Experiential Insight: on Carrying out Primary Research

In the research community across the world, several research methodologies, research models, survey methods, sampling methods, and different data collection methods are being used in different areas of management and social sciences research. There are certain best practices in carrying out primary research, also known as empirical research. The primary research is the one, done on the firsthand data; that is; the data is collected for the very first time; and it does not exist earlier. Basically, several factors effect the primary research. This specific experiential insight is particularly based on my experience of carrying out several primary research studies. Following are some of the best practices and challenges encountered while carrying out primary research studies:

1. Clarity in Problem and Deciding the Scope: this is very much important because, the management or social sciences research is an ocean. We should restrict ourselves to a research problem which is possible to be completed within the specified time. Instead of large scoped research project, one can focus on several tiny research studies; and finally consolidate findings of all those tiny research studies.
2. Limited Number of Hypotheses in-line with the Research Problem: It is best practice to have up to Five hypotheses. These hypotheses designed are to be in sync with the research problem, and research objectives. These are just derived/decomposed from research problem. Also, just by reading the hypothesis, one should be able to visualize/foresee the statistical technique going to the used in data analysis.
3. Deciding the Questionnaire: Here several factors come into picture. Response rate of surveys depends on the number of questions in the questionnaire, time taken to fill the questionnaire, level of ease in questions, and the kind of data is being asked for. Also, response rate depends on the type of data collection method. That is, to compare with face-to-face data collection, online surveys attract less response rate.
4. Conduct Interviews for Qualitative Data: It is best practice to conduct interviews, if the researcher wants to collect qualitative data from respondents. In this, specific interviews, particularly, open-ended questions can be asked. Also, it is best practice to have time limit for each respondent in conducting interviews.
5. Selection of Respondents: This is very much important. The respondents are to be chosen based on the kind of research problem, and the domain into which the research problem comes into. For example, if one wants to find out, what new features can be added to a new TV product, the potential respondents would be the housewives. For example, one wants to find out customer drives/triggers in purchasing a BMW car, the respondents should be selected from high income and high net-worth individuals.
6. Data Coding: This is the potential area, where in an error can unknowingly enter into the consolidated data. For example, one has collected data using a hard copy survey questionnaire. Then, entering the data into excel sheet or SPSS (Statistical Package for Social Sciences) has to be done with care. There is a possibility for human error here.
7. Delay in Data Analysis: This happens with many researchers across the world. Once data is collected, because of the other priorities, researchers delay in analyzing the data. Specifically, if the research study is done for industry purpose, by the time, research findings come out, outside business environment, technological environment, business context changes. For example, research studies carried out to find Top Technology Trends for 2021, Most Admired Companies-2021, Employee Salary Surveys-2021, etc. loose relevance if delay is there in data analysis.
8. Better to Use Simple Statistical Techniques: Unless one is mathematician, it is best practice to use simple statistical techniques, simple analysis tools. This is because, several times, researchers struggle in interpreting the output of tough statistical techniques. When, researcher is looking for depth in study, then complex statistical techniques are advised.
9. Challenges in Interpretation: Level of statistical understanding varies from researcher to researcher. Here, there is a chance that, for the same statistical data analysis output, different researchers may interpret the results in different ways. This is because of the variance in their level of understanding of the statistical techniques.
10. Clarity in Report Writing and Presentation: Usually, one ignores all the effort put by the researcher if these is no clarity in written report, and in presenting facts, data tables, analysis tables, graphs etc. Specific research report templates are to be followed in a systematic approach. Tables, Figures, Graphs and Facts should be at right place in the report with right context. Easy to read research reports attract more attention. Also, these Illustrations or value additions are to be linked to the running text in the research report at appropriate places/context.

Thus, several best practices and approaches can help one to carryout the primary research studies. 

Hope this helps.

Best Regards,

Dr.Goparaju Purna Sudhakar, PhD (Business Administration), PMP

(http://www.gpsudhakar.com)