (see Table 2). This reflected post‐1945 patterns of immigration to North West England from the Indian subcontinent. Most Pakistanis originated from either Punjab or Kashmir, whilst most Indians in the two towns originated from Gujerat (Anwar [<reflink idref="bib3" id="ref62">3</reflink>]; Penn [<reflink idref="bib59" id="ref63">59</reflink>]). Bangladeshis, predominantly from Sylhet, were more recent arrivals and smaller in overall numbers in the North West (Penn and Scattergood [<reflink idref="bib61" id="ref64">61</reflink>], [<reflink idref="bib62" id="ref65">62</reflink>]).</p> <p>Table 2. Ethnic grouping and religious affiliation.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>Christian</td><td>Moslem</td><td>Other</td><td>None</td></tr></thead><tbody><tr><td>White</td><td char=".">299</td><td char=".">4</td><td char=".">11</td><td char=".">96</td></tr><tr><td>Pakistani</td><td char=".">0</td><td char=".">163</td><td char=".">1</td><td char=".">0</td></tr><tr><td>Indian</td><td char=".">1</td><td char=".">102</td><td char=".">2</td><td char=".">0</td></tr><tr><td>Bangladeshi</td><td char=".">4</td><td char=".">34</td><td char=".">1</td><td char=".">2</td></tr></tbody></table> </ephtml> </p> <p>A range of languages were used at home with parent(s). English was the most frequent (see Table 3) but a range of South Asian languages were also evident reflecting the variety of linguistic backgrounds amongst ethnic minority respondents in the sample.</p> <p>Table 3. Language used at home with parent(s).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>White</td><td>Pakistani</td><td>Indian</td><td>Bangladeshi</td></tr></thead><tbody><tr><td>English</td><td char=".">400</td><td char=".">67</td><td char=".">28</td><td char=".">23</td></tr><tr><td>Urdu</td><td char=".">0</td><td char=".">35</td><td char=".">1</td><td char=".">1</td></tr><tr><td>Punjabi</td><td char=".">0</td><td char=".">38</td><td char=".">5</td><td char=".">2</td></tr><tr><td>Gujerati</td><td char=".">0</td><td char=".">2</td><td char=".">67</td><td char=".">0</td></tr><tr><td>Mirpuri</td><td char=".">0</td><td char=".">10</td><td char=".">0</td><td char=".">0</td></tr><tr><td>Bengali</td><td char=".">0</td><td char=".">0</td><td char=".">0</td><td char=".">13</td></tr><tr><td>Other</td><td char=".">0</td><td char=".">3</td><td char=".">3</td><td char=".">0</td></tr></tbody></table> </ephtml> </p> <hd id="AN0035020633-12">4 The data</hd> <p>Respondents were asked to provide details of their GCSE results and also to provide the name of the school that they attended during their GCSE year (i.e. when they were 15 and 16 years old). This allowed the research team to construct a response variable based upon scoring each GCSE result[<reflink idref="bib1" id="ref66">1</reflink>] and calculating an overall GCSE score. This varied between 0 and 87. Respondents also provided information on their route out of compulsory education after their GCSE examinations. These were classified into four 'tracks'. A subset of respondents provided their 'A'‐level results. These three response variables – GCSE scores, post‐16 'tracks' and 'A'‐level scores – permitted a multi‐stage analysis of the determinants of routes through the educational system (see the next section).</p> <p>The name of the school where the respondent had taken his/her GCSE examinations permitted the construction of a range of explanatory variables associated with school type.[<reflink idref="bib2" id="ref67">2</reflink>] These included the age range of pupils, whether the school was selective, whether it was a denominational or 'faith' school,[<reflink idref="bib3" id="ref68">3</reflink>] whether it was a private or a state school, the local education authority (LEA) in which the school was situated, whether it was single‐sex or mixed, whether it was 'specially designated',[<reflink idref="bib4" id="ref69">4</reflink>] and a contextual variable that measured the percentage of pupils who had achieved five GCSE passes at grades A*–C over the previous four years.[<reflink idref="bib5" id="ref70">5</reflink>]</p> <p>The information on the schools where respondents had taken their GCSEs also permitted the construction of a locality variable. Most respondents (75.5%) had undertaken their GCSEs at schools in Blackburn (31.1%) and Rochdale (44.4%). A significant minority (17.7%) had obtained their GCSEs in localities contiguous to Blackburn and Rochdale. This is due to the not uncommon practice of parents selecting schools outside their immediate locality for their children at the transition from primary to secondary schools. (This is at age 11 in both Blackburn and Rochdale and in most other localities in the North West of England.)</p> <p>Rochdale and Blackburn have very different histories in terms of secondary school education. Rochdale has been an Independent LEA since 1974. It is a non‐selective authority with no grammar schools and no Independent (i.e. private) secondary schools of any size within its boundaries. However, it is contiguous with other LEAs, such as Rossendale, which are selective at the age of 11 years, and Bolton/Bury that has a significant presence of Independent secondary schools. Blackburn, on the other hand, was part of Lancashire Education Authority until 1998. Its schools were non‐selective but it is contiguous with LEAs that have selection at the age of 11 years and that contain several major Independent schools.</p> <p>An important difference between Blackburn and Rochdale LEA schools involves their ethnic mix. Rochdale has had a longstanding commitment to ethnic balance within its secondary schools (see Penn and Scattergood [<reflink idref="bib62" id="ref71">62</reflink>]) by the use of unusual catchment areas. Blackburn has not had such a policy – partly because it was controlled by Lancashire County Council – and it has marked segregation within its schools. Some are over 95% South Asian whilst others are almost 100% White. This strongly reflects the very high levels of residential segregation historically in Blackburn (see Rees and Phillips [<reflink idref="bib63" id="ref72">63</reflink>]).</p> <p>The data presented in this paper should not be thought of as entirely representative of educational attainment in Britain. Their relevance for debates about the social determinants of educational attainment in Britain is two‐fold. Firstly, they involve an in‐depth analysis of young adults in two different localities in a region of Britain with a high proportion of children of international migrants. The ethnic minority respondents in Rochdale and Blackburn come from the three largest international migrant communities in contemporary Britain: Indians, Pakistanis and Bangladeshis. The structure of the data collected also permitted a three‐level modelling strategy that can encompass individual characteristics as well as school‐level and locality‐level effects.</p> <p>The data also permitted an assessment of the relative success of children of international migrants in two different localities. Britain has a complex mosaic of ethnic settlement. Most South Asians in Rochdale are Pakistanis from rural areas in the Punjab and Kashmir, whilst a minority are Sylhetis from Bangladesh (see Penn and Scattergood [<reflink idref="bib61" id="ref73">61</reflink>]). Blackburn has a different mix of South Asians: around one‐half are Indian Gujeratis whilst the other one‐half are Pakistani Punjabis mainly from urban backgrounds. Almost all South Asians in Blackburn and Rochdale are Moslem, albeit from a wide range of differing traditions and sects.</p> <p>A set of general explanatory variables drawn from the sociological literature on the determinants of educational attainment were also used in the analysis, including gender, age, socio‐economic background of family, language spoken at home with parent(s) and ethnicity (see Appendix 1). Fifty‐nine per cent of respondents were female and just over one‐half (54%) were aged between 16 and 19 years. Thirty‐one per cent of respondents had at least one parent who worked (or had last worked) in a routine, non‐manual job. A further 16.2% had a parent who was a skilled manual worker. There were also 14.8% of respondents with a professional parent, 14.5% with a managerial parent and 13.7% with a parent who was a business proprietor. A total of 9.8% reported non‐active parent(s).</p> <p>Most respondents (93%) had attended a mixed‐sex school. However, only 33% had attended a school for students 11–18 years old. This reflected the predominance of pupils who had attended Blackburn and Rochdale LEA schools. None of the former and most of the latter were schools for 11–16 year olds.[<reflink idref="bib6" id="ref74">6</reflink>] Most schools attended at age 16 were non‐selective. Thirty‐seven respondents had attended a positively selective school and 18 respondents a negatively selective school (this again reflected the abolition of selection in Blackburn and Rochdale LEAs but not in adjacent LEAs). Most respondents had also attended state schools (93%), which again mirrored the scarcity of private school places in these two localities. One‐fifth of respondents had attended 'specially designated' schools (these comprised specialist technology, sports and modern language schools).[<reflink idref="bib7" id="ref75">7</reflink>] There were also a small number of respondents from Islamic schools (18 in all) but 153 (just over 20%) had attended Christian denominational schools. These were either Anglican or Roman Catholic Schools.</p> <hd id="AN0035020633-13">5 Modelling framework</hd> <p></p> <hd id="AN0035020633-14">5.1 Structure of the data</hd> <p>Educational data often contain two or more hierarchies (or 'levels'). For example, there is typically information both on the performance of individual pupils and on the individual background characteristics of these pupils such as gender, age and socio‐economic background of the family, as well as data on the characteristics of the schools attended by such pupils themselves. These include such factors as whether they are selective or non‐selective and whether they are single sex or mixed. These are termed 'school level' effects.</p> <p>The performance of pupils within the same school often tends to be more correlated with each other than with the performance of pupils from other schools. Variation within schools may often be lower than variation between schools. At a higher level or hierarchy within the data structure, the performance of schools within the same locality may be more correlated with each other than with the performance of schools from other localities. In other words, variation within localities may often be lower than variation between localities. The need to handle such school‐specific and locality‐specific heterogeneity led to the development of multi‐level modelling in the 1980s and 1990s (see, for example, Aitken and Longford [<reflink idref="bib1" id="ref76">1</reflink>]; Gray, Jesson, and Sime [<reflink idref="bib37" id="ref77">37</reflink>]; Aitken and Zuzovsky [<reflink idref="bib2" id="ref78">2</reflink>]; Goldstein and Healy [<reflink idref="bib34" id="ref79">34</reflink>]; Goldstein and Spiegelhalter [<reflink idref="bib35" id="ref80">35</reflink>]; Goldstein [<reflink idref="bib33" id="ref81">33</reflink>]). In this paper, we apply three‐level models to the three response variables under consideration. The three levels are individual, school and locality (LEA).</p> <hd id="AN0035020633-15">5.2 Modelling GCSE scores</hd> <p>The EFFNATIS data on GCSE results were initially categorized into the standard binary outcome used in almost all educational research (Demack, Drew, and Grimsley [<reflink idref="bib17" id="ref82">17</reflink>]). Responses were dichotomised into those with at least five GCSE passes at grades A*–C and those without (Scott [<reflink idref="bib65" id="ref83">65</reflink>]; Bradley and Taylor [<reflink idref="bib9" id="ref84">9</reflink>]). In preliminary analyses not reported here, three‐level binary logistic regression was used to relate this binary outcome to the individual‐level, school‐level and locality‐level explanatory variables. However, it became evident that modelling continuous GCSE scores using linear regression, rather than analysing the conventional binary measure used both in official publications and by most educational sociologists, produced a clearer picture of the structural factors that determined educational attainment. Clearly a continuous measure allows for greater resolution, both in the response variable itself and in the modelling process. Therefore, in this paper we report the results of applying three‐level linear regression models to the measure of continuous GCSE score, which allowed us to test the following research hypothesis:</p> <p>H1: Respondent's GCSE score was significantly related to individual‐level, school‐level and locality‐level explanatory variables.</p> <hd id="AN0035020633-16">5.3 Modelling track taken at age 16</hd> <p>Respondents followed four distinct tracks at the end of compulsory education. One track involved taking three 'A'‐levels with a probable aim of entering higher education at 18 or 19 years of age (this included 202 respondents). The second entailed remaining in full‐time education but following lower level academic courses. These included either one 'A'‐level or further GCSE courses or GNVQ courses or other college‐based courses or a combination of all of these. These courses were heavily marketed by the respective Further Education Colleges in Blackburn and Rochdale but did not standardly, in themselves, provide appropriate credentials for entry into higher education (this involved 214 respondents). The third track involved some form of organized or formal training in conjunction with full‐time employment. These included both apprenticeships and youth training courses and involved 205 respondents. The fourth and final track involved those who left school at age 16 generally for full‐time employment that did not involve any type of organized or formal training (102 respondents followed this track).</p> <p>The outcomes could have been modelled in two ways. The first would have involved an ordered logit model and would have assumed that the tracks were ordered hierarchically. Such an ordinality assumption is conceptually plausible but in practical terms may not provide sufficient resolution in the analysis. The results of fitting an ordered logit model would allow us to conclude, for example, that respondents were more likely to follow a higher track (i.e. either Lower Academic or Higher Academic) rather than to follow a lower track (i.e. either Left School at 16 or Vocational Education/Training). However, the ordered logit model would not allow us to test, within the higher tracks, whether respondents were more likely to pursue a Lower Academic track rather than to follow a Higher Academic route. This increase in resolution would only be possible using the second approach, the multinomial logit model, which would relax the assumption of ordinality and would model the outcomes as a set of distinct categories. We present the multinomial logit model in this analysis, partly for the reasons outlined above, and partly for ease of interpretation.</p> <p>Under the multinomial logit framework, a baseline category was contrasted with each of the other three categories. By default, the modal category was taken to be the baseline. In this analysis, the modal category was Lower Academic, with 214 respondents. Thus, the three contrasts modelled in this analysis were:</p> <p></p> <ulist> <item> • C1: Left School at 16 versus Lower Academic</item> <p></p> <item> • C2: Vocational Education/Training versus Lower Academic</item> <p></p> <item> • C3: Higher Academic versus Lower Academic.</item> </ulist> <p>A three‐level multinomial logit model allowed us to test the following research hypothesis:</p> <p>H2: Track taken at 16 (as represented by the contrasts C1, C2 and C3) is significantly related to individual‐level, school‐level and locality‐level variables, having controlled for prior GCSE score.</p> <p>It is probable that respondents' prior GCSE score would have acted as a powerful determinant of track taken at age 16. This is particularly the case for the Higher Academic and Lower Academic tracks since a set of good GCSE results is generally a precondition for entry into higher academic courses in most educational establishments.</p> <hd id="AN0035020633-17">5.4 Modelling 'A'‐level scores</hd> <p>Respondents' 'A'‐level scores varied between 0 and 30. This was based on scoring an A grade as 10 points, a B grade as 8 points, and so on, following the standard convention. A three‐level linear regression model was fitted to test the following research hypothesis:</p> <p>H3: 'A'‐level score was significantly related to individual‐level, school‐level and locality‐level explanatory variables, having adjusted for prior GCSE score.</p> <p>The individual‐level variables once again were gender, age, ethnicity, socio‐economic background and language spoken at home. The school‐level variables characterized the schools attended at age 16 during the respondents' GCSE year and not the establishment where the respondent had undertaken his/her 'A'‐levels, although in the majority of cases these were the same.[<reflink idref="bib8" id="ref85">8</reflink>]</p> <hd id="AN0035020633-18">5.5 General modelling issues</hd> <p>For each of the three measures of educational attainment, the ultimate objective of our model‐building procedure was to determine the most parsimonious model. In other words, the model that explained the highest proportion of variation with the smallest number of explanatory variables. This procedure necessitated the removal of non‐significant explanatory variables.</p> <p>We present the results of our analyses in terms of the parameter estimates, standard errors and levels of significance (or <emph>p</emph> values) associated with those significant explanatory variables left in the most parsimonious model for each measure of educational attainment. To permit a direct comparison of the effect of explanatory variables from one measure to another, we present a summary of the results for all parsimonious models in Appendix 2. A summary of the results of fitting full models (i.e. those models that contain all explanatory variables, regardless of whether they are significant or not) is presented in Appendix 3.</p> <p>We also fitted one‐level models including only the individual‐level explanatory variables, and two‐level models including both individual‐level and school‐level explanatory variables. Due to space limitations, we will not present the results of fitting these models in this paper.</p> <hd id="AN0035020633-19">6 Results</hd> <p></p> <hd id="AN0035020633-20">6.1 Modelling GCSE scores</hd> <p>The most parsimonious three‐level linear regression model of GCSE scores explained a highly significant amount of variation, as indicated by the change in deviance of 143.6 on nine degrees of freedom (see Table 4). The parameter estimates, standard errors and <emph>p</emph> values of those explanatory variables remaining in the most parsimonious three‐level linear regression model of GCSE score are presented in Table 4. Gender, age and social class were all powerful determinants of educational attainment. Females outperformed males and younger respondents scored better than older ones. The latter finding is consistent with both the notion that teaching has improved in secondary schools and the contention that GCSE examinations have become easier overall. Indeed, the results are consistent with both possibilities acting simultaneously. The model indicated significantly poorer results amongst Indians (most of whom were Gujerati Moslems living in Blackburn). Parental background was also a very powerful factor. Children of parents from professional, managerial and family business backgrounds obtained significantly better GCSE scores. Indeed, children from professional family backgrounds obtained over eight additional points overall (equivalent to an A* grade). The results suggest the continued centrality of a social class divide between families performing professional and managerial work and those undertaking skilled manual and routine work.</p> <p>Table 4. Final three‐level random intercept model of overall GCSE score.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Explanatory variable</td><td>Coefficient</td><td>Standard error</td><td><italic>p</italic></td></tr></thead><tbody><tr><td>Gender: female</td><td char=".">5.12</td><td char=".">1.09</td><td>***</td></tr><tr><td>Age (years)</td><td char=".">−0.93</td><td char=".">0.21</td><td>***</td></tr><tr><td>Indian</td><td char=".">−8.94</td><td char=".">1.69</td><td>***</td></tr><tr><td>Parent(s) = business</td><td char=".">4.61</td><td char=".">1.58</td><td>**</td></tr><tr><td>Parent(s) = professional</td><td char=".">8.13</td><td char=".">1.57</td><td>***</td></tr><tr><td>Parent(s) = manager</td><td char=".">5.75</td><td char=".">1.55</td><td>***</td></tr><tr><td>Single‐sex school</td><td char=".">−6.30</td><td char=".">3.23</td><td char=".">†</td></tr><tr><td>Positively selective school</td><td char=".">17.77</td><td char=".">3.56</td><td>***</td></tr><tr><td>Christian school</td><td char=".">4.99</td><td char=".">1.66</td><td>**</td></tr><tr><td>Constant</td><td char=".">54.04</td><td char=".">4.37</td><td>***</td></tr><tr><td>Notes: Null model: log likelihood = −3003.9. Final model: log likelihood = −2932.1. Change in deviance = 2 × (3003.9 − 2932.1) = 143.6 on nine degrees of freedom. ***Significant at 0.001 level, **significant at 0.01 level, *significant at 0.05 level, †significant at 0.10 level.</td></tr></tbody></table> </ephtml> </p> <p>Some school‐level variables were significant, but others were not. Clearly a multi‐level approach is more appropriate than one that ignores school effects in the modelling strategy. The overall academic context of the school (measured in terms of an average of the percentage of pupils gaining five GCSE passes at grades A*–C over the previous four years) was not significant. This suggested that the so‐called 'academic culture' of schools was not significant <emph>per se</emph>. Respondents at Independent schools (albeit small in numbers) and those at 'specially designated' schools did not achieve significantly different GCSE results overall. Pupils who had attended positively selective schools, unsurprisingly, performed much better on average at GCSE, scoring an average of almost 18 points more than others. Pupils who had attended Christian denominational schools also reported significantly better results, scoring over five additional points overall. However, those at Islamic schools did not report significantly better results, although the number of such pupils was small. Respondents who had attended mixed‐sex schools also performed better (albeit only at the 10% level) than those at single‐sex schools. This is partly explained by the fact that single‐sex schools were also predominantly Independent schools.</p> <p>These results suggest a gender paradox. Gender <emph>per se</emph> was a significant factor: girls out‐performed boys at GCSE. However, pupils at mixed‐sex schools achieved significantly better overall GCSE results, indicating that the gender of the respondent, but not the gender milieu of his/her peers at the school attended, was the more important determinant of GCSE score.</p> <p>There were no significant locality effects suggesting that, despite the variations between Rochdale, Blackburn and surrounding contiguous LEAs in terms of policies with regards to ethnic diversity within secondary schools, GCSE results were affected by generic social factors such as gender and social class rather than locality differences <emph>per se</emph>.</p> <hd id="AN0035020633-21">6.2 Modelling track taken at age 16</hd> <p>There was a powerful association between number of GCSE passes at grades A*–C and track taken at age 16 (see Table 5). In order to assess the effect of prior GCSE score upon subsequent post‐16 track, the respondents' GCSE score was included in a three‐level multinomial logit model of track taken at age 16. The most parsimonious model explained a highly significant amount of variation, as shown by the change in deviance of 645.7 on 15 degrees of freedom (see Table 6). The parameter estimates, standard errors and <emph>p</emph> values of those explanatory variables left in the most parsimonious model are presented in Table 6.</p> <p>Table 5. Track taken at post‐16 and number of GCSE passes (row percentages).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>Number of GCSE passes, grades A*−C</td><td /><td /></tr><tr><td>Track</td><td>0 + 1</td><td>2 + 3</td><td>4 + 5</td><td>6 + 7</td><td>8</td><td>9 or more</td><td><italic>n</italic></td></tr></thead><tbody><tr><td>Higher Academic</td><td char=".">–</td><td char=".">1.5</td><td char=".">0.5</td><td char=".">4.0</td><td char=".">14.9</td><td char=".">79.2</td><td char=".">202</td></tr><tr><td>Lower Academic</td><td char=".">3.7</td><td char=".">3.2</td><td char=".">20.1</td><td char=".">43.5</td><td char=".">17.3</td><td char=".">12.2</td><td char=".">214</td></tr><tr><td>Vocational Education/Training</td><td char=".">21.4</td><td char=".">19.5</td><td char=".">21.0</td><td char=".">20.5</td><td char=".">8.8</td><td char=".">8.8</td><td char=".">205</td></tr><tr><td>Left School at 16</td><td char=".">27.4</td><td char=".">15.7</td><td char=".">31.4</td><td char=".">15.6</td><td char=".">4.9</td><td char=".">4.9</td><td char=".">102</td></tr><tr><td><italic>n</italic></td><td char=".">80</td><td char=".">66</td><td char=".">119</td><td char=".">159</td><td char=".">90</td><td char=".">209</td><td char=".">723</td></tr></tbody></table> </ephtml> </p> <p>Table 6. Final three‐level multinomial logit model of post‐16 track (including GCSE score).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>Contrast</td></tr><tr><td /><td>Left School at 16 versus Lower Academic</td><td>Vocational Education/Training versus Lower Academic</td><td>Higher Academic versus Lower Academic</td></tr><tr><td>Explanatory variable</td><td>Coefficient</td><td>Standard error</td><td><italic>p</italic></td><td>Coefficient</td><td>Standard error</td><td><italic>p</italic></td><td>Coefficient</td><td>Standard error</td><td><italic>p</italic></td></tr></thead><tbody><tr><td>Gender</td><td char=".">−0.49</td><td char=".">0.27</td><td>†</td><td char=".">−0.11</td><td char=".">0.29</td><td /><td char=".">−0.18</td><td char=".">0.39</td><td /></tr><tr><td>Age</td><td char=".">−0.20</td><td char=".">0.05</td><td>***</td><td char=".">−0.24</td><td char=".">0.05</td><td>***</td><td char=".">−0.16</td><td char=".">0.07</td><td>*</td></tr><tr><td>Pakistani</td><td char=".">0.39</td><td char=".">0.35</td><td /><td char=".">0.98</td><td char=".">0.35</td><td>**</td><td char=".">0.90</td><td char=".">0.45</td><td>*</td></tr><tr><td>GCSE score</td><td char=".">0.02</td><td char=".">0.01</td><td>*</td><td char=".">0.07</td><td char=".">0.01</td><td>***</td><td char=".">0.34</td><td char=".">0.03</td><td>***</td></tr><tr><td>11–18 school</td><td char=".">−0.18</td><td char=".">0.35</td><td /><td char=".">1.18</td><td char=".">0.34</td><td>***</td><td char=".">1.73</td><td char=".">0.42</td><td>***</td></tr><tr><td>Constant</td><td char=".">5.09</td><td char=".">1.16</td><td>***</td><td char=".">3.31</td><td char=".">1.25</td><td>**</td><td char=".">−12.16</td><td char=".">2.07</td><td>***</td></tr><tr><td>Notes: Null model: log likelihood = −962.7. Final model: log likelihood = −639.9. Change in deviance = 2 × (962.7 − 639.9) = 645.7 on 15 degrees of freedom. ***Significant at 0.001 level, **significant at 0.01 level, *significant at 0.05 level, †significant at 0.10 level.</td></tr></tbody></table> </ephtml> </p> <p>Prior GCSE score was a significant and powerful predictor in the models contrasting Vocational Education/Training and the Lower Academic tracks (contrast C2), and Higher and Lower Academic tracks (contrast C3). Respondents with higher prior GCSE scores were significantly more likely to have entered Vocational Education/Training or to have followed a Higher Academic route rather than to take the Lower Academic track. Prior GCSE score had a significant effect, albeit only marginally at the 5% level, in the contrast C1 model (Left School at 16 versus Lower Academic). Respondents with higher prior GCSE scores were also more likely to have left school at age 16 rather than to have taken the Lower Academic track.</p> <p>Gender proved significant, albeit only at the 10% level, in the contrast C1 model. Females were significantly more likely to follow the Lower Academic track rather than leave school at age 16, once their GCSE results had been adjusted for. There was no significant gender effect in the models for contrasts C2 and C3. Age was a consistent and powerful determinant of track taken at age 16 in the models for all three contrasts. Younger respondents were significantly more likely to have taken the Lower Academic track rather than to have left school at age 16, once their prior GCSE scores had been taken into account. The younger the respondent, the more likely he/she was to have taken the Lower Academic track rather than to have entered Vocational Education/Training. Younger respondents were also significantly more likely to have taken the Lower Academic track rather than to have followed a Higher Academic route.</p> <p>Ethnicity was a powerful factor in the model for contrast C2. Pakistanis were more likely to have entered Vocational Education/Training rather than take the Lower Academic track, once their prior GCSE scores had been adjusted for, in comparison with other ethnic groups. However, this result is likely to be highly influenced by a relatively small number of respondents with GCSE scores between 21 and 30. Thirteen Pakistani respondents with GCSE scores between 21 and 30 chose either Vocational Education/Training or the Lower Academic track. Of these respondents, 12 chose Vocational Education/Training and one respondent chose the Lower Academic track. Therefore, the odds of a Pakistani respondent with a GCSE score between 21 and 30 having chosen Vocational Education/Training rather than the Lower Academic track were 12/1 = 12. In contrast, 42 non‐Pakistani respondents with GCSE scores between 21 and 30 chose either Vocational Education/Training or the Lower Academic track. Of these respondents, 30 chose Vocational Education/Training and 12 chose the Lower Academic track. The odds of a non‐Pakistani respondent with a GCSE score between 21 and 30 having chosen Vocational Education/Training rather than the Lower Academic track were 30/12 = 2.5. A Pakistani respondent with a GCSE score between 21 and 30 was almost five times more likely than a non‐Pakistani respondent with a GCSE score in the same interval to choose Vocational Education/Training rather than the Lower Academic track. By contrast, a Pakistani respondent with a GCSE score in any other interval was less likely than a non‐Pakistani respondent with a GSCE score in the same interval to choose Vocational Education/Training rather than the Lower Academic track. Consequently, the positive Pakistani effect indicating a significant ethnic difference should be treated with a high degree of caution here.</p> <p>Respondents who had attended schools for 11–18 year olds were more likely to have entered Vocational Education/Training or to have followed a Higher Academic route rather than take the Lower Academic track, once their prior GCSE results were controlled for.</p> <p>The earlier modelling of respondents' overall GCSE score demonstrated that a number of explanatory variables were highly correlated with GCSE performance. The individual‐level explanatory variables significantly correlated with overall GCSE score were gender, age, Indian ethnic group, professional, business and managerial family backgrounds. School‐level explanatory variables that proved significant predictors of GCSE performance were positively selective schools, single‐sex schools and Christian schools. A number of these explanatory variables, namely familial socio‐economic background, positively selective schools and single‐sex schools, were also significant predictors in the model of track taken at age 16, which did not include respondents' overall GCSE score. However, once GCSE score had been added to the model of track taken at age 16, the significant correlations outlined above rendered these explanatory variables redundant and they were removed from the model. The addition of GCSE score to the model of track taken at age 16 also led to the elimination of two further significant explanatory factors: these were English spoken at home and school's average GCSE score.</p> <hd id="AN0035020633-22">6.3 Modelling 'A'‐level scores</hd> <p>The most parsimonious three‐level linear regression model of 'A'‐level scores explained a highly significant amount of variation, as indicated by the change in deviance of 75.4 on five degrees of freedom (see Table 7). Parameter estimates, standard errors and <emph>p</emph> values of those explanatory variables remaining in the most parsimonious model are presented in Table 7. The results revealed that Pakistanis obtained significantly better 'A'‐level scores than other ethnic groups, after controlling for prior GCSE results. There were no significant differences between males and females. Recent evidence[<reflink idref="bib9" id="ref86">9</reflink>] shows that the gap in overall performance at 'A'‐level nationally between males and females has narrowed for the third successive year, with females only just outperforming males. Our results mirror this process. The results in Table 7 indicated that there were also significant social class effects: children from professional backgrounds scored significantly better than other socio‐economic groups. Those who had attended a Christian school at age 16, on the other hand, subsequently scored significantly lower 'A'‐level results. Age also proved significant. Older respondents reported higher 'A'‐level scores. This could have been an artefact of the way 'A'‐level score had been calculated: the measure included all 'A'‐levels and this meant that older respondents had a much greater opportunity to accumulate higher overall 'A'‐level scores.</p> <p>Table 7. Final three‐level linear regression of 'A'‐level scores (including GCSE score).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Explanatory variable</td><td>Coefficient</td><td>Standard error</td><td><italic>p</italic></td></tr></thead><tbody><tr><td>Age</td><td char=".">0.72</td><td char=".">0.26</td><td>**</td></tr><tr><td>Pakistani</td><td char=".">2.60</td><td char=".">1.28</td><td>*</td></tr><tr><td>Parent(s) = professional</td><td char=".">3.38</td><td char=".">1.35</td><td>*</td></tr><tr><td>Christian school</td><td char=".">−3.70</td><td char=".">1.31</td><td>**</td></tr><tr><td>GCSE score</td><td char=".">0.30</td><td char=".">0.04</td><td>***</td></tr><tr><td>Constant</td><td char=".">−13.41</td><td char=".">6.25</td><td>*</td></tr><tr><td>Notes: Null model: log likelihood = −733.7. Final model: log likelihood = −696.0. Change in deviance = 2 × (733.7 − 696.0) = 75.4 on five degrees of freedom. ***Significant at 0.001 level, **significant at 0.01 level, *significant at 0.05 level, †significant at 0.10 level.</td></tr></tbody></table> </ephtml> </p> <hd id="AN0035020633-23">7. Overall conclusions</hd> <p>It is evident from the summary of the final models for GCSE results, educational track and 'A'‐level scores that there was no general and consistent pattern of significant explanatory factors (see Appendix 2). None of the explanatory variables had either a systematically positive or a systematically negative effect upon the three response variables.</p> <p>In terms of gender, females performed significantly better at GCSE but not at 'A'‐level (once their prior GCSE performance had been controlled for). Females' greater attainment levels at GCSE were not reproduced, <emph>pari passu</emph>, either in terms of transitions at age 16 nor in terms of subsequent 'A'‐level performance. Such findings suggested that the central position allocated to gender by writers such as Scott ([<reflink idref="bib65" id="ref87">65</reflink>]) and Bradley and Taylor ([<reflink idref="bib9" id="ref88">9</reflink>]) features far more in relation to GCSE results than subsequent educational outcomes.</p> <p>Younger respondents performed better at GCSE level. This could have been the result of improved teaching and performance at GCSE or the result of the examinations themselves having become relatively easier (or indeed of both factors operating simultaneously!). Older respondents were more likely to have entered a Lower Academic track after the end of compulsory education at age 16. This is consistent with the general improvement in GCSE scores over time, which would have made it relatively easier for younger respondents to have acquired the set of GCSE results sufficient for entry into the Higher Academic track. Older respondents also reported higher 'A'‐level scores, which was partially an artefact of the question itself that asked for overall 'A'‐level results. This could have enabled older respondents to have accumulated better overall results over time. These findings indicated that the relationship between educational attainment and age is by no means consistent and is highly context‐specific.</p> <p>There was no evidence that ethnic minority groups were disadvantaged systematically at any of the three stages examined. There was no evidence of a dichotomy between the ethnic majority White respondents and ethnic minorities as a whole. At GCSE, Indians underperformed compared with other ethnic groups. Pakistanis were more likely to have entered Vocational Education/Training once their GCSEs were controlled for. Ethnicity had no significant effect on subsequent 'A'‐level scores once prior GCSE scores had been controlled for. Such results strongly vindicated the ethnic heterogeneity thesis associated with writers such as Modood and Berthond ([<reflink idref="bib52" id="ref89">52</reflink>]).</p> <p>Family socio‐economic background (social class) had no significant effect on track entered post‐16 or on 'A'‐level results. However, there was a marked effect on GCSE scores. Children from managerial, professional and business backgrounds performed significantly better than their counterparts. This revealed the continued salience of the 'middle‐class'/'working‐class' divide at this level. Once again, however, social class effects were highly context‐specific: they applied at the first stage in the process analysed, but not at subsequent stages.</p> <p>Language spoken at home with parent(s) had no effect on any of the three outcomes modelled. This suggested that the notion of 'linguistic deprivation' associated with Dustmann ([<reflink idref="bib23" id="ref90">23</reflink>]), Esser ([<reflink idref="bib27" id="ref91">27</reflink>]) and Diefenbach ([<reflink idref="bib19" id="ref92">19</reflink>]) was not applicable in these localities in North West England. When added to the results on ethnicity reported earlier, this suggested strongly that there was no significant pattern of overall ethnic disadvantage.</p> <p>Nor were there strong school‐level differences. Attending a positively selective school did have a powerful effect on educational attainment at age 16. However, it is worth reiterating the point that there were no such positively selective schools in either Rochdale or Blackburn. This indicated that only the relatively small number of pupils living in these two towns who had attended selective schools outside these two localities had obtained significantly better overall GCSE scores. The possibility of a wide range of selection effects associated with socio‐economic background governing these results is evident here.</p> <p>Pupils who had attended Christian denominational schools also reported significantly better GCSE results. Many of the secondary schools in Rochdale and Blackburn are Christian. However, they also include many non‐Christians, including large numbers of Moslem pupils, amongst their pupil bodies. Indeed, there is anecdotal evidence from Blackburn in particular that Moslem parents prefer Christian denominational secondary schools over non‐religious, secular establishments.</p> <p>Attending a school for students aged 11–18 years did not have a significant effect on GCSE scores. This suggested that the presence or absence of an integral 'sixth form' was not a significant determinant of results at age 16. However, attending such a school for 11–18 year olds did have an effect on the track taken subsequently. Pupils at schools with continuity at age 16 were more likely to have taken a Higher Academic track or to have entered Vocational Education/Training than to have entered the Lower Academic track once their GCSE results had been controlled for. As noted earlier, such Lower Academic courses are very much the preserve of the further education colleges in Rochdale and Blackburn and are eschewed, for the most part, by secondary schools with integral 'sixth forms'.</p> <p>Other school‐level variables such as the gender mix of pupils and the school's average GCSE score over the preceding four years made no significant difference to respondents' GCSE score, the track taken at age 16 or subsequent 'A'‐level results. This indicated that the notion of 'school effects' needed to be decomposed into constituent elements. Some school effects proved significant depending upon the context, whilst others proved non‐significant at all stages in the analysis.</p> <p>The data analysed in this paper should not be seen as statistically representative of England and Wales as a whole. However, they do reflect two key elements within the educational system of these two countries. Schools in England and Wales are highly variegated in terms of their type and structure. The same locality will have a wide range of secondary schools, both in terms of the gender mix of pupils and of such factors as religious denomination and the age range within schools. Britain has also become a highly differentiated society culturally over the past 60 years, in terms of both ethnicity and religion. The two localities where respondents lived at the time of the survey – Rochdale and Blackburn – both have a complex mosaic of ethnic settlement, predominantly from South Asia. As such they can be seen as indicative of the situation in the North West of England.</p> <p>These features of the data were explicitly modelled in the analysis. The three‐level modelling allowed us to test the significance of both locality‐level and school‐level variations. We found no evidence of any locality‐level effects. This suggested that, despite outward appearances, there were few specifically local‐level differences in the determinants of educational attainment in this region of England. Overall, it is clear that none of the explanatory variables conventionally considered to affect educational attainment had a consistent effect across all three stages in our analysis. Rather, each had a contingent effect at specific points within the overall trajectory of educational outcomes.</p> <hd id="AN0035020633-24">Appendix 1. Explanatory variables</hd> <hd1 id="AN0035020633-25">Individual Level</hd1> <p>Gender: Male, Female</p> <p>Age in Years: 16–25</p> <p>Ethnicity: White, Pakistani, Indian, Bangladeshi</p> <p>Parent(s) Occupational Grouping: Business Owner, Professional, Manager, Skilled Manual, Routine Worker, Inactive</p> <p>English Spoken at Home with Parent(s): Yes, No</p> <p>Location in GCSE year: Blackburn Resident/Blackburn LEA;</p> <p>Blackburn Resident/Other LEA;</p> <p>Rochdale Resident/Rochdale LEA;</p> <p>Rochdale Resident/Other LEA.</p> <hd1 id="AN0035020633-26">School Level</hd1> <p>Age Range of Pupils: 11–16, Other</p> <p>Gender of Pupils: Mixed‐Sex, Single‐Sex</p> <p>Type of Selection: Non‐Selective, Positively Selective, Negatively Selective</p> <p>Denomination: Christian, Moslem, Secular</p> <p>Fee Paying: Independent, State</p> <p>Specially Designated: Non‐Designated, Designated (Technology, Sports and Modern Languages)</p> <p>Academic Context: Percentage of pupils gaining 5 GCSE passes at grades A*–C over previous 4 years.</p> <hd1 id="AN0035020633-27">Locality Level</hd1> <p>Locality: LEA where respondent's school was located at the time of sitting his/her GCSEs</p> <hd id="AN0035020633-28">Appendix 2. Overall patterns of educational trajectories (based on final three‐level models i...</hd> <p></p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td>GCSEs</td><td>Track (Table 6)</td><td>'A'‐Levels</td></tr><tr><td>Explanatory Variables</td><td>(Table 4)</td><td>C1</td><td>C2</td><td>C3</td><td>(Table 7)</td></tr></thead><tbody><tr><td>Gender: Female</td><td>+++</td><td /><td /><td /><td /></tr><tr><td>Age</td><td>− − −</td><td>− − −</td><td>− − −</td><td /><td>++</td></tr><tr><td>Pakistani</td><td /><td /><td>++</td><td /><td /></tr><tr><td>Indian</td><td>− − −</td><td /><td /><td /><td /></tr><tr><td>Bangladeshi</td><td /><td /><td /><td /><td /></tr><tr><td>Parent(s) = Professional</td><td>+++</td><td /><td /><td /><td /></tr><tr><td>Parent(s) = Managerial</td><td>+++</td><td /><td /><td /><td /></tr><tr><td>Parent(s) = Business</td><td>++</td><td /><td /><td /><td /></tr><tr><td>Parent(s) = Skilled</td><td /><td /><td /><td /><td /></tr><tr><td>Parent(s) = Non‐active</td><td /><td /><td /><td /><td /></tr><tr><td>English Spoken at Home</td><td /><td /><td /><td /><td /></tr><tr><td>School's Average GCSE Score</td><td /><td /><td /><td /><td /></tr><tr><td>11–18 School</td><td /><td /><td>+++</td><td>+++</td><td /></tr><tr><td>Single Sex School</td><td /><td /><td /><td /><td /></tr><tr><td>Positively Selective School</td><td>+++</td><td /><td /><td /><td /></tr><tr><td>Negatively Selective School</td><td /><td /><td /><td /><td /></tr><tr><td>Christian School</td><td>++</td><td /><td /><td /><td>− −</td></tr><tr><td>Islamic School</td><td /><td /><td /><td /><td /></tr><tr><td>Specially Designated School</td><td /><td /><td /><td /><td /></tr><tr><td>C1: Left School at 16 versus Lower AcademicC2: Vocational Education/Training versus Lower AcademicC3: Higher Academic versus Lower AcademicKey: +++ (− − −) = positive (negative) and significant at 0.001 level ++ (− −) = positive (negative) and significant at 0.01 level</td></tr></tbody></table> </ephtml> </p> <hd id="AN0035020633-29">Appendix 3. Overall patterns of educational trajectories (based on full three‐level models in...</hd> <p></p> <p> <ephtml> <table><thead valign="bottom"><tr><td /><td /><td>Track</td><td /></tr><tr><td>Explanatory Variables</td><td>GCSEs</td><td>C1</td><td>C2</td><td>C3</td><td>'A'‐Levels</td></tr></thead><tbody><tr><td>Gender: Female</td><td>+++</td><td /><td /><td /><td /></tr><tr><td>Age</td><td>− − −</td><td>− − −</td><td>− − −</td><td>(−)</td><td>+</td></tr><tr><td>Pakistani</td><td>++</td><td /><td>+</td><td>+</td><td>(+)</td></tr><tr><td>Indian</td><td>−</td><td /><td /><td /><td /></tr><tr><td>Bangladeshi</td><td>+</td><td /><td /><td /><td /></tr><tr><td>Parent(s) = Professional</td><td>+++</td><td /><td /><td /><td>++</td></tr><tr><td>Parent(s) = Managerial</td><td>++</td><td /><td /><td /><td>+</td></tr><tr><td>Parent(s) = Business</td><td>+</td><td /><td /><td /><td /></tr><tr><td>Parent(s) = Skilled</td><td /><td /><td /><td /><td>+</td></tr><tr><td>Parent(s) = Routine</td><td /><td /><td /><td /><td>+</td></tr><tr><td>English Spoken at Home</td><td>(+)</td><td /><td /><td /><td /></tr><tr><td>Respondent's GCSE Score</td><td char=".">N/A</td><td /><td>+++</td><td>+++</td><td>+++</td></tr><tr><td>School's Average GCSE Score</td><td /><td>+</td><td /><td /><td /></tr><tr><td>11–18 School</td><td /><td>− −</td><td>+++</td><td /><td /></tr><tr><td>Single Sex School</td><td /><td /><td /><td /><td /></tr><tr><td>Positively Selective School</td><td>++</td><td /><td /><td /><td /></tr><tr><td>Negatively Selective School</td><td /><td char=".">N/I</td><td char=".">N/I</td><td char=".">N/I</td><td /></tr><tr><td>Christian School</td><td>++</td><td /><td /><td /><td>− −</td></tr><tr><td>Islamic School</td><td /><td char=".">N/I</td><td char=".">N/I</td><td char=".">N/I</td><td /></tr><tr><td>Independent School</td><td /><td /><td /><td /><td /></tr><tr><td>Specially Designated School</td><td /><td /><td>(+)</td><td /><td /></tr><tr><td>C1: Left School at 16 versus Lower AcademicC2: Vocational Education/Training versus Lower AcademicC3: Higher Academic versus Lower AcademicKey: +++ (− − −) = positive (negative) and significant at 0.001 level ++ (− −) = positive (negative) and significant at 0.01 level + (−) = positive (negative) and significant at 0.05 level (+) ((−)) = positive (negative) and significant at 0.10 level N/A = not applicable N/I = not included in the full model; effect cannot be estimated due to small sample size</td></tr></tbody></table> </ephtml> </p> <hd id="AN0035020633-30">Notes</hd> <ref id="AN0035020633-31"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref66" type="bt">1</bibl> <bibtext> 1. See Department of Education and Skills website (<ulink href="http://www.dfes.gov.uk/performancetables/nscoringsystem">www.dfes.gov.uk/performancetables/nscoringsystem</ulink>). The scoring system gave an A* grade eight points, an A grade seven points, a B grade six points, and so on.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref67" type="bt">2</bibl> <bibtext> 2. These were obtained online (<ulink href="http://www.dfee.gov.uk/cgi‐bin/shschool1‐99?school=">www.dfee.gov.uk/cgi‐bin/shschool1‐99?school=</ulink>).</bibtext> </blist> <blist> <bibl id="bib3" idref="ref62" type="bt">3</bibl> <bibtext> 3. The effects of denominational schools on educational attainment are discussed in Morris ([53]).</bibtext> </blist> <blist> <bibl id="bib4" idref="ref69" type="bt">4</bibl> <bibtext> 4. The growth of specially designated schools is outlined in Taylor, Fitz, and Gorard ([70]).</bibtext> </blist> <blist> <bibl id="bib5" idref="ref6" type="bt">5</bibl> <bibtext> 5. These were based upon the average of the previous four years' GCSE results.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref37" type="bt">6</bibl> <bibtext> 6. Benton et al. ([4]) have provided a study of results in maintained secondary schools in England that revealed the importance of different types of schools for GCSE attainment scores.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref11" type="bt">7</bibl> <bibtext> 7. Specialist schools have been studied by Jesson ([42]) and Schagen et al. ([64]) and Levacic and Jenkins ([45]). The latter article used a three‐level multi‐level model similar in design to our own. The three levels were LEA, school attended and pupil. The authors reported that attending a specialist school had a small positive effect on pupils' GCSE point scores.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref51" type="bt">8</bibl> <bibtext> 8. Respondents lived in Blackburn or Rochdale. 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  Label: Title
  Group: Ti
  Data: Modelling Trajectories through the Educational System in North West England
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Penn%2C+Roger%22">Penn, Roger</searchLink><br /><searchLink fieldCode="AR" term="%22Berridge%2C+Damon%22">Berridge, Damon</searchLink>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Education+Economics%22"><i>Education Economics</i></searchLink>. Dec 2008 16(4):411-431.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 21
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2008
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Adults%22">Young Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Attainment%22">Educational Attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Influences%22">Influences</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="DE" term="%22Exit+Examinations%22">Exit Examinations</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnic+Groups%22">Ethnic Groups</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Class%22">Social Class</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Environment%22">Educational Environment</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22United+Kingdom+%28England%29%22">United Kingdom (England)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/09645290802024744
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0964-5292
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The main aim of this paper is to identify those school-level and locality-level factors that significantly affect each of the three stages in a young adult's educational trajectory in North West England: GCSE results, track taken at age 16 and "A"-level scores. By applying three-level models to data collected as part of the EFFNATIS project, we find no evidence of any locality-level effects. Overall, none of the explanatory variables conventionally considered to affect educational attainment had a consistent effect across "all" three stages. Rather, each explanatory variable had a contingent effect at specific points within the overall trajectory of educational outcomes. (Contains 7 tables and 9 notes.)
– Name: AbstractInfo
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  Data: As Provided
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  Data: 75
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  Data: 2008
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  Label: Accession Number
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  Data: EJ816573
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      – Type: doi
        Value: 10.1080/09645290802024744
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 411
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Young Adults
        Type: general
      – SubjectFull: Educational Attainment
        Type: general
      – SubjectFull: Influences
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Secondary Education
        Type: general
      – SubjectFull: Exit Examinations
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Gender Differences
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      – SubjectFull: Age Differences
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      – SubjectFull: Ethnic Groups
        Type: general
      – SubjectFull: Social Class
        Type: general
      – SubjectFull: Educational Environment
        Type: general
      – SubjectFull: United Kingdom (England)
        Type: general
    Titles:
      – TitleFull: Modelling Trajectories through the Educational System in North West England
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            NameFull: Penn, Roger
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            NameFull: Berridge, Damon
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              Y: 2008
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            – TitleFull: Education Economics
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