Secondary data

Secondary data is data collected by someone other than the user. Common sources of secondary data for social science include censuses, surveys, organizational records and data collected through qualitative methodologies or qualitative research. Primary data, by contrast, are collected by the investigator conducting the research.


Secondary data analysis saves time that would otherwise be spent collecting data and, particularly in the case of quantitative data, provides larger and higher-quality databases than would be unfeasible for any individual researcher to collect on their own. In addition to that, analysts of social and economic change consider secondary data essential, since it is impossible to conduct a new survey that can adequately capture past change and/or developments.

Sources of secondary data


As is the case in primary research, secondary data can be obtained from two different research strands:

Quantitative: Census, housing, social security as well as electoral statistics and other related databases.

Qualitative: Semi-structured and structured interviews, focus groups transcripts, field notes, observation records and other personal, research-related documents.

A clear benefit of using secondary data is that much of the background work needed has been already been carried out, for example: literature reviews, case studies might have been carried out, published texts and statistic could have been already used elsewhere, media promotion and personal contacts have also been utilized.


This wealth of background work means that secondary data generally have a pre-established degree of validity and reliability which need not be re-examined by the researcher who is re-using such data.

Furthermore, secondary data can also be helpful in the research design of subsequent primary research and can provide a baseline with which the collected primary data results can be compared to. Therefore, it is always wise to begin any research activity with a review of the secondary data.


Secondary analysis or re-use of qualitative data

Qualitative data re-use provides a unique opportunity to study the raw materials of the recent or more distant past to gain insights for both methodological and theoretical purposes.


In the secondary analysis of qualitative data, good documentation can not be underestimated as it provides necessary background and much needed context both of which make re-use a more worthwhile and systematic endeavour . Actually one could go as far as claim that qualitative secondary data analysis “can be understood, not so much as the analysis of pre-existing data; rather as involving a process of re-contextualising, and re-constructing, data”



Overall challenges of secondary data analysis

There are several things to take into consideration when using pre-existing data. Secondary data does not permit the progression from formulating a research question to designing methods to answer that question. It is also not feasible for a secondary data analyst to engage in the habitual process of making observations and developing concepts. These limitations hinder the ability of the researcher to focus on the original research question.

Data quality is always a concern because its source may not be trusted. Even data from official records may be unreliable because the data is only as good as the records themselves, in terms of methodological validity and reliability.

Furthermore, in the case of qualitative material, primary researchers are often reluctant to share “their less-than-polished early and intermediary materials, not wanting to expose false starts, mistakes, etc.”

So overall, there are six questions that a secondary analyst should be able to answer about the data they wish to analyze.

Case 2:-

Secondary data is the data that have been already collected by and readily available from other sources. Such data are cheaper and more quickly obtainable than the primary data and also may be available when primary data can not be obtained at all.


Advantages of Secondary data

It is economical. It saves efforts and expenses.

It is time saving.

It helps to make primary data collection more specific since with the help of secondary data, we are able to make out what are the gaps and deficiencies and what additional information needs to be collected.

It helps to improve the understanding of the problem.

It provides a basis for comparison for the data that is collected by the researcher.

Disadvantages of Secondary Data

Secondary data is something that seldom fits in the framework of the marketing research factors. Reasons for its non-fitting are:-

Unit of secondary data collection-Suppose you want information on disposable income, but the data is available on gross income. The information may not be same as we require.

Class Boundaries may be different when units are same.

Before 5 Years After 5 Years

2500- 5000   5000-  6000

5001-  7500   6001- 7000

7500- 10000  7001-10000

Thus the data collected earlier is of no use to you.

Accuracy of secondary data is not known.

Data may be outdated.

Evaluation of Secondary Data

Because of the above mentioned disadvantages of secondary data, we will lead to evaluation of secondary data. Evaluation means the following four requirements must be satisfied:-

Availability- It has to be seen that the kind of data you want is available or not. If it is not available then you have to go for primary data.

Relevance- It should be meeting the requirements of the problem. For this we have two criterion:-

Units of measurement should be the same.

Concepts used must be same and currency of data should not be outdated.

Accuracy- In order to find how accurate the data is, the following points must be considered: -

Specification and methodology used;

Margin of error should be examined;

The dependability of the source must be seen.

Sufficiency- Adequate data should be available.

Robert W Joselyn has classified the above discussion into eight steps. These eight steps are sub classified into three categories. He has given a detailed procedure for evaluating secondary data.

Applicability of research objective.

Cost of acquisition.

Accuracy of data.