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Topic Selection strategy for women Ph.D. scholars currently working as associate / assistant professors in India
Topic Selection strategy for women Ph.D. scholars currently working as associate / assistant professors in India
Written by Ankit Gupta for CrossTab PhD Thesis Advisor from Dehradun, Uttarakhand, India
Data availability, research methodology and research gap are the three basic building blocks of any PhD research topic selection
Data availability, research methodology and research gap are the three basic building blocks of any PhD research topic selection
If your career focus is that of teaching at an advanced degree level in India and not that of a career in scientific research, my advise for topic selection for your PhD in India is to prioritise and keep your foci on ease of completion rather than any other aspect. The second most important aspect of PhD thesis acceptance in India is based on quality of research work. Quality of research work can be easily established with quantitative numerical data, its analysis and interpretation. Third and final aspect of PhD research work would be its impact, and impact of any data driven research work is its ability to statistically prove or disapprove a single or multiple hypotheses.
Therefore, the three most important criteria to keep in mind while selecting a topic for your PhD are:1. Ease of completing your PhD research work within the time allotted2. Quality of PhD research work that can be attained by you with ease3. Impact of PhD research work that you can establish with ease
And, since research work that is driven by quantitative numerical data will help you achieve points 2 and 3 above, in order to achieve point 1 above you should consider selecting that topic for which you can easily obtain relevant quantitative numerical data. Although, obtaining quantitative numerical data depends largely on the area or discipline or faculty or department of research work, it can be classified as follows:1. Sampled Survey data (Questionnaire or Interview or Test etc. while interacting and by taking inputs from the research subject)2. Sampled Measurement data (Physically, through an instrument or through a technique etc. while interacting with a research subject but without taking inputs from the research subject)3. Sampled Observation data (Without interacting with the research subject and without taking inputs from the research subject, this may also include meta data that is data about data)4. Sampled Secondary data (Existing data that is already available and was originally collected or left behind at an earlier time by a different person for a different purpose, but not for direct analysis for the current research, this may also include meta data that is data about data)
Thus, you should first consider what type of data is already available with you or is within your reach and does not require additional effort. If this is not possible then you should consider that class of data which you can collect with minimal additional effort and if that is also not possible, only then you should consider that data for which you would have to put maximum effort. Generally speaking, sampled secondary data is the easiest to find and obtaining sampled survey data is where you would have to put maximum effort. While selecting the class of data you should also consider qualitative data (non-numerical data) that can be quantified or coded into numerical values though an appropriate approach or technique or method.
By the time you are able to find or close in on your class of data, you will be in a position to at-least identify the area or sub-area or category or keyword of your research topic. This is where your creativity will have to kick in. In order to gain starting ideas for your topic selection you can use the following resources:1. Google scholar alerts2. Google alerts3. Shodhganga4. Journals and publications5. Online / college / University library research databases
Just use the keyword identified by you earlier and use it across above mentioned resources. Once you obtain various published articles or research papers or theses look into their research methodology and the tools that they have used. Identify different methods, techniques and tools for research that may also be applicable to chosen class of data and the data set. After you have done this you will have a more clear picture of the research topic or topics that you can pursue with ease. From here you will have to do a broader literature survey to identify a research gap in-order to finalize your topic. The end result of the above mentioned exercise would be a topic that would have already considered data availability, research methodology and research gap which are the three basic building blocks of any PhD research topic selection.
You as a PhD scholar may not have an inclination towards a research career, and even if you do, you may still lack the experience needed to develop a specific topic within a broader scientific research area. When starting out to finalize your PhD research topic, do not begin by jumping straight for a solution to a problem. Instead give the problem adequate thought or try to define a problem. As a novice researcher do not approach an unfamiliar problem by focusing almost immediately on the solution while ignoring the fundamental steps involved in defining the problem and examining the alternatives. The difficulty this will present is that you will not be prepared to engage in the appropriate activities needed to adequately select a topic based on a defined problem. Before selecting a viable topic, you should become ready mentally to fully engage in the exploration and development process.
Therefore, the three most important criteria to keep in mind while selecting a topic for your PhD are:1. Ease of completing your PhD research work within the time allotted2. Quality of PhD research work that can be attained by you with ease3. Impact of PhD research work that you can establish with ease
And, since research work that is driven by quantitative numerical data will help you achieve points 2 and 3 above, in order to achieve point 1 above you should consider selecting that topic for which you can easily obtain relevant quantitative numerical data. Although, obtaining quantitative numerical data depends largely on the area or discipline or faculty or department of research work, it can be classified as follows:1. Sampled Survey data (Questionnaire or Interview or Test etc. while interacting and by taking inputs from the research subject)2. Sampled Measurement data (Physically, through an instrument or through a technique etc. while interacting with a research subject but without taking inputs from the research subject)3. Sampled Observation data (Without interacting with the research subject and without taking inputs from the research subject, this may also include meta data that is data about data)4. Sampled Secondary data (Existing data that is already available and was originally collected or left behind at an earlier time by a different person for a different purpose, but not for direct analysis for the current research, this may also include meta data that is data about data)
Thus, you should first consider what type of data is already available with you or is within your reach and does not require additional effort. If this is not possible then you should consider that class of data which you can collect with minimal additional effort and if that is also not possible, only then you should consider that data for which you would have to put maximum effort. Generally speaking, sampled secondary data is the easiest to find and obtaining sampled survey data is where you would have to put maximum effort. While selecting the class of data you should also consider qualitative data (non-numerical data) that can be quantified or coded into numerical values though an appropriate approach or technique or method.
By the time you are able to find or close in on your class of data, you will be in a position to at-least identify the area or sub-area or category or keyword of your research topic. This is where your creativity will have to kick in. In order to gain starting ideas for your topic selection you can use the following resources:1. Google scholar alerts2. Google alerts3. Shodhganga4. Journals and publications5. Online / college / University library research databases
Just use the keyword identified by you earlier and use it across above mentioned resources. Once you obtain various published articles or research papers or theses look into their research methodology and the tools that they have used. Identify different methods, techniques and tools for research that may also be applicable to chosen class of data and the data set. After you have done this you will have a more clear picture of the research topic or topics that you can pursue with ease. From here you will have to do a broader literature survey to identify a research gap in-order to finalize your topic. The end result of the above mentioned exercise would be a topic that would have already considered data availability, research methodology and research gap which are the three basic building blocks of any PhD research topic selection.
You as a PhD scholar may not have an inclination towards a research career, and even if you do, you may still lack the experience needed to develop a specific topic within a broader scientific research area. When starting out to finalize your PhD research topic, do not begin by jumping straight for a solution to a problem. Instead give the problem adequate thought or try to define a problem. As a novice researcher do not approach an unfamiliar problem by focusing almost immediately on the solution while ignoring the fundamental steps involved in defining the problem and examining the alternatives. The difficulty this will present is that you will not be prepared to engage in the appropriate activities needed to adequately select a topic based on a defined problem. Before selecting a viable topic, you should become ready mentally to fully engage in the exploration and development process.
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Last Updated: 30-05-2025 IST
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Public Key: 5F0A7C59DA493504 on https://pgp.mit.edu/
Licences: This work is licensed under Attribution-NonCommercial-NoDerivatives 4.0 International and is owned by Ankit Gupta
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