Learning Skills

Observational Research and Secondary Data

Part of: Research Methods

This page covers two major potential sources of research data: observational research, and use of someone else’s already-published data, known as secondary data.

For other sources of data collection see our pages: Sampling and Sample Design, Surveys and Survey Design and Quantitative and Qualitative Research Methods.

Observational methods of collecting data have been used by scientists and researchers for many years. As far back as the Middle Ages, scientists were observing what happened as a result of their experiments. Similar methods are widely used in all types of research, from laboratory work right through to management research, and even fieldwork in the jungle.

Secondary data may be used in both quantitative and qualitative research, but involves the use of previously-published information for analysis. Such data may include historical archives, company records, and census data.

Collecting Observational Data

Observations range from counting something that happens, for example, ‘pings’ on a computer screen, through to observing and/or coding behaviour.

Observations may be used in a laboratory or in the field, for example, in a meeting at an office. They allow researchers to uncover things that are not known, or not spoken about, which would not be disclosed by interviewing people or using surveys. Examples might include the informal power relationships within a group.

Endless Observations

It will almost certainly be obvious that there are as many types of observational data as there are phenomena in the world; probably more, because behaviour counts as many different types of data. It is therefore impossible to discuss every type.

Methods of collecting are also wide-ranging.

Data tend to be either seen and written down, or recorded on a computer. Observations may be made and recorded immediately as observations, or the data may be recorded ‘raw’ and analysed later. This technique is often used in management research to record meetings for later analysis of the conversation.

There are also two different options for the observer: he or she may be either an observer from the outside, or they may be a participant.

This status will change the experiment, often significantly.

For example, it is probably obvious that whether someone observing a meeting is an ‘outsider’ or a member of the team will alter the behaviour of the team. People tend to be wary of ‘washing dirty linen in public’, but may also say more to an outsider because they do not fear that it will alter their position in the team.

Other Factors Affecting Observational Data

Data is Affected by Being Observed

In behavioural terms this is obvious: people behave differently when they’re being observed.

For this reason, observations about behaviour tend to be stronger when a group is observed constantly over a long period. Under these circumstances, the group gets used to being observed, and starts to behave more naturally. However, this type of design lends itself more to some types of research than others.

For example, it’s quite hard to design management research that allows you to sit in an office observing how a team behaves all day, every day for several months. However, researchers like Dian Fossey and Jane Goodall used this technique to observe wild apes. At first, the animals were very wary and spent most of their time watching the researcher but, as the animals became accustomed to the presence of a human, they started to ignore them and behave naturally.

The same principle applies to ‘pure’ scientific research. For example, the very act of observing an electron affects its location, which means that measurement alters the experiment. You always need to be aware of ‘observer effects’.

Observational Data is Affected by what is Sampled

Even if you have managed to design research which allows you to watch everything that goes on for several months, you are still going to select what you notice, whether consciously or unconsciously. It’s human nature. The key is to notice all the data that is relevant to what you are studying and not just the data that fits with your hypothesis.

This is where recording and re-examining later is helpful as you can go over the data several times and ensure that you have included everything relevant. Another way to avoid this so-called observer bias is to involve someone else in your re-examination and coding. Much management research involves two observers and four or more coders working in duplicate on transcripts or recordings later as a way of resolving observer bias without introducing inconsistencies of coding.

Alternatively, you can sample at various time intervals, such as every ten minutes or every hour.

Using Secondary Data

In subjects such as history and classical studies, secondary data is usually the only available source of information.

Data may include eye witness accounts, contemporary reports of events, or later reports. Historians generally give the greatest credence to the first, then the second, and finally the third, although there is a place for all of them in research.

For example, a formal record of an event, created for an official purpose, may not be directly contemporaneous, but may draw on all available eye witness accounts, and therefore give a better picture of events than any single eye witness account. In general, government and formal company documents are higher quality than personal documents, but you always need to be aware of why they were written.

In more scientific research, secondary data is often regarded as ‘second-best’ although it is widely used particularly for public health and epidemiological research. Suitable data sources for such research include national and international health surveys, often funded by governments. The quality of such data depends on:

  • The size of the sample: the larger, the better, because the answer will be more precise (and see our page on Sampling and Sample Design for more); and
  • The quality of the data collection, including how representative the sample is of the population as a whole but also whether any bias has crept in during data collection.
As a general rule of thumb, you can place high reliance on a large-scale survey carried out by a highly reputable research institute and funded by government. Smaller-scale studies are less reliable.

In social sciences, including management and business research, the position on secondary data is more nuanced. Many studies will draw on some kind of secondary data, but often supplement with primary data as well.

Examples of secondary data in these fields include:

  • Financial databases, such as archived company accounts;
  • Collections of newspaper reports; and
  • Census data.

It is important to assess the quality of the information before use, which depends on several factors including its completeness and accuracy, and what information was collected.

All these will depend on the purpose for which the information was collected in the first place. Generally, if the purpose for which the data was collected is similar to the purpose of your research, you are likely to find the data useful and be able to rely on it in your study.

You also need to be aware of any changes to the data series over time, for example, when one particular item was redefined to fit a different purpose. These may affect what time period you can study, or make one time period less comparable with another.

In conclusion…

Observational research and secondary data both have a place to play in all types of research.

As with any research design, the important aspect is to be guided by your research questions to draw on the data that will answer those, and also to assess the quality of your chosen methods to identify strengths and limitations.