When you carry out any study you will almost always need to enter the data into a computer package. Computers are invaluable for improving the accuracy and speed of data collection and analysis, making it easy to check for errors, produce graphical summaries of the data and generate new variables. It is worth spending some time planning data entry – this may save considerable effort at later stages.
Formats for Data Entry
There are a number of ways in which data can be entered and stored on a computer. Most statistical packages allow you to enter data directly. However, the limitation of this approach is that often you cannot move the data to another package. A simple alternative is to store the data in either a spreadsheet or database package. Unfortunately, their statistical procedures are often limited, and it will usually be necessary to output the data into a specialist statistical package to carry out analyses.
A more flexible approach is to have your data available as an ASCII or text file. Once in an ASCII format, the data can be read by most packages. ASCII format simply consists of rows of text that you can view on a computer screen. Usually, each variable in the file is separated from the next by some delimiter, often a space or a comma. This is known as free format.
The simplest way of entering data in ASCII format is to type the data directly in this format using either a word processing or editing package. Alternatively, data stored in spreadsheet packages can be saved in ASCII format. Using either approach, it is customary for each row of data to correspond to a different individual in the study, and each column to correspond to a different variable, although it may be necessary to go on to subsequent rows if data from a large number of variables are collected on each individual.
Planning Data Entry
When collecting data in a study you will often need to use a form or questionnaire for recording the data. If these forms are designed carefully, they can reduce the amount of work that has to be done when entering the data. Generally, these forms/questionnaires include a series of boxes in which the data are recorded – it is usual to have a separate box for each possible digit of the response.
Categorical Data
Some statistical packages have problems dealing with non-numerical data. Therefore, you may need to assign numerical codes to categorical data before entering the data into the computer. For example, you may choose to assign the codes of 1, 2, 3 and 4 to categories of ‘no pain’, ‘mild pain’, ‘moderate pain’ and ‘severe pain’, respectively. These codes can be added to the forms when collecting the data. For binary data, e.g. yes/no answers, it is often convenient to assign the codes 1 (e.g. for ‘yes’) and 0 (for ‘no’).
- Single-coded variables – there is only one possible answer to a question, e.g. ‘is the patient dead?’. It is not possible to answer both ‘yes’ and ‘no’ to this question.
- Multi-coded variables – more than one answer is possible for each respondent. For example, ‘what symptoms has this patient experienced?’. In this case, an individual may have experienced any of a number of symptoms. There are two ways to deal with this type of data depending upon which of the two following situations applies.
- There are only a few possible symptoms, and individuals may have experienced many of them. A number of different binary variables can be created which correspond to whether the patient has answered yes or no to the presence of each possible symptom. For example, ‘did the patient have a cough?’, ‘did the patient have a sore throat?’
- There are a very large number of possible symptoms but each patient is expected to suffer from only a few of them.
A number of different nominal variables can be created; each successive variable allows you to name a symptom suffered by the patient. For example, ‘what was the first symptom the patient suffered?’, ‘what was the second symptom?’. You will need to decide in advance the maximum number of symptoms you think a patient is likely to have suffered.
Numerical Data
Numerical data should be entered with the same precision as they are measured, and the unit of measurement should be consistent for all observations on a variable. For example, weight should be recorded in kilograms or in pounds, but not both interchangeably.
Multiple Forms Per Patient
Sometimes, information is collected on the same patient on more than one occasion. It is important that there is some unique identifier (e.g. a serial number) relating to the individual that will enable you to link all of the data from an individual in the study.
Problems with Dates and Times
Dates and times should be entered in a consistent manner, e.g. either as day/month/year or month/day/year, but not interchangeably. It is important to find out what format the statistical package can read.
Coding Missing Values
You should consider what you will do with missing values before you enter the data. In most cases you will need to use some symbol to represent a missing value. Statistical packages deal with missing values in different ways. Some use special characters (e.g. a full stop or asterisk) to indicate missing values, whereas others require you to define your own code for a missing value (commonly used values are 9, 999 or −99). The value that is chosen should be one that is not possible for that variable. For example, when entering a categorical variable with four categories (coded 1, 2, 3 and 4), you may choose the value 9 to represent missing values. However, if the variable is ‘age of child’ then a different code should be chosen. Missing data are discussed in more detail in Chapter 3.
Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
