In your own research, you can therefore reuse this qualitative data to form a questionnaire, which you then give to a larger population of people who live on boats. This will help you to generalise the previously-obtained qualitative results to a broader population. Importantly, you can also re-assess a qualitative data set in your research, rather than using it as a basis for your quantitative research.
Both can be used when you want to a inform your current research with past data, and b re-assess a past data set.
Internal sources of data are those that are internal to the organisation in question. For instance, if you are doing a research project for an organisation or research institution where you are an intern, and you want to reuse some of their past data, you would be using internal data sources. The benefit of using these sources is that they are easily accessible and there is no associated financial cost of obtaining them.
External sources of data, on the other hand, are those that are external to an organisation or a research institution. The benefit of external sources of data is that they provide comprehensive data — however, you may sometimes need more effort or money to obtain it. There are several types of internal sources. Each organisation keeps a track of its sales records, and thus your data may provide information on sales by geographical area, types of customer, product prices, types of product packaging, time of the year, and the like.
The purpose of using this data could be to conduct a cost-benefit analysis and understand the economic opportunities or outcomes of hiring more people, buying more vehicles, investing in new products, and so on.
Another type of internal data is transport data. Here, you may focus on outlining the safest and most effective transportation routes or vehicles used by an organisation. Alternatively, you may rely on marketing data , where your goal would be to assess the benefits and outcomes of different marketing operations and strategies. The list of the types of internal sources of secondary data can be extensive; the most important thing to remember is that this data comes from a particular organisation itself, in which you do your research in an internal manner.
The list of external secondary data sources can be just as extensive. One example is the data obtained through government sources. Government agencies tend to conduct a lot of research, therefore covering almost any kind of topic you can think of.
Another external source of secondary data are national and international institutions , including banks, trade unions, universities, health organisations, etc. As with government, such institutions dedicate a lot of effort to conducting up-to-date research, so you simply need to find an organisation that has collected the data on your own topic of interest.
Alternatively, you may obtain your secondary data from trade, business, and professional associations. These usually have data sets on business-related topics and are likely to be willing to provide you with secondary data if they understand the importance of your research.
If your research is built on past academic studies, you may also rely on scientific journals as an external data source. Once you have specified what kind of secondary data you need, you can contact the authors of the original study. As a final example of a secondary data source, you can rely on data from commercial research organisations.
These usually focus their research on media statistics and consumer information, which may be relevant if, for example, your research is within media studies or you are investigating consumer behaviour. TABLE 5 summarises the two sources of secondary data and associated examples: Internal to the organisation or research institution where you conduct your research Definition: External to the organisation or research institution where you conduct your research Examples: Now it may be useful to focus on the actual process of doing secondary research.
This next section is organised to introduce you to each step of this process, so that you can rely on this guide while planning your study. At the end of this blog post, in Table 6 , you will find a summary of all the steps of doing secondary research.
For an undergraduate thesis, you are often provided with a specific research question by your supervisor. But for most other types of research, and especially if you are doing your graduate thesis, you need to arrive at a research question yourself. The first step here is to specify the general research area in which your research will fall.
For example, you may be interested in the topic of anxiety during pregnancy, or tourism in Greece, or transient lifestyles. Since we have used these examples previously, it may be useful to rely on them again to illustrate our discussion.
Once you have identified your general topic, your next step consists of reading through existing papers to see whether there is a gap in the literature that your research can fill. Having found your topic of interest and identified a gap in the literature, you need to specify your research question. In our three examples, research questions would be specified in the following manner: It is at this point, after reviewing the literature and specifying your research questions, that you may decide to rely on secondary data.
You will do this if you discover that there is past data that would be perfectly reusable in your own research, therefore helping you to answer your research question more thoroughly and easily.
But how do you discover if there is past data that could be useful for your research? You do this through reviewing the literature on your topic of interest. During this process, you will identify other researchers, organisations, agencies, or research centres that have explored your research topic. Somewhere there, you may discover a useful secondary data set.
You then need to contact the original authors and ask for a permission to use their data. Note, however, that this happens only if you are relying on external sources of secondary data. If you are doing your research internally i. In any case, you need to ensure that a secondary data set is a good fit for your own research question. Once you have established that it is, you need to specify the reasons why you have decided to rely on secondary data.
For instance, your choice to rely on secondary data in the above examples might be as follows: Because such disadvantages of secondary data can limit the effectiveness of your research, it is crucial that you evaluate a secondary data set. To ease this process, we outline here a reflective approach that will allow you to evaluate secondary data in a stepwise fashion.
During this step, you also need to pay close attention to any differences in research purposes and research questions between the original study and your own investigation. As we have discussed previously, you will often discover that the original study had a different research question in mind, and it is important for you to specify this difference.
The aim of the first research example was to investigate mental difficulties e. How does this aim differ from your research aim? Well, you are seeking to reuse this data set to investigate national differences in anxiety experienced by women during different pregnancy stages. Finally, in our third example, the original research was a qualitative investigation into the reasons for living on boats.
Your research question is different, because, although you are seeking to do the same investigation, you wish to do so by using a quantitative methodology. Importantly, in all three examples, you conclude that secondary data may in fact answer your research question. If you conclude otherwise, it may be wise to find a different secondary data set or to opt for primary research. Original authors should have documented all their sample characteristics, measures, procedures, and protocols.
This information can be obtained either in their final research report or through contacting the authors directly. It is important for you to know what type of data was collected, which measures were used, and whether such measures were reliable and valid if they were quantitative measures.
You also need to make a clear outline of the type of data collected — and especially the data relevant for your research. In our second example, the authors might have crafted their own measure to assess interest in Greek tourism, but there may be no established validity and reliability for this measure. And in our third example, the authors have employed semi-structured interviews, which cover the most important reasons for wanting to live on boats.
Ideally, you want your secondary data to have been collected within the last five years. We have already noted that you need to evaluate the reliability and validity of employed measures. In addition to this, you need to evaluate how the sample was obtained, whether the sample was large enough, if the sample was representative of the population, if there were any missing responses on employed measures, whether confounders were controlled for, and whether the employed statistical analyses were appropriate.
Any drawbacks in the original methodology may limit your own research as well. Note that, since this was a qualitative investigation, there is no need to evaluate the number of missing values and the use of confounders.
We would conclude that the secondary data from our first research example has a high quality. Data was recently collected by professionals, the employed measures were both reliable and valid, and the methodology was more than sufficient. We can be confident that our new research question can be sufficiently answered with the existing data. Thus, the data set for our first example is ideal. The two secondary data sets from our second research example seem, however, less than ideal.
Although we can answer our research questions on the basis of these recent data sets, the data was collected by an unprofessional source, the reliability and validity of the employed measure is uncertain, and the employed methodology has a few notable drawbacks. Finally, the data from our third example seems sufficient both for answering our research question and in terms of the specific evaluations data was collected recently by a professional source, semi-structured interviews were well made, and the employed methodology was sufficient.
The final question to ask is: In this instance, you can only note the drawbacks of the original data set, present its limitations, and conclude that your own research may not be sufficiently well grounded. Your first sub-step here if you are doing quantitative research is to outline all variables of interest that you will use in your study. In our first example, you could have at least five variables of interest: In our second example, you will have two variables of interest: Once your variables of interest are identified, you need to transfer this data into a new SPSS or Excel file.
Remember simply to copy this data into the new file — it is vital that you do not alter it! Once this is done, you should address missing data identify and label them and recode variables if necessary e. You may also need to reverse-score some items, so that higher scores on all items indicate a higher degree of what is being assessed.
Most of the time, you will also need to create new variables — that is, to compute final scores. For instance, in our example of research on anxiety during pregnancy, your data will consist of scores on each item of the State Anxiety Inventory, completed at various times during pregnancy.
You will need to calculate final anxiety scores for each time the measure was completed. Your final step consists of analysing the data. You will always need to decide on the most suitable analysis technique for your secondary data set.
In our first research example, you would rely on MANOVA to see if women of different nationalities experience different stress levels at the beginning, at three months, at six months, and at nine months of pregnancy ; and in our second example, you would use an independent samples t-test to see if interest in Greek tourism differs between Germans and Britons. The process of preparing and analysing a secondary data set is slightly different if your secondary data is qualitative.
In our example on the reasons for living on boats, you would first need to outline all reasons for living on boats, as recognised by the original qualitative research. Then you would need to craft a questionnaire that assesses these reasons in a broader population. Note that this example combines qualitative and quantitative data. But what if you are reusing qualitative data, as in our previous example of re-coding the interviews from our study to discover the language used when describing transient lifestyles?
Here, you would simply need to recode the interviews and conduct a thematic analysis. Outline all reasons for living on boats; Craft a questionnaire that assesses these reasons in a broader population; Analyse the data. Recent Posts How often should you reference? A great example of a reflective essay How to write a captivating conclusion to your essay How to write a dissertation literature review: How to structure an essay Top 10 essay referencing tips.
The Ultimate Guide to Writing a Dissertation. How to do your dissertation secondary research in 4 steps. TABLE 1 outlines the differences between primary and secondary research:.
Whatever type of research you are conducting, always be aware of its strengths and limitations. If you look at the table above, you should already be able to discern some advantages of secondary research. But this is far from the only consideration. By now you may have concluded that using secondary data is a perfect option for your graduate or undergraduate dissertation. TABLE 2 provides a full summary of advantages and disadvantages of secondary research:.
So far, we have defined secondary research and outlined its advantages and disadvantages. TABLE 3 summarises particular methods and purposes of secondary research: The two most common types of secondary research are, as with all types of data, quantitative and qualitative. Secondary research can, therefore, be conducted by using either quantitative or qualitative data sets.
TABLE 4 highlights the two main types of secondary data and their associated purposes: The two most common types of secondary data sources are labelled as internal and external. TABLE 5 summarises the two sources of secondary data and associated examples:. In previous sections of this guide, we have covered some basic aspects of doing secondary research. We have defined secondary data, outlined its advantages and disadvantages, introduced the methods and purposes of secondary research, and outlined the types and sources of secondary data.
At this point, you should have a clearer understanding of secondary research in general terms. Secondary research begins exactly like any type of research: As we mentioned above, most research begins by specifying what is already known on the topic and what knowledge seems to be missing.
This process involves considering the kind of data previously collected on the topic. If you recall our previous discussion on the disadvantages of secondary data, you will remember us specifying that: When evaluating secondary data, you first need to identify the aim of the original study.
A further step in evaluating a secondary data set is to ask yourself who has collected the data. To what institution were the authors affiliated? Were the original authors professional enough to trust their research? Usually, you will be able to obtain this information through quick online searches. If the study on which you are basing your research was conducted in a professional manner, you can expect to have access to all the essential information regarding this research.
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