In any study there is always the potential for errors to occur in a data set, either at the outset when taking measurements, or when collecting, transcribing and entering the data into a computer. It is hard to eliminate all of these errors. However, you can reduce the number of typing and transcribing errors by checking the data carefully once they have been entered. Simply scanning the data by eye will often identify values that are obviously wrong. In this chapter we suggest a number of other approaches that you can use when checking data.

Typing Errors

Typing mistakes are the most frequent source of errors when entering data. If the amount of data is small, then you can check the typed data set against the original forms/questionnaires to see whether there are any typing mistakes. However, this is time-consuming if the amount of data is large. It is possible to type the data in twice and compare the two data sets using a computer program. Any differences between the two data sets will reveal typing mistakes. Although this approach does not rule out the possibility that the same error has been incorrectly entered on both occasions, or that the value on the form/questionnaire is incorrect, it does at least minimize the number of errors. The disadvantage of this method is that it takes twice as long to enter the data, which may have major cost or time implications.

Error Checking

• Categorical data – It is relatively easy to check categorical data, as the responses for each variable can only take one of a number of limited values. Therefore, values that are not allowable must be errors.
  
• Numerical data – Numerical data are often difficult to check but are prone to errors. For example, it is simple to transpose digits or to misplace a decimal point when entering numerical data. Numerical data can be range checked – that is, upper and lower limits can be specified for each variable. If a value lies outside this range then it is flagged up for further investigation.
  
• Dates – It is often difficult to check the accuracy of dates, although sometimes you may know that dates must fall within certain time periods. Dates can be checked to make sure that they are valid. For example, 30th February must be incorrect, as must any day of the month greater than 31, and any month greater than 12. Certain logical checks can also be applied. For example, a patient’s date of birth should correspond to his/her age, and patients should usually have been born before entering the study (at least in most studies). In addition, patients who have died should not appear for subsequent follow-up visits!

With all error checks, a value should only be corrected if there is evidence that a mistake has been made. You should not change values simply because they look unusual.

Handling Missing Data

There is always a chance that some data will be missing. If a very large proportion of the data is missing, then the results are unlikely to be reliable. The reasons why data are missing should always be investigated – if missing data tend to cluster on a particular variable and/or in a particular subgroup of individuals, then it may indicate that the variable is not applicable or has never been measured for that group of individuals. If this is the case, it may be necessary to exclude that variable or group of individuals from the analysis. We may encounter particular problems when the chance that data are missing is strongly related to the variable of greatest interest in our study (e.g. the outcome in a regression analysis – Chapter 27). In this situation, our results may be severely biased (Chapter 34). For example, suppose we are interested in a measurement which reflects the health status of patients and this information is missing for some patients because they were not well enough to attend their clinic appointments: we are likely to get an overly optimistic overall view of the patients’ health if we take no account of the missing data in the analysis. It may be possible to reduce this bias by using appropriate statistical methods¹ or by estimating the missing data in some way², but a preferable option is to minimize the amount of missing data at the outset.

Outliers

What Are Outliers?

Outliers are observations that are distinct from the main body of the data, and are incompatible with the rest of the data. These values may be genuine observations from individuals with very extreme levels of the variable. However, they may also result from typing errors or the incorrect choice of units, and so any suspicious values should be checked. It is important to detect whether there are outliers in the data set, as they may have a considerable impact on the results from some types of analyses (Chapter 29).

For example, a woman who is 7 feet tall would probably appear as an outlier in most data sets. However, although this value is clearly very high, compared with the usual heights of women, it may be genuine and the woman may simply be very tall. In this case, you should investigate this value further, possibly checking other variables such as her age and weight, before making any decisions about the validity of the result. The value should only be changed if there really is evidence that it is incorrect.

Checking for Outliers

A simple approach is to print the data and visually check them by eye. This is suitable if the number of observations is not too large and if the potential outlier is much lower or higher than the rest of the data. Range checking should also identify possible outliers. Alternatively, the data can be plotted in some way (Chapter 4) – outliers can be clearly identified on histograms and scatter plots (see also Chapter 29 for a discussion of outliers in regression analysis).

Handling Outliers

It is important not to remove an individual from an analysis simply because his/her values are higher or lower than might be expected. However, the inclusion of outliers may affect the results when some statistical techniques are used. A simple approach is to repeat the analysis both including and excluding the value – this is a type of sensitivity analysis (Chapter 35). If the results are similar, then the outlier does not have a great influence on the result. However, if the results change drastically, it is important to use appropriate methods that are not affected by outliers to analyse the data. These include the use of transformations (Chapter 9) and non-parametric tests (Chapter 17).

Stay updated, free articles. Join our Telegram channel

Join