Early Years HLE 0.18
(N= 1832) 0.29 (N=1832) KS1 HLE Computing 0.22 (N= 1485) 0.03ns (N= 1485) KS1 HLE Interactions 0.08 (N=1485) 0.14 (N=1485) KS1 HLE Outings 0.17 (N=1485) 0.18 (N=1485) KS1 HLE Play 0.11 (N=1485) 0.37 (N=1485)
Table 1.26: Academic Attainment in Year 9 by KS2 HLE Educational Computing – Original and Imputed Data
Original Data Pooled Sample Imputed Data KS2 HLE
Educational Computing Mean
Std.
Dev. N Mean Std. Dev. N Year 9 English Teacher Assessment Low KS2 HLE 4.9 1.1 272 4.9 1.1 497 Medium KS2 HLE 5.4 1.0 1149 5.3 1.1 2072 High KS2 HLE 5.3 .9 215 5.2 1.0 433 Year 9 Maths Teacher Assessment Low KS2 HLE 5.3 1.5 272 5.2 1.4 497 Medium KS2 HLE 6.0 1.3 1151 5.8 1.4 2072 High KS2 HLE 5.8 1.3 216 5.7 1.4 433 Year 9 Science Teacher Assessment Low KS2 HLE 5.0 1.2 272 5.0 1.2 497 Medium KS2 HLE 5.5 1.0 1149 5.4 1.1 2072 High KS2 HLE 5.5 1.0 217 5.3 1.1 433
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Table 1.27: Academic Attainment in Year 9 by KS2 HLE Individual Activities – Original and Imputed Data
Original Data Pooled Sample Imputed Data
KS2 HLE Individual Activities Mean
Std. Dev. N Mean Std. Dev. N Year 9 English Teacher Assessment Low KS2 HLE 4.9 1.0 253 4.8 1.1 466 Medium KS2 HLE 5.4 1.0 1144 5.2 1.1 2068 High KS2 HLE 5.5 .9 239 5.4 1.0 468 Year 9 Maths Teacher Assessment Low KS2 HLE 5.5 1.4 252 5.4 1.4 466 Medium KS2 HLE 5.9 1.3 1149 5.8 1.4 2068 High KS2 HLE 5.8 1.3 238 5.8 1.3 468 Year 9 Science Teacher Assessment Low KS2 HLE 5.1 1.2 253 5.0 1.1 466 Medium KS2 HLE 5.5 1.1 1146 5.4 1.1 2068 High KS2 HLE 5.5 1.0 239 5.5 1.1 468 Pre-school Attendance
Findings from earlier analyses (start of primary school, at the end of Year 1, Year 2 and Year 6) showed beneficial effects of attending a pre-school on academic outcomes when compared with not attending a pre-school. At the end of Year 9, students who had attended pre-school still have higher average TA levels than students who had not attended pre-school (see Table 1.28).
Due to the very different characteristics of the ‘home’ group (for example, disadvantaged students are over-represented in this group) and very different characteristics of students who went to different types of pre-school centre, these raw differences need to be interpreted with considerable caution. Further analyses are required to separate the ‘net’ pre-school effects from those related to background characteristics. Section 3 investigates the impact of attendance, quality and effectiveness of pre-school in more detail, controlling for the influence of differences in students’ background characteristics.
Table 1.28: Academic Attainment in Year 9 by Pre-school Attendance – Original and Imputed Data
Original Data Pooled Sample Imputed Data Pre-school
Attendance Mean
Std.
Dev. N Mean Std. Dev. N Year 9 English Teacher Assessment Pre-school Experience 5.2 1.0 2325 5.2 1.0 2721 No Pre-school Experience 4.7 1.1 249 4.8 1.1 281 Year 9 Maths Teacher Assessment Pre-school Experience 5.7 1.4 2328 5.8 1.4 2721 No Pre-school Experience 5.0 1.4 246 5.1 1.4 281 Year 9 Science Teacher Assessment Pre-school Experience 5.4 1.1 2325 5.4 1.1 2721 No Pre-school Experience 4.8 1.1 250 4.8 1.1 281
It would be inappropriate to explore any continuing influence of pre-school, primary or secondary school on subsequent educational outcomes at the end of Year 9 unless proper statistical control is made of the influence of intake differences. The next section therefore examines the net influence of different individual student, family and HLE characteristics in contextualised multilevel statistical models, which identify and separate the various influences simultaneously. The additional ‘net’ influence of pre-school, primary and secondary school experience are then explored for the whole EPPSE 3-14 sample and for relevant sub-groups.
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2. Students’ Academic Attainment at the End of Year 9 in Secondary
School: The Impact of Different Individual Student, Family and Home
Learning Environment (HLE) Characteristics
This section presents the results of contextualised multilevel analyses establishing the pattern of relationships between various individual student, family and HLE characteristics and students’ academic attainment at the end of Year 9. Background details concerning the students’ earlier childcare experiences, health, family and HLE during the pre-school period were obtained from parental interviews conducted when students entered the EPPE study, a parent questionnaire completed by the parents when students were in KS1 of primary school education and a parent questionnaire completed by the parents when students were in KS2 of primary school education. As potentially influencing background factors, the following measures have been used in the analyses:
Individual student factors (i.e., gender, birth weight, number of siblings, early developmental problems, early behavioural problems, early health problems, ethnicity).
Family factors (i.e., socio-economic status [SES], parent’s qualification levels, family income16).
Home learning environment (HLE) in the early years (parents reported how often they read to the child, taught the child the alphabet, played with letters & numbers, taught songs & nursery rhymes, painted & drew etc.) before starting primary school.
Parental activities during KS1 such as the frequency of reading to the child, taking the child out to educational visits, computing activities, play, etc. (see Appendix 5 for details of these measures).
KS2 HLE included activities such as computing, playing, reading etc. (see Appendix 5 for details of these measures).
Figure 2.1 illustrates the strategy of the statistical analysis. The analyses investigated the associations between academic attainment and individual student, family and HLE characteristics when the students reach the end of Year 9 of secondary school education17. The analysis of the influence of individual student, family and HLE characteristics on academic outcomes is an important step as only on this basis, is it possible to separately identify and quantify the ‘net’ influence of pre-school, primary school education and secondary school. These influences will be explored in Section 3. The extent of differences in TA levels attributable to student background is also of considerable policy interest given the equity implications for later progress at school. The ‘net’ effects of particular individual student, family and HLE characteristics reported in this section were derived by contextualised multilevel analyses and therefore take into account any clustering related to the secondary school attended.
16
Marital status at KS2 was also included in initial analysis but did not prove significant.
17
It should be noted that all the analyses also accounted for associations between the predictors which could have been illustrated by additional arrows. For simplicity these arrows are not shown in Figure 2.1.
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Figure 2.1: Strategy of Statistical Analysis of Background Influences
2.1. Null Models
In order to control for potential secondary school influences and to take account of the clustering in the data, multilevel analyses were used to partition the variance in the TA levels that is attributable to the school (Level 2) and the individual student (Level 1). This models the effects of clustering in the data (because students are nested in schools) and is widely recognized as essential in studying school influences (Creemers, Kyriakides & Sammons, 2010; Goldstein, 1995; 2003; Teddlie & Reynolds, 2000).
Table 2.1, Table 2.3 and Table 2.5 show the null models for TA levels in English, maths and science. For English TA levels, the school and student level variances are very similar for the original and imputed data. Similarly, for maths and science, the imputed models show similar variances at both student and school levels on the original data (with the exception for science, where the school level variance for imputed data is slightly higher than the corresponding variance on the original data). These initial results suggest that the imputation procedure was robust in relation to the multilevel structure of the data set.
The intra-school correlations (ICC) for all three academic outcomes show that there is significant school level variation (approximately 20-24%) so that pursuing the analyses with multilevel models is essential to avoid bias in estimating the effects of the predictors.