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Fixed characteristics

We identified four fixed characteristics that are associated with weak performance in the literacy assessment. In order of predictive power these are:

1. Not having English as first language, especially for some ethnic groups 2. Neither parent staying in education beyond the age of 16

3. Having a (self-assessed) learning difficulty 4. Being aged 45 or older.

Those for whom English is not a first language (ENFL) tended to perform relatively weakly on the literacy assessment. However, there was significant variation by ethnic group. In particular, those self-identifying in the Pakistani group performed at a lower standard than others. It is noticeable that some variance by ethnic group was also observed among those for whom

English is first language (EFL). The Indian, Pakistani and Black African ethnic groups performed at a lower standard than the white and Black Caribbean groups.

Those for whom at least one parent stayed in education beyond age 16 were very unlikely to have weak literacy skills once other factors are controlled for.

Inevitably, those reporting a learning difficulty struggled with the assessment more than others. It would be very valuable to distinguish between different types of learning difficulty but the statistical power is lacking for that analysis.

Sex was not a significant factor and age band only marginally significant.

Application of this four-term regression model allowed us to create three equal-sized groups with different base likelihoods of weak Literacy assessment performance. Analysis of the impact of ‘acquired’ characteristics is carried out both for the total sample and separately for each of these groups.

Chapter 6: Understanding the relationship between skills and personal characteristics

105 Group 2: probability of weak assessment performance = 10-14 per cent (mean = 12 per cent) Group 3: probability of weak assessment performance = 14-89 per cent (mean = 26 per cent) Model fit (fixed characteristics only)

The total explanatory power was 17.1 per cent. This is allocated as follows: ethnic

group/language (11.4 per cent), parental education (2.7 per cent), learning difficulty status (2.4 per cent), age-band (0.6 per cent). There are no obvious problems with model fit.

Acquired characteristics

We identified six acquired characteristics that are associated with weak performance in the literacy assessment. In order of predictive power these are:

1. Working in some industry sectors (although cannot draw firm conclusions about which ones are most closely associated with weak assessment performance )

2. Infrequent or zero use of computers

3. Highest qualification is rated at Level 1 or below 4. No English GCSE/equivalent A*-C

5. Working in routine occupations (or long-term unemployed) 6. Never been on an ICT course

In terms of industry sector, even with a fairly large survey like the Skills for Life 2011 Survey (SfL2011), the sample size per industry sector is small so conclusions can only be tentative. Working in the Education and Public sector administrative sectors appears to lessen the odds of weak assessment performance but there are no other significant sector-level findings despite the strong influence of the variable as a whole.

Those using computers every day tended to achieve a higher Literacy Level than others, and those with any experience of computers performed better than those who had never used a computer. These associations survive even when controlling for other factors suggesting that frequency of computer use is an important behavioural variable over and above education and work status. However, frequent computer use may be something that both promotes good literacy and follows from it (i.e. it has a circular, reinforcing quality).

The association between highest qualification and literacy assessment performance is generally high but there is little difference between those with Level 2 qualifications and those with higher qualifications. Individuals with any of these qualifications were unlikely to perform weakly on the literacy assessment. The distinction between a highest qualification at Level 2 and a highest qualification at Level 1 is not particularly large but holding no qualifications (or an unclassifiable qualification) was strongly associated with weak performance.

As expected, holding a qualification relevant to literacy (a Level 2 English language qualification) is associated with better performance on the assessment, even controlling for general

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In terms of occupation, there appears to be a clear divide between what might be termed “white collar” and “blue collar” occupations, beyond that expected given educational level. This

suggests that access to “white collar” work not only requires a good minimum standard of literacy but may also help individuals retain skills in a way that “blue collar” work does not. Within the “blue collar” group, those working in Routine occupations performed at a lower standard than those working in Semi-routine or Lower supervisory occupations. There was no such subgroup distinction within the “white collar” group.

Basic skills training was not an influential factor and was excluded from the model. This counter- intuitive result may be explicable if the impact of such training is to bring students up to the average for their particular combination of personal characteristics. In this scenario, basic skills training does make a difference but its impact is hidden in a cross-sectional survey like this one. Ultimately, it requires longitudinal data or formal experimental data to tease out the truth. However, evidence of having undertaken an ICT training course was a positive indicator. ICT courses are somewhat different from basic skills courses because the attendees are not necessarily behind their statistical peers (those others with the same combination of personal characteristics). They may simply have greater motivation to improve their skills.

Health status had no independent predictive power with regards to the literacy assessment. Fixed and acquired characteristics model fit

Addition of these acquired variables nearly doubles the explanatory power of the model from 17.1 per cent to 35.6 per cent. In the full model, this is allocated as follows: ‘fixed’

characteristics (18.1 per cent), industry sector (3.7 per cent), computer use (3.4 per cent), highest qualification (3.3 per cent), whether has Level 2 English qualification (3.2 per cent), occupational category (2.9 per cent) and whether gone on an ICT course (1.0 per cent). Note that the allocation of explanatory power to the ‘fixed characteristics’ is slightly different once the acquired characteristics are added to the model. This is due to varied correlation between the acquired and fixed characteristics. There are no obvious problems with model fit.

Differences between base groups

The higher the base likelihood of weak performance in the literacy assessment, the more important the acquired characteristics are. One way of looking at this is to compare the

explanatory power of the full model for each of base groups 1, 2 and 3. This varies from 12 per cent for group 1 (the group with the lowest likelihood of weak assessment performance), to 25 per cent for group 2 and 42 per cent for group 3 (the group with the highest likelihood of weak assessment performance).

The models for groups 1 and 2 can be minimised without losing significant explanatory power. For group 1, it is possible to base a model entirely on the education variables, suggesting that the work variables, while statistically significant in isolation, explain much the same variance as the education variables. In short, work status does not alter assessment performance

expectations that are based solely upon knowledge of ‘fixed’ characteristics and educational level.

For group 2, occupational category does have some additional predictive power (in the direction expected, although sample sizes are small for some categories) but industry sector is

Chapter 6: Understanding the relationship between skills and personal characteristics

107 Both work variables (occupational category and industry sector) form key and independent parts of the model for group 3 and, overall, have a slightly stronger influence than education. The directions of influence for both the work and education variables are more or less the same as for the total sample model but, interestingly, the influence of highest qualification is weaker for group 3 than it is for groups 1 and 2. Achievement of Level 2 or higher qualifications (as opposed to lower level qualifications) does not seem to make much difference for this group, although holding no qualifications at all remains associated with weak performance on the assessment.

One crucial difference is in the influence of ‘fixed’ characteristics. Group 3 is highly varied in terms of the base likelihood of weak assessment performance, ranging from 14 per cent to 89 per cent. Given this range, it is not surprising that the ‘fixed’ characteristics retain their weight in the model.