PRINCIPALES ÁMBITOS DE LA POLÍTICA COMERCIAL 1 ARANCEL

The characteristics considered for the models are personal and do not include geographic indicators or household characteristics such as tenure, presence or otherwise of an internet connection, or the status of the head of household. Although these variables might have

predictive power, they are not particularly informative about the kinds of people with weak skills. Table 6.1 describes the personal characteristics that were considered for each model.

Broadly speaking, the acquired characteristics cover education, work, basic skills training, computer use135 _{and health. The ten ‘attitudes to learning’ variables were also considered but}

the two statements with the strongest associations ‘learning isn’t for people like me’ and ‘I didn’t get anything out of school’) are too closely related to educational attainment to be additionally informative.

The models presented here are ‘main effects’ models despite the fact that the explanatory power of some models could be improved if two-way interaction terms were included.136 _{The deliberate}

omission of interaction terms from the presented models is not to say that these effects do not exist, rather that the evidence we have is insufficiently clear to warrant further complication of the model. To a great extent, this limitation is due to small sample sizes in many ‘interaction’ categories.

There is one exception to this general rule: the ethnic group and ‘first language’ variables have been combined together due to the naturally strong correlation between the two. This

correlation makes the respective ‘strength of association’ measures somewhat unstable when the two variables are separate. Because first language status has a more obvious connection with English literacy, it would be a reasonable approach to omit the ethnic group term altogether. However, despite small sample sizes, it seems more likely than not that ethnic group has some independent influence.

Model fit has been largely measured through two summary outputs: (a) Nagelkerke’s pseudo R2

measure of explanatory power, and (b) Hosmer and Lemeshow’s goodness-of-fit test (i.e. relative fit of the model across the range of modelled probabilities of weak assessment performance). To avoid inclusion of terms that significantly improve model fit in a statistical sense but not a substantive sense, terms have only been included if they increase the pseudo R2 _{value by 0.5 percentage points or more or increase it by less than this but improve relative}

fit.137

135 _{This was not included in the ICT model because it is too closely correlated with ICT assessment outcomes to}

be informative.

136 _{An interaction term would be necessary if, for example, the effect of parental education attainment on}

assessment performance varied significantly between men and women.

137 _{The weight of each variable in the model is determined by total change in the model’s ‘deviance difference’ if}

the variable is removed. The total R2 _{of the model is allocated to each variable using the same calculation. The}

102

Annexes 7 and 8 include the regression model coefficients and tree diagrams based on the regression model variables. The text in this chapter is a qualitative interpretation of those coefficients.

Table 6.1 Personal characteristics considered for regression models ‘FIXED’ CHARACTERISTICS Sex _Male Female Age group _16-19 20-24 25-34 35-44 45-54 55-65

Ethnic group/ ‘first’ language _{White British/Irish (almost all EFL)} White Other: EFL

White Other: ENFL Indian: EFL Indian: ENFL Pakistani: EFL Pakistani: ENFL

Other South Asian (mostly ENFL)

Black Caribbean and mixed Black Caribbean/White (almost all EFL) Other Black and mixed Black/White: EFL

Other Black and mixed Black/White: ENFL Other: EFL

Other: ENFL

Parental educational attainment One or more parents stayed in education beyond age 16 Neither parent stayed in education beyond age 16 (or DK) *Whether has a learning difficulty _Yes

No

‘ACQUIRED’ CHARACTERISTICS

Highest qualification _{Degree level qualification}

Non-degree level HE qualification Level 3 qualification

Level 2 qualification

Level 1 qualification or below Other qualification: level unknown No qualifications

Whether has A*-C English GCSE

or equivalent Yes _No Whether has A*-C Maths GCSE or

equivalent Yes _No

Chapter 6: Understanding the relationship between skills and personal characteristics

103 Table 6.1 Personal characteristics considered for regression models

‘ACQUIRED’ CHARACTERISTICS (continued)

Computer use _Daily

Less than daily Never

Whether been on an ICT training

course Yes _No

Basic skills training in English (any) Yes No Basic skills training in Maths _Yes

No Whether has a limiting long-term

illness/disability Yes _No Current / most recent occupational

type “White collar “occupations: _{Higher managerial and professional occupations} Lower managerial and professional occupations Intermediate occupations

Small employers and own account workers “Blue collar” occupations:

Lower supervisory and technical occupations Semi-routine occupations

Routine occupations

Never worked/ long term unemployed Full-time student

Current / most recent industry

sector Agriculture, Forestry and Fishing _{Manufacturing} Construction

Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles

Transport and Storage

Accommodation and Food Service Activities Information and Communication

Financial and Insurance Activities

Professional, Scientific and Technical Activities Administrative and Support Services Activities

104

Table 6.1 Personal characteristics considered for regression models

Public Administration and Defence; Compulsory Social Security Education

Human Health and Social Work Activities Arts, Entertainment and Recreation Other Service Activities

Other (inc. long term unemployed and students)

It is arguable whether a learning difficulty counts as a fixed characteristic or as an acquired characteristic. Almost certainly it differs between individuals and between types of learning difficulty. Although type of learning difficulty was recorded, there are too few cases in each category to include in general models like these.