GCSE ICT LESSON 5 Booklet Sections: 6 & 7 Data Capture & Checking Data

What is data? Data is raw facts and figures. In order to process data it has to be collected. The method of collecting data depends upon what it is going to be used for.

Designing a data capture sheet The process of collecting data is called DATA CAPTURE. If a printed form is used to collect data it is called a DATA CAPTURE SHEET.

Designing a data capture sheet The design of a data capture sheet should help the user to collect data efficiently and accurately. It should also enable the data to be entered into a database efficiently and accurately.

Designing a data capture sheet A good design will have: Informative headings. Simple instructions. An easy to use layout. Logical divisions into sections. Been tested before use.

An example of a data capture sheet Informative headings. Simple instructions. An easy to use layout. Logical divisions into sections. Personal data Academic data

Questions Data capture is about getting the answers to specific questions. There are two types of question. These are: –Closed questions –Open-ended questions

Closed questions Closed questions are the easiest to answer and easiest to process. Closed questions can produce: – Yes/No or True/False answers.

Open-ended questions Open-ended questions are the more difficult to answer and less easy to process. This is because the answers are ‘open- ended’ (i.e. they allow users to enter any answers they wish).

Examples of closed and open- ended questions Closed questions Open-ended questions Only grades from A to U can be entered Only percentages can be entered Written comments can be added

Coding data Because closed questions only allow users to enter answers from a specific list of alternatives it is easy to encode the data before entering it into a database.

Coding data For example, examination grades can be entered a single letters (A, B, C, D, E, F, or U). Another example is gender, which can be entered as M (for male) or F (for female). Individuals can also be given their own code number to identify them (e.g. National Insurance Number).

Coding data Why use codes?They are quicker to enter. It reduces the size of computer files, which increases the speed at which data can be handled. Codes can be unique.

Entering data into a database Once data has been captured, it will need to be entered into a database. This can be done by a variety of methods including: –Keying in using a keyboard –Magnetic Ink Character Recognition (MICR) –Optical Mark Reading (OMR)

Entering data into a database This can also be done by: –Optical Character Recognition (OCR) –Barcode readers –Automatic data capture –Turnaround documents

Keying in data This is the most common form of data entry as it is flexible and can cope with different types of data. However it is time consuming and errors can occur very easily.

Automatic data capture Some data can be entered directly into a database by electronic signals. Examples of automatic data capture include: –EPOS (Electronic Point Of Sale) –Remote sensing of traffic flow

Why do errors happen? Computers do not make mistakes. However if incorrect data is put in errors happen. In ICT this is called GIGO – Garbage in, garbage out! The way to avoid incorrect data being entered is to check it thoroughly.

Checking Data There are two main methods of avoiding errors. These are: –Verification –Validation

Verification Verification is the process of checking data. This can be done visually by the person entering the data. This can be done by double data entry (i.e. two people enter the same data, and if it is identical the data is accepted).

Validation Validation is the process of detecting inaccurate, incomplete, or unreasonable data. This can be done by the computer as the data is entered and as a result it is much less likely that errors will occur.

Ways to Validate Data These include: Character type checks Range checks Presence checks Length checks Lookup tables Spelling checkers

Validation Others include:Hash totals Control total Check digits Custom dictionaries Parity errors

Character type checks Character type checks make sure that the correct type of character has been entered (e.g. that a number has not been entered where a letter should have been or that a letter have not been entered where a number should have been.

Range checks Range checks make sure that numerical data falls between pre- determined limits (i.e. within a certain range of numbers). For example, that the age of a pensioner who is a member of a pension scheme and who is 81 is not entered as 18. This is done by setting a bottom limit on the age range.

Range checks Range checks are not infallible. In the example of the pension scheme data, if the age of the pensioner had been 96 and it had been entered as 69, the mistake would not have been detected by a range check.

Presence checks Presence checks ensure that data that must be entered is entered. For example, the pension scheme would need a pensioner’s National Insurance number. If that was not entered the presence check would detect this and notify whoever was entering the data.

Length checks Certain types of data are always the same length. For example, a National Insurance number will have 2 letters, followed by 6 numbers, followed by 1 letter (e.g. YY232425A). A length check will identify any NI numbers that have more or fewer characters.

Lookup tables A lookup table contains a list of valid codes that can be used to enter data. If a code that is not in the lookup table is entered. it is rejected. This prevents any incorrect data from being processed.

Spelling checkers Although spellcheckers are usually found in word processing programs, they can also form part of data- handling programs. They can be ‘enabled’ (switched on) so that they check data as it is entered and automatically identify any mistakes.

Custom dictionaries If a spellchecker is used it will automatically identify any words that it does not recognise. In particular spellcheckers often fail to recognise proper names (i.e. the names of people or places) or specialist words or jargon.

Common errors Common types of error are: –Transcription errors – these usually occur when people entering data misread what they are entering (e.g. mistaking 5 for S or O for 0). –Transposition errors – these occur when people entering data get characters out of order or back to front (e.g. 619 instead of 916 or ‘form’ instead of ‘from’).