Using secondary quantitative data

‘Secondary analysis is the re-analysis of data for the purpose of answering the original research question with better statistical techniques, or answering new questions with old data. Secondary analysis is an important feature of the research and evaluation enterprise’ (Glass, 1976, pp. 3–8). Greener (2008, p. 73) described secondary data ‘as data which the researcher did not collect for themselves directly from respondents or subjects’. This statement suggests that secondary data is not collected with the researcher’s purpose and objectives in mind. This data could be collected by other researchers, in the process of normal operations, or by institutions (Greener, 2008). Secondary data analysis could take many forms and have many benefits as well (Morrow, Boddy, & Lamb, 2014). Adams, Khan, Reaside, and White (2007) described secondary data as data collected by someone else and this data is available in books, libraries and on the web. A researcher can use this data as the main source for the research to answer the research question or to supplement the collected data. In many cases, secondary data is used to validate the collected data (Adams, Khan, Raeside, & White, 2007). In this study, the researcher use old data (according to Glass and Greener) from a process of normal operations to answer new questions. In addition to qualitative data (primary data) from expert interviews, the researcher can also use confidential quantitative data from the Business Intelligence software of a company. The available secondary quantitative data material amounts to several thousands of B2B clients. This secondary data allows the researcher to verify the findings of the face-to-face interviews. The researcher defines secondary data as the data gathered by a given party—in this case, a company—for one purpose; the data is then utilized by another party—the researcher—for a different purpose. In general, secondary data can be comprised of published research, internet materials, media reports, and data that has been cleaned, analysed, and collected for a purpose other than needs assessment, such as academic research or an agency or sector- specific monitoring reports (Micheel, 2010). In the academic world, different researchers like Bryman (1989), Dale, Arber, and Proctor (1988), and Robson (2002) created some varient types of classifications of secondary data. Saunders, Lewis, and Thornwill (2009) generated three sub-groups with several subitems. This groups are shown in Figure 51.

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Documentary secondary data, written or non-written, is often used where the researcher also use primary data e.g. the research follows a mixed method approach (Saunders, Lewis, & Thornwill, 2009).

Both written and non-written material can be used and analysed for qualitative, quantitative, or mixed method research approaches.

Survey

Survey-based secondary data could be available as raw data or as already analysed data. The first sub-type is census. This data is normally carried out by governments or state organisations because in most cases these data is about population of continents, countries, or regions (Hakim, 2000).

Continuous and regular surveys are repeated over a period of time at regular intervals e.g. every year. These surveys are usually done by governments or private organisations. This secondary data could be useful if the research focuses for example on social trends. But a researcher has to check in advance if the data answers the research question and meets the research objectives, because in some cases the data is very detailed. For a researcher, it is also important to check when was the data collected because the gap between collecting and publication could be in some cases more than two years (Hakim, 1982). This could be a problem because maybe relevant circumstances had changed.

Saunders, Lewis, and Thornwill (2009) described ad hoc surveys as very specific one-off surveys. These surveys in most cases deal with a very specific matter and are executed by independent researchers, the government, or organisations. Regarding these matter, it is difficult to find relevant data. In the case of non-public secondary data, the researcher has to check if he is allowed to use this data.

Multiple source

This type of secondary data is made by combining two different data sets. This could be two surveys, two documentaries or a combination of both. One method is the time series, in which a survey has been repeated a number of times to generate the data (Saunders, Lewis, & Thornwill, 2009). According to Hakim (2000), different sources of secondary data could be

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combined as well, if they have the same geographical basis. Researchers call such data ‘area– based’.

Adams, Khan, Reaside, and White (2007) outlined some advantages and disadvantages that have to be considered while using secondary data. The main questions for these researchers are:

• Is the data really relevant to the researcher’s work? • Is the data really representative?

Table: 35 Advantage and disadvantage of using secondary data (Adams, Khan, Raeside, & White, 2007)

Greener (2008) pointed out some disadvantages of using secondary data. According to her, the researcher has to keep in mind seven points before using secondary data.

1. Difference of purpose

Difference of purpose could be an issue if researchers use secondary data from other researchers or institutions, because the original researcher may have had a different

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purpose and the data would not be compatible with the study. In this study, this is not the case because this researcher uses ‘grey’ data/material, i.e. existing data is original sales figures from a normal operation.

2. Cost or access constraint

This could happen if the researcher searches for corporate data from market research companies or agents, because this data is normally not free of charge. It is possible to avoid this issue if the researcher obtained the data from university libraries. In this case, there are no constraints because the data already exists.

3. Aggregation and presentation of data

Other researchers will aggregate the original data for their purpose. The original re- searcher perhaps needed different customer cluster or time frames. It is difficult to work with such clusters or time frames in the current study if researchers need differ- ent ones. It is the same issue regarding the presentation of data. In this study, it is not an issue because the researcher has access to the original data.

4. Data quality

The quality of the secondary data is a major point of doubt. If the researcher does not know the original researcher, the institution, or the original study very well, it is a leap of faith for him to use the secondary data. The researcher knows the exact quality of the secondary data because he knows the organisation where the data comes from.

5. Measurement validity

A researcher cannot expect secondary data to be completely true because the data reflects the purpose and preconceptions of the original researcher. In this study, the researcher knows the validity of measurement.

189 6. Data coverage

In some cases, secondary data does not cover all the necessary information. To cover all discussed customer segmentation methods, some very specific customers are nec- essary. For the researcher, it was impossible to get all this sensitive customer data.

7. Data use

In some cases it is not possible to use the secondary data because it is in a format that does not match the statistical analysis. This is not the case in this study.

The existing secondary data covers six out of seven critical points. Thus, no problems regarding the points above are to be expected during the use of existing data.

For every researcher, it must be clear, secondary data must be viewed with the same caution as any primary data that the researcher collected by himself (Saunders, Lewis, & Thornwill, 2009). It is absolutely necessary for the researcher to feel certain about the following points:

• The secondary data help to answer the research questions and meet the research ob- jectives.

• The benefits are greater than the cost. • The data is valid and reliable.

• The researcher is allowed to use the secondary data.

If the researcher answers these four points with yes, using secondary data has an advantage compared to using primary data (Stewart & Kamins, 1993). One advantage is, the researcher can evaluate the data before he use it. This is very important, especially if the researcher has more than one source of secondary data. To make sure, the secondary data is valid and reliable, the researcher could use a three-stage process.

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Figure: 52 Evaluating potential secondary data sources (Saunders, Lewis, & Thornwill, 2009)

a) Measurement validity

Measurement validity is one of the most important criteria of any secondary data set. If the researcher does not get the needed information to answer the research questions, the research objectives will result in invalid answers (Kervin, 1999). Jacob (1994) argued that when a researcher uses secondary survey data, most of the measures used do not quite match those the researcher needs.

b) Coverage including unmeasured variables

Coverage is the second important criteria for suitability. The researcher must be absolutly sure that the secondary data includes main information that the researcher need, the correct period of time, and the right variables to answer the research questions and research objectives. Hakim (2000) pointed out two major issues. First, the researcher must be sure that

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unwanted data can be eliminated. Second, sufficient data must remain for the analysis to take place once unwanted secondary data has been eliminated.

c) Reliability and validity

As mentioned before, the reliability and validity of secondary data, i.e. the sources of the secondary data and the method by which the data was collected, are important factors. In many cases, a view of the sources of the data is sufficient because Dochartaigh (2002) claimed that the authority or reputation of the sources is helpful. If the data comes from the government or from a large and well-known company or organisation, the source is, in all likelyhood, trustworthy and reliable.

d) Measurement error or bias

Kervin (1999) stated that measurement bias can occur due to two reasons. One reason is the deliberate distortion of data. Second reason is a change in the data since collection. But sometimes these errors or biases are not published if the source of the issue is not an error but a differences in the data. One solution for the researcher is to contact the company or organisation responsible for the data collection to remove ambiguity.

e) Cost and benefits

Last step in the process given by Saunders, Lewis, and Thornwill (2009) to evaluate potential secondary data sources is a comparison of the costs and benefit of the secondary data. According to Greener (2008), secondary data is often cost-free, but cost does not only include the money the researcher has to pay for the secondary data. It also includes the financial resources and time needed to evaluate the data (Kervin, 1999).

Dillon, Madden, and Firtle (1994) suggested that a search for secondary data should precede any primary research activity, because secondary data solves the research questions and objectives already or helps to solve theses topics. According to Adams, Khan, Raeside, and White (2007), before a researcher starts collecting secondary data, he should follow some important guidelines:

1. Plan the data collection 2. Develop a strategy

3. Identify the right type of data

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Apart from these guidelines, the researcher has to consider all advantages and disadvantages of using the secondary data. The researcher should also keep in mind that all analyses, whether primary or secondary, qualitative or quantitative, are a matter of interpretation ‘because meanings cannot be grasped directly and all meanings are essentially indeterminate in any unshakeable way, interpretation becomes necessary, and this is the work of the hermeneutic enterprise’ (Josselson, 2004, p. 3).