Train to Gain Sample Code

Note: This page is from an older version of the Data Dictionary and may therefore contain information that is now out of date. It is included here for reference only.

1 The following pseudo code and SPSS code is provided to illustrate the Train to Gain data definition.

A_TTGAIN, L_TTGAIN

2 The following table shows the steps required to derive A_TTGAIN

FE, UfI and ACL

Step

Condition

Action if true

Action if false

1

Does A10=60

Set A_TTGAIN to 2

Go to 2

2

Is A_AGESTB ge 3

Go to 3

3

Does A10=20

Go to 4

4

Does A46A=64

Set A_TTGAIN to 1

Go to 5

5

Does A46B=64

Set A_TTGAIN to 1

Set A_TTGAIN to 0

WBL, ESFSR

Step

Condition

Action if true

Action if false

1

Does A10=60

Set A_TTGAIN to 2

Value

Label

0

Not Train to Gain Aim

1

Train to Gain Like Aim

2

Funded Train to Gain Aim

3 The following table shows the steps required to derive L_TTGAIN

Step

Condition

Action if true

Action if false

1

Does any aim have A_TTGAIN=0

Set L_TTGAIN to 0, Go to 2

2

Does any aim have A_TTGAIN=1

Set L_TTGAIN to 1, Go to 3

3

Does any aim have A_TTGAIN=2

Set L_TTGAIN to 2

Value

Label

0

Not Train to Gain Learner

1

Train to Gain Like Learner

2

Train to Gain Learner

4 The following table shows the steps required to derive A_TTGBROKE

Step

Condition

Action if true

Action if false

1

Is A_TTGAIN>0

Go to 2

2

Does A54 have a value

Go to 3

3

Does A54 <> 9999999999

Set A_TTGBROKE to 1

4

Does A54 = 9999999999

Set A_TTGBROKE to 2

A_TTGBROKE

Value

Label

0

Not Brokered

1

Fully Brokered

2

Not Fully Brokered

Creator

Analysis and MI Team

Date issued

29 September 2006

Date created

22 December 2005

Document ref.

G:\Organisational Data\DCA\A&MI\Documentation\web data dictionary\200607\DataDefinitions\SampleCode\nat-traintogainsamplecode-report-29sep2006.doc

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