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Grounded Theory is a qualitative research method that is grounded in data. It involves collection, clean up and analysis of the data to discover the underlying information. In this approach rather than starting with an presumptive hypothesis, we start with the data and hypothesis are decided basis the data.


An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Subodh Tripathi on 22nd Jan 2021.


Applause for all the respondents - Sanjay Singh, Subodh Tripathi, Santosh Sharma


Q 332. What is Grounded Theory? How is it different from traditional hypothesis testing?


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Grounded Theory (GT) is a form of qualitative research that studies peoples experience for some kind of process and then generates a theory or explanation of how that process works. Its important to note here that the theory that’s created in grounded theory is generated only from data that’s collected in the study. The theory does not come from other sources like other theories or textbooks or the researchers own opinions or so on. That's why it is called Grounded Theory.


1. The first step of conducting grounded theory is to Determine if grounded theory will answer your research question. Grounded theory is good to use when there are no existing theories regarding the process that’s of interest to the researcher or the theories which were created from assert for a certain group of people that the researcher is interested in. For example, there is no theory that exists in terms of the process of becoming a regular smoker while attending high school college.


2. The second step is Recruitment of Participants who have gone through or are going through the process of interest. This is known as theoretical sampling. Finding a sample of participants who have experienced the process and can help develop a well rounded theory. For our example, we can recruit smokers/former smokers from high school, colleges and recruit people from a wide range of racist ethnicity or social economic backgrounds.


3. The third step is Data Collection. The main type of data collected in grounded theory is interview data although other forms of data can also be used for a triangulation. The main data that comes from interviews and interviews are conducted until saturation is reached. Interview questions are open ended like

a) What was the process you went through to become a smoker? Such questions address the core phenomena.

b) What influenced that process to occur? These are known as casual conditions.

c) What actions were taken by the person in response to the process of becoming a regular smoker? These are known as strategies.

d) What were the outcomes or effects of those actions? This known as consequences.

These questions help in creating a good solid theory.

4. The fourth step is Data Analysis. There are three stages of analysis:

i. Open Coding: This is when researcher reads the transcripts and determines different kinds of categories or themes that are found in data. Think of open coding as having an open mind, being open to what the participants are saying and open to different kinds of categories that you are seeing in the data. Coding is when you circle a chunk of text and write down the name of the category you have come up with next to that chunk of text. There you just coded that data. During open coding, researchers are also conducting constant comparative analysis which is when researchers constantly compare the data to the category to see if you are being consistent in how you are coding the data in each category. If a new chunk of data does not fit into a particular category, maybe you need to create a new category. During open coding, researchers also conduct memoing which is when researchers make memos to themselves throughout open coding regarding how the categories are beginning to explain the process and how those categories can be formed into a theoretical model. Open coding is finished when you have compared the data and categories to each other over and over that you feel like there are no new categories coming from the data.

ii. Axial Coding: This is when the researchers uses the codes and memos and thinks about how each of the categories relate to each other. This is the part of analysis that actually develops the theory. The researchers looks for categories that may be the core phenomena, casual conditions, strategies and consequences. Then he/she thinks how these categories connect to each other. They show these connections through a coding paradigm or a logic diagram which is visual model that shows the categories with lines and arrows to show an explanation of how the process works.

iii. Selective Coding: Here the researchers write a storyline about how the theory explains the core process, how all of the categories are related. It’s overall explanation of the theory.

5. The final step is Discriminant sampling. In this, the researchers recruit whole new group of participants who are similar to the original participants and same research is conducted. The point of this is to determine if the new participants experiences with the process of interest will be similar to the theory that was already created. This helps to test and verify if the theory is accurate or not.





1. It can be hard to recruit participants depending upon the process of interest.

2. It can take a lot of time to gather data, analyze data and then come up with a model and so on.

3. Analysis can be difficult to categorize and code all of that data.

4. There may be researchers who are biased in terms of the study and what the categories are and so on.

5. Small samples of participants makes it difficult to say that their experience with that process is what’s been felt by others, although discriminant analysis helps to verify the model if only we are given with a small number of participants of a particular area.


Hypothesis Testing on the other hand is when statistics of a sample are analyzed to determine if a hypothesis (which is explanation of something) is null hypothesis or alternative hypothesis. We use a small sample of data to verify our hypothesis rather than testing as much as data as possible. If the hypothesis turns out to be not verified then we call it a null hypothesis. In response, we create a small new hypothesis which is less relevant as compared to Grounded Theory.


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Grounded theory is an inductive research method, Grounded theory helps to discover or construct theory from data, systematically obtained and analyzed using comparative analysis. While grounded theory is intrinsic flexible, it is a complex methodology. 


when conducting experimental research, we normally start from a predetermined theory, typically in the form of one or more hypotheses, we then conduct experiments to collect data and use the data to prove the theory. But grounded theory emerges from a set of empirical observations and systematically collected data and we aim to develop a well-grounded theory from these data.

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This theory basically involves sort of techniques which enable researchers to effectively analyse ‘rich’ (detailed) qualitative data effectively.
It reverses the classic hypothesis-testing approach to theory development by defining data collection because the first stage and requiring that theory is closely linked to everything of the info .
The researcher keeps on the brink of the info when developing theoretical analyses – during this manner the analysis is ‘grounded’ within the data rather than being supported speculative theory which is then tested using hypotheses derived from the thought .
It employs a unbroken process of comparison back and forwards between the varied aspects of the analysis and also the info .
Grounded theory doesn't suggest that there are theoretical concepts just waiting within the info to be discovered. It means the thought is anchored within the info .
In grounded theory, categories are developed and refined by the researcher so on explain no matter the researcher regards because the many features of the data .



Highlights of grounded theory:

  • It consists of guidelines for conducting data collection, data analysis and theory building, which may cause research which is closely integrated to social reality as represented within the data.
  • The analysis of data to urge theory isn't enthusiastic to a stroke of genius or divine inspiration, but on perspiration and application of general principles or methods.
  • Grounded theory involves inductive guidelines instead of deductive processes. This is very different from what's often considered conventional theory building (sometimes described because the ‘hypothetico-deductive method’).
  • It should develop out of an understanding of the complexity of the topic matter. It knit the complexity of the info into a coherent whole. Primarily, such theories could also be tested effectively only in terms of the fit between the categories and thus the info , and by applying the categories to new data. In some ways this contrasts markedly with mainstream quantitative psychology where there's no requirement that the analysis fits all of the data closely – merely that there are statistically significant trends, no matter magnitude, which confirm the hypothesis derived from the thought . The unfitting data are considered measurement error rather than a reason to explore the data further so as to provide a much better analysis, because it could even be in qualitative research.
  • The theory-building process could also be endless one rather than a sequence of critical tests of the idea through testing hypotheses. In some ways , it's impossible to separate the various phases of the research into discrete components like theory development, hypothesis testing, followed by refining the idea . the info collection phase, the transcription phase and therefore the analysis phase all share the common intent of building theory by matching the analysis closely to the complexity of the subject of interest.
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Subodh Tripathi has provided the best answer to this question by highlighting the characteristics and detailed procedure of Grounded Theory and by also providing its comparison with traditional hypothesis testing. 

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