If you are here, you are probably conducting secondary research to support your dissertation instead of primary research. This would be fantastic news if it is true. It is the simplest kind of study to do secondary research! Congratulations!

Secondary research is considerably simpler. It's so easy that, in fact, it was possible to explain how to do it in just the four stages (see the next section). The time and effort necessary to perform primary research may be saved by conducting secondary research. Like contacting people, selecting and establishing measurements, and then sifting through data takes days or months at a time.

In the end, you'll still need to be able to conduct additional research. This is the reason why you're here. Therefore, make an adequate cup of your favourite drink (along with a glass of water) and then return to the sofa to enjoy a moment of relaxation.

THE FUNDAMENTALS: WHAT IS SECONDARY RESEARCH?

SECONDARY RESEARCH COMPREHENSION

So, just what do we mean by "secondary research"?

To address this topic, we must first define primary research. Primary research, as the name implies, is conducted by the researcher themselves. Real-time data is used by the researcher. Data gathered during certain research is under their direct supervision.

Using data that has previously been collected by another researcher is known as secondary research. The term "past data" refers to this sort of information, which may be found in earlier studies, government papers, and a wide range of online and offline sources.

Secondary research is, in a nutshell, the re-analysis, interpretation, or evaluation of previous data. It is up to the researcher to explain how information from the past affects the current research.

Because the researcher is not involved in data collection, secondary research is easier than primary research. Secondary research is also less time-consuming and more cost-effective. You don't have to pay any fees or compensate your subjects.

SECONDARY RESEARCH ADVANTAGES

Be aware of the strengths as well as the limitations of any research you do. You should see some of the advantages of secondary research by looking at the table.

Secondary research can be more affordable than original research. This is one of its most obvious benefits. Primary research usually requires a substantial financial investment. For example, members of the study group should be paid. It is common to incur travel and transportation costs. It is possible to purchase office space, equipment, and reward employees for their time. You may also need to pay for other expenses.

These costs are not necessary when conducting the secondary research. While secondary data may be required by researchers, it is usually less costly than doing the research from scratch.

A dissertation thesis for an undergraduate student or graduate student doesn't have to be expensive. It is possible to reduce your expenses by using publicly available secondary data sets.

This is not the only thing to be aware of.

Secondary research has another significant advantage that students appreciate. It saves time. Primary research can take months to recruit individuals, conduct interviews or administer questionnaires to them, clean the data collection and analyse the results. You can save most of the time and effort by selecting, preparing, or analysing existing data.

Secondary data is usually easy to collect, so it won't take you long to compile your secondary data set. Students used to have to spend hours looking through libraries to find the right data. The new technology makes this process much easier. You can usually find secondary data using web search tools or by emailing previous researchers.

Access to large amounts of data makes it possible to create your own study. This is another benefit of secondary research. If you were to collect large amounts of data on your own, it would be a lot of work. Primary research can take many years and would make it difficult to use longitudinal data in graduate or undergraduate studies. Because longitudinal data requires a periodic reassessment of a group, this is why it is important to use it.

Working with enormous datasets generated by someone else is possible when you use secondary data. You may also use longitudinal data to look for patterns and changes over time in a variety of events.

Secondary research allows you to rely on both broad data sets and expertly gathered material. Secondary research also has another benefit. For example, secondary research data that you use for your project will likely have been collected by researchers who are experienced in recruiting representative samples and designing studies.

Your lack of experience in comparison to these researchers means that your data collection is likely to be less successful than if you had collected this information by yourself.

SECONDARY RESEARCH DISADVANTAGES

You may have come to the conclusion that employing secondary data for your graduate or undergraduate dissertation is the best alternative by now. Don't ignore, nevertheless, the drawbacks of conducting secondary research.

Your secondary data might not be suitable for your study goals. This is the first problem. This is simply because you didn't get the information yourself.

It's important to have a precise study question in mind when you gather your data. In this way, you'll be able to locate the information you want quickly. Even so, secondary data was gathered to aid in the pursuit of other researchers' aims and objectives.

No matter how much secondary data may teach you, it will almost certainly be irrelevant to your research question. There are several reasons for this. You may be interested in statistics on a certain population in a specific location over a given period of time. It's possible that the secondary data you're using comes from a different time period, a different place, or a different population entirely.

Data from secondary sources may not be appropriate for your research, or it may be in a format that is not compatible with what you need. Participants ages may be a continuous variable if that's what you're looking for. That's right, you'd want your participants to put in a number for how old they are. Participants ages may have been included in the secondary data set as a categorical variable (e.g., 20-29, 30-39, 40-49, etc.). Another illustration A secondary data collection could just include the racial groupings "White," "Black or African American," "American Indian," and "Asian," but you'd want to include all of these as well. Because of these changes, secondary data may not be appropriate for your study.

The aforementioned two drawbacks might also lead to a third one: the existing data collection could not provide the best solution to your own research question(s). The application of secondary data to your own research goal may be constrained since, as was said above, it was gathered with a different study issue in mind.

Sadly, there are still more drawbacks to be mentioned. The fact that you have no control over the data's quality is another flaw with secondary data. All researchers must demonstrate the validity and reliability of their data. However, if the initial researchers did not prove the validity and reliability of their data, this may also restrict the validity and reliability of the data for your research. You are often recommended to critically assess how the data was acquired, analysed, and presented in order to demonstrate reliability and validity.

The third drawback of using secondary sources is that sometimes the original researchers don't disclose enough details on how their study was carried out. It's possible that you won't have enough knowledge of things like hiring practices, sample representativeness, data collection techniques, measurement instruments used, statistical analysis, and other things. If it is even feasible, you might need to go above and beyond to get this information.

TECHNIQUES AND OBJECTIVES OF SECONDARY RESEARCH

We have discussed the benefits and drawbacks of secondary research so far.

What are the best techniques for secondary research? How and when should each technique be used? In this context, there are three secondary research techniques that can be distinguished: using a single secondary data set; merging two secondary sets of data, and integrating main and secondary data sets. Let's go over each one in detail, and then discuss when each technique should be used.

A secondary data set can initially be used alone, that is, without integrating it with other data sets. You sift through the data until you locate a set that is relevant to your study, and then you utilize that set as the foundation for your whole investigation. When you wish to evaluate a data set again with a different research topic in mind, you do this.

Let's use a straightforward example to show this. Consider the possibility that you wish to find out whether pregnant women of various nations differ in their degrees of anxiety at various phases of their pregnancies. You have a suspicion based on the research that the association between pregnancy and anxiety may be affected by nationality.

You would need to enlist several expectant mothers from various countries and measure their levels of worry throughout their pregnancies if you wanted to investigate this association by gathering your own data. You could finish this study project in at least a year.

Instead of going through with this lengthy process, you choose to locate a secondary data collection that looked into, for example, a range of challenges faced by pregnant women in a countrywide sample. This study's original research question may have been: "How frequently do pregnant women experience a variety of mental health issues, such as stress, anxiety, mood disorders, and paranoid thoughts?" Although the initial researchers may have included the nationalities of the women, they were not especially interested in examining the relationship between nationality and anxiety at various stages of pregnancy. As a result, you are evaluating their data set in light of your own study question.

Your study may require the fusion or combination of two data sets. This approach is used when you want to compare two research results or examine the relationship between variables in two data sets.

Let me illustrate: In certain cases, secondary data collection may include information about a target population's smoking habits. Data on alcohol use might also be included in the second batch of data. As a consequence, you have the option to investigate whether or not this particular population is more likely to smoke or drink.

As a follow-up, here is another example: Your two secondary data sets may have the same outcome variable, such as the percentage of people who vacation in Greece during the summer. Even if one piece of data was collected in Germany and the other in the United Kingdom. By comparing these two sets of data, you may discover which nation visits Greece the most often.

For your research endeavours, it may be necessary to combine primary and secondary data. If you are looking for information that could be useful in your primary research, this is something you might consider.

Let's take another example. Assume that your subject is the attitudes of Americans and Britons towards racial discrimination. Imagine that you could locate recent research on Americans' views of this topic and use a set of measurements to analyse it. However, your search does not yield any current research regarding opinions among Britons. It would be difficult for you, if you live in London, to assess American sentiments on the subject. However, it would be much easier for you to do primary research about British attitudes.

The data from the American research may easily be replicated for use with British subjects, and the same controls can be used. Data from both primary and secondary sources are being brought together in your analysis. Secondary data may also be combined if it's for the goal of summarising descriptive information to help your research. For example, secondary data may be utilised to supplement primary research on people's perceptions of McDonald's.

SECONDARY DATA TYPES

Like all data types, the most common categories for secondary research are qualitative and quantitative. Secondary research can be done with both qualitative and quantitative data.

There are many examples of quantitative secondary data being used. This type of data is used after initial research has examined a population's propensity for smoking or alcohol consumption, the number of people from different nationalities visiting Greece in the summer, and the percentage of women who are pregnant who worry.

Each of these cases involved the evaluation of outcome variables via questionnaires. The data collected was numerical.

Secondary research that is qualitative rather than quantitative is more prevalent. Qualitative secondary data may still be used in your study, though. Secondary data is information that has already been gathered and is being utilised to supplement the findings of a current investigation. Qualitative data may be tested quantitatively using this method.

Qualitative research, for example, may have been focused on factors that influence people's decisions to live on boats. There are four main reasons why people choose to live on boats: (1) They can live a more flexible lifestyle; (2) they feel freer; (3) they feel like "world citizens"; (4) it is easier to visit family members who reside in other places. The study could have included 30 participants. This qualitative data can be used to create your own questionnaire, which you can then distribute to a wider group of people who live aboard boats. This is so you can use the qualitative data you've already gathered and reach a larger audience.

Qualitative data gathered as a basis for your quantitative study might also be evaluated, so keep that in mind. Let's imagine your study is focused on nomadic people's use of language to depict their way of life on boats. Since the original study did not address the research question, you may "extract" participant descriptions of a transitory lifestyle from the interviews.

SECONDARY DATA SOURCES

The terms "internal" and "external" refer to the two most popular categories of secondary data sources.

Data that comes from internal sources is information that is held by the organisation in the subject. For instance, you might utilise internal data sources if you were conducting research for a company (or research institution) when you were an intern and wanted to use some of their historical data.

Utilising these resources has the advantage of being simple to get and being free of related costs.

On the other hand, external data sources are those that are not part of a company or a research organisation. In the strictest sense, "someone else" has gathered this kind of information. External data sources have the advantage of providing extensive data, but often additional work (or money) is required to get it.

Now let's concentrate on various internal and external secondary data source kinds.

Internal sources come in many different forms. For instance, you may take sales data from the organisation if your study is concerned with profitability. Each business maintains track of its sales figures; therefore, your data might contain details on sales by region, client kinds, product pricing, product packaging, season, and other factors.

As an alternative, you may use financial information from a company. The goal of employing this information may be to perform a cost-benefit analysis and comprehend the economic potential or results of investing in new items, purchasing additional automobiles, and so forth.

Transport data is another category of internal information. Here, you might concentrate on describing the safest and most efficient modes of transportation or cars a company uses.

Alternately, you might rely on marketing data with the aim of evaluating the advantages and results of various marketing initiatives and methods.

Other suggestions include using customer information to identify the ideal clientele or safety information to examine the degree to which staff members adhere to safety policies.

There are many types of internal sources for secondary data. However, the most important thing to remember is that these data come from the organization where you did your internal research.

It is also possible to have a long number of external secondary data sources. The data received from official sources is one instance. This could be information on imports and exports socio-economic surveys, production figures agriculture statistics, health data as well as energy expenditure figures censuses of the population, and more. Governmental organizations often conduct research, which makes them experts on almost any topic you could think of.

international and national levels, including trade unions, banks, health organizations, colleges, and so on. They are also a third-party source of secondary information. All you need to do is find an organisation that has collected the information you require on your particular topic of interest, since, as with those of the federal government, these organisations invest a lot of effort in conducting current research.

It is also possible to obtain secondary data from organisations that deal with business, commerce, or professionals. They usually contain data sets about matters related to business. If they know how important your research is and are willing to give you information from other sources.

You may use scholarly publications as an external data source if your study is based on prior academic research.

You can get in touch with the original study's authors once you have identified the type of secondary data you want.

As a last illustration of a secondary data source, consider data from for-profit research institutions. They usually focus their research on the media's statistics and consumer information. This could be relevant to your work in the event that, for example, it falls into the study of media studies or if you're studying consumer behaviour.

PROCESS OF SECONDARY RESEARCH IN 4 STEPS

Some fundamental facets of conducting secondary research have been discussed in earlier sections of this book. We have defined secondary data, discussed its benefits and drawbacks, presented secondary research's techniques and objectives, and described the categories and sources of secondary data.

You ought to now have a better overall concept of what secondary research is.

We should now concentrate on the actual act of conducting secondary research. You will learn about each stage in this procedure in the section that follows, so you can use it as a reference when organising your research. A list of all the stages involved in conducting secondary research is provided in Table 6 at the conclusion of this article.

STEP 1: CREATE YOUR RESEARCH QUESTION(S)

Just like with any other sort of study, secondary research begins with establishing your research topic (s).

Your supervisor will frequently provide you with a clear research issue for your undergraduate thesis. However, for the majority of other sorts of research, and particularly if you are writing your graduate thesis, you must develop your own research topic.

The first stage in this process is to choose the broad research field that your study will fit within. For instance, you could be interested in learning about pregnancy anxiety, Greece tourism, or transitory lives. Since we have previously used these instances to explain our subject, it could be helpful to do so once more.

Reading through previous articles to determine whether there is a gap in the literature that your study can fill is the next step after deciding on your main topic. You could find that prior study did not look at regional variations in anxiety during pregnancy, regional variations in the propensity to spend the summer in Greece, or regional variations in the literature that attempts to generalise the findings on the choice of individuals to live on boats.

Once you have identified your area of interest, and located a knowledge gap in your field, it is time to define your research question. The following three research questions are more specific:  (1) "Are there any variations in interest in Greek tourism between Germans and Britons?" (2) "Do women of various nationalities suffer varying degrees of worry throughout different stages of pregnancy?" (3) "Why do individuals want to live on boats?"

Reading through previous articles to determine whether there is a gap in the literature that your study can fill is the next step after deciding on your main topic. You could find that prior study did not look at regional variations in anxiety during pregnancy, regional variations in the propensity to spend the summer in Greece, or regional variations in the literature that attempts to generalise the findings on the choice of individuals to live on boats.

After discovering your area of interest and locating a knowledge gap, you must define your research question. Our three examples' research questions would be more specifically worded as follows: (1) "Are there any variations in interest in Greek tourism between Germans and Britons?" (2) "Do women of various nationalities suffer varying degrees of worry throughout different stages of pregnancy?" (3) "Why do individuals want to live on boats?"

STEP 2: CHOOSE A SECONDARY DATA COLLECTION

As we previously discussed, the majority of research starts by outlining what is currently known about the subject and what information appears to be lacking. The type of data that has already been gathered about the subject is taken into account throughout this procedure.

You may choose to use secondary data after doing a literature review and determining your study objectives. There are already existing data that you can readily reuse, which will aid in addressing your research topic more effectively.

How can you determine if historical data is relevant to your research? This is done by looking through the literature in your field of interest. This will help you find other researchers, agencies, research centres, and organisations that have studied your subject.

There may be a useful supplementary data collection. You must then get in touch with the original authors to obtain their permission to use their data. This only applies if secondary data is used from other sources. You can use prior data from within your organisation to conduct your research rather than looking through literature for secondary data.

You must ensure that the secondary data set you are using is appropriate for your research question. After you've checked it, you'll have to explain why you used secondary data.

If you choose to use secondary data, for example, in the above examples, it might be because:

(1) This data could be used again after a recent study examined a range of mental health issues that women all over the globe face.

(2) Already, statistics exist about how many Britons and Germans want to visit Greece. These numbers can be compared.

(3) An existing qualitative study about why people like living on boats can be used as a basis for a quantitative study.

STEP 3: EVALUATION OF SECONDARY DATA SET.         

If you remember when we talked about the problems with secondary data, you'll remember that we said: 

(1) secondary data might not be the best choice for your research,

(2) The format of the secondary data may be different from what you need.

(3) Secondary data may not be as valid or reliable,

(4) Your research question may not be answered by secondary data, and

(5) The original researchers may not have given enough information about their research.

These problems can make your research less valuable. It is important to assess a set of secondary data. This will make the process much easier. We'll show you how to evaluate secondary information in a step-by-step manner.

STEP 3 (A): WHAT WAS YOUR ORIGINAL PURPOSE FOR THE STUDY?

Before you can evaluate secondary data, it is important to determine the original study's purpose. This is important because the original authors' goals will have influenced a number of crucial elements of your research, including the sample size, population, measurement techniques, and overall environment.

Throughout this stage, you must pay attention to any differences in the original study's research objectives or research questions. If, as we've said, you find that the original study was about something else, you must figure out what's different.

Let's look at our three research examples as examples of how to determine the goal of your original study. The first example was to study mental health issues such as anxiety, stress, mood disorders, and paranoid thinking in a diverse group consisting of expectant mothers.

What is the difference between this and your research goals? This data set will be used again to examine regional variations in women's anxiety during different stages of pregnancy. Concerning the second example study, you used two secondary data sets. One was to learn more about Germany, and one was to learn more details about British interest in Greek tourism.

Your research, unlike the previous studies that focused on specific populations, focuses on the tendency of Germans and Britons in Greece to visit during summer vacation. The original research was qualitative and focused on the reasons for living aboard boats. Although the inquiry is similar, you will conduct it differently because you are using a quantitative method.

You must conclude from each case that secondary data can actually answer your research question. It is possible to come up with a different conclusion and choose to do primary research or another type of secondary data collection.

STEP 3 (B) WHO COLLECTED THIS DATA?

Consider who collected the data to improve your assessment of secondary data collection. Which organisation were the writers affiliated with? Did the original authors have enough confidence in their work to trust them? This information is usually found by doing a few web searches.

Let's say that the UK government collected data on pregnant women, that a travel agency collected data on Greek tourism, and that researchers at a UK university collected data on why people live on boats.

Imagine that you have done extensive research on these organisations and found that all of them have sufficient professional experience. The exception is the travel agency. The level of professionalism displayed by this data source is unknown. Even though, for example, it wasn't published and very little is known about the researchers, this is still the case.

STEP 3(C): WHAT TECHNIQUES WERE USED?

You may anticipate having access to all the crucial details about this research if the study on which your research is based was completed expertly.

All of the first authors' sample characteristics, measurements, processes, and protocols need to have been documented. You can get this information in their final study report or by getting in touch with the authors directly.

You should be aware of the types of data that were gathered, the methods employed, and the validity and reliability of those methods (if they were quantitative measures). Additionally, you must provide a detailed summary of the types of data gathered, paying particular attention to the material pertinent to your research.

Let's say that researchers used the State Anxiety Index to assess women's anxiety at different stages of pregnancy. A demographic measure was also used to determine the women's nationalities. Both of these methods are valid and trusted. The writers might have created their own metric for gauging interest in Greek travel. However, there are no established standards. In our third example, semi-structured interviews were used by the authors to discuss the reasons why they wanted to live on boats.

STEP 3(D): WHEN WAS THE DATA GATHERED?

You should take into account the date the data was gathered while analysing secondary data. The reason for this is straightforward: if the data was gathered in the past, you could assume that it is out-of-date. What good is it to reuse data that is already out of date?

Within the previous five years, your secondary data should have been gathered Please assume that all three original investigations were completed within the provided time range for these cases.

STEP 3(E): SPECIFY THE DATA COLLECTION PROCESS.

The most crucial step in determining the quality and reliability of secondary data collection is the examination of the used approach.

We have already said that you must assess the validity and reliability of applied measurements. You must also evaluate the method of collection, its size, representativeness of the population, missing responses to employed measures, control for confounders, and the accuracy of the statistical analyses. You may be limited by flaws in the original method.

Let's suppose that the second case study used a slightly different approach. The two samples had enough participants (N1 = 453, N2 = 488) and low missing values. However, the authors didn't disclose how they selected the participants or whether they considered potential confounders.

The authors did not email you any extra material, so let's pretend they didn't. Allowing for qualitative investigations, let's also suppose that the third study example used a suitable technique, adequate analysis, and a large enough sample size (N = 30). Participants in the study came from a wide range of boating communities and ethnicities (thematic analysis).

This qualitative research does not require the assessment of the prevalence of missing values or the use of confounders.

STEP 3(F): CONDUCTING A FINAL ASSESSMENT

What conclusions can you draw about the calibre of your secondary data collection after taking all the factors mentioned in the previous phases into account? Let's think over our three cases once again.

From our first study example, we would say that the secondary data is of good quality. Professionals recently gathered the data, the metrics used were genuine and dependable, and the approach was more than enough. We are certain that the data already available will allow us to adequately address our new study issue. The data set for our first example is therefore perfect.

However, it appears that the two secondary data sets from our second study example are less than perfect. These current data sets allow us to answer our study questions. However, they were acquired by an unreliable source, the measure used is not shown to be reliable or valid, and the technique used has some significant flaws.

STEP 4: GATHER AND ANALYSE SECONDARY DATA.

As you examine the secondary data, you will become familiar with the primary research. Then, prepare a secondary data collection.

When you do quantitative research, the first thing you should do is list all the variables that you would like to use in your study. You might be interested in at least five factors. These include the women's nationality and anxiety levels at the start of their pregnancies. Their anxiety levels at three, six, and nine months the second scenario will focus on two variables: the participants' nationality and whether they are interested in summer vacation to Greece. After identifying your variables of interest, you must transfer your data into an Excel or SPSS file. That data must be transferred into the new file.

Next, look for and label any missing data, and recode variables if required (e.g., giving 1 to German participants and 2 to British). Some things may need to be re-scored in order to receive the best possible grades.

Most of the time, in order to calculate final scores, you will also need to construct new variables. For our example study on anxiety during pregnancy, the scores on each State Anxiety Inventory item that were completed at different points during the pregnancy will make up your data. You must determine the ultimate anxiety ratings for each instance the test was taken.

The final phase is the analysis of the data. You will need to choose the best method for secondary data collection. In our first example, MANOVA would be used to determine whether pregnant women in different countries experience different levels of stress at three, six, and nine months. To determine if Britons and Germans are interested in Greek tourism, we would use an independent sample t-test.

The process for collecting and analysing secondary information that is qualitative is slightly different. As a result of the qualitative investigation, you will need to list all reasons for living on a boat in this scenario. Next, you would need to make a survey that looks at the factors in a larger group.

To analyse the data, you will need to use statistical techniques.

As you might have noticed, this example combines quantitative and qualitative data. But what if you're recycling qualitative data? As in the earlier example, where we recoded interviews from our research to determine the terminology used for describing impermanent lives? This would mean that recording interviews and performing theme analysis would suffice.

SUMMARY

By now, you should have a good handle on how to do secondary research after reading this lengthy article. Hopefully, you've learned that secondary research is not as difficult as you thought.