There is nothing that requires you to use the same method for both types of data, but you would need a good reason for doing so in order to convince your reviewers that you made an appropriate decision.

In particular, there would need to be strengths of each analysis method that were directly connected to each form of data collection. I am familiar with both thematic analysis and grounded theory, and I personally don't see any obvious reason to use one for diary data and the other interview data.

I definitely share the views of David. Ithe is unclear at this stage why you would want to use different analytical approaches to both sets of data when ideally thematic analysis could be applied. Unless as David suggests there is strong reason for using the grounded theory approach as a second analytic method.

David and Ferdinand are correct. To me, the only exception would be if you wanted to adopt a mixed methods approach on the basis of data triangulation and pragmatics. That said, you have to approach mixed methods very carefully and with the intention to purposefully collect and seperate out data using different methods from the outset of the research. The attached chapter may assist.

In my opinion, both thematic analysis (as a data analysis method) and grounded theory method (as a general strategy to inquiry, along with narrative study, case study method, ethnography, or phenomenology), can be considered as a way of analysing data. They provide us with methods and techniques to organise, validate, connect every data segments to build up a whole story, aiding in interpreting those data to answer your research questions. Each helps you back up your answer to the epistemological question - how do you know the nature of phenomenon in study by interpreting and analysing your data? Therefore, as long as you can explain the epistemological motivation behind the use of both methods in your analysis, you can use each of them for each of type of data.

HOWEVER, for me, each method has different epistemological nature, thus, they have different focus and techniques in analysing data.

If thematic analysis helps you focus on finding any segments and details present in your data pieces to depict the big theme, grounded theory method aims to develop theories emerging in data segments in terms of ideas (in open sampling to open coding and memoing), relationships between ideas (in relational sampling to axial coding and memoing), theory (in discriminate sampling to selective coding).

In sum, my recommendation is to use only one method for data analysis for more coherent and logic interpretation of data.

Cheers.

The sequence of the data collection procedures (and approaches to collecting the data) are also important. These are not clearly mentioned in your question, so it would be risky to give any definitive advice. Also, as David Morgan mentioned above, you would need to persuade the reviewers (examiners) that the blending of the two analytical methods was appropriate. (I personally think this could be very challenging).

Regarding your data, you might need to consider whether the diary data precede the data from the interviews? Were the participants encouraged to write their diaries freely or only on some selected issues that interest the researcher? If the diaries data do precede the interviews data and the diaries were written in a "free style/free flow" manner -- and you you want to adopt both Grounded Theory and thematic analysis -- then it would be intuitively more appropriate to use Grounded Theory for the diaries. Then, after one will have identified the codes and categories based on the diaries entries one could, in theory, proceed to analyze the interviews using thematic analysis. (Actually, this even could be considered as an "abbreviated version" of Grounded Theory [Willig, 2001], where you triangulate the findings from the diaries through the interviews).

Finally, it would be useful to know in which academic field is your study? If in the Social Sciences, then you might want to consider Grounded Theory as the most appropriate method to analyze complex social processes. If it is in other areas (e.g., psychology, management studies, education), then doing a thorough thematic analysis for the both kinds of the data could be a better choice.

Willig, C. (2001). Introducing qualitative research in psychology Adventures in theory and method. Open University Press

thanks. my project has to do with education.

the first part of my research involves a quantitative phase( Questionnaire)

the second phase is qualitative ( Diary and interview ).

If the questionnaire in your first phase has 20 open-ended items, I'm not sure how that is quantitative , although one option would be to use a quantitative orientation to content analysis and count specific sets of codes.

Even so, my own view of mixed methods research is that it should integrate the results for both a qualitative and a quantitation study. So, the issue would be what kind of research question is associated with each method, and how will the results from those two methods be linked.

The first (quantitative part) was not mentioned in your original question. This new information puts everything in a new light (just like a new clue in a detective story :-) ).

This means that your study would be a mixed-methods research. I also assume that the qualitative data from the diaries and interviews would (most probably) be used to triangulate the quantitative findings. Therefore, I would suggest to definitely not to adopt Grounded Theory. The method simply is not suitable for your study (as I understand it). Thematic or even content analysis would be a more appropriate option.

Good luck!

Larisa Nikitina

thanks a lot for your insightful comments

Ask yourself what method or methods will be most appropriate for comprehensively answering your research question.

It seems to me that what you suggested is the right way to proceed. Because diary entries are necessarily records of random (not in statistical sense) occurrences and interviews, in the way that they are usually used in a grounded theory research, are structured, they two categories of data may not lend themselves to the same form of analysis. Your proposal seems apt.

I havent read all the answers bu think a practical trick or two is missing. Due to simultaneous data collecton and analysis during three coding stages there is little justification left for using thematic analysis..

From your post immediately above, you seem to have analysed both diaries and interviews using Grounded Theory; then forget Thematic Analysis.

Following

For example, analysis of social behavior, changes in the opinions of users of social media portals on specific topics. such issues can be examined in this way by analyzing large collections of data downloaded from the Internet.

The purpose of grouping behavior of users of social media portals into specific classes of behavior should first define these defined classes. Sentiment analysis using large data sets collected from entries and comments from social media portals and transferred to Big Data database platforms may be helpful. Then, watching changes in certain types of behaviors of users of social media portals, you can analyze data collected in Big Data according to these observation results. In addition, a useful tool can be an analysis of the behavior of users of social media portals based on up-to-date posts, posts and comments on specific social media sites, statistical analysis of comments on specific topics of posts. This type of research is carried out by online technology companies that run social media portals and use the results of this research to develop their viral marketing services, because this area of ??marketing is a key determinant of revenue generated by these companies on the sale of advertising on social media portals.

I invite you to the discussion

Below I give other examples of the analysis of large collections of information collected in Big Data database systems for the purposes of conducted scientific research.

Will the development of data processing technology accumulated in the Big Data banking database systems improve the credit risk management process or will it contribute to the development of Shadow Banking and the use of unethical practices for the surveillance of potential borrowers?

Large commercial banks generate high financial surpluses allowing for the implementation of modern integrated teleinformatic internet banking systems, Business Intelligence data analysis systems, data processing platforms in Big Data database systems, etc.

There were already situations of unethical use of modern ICT solutions, analysis of comments on social media portals, during which the bank verified the customer's data entered into the loan application by also scanning information that the potential borrower types in social media portals.

This informal verification took place without the knowledge of a potential borrower and could then be the basis for suing the bank.

However, the bank's client is not always aware of the fact that it can be invigilated in such a way by the public trust institutions that the bank should be.

Of course, these types of cases, which we know from the media is supposedly a margin of entire banking, which can be one of the categories of a new type of unethical practices typical of the so-called Shadow Banking.

However, only part of this type of information gets to the media.

Maybe this is just so-called "the tip of the iceberg" of this problem.

The situation is similar in the situation of cybercriminals' attack on bank IT systems or electronic banking platforms.

If it is possible to keep this type of events secret, then customers do not find out about it.

This is because media only receive information about some of these types of events.

Does any of you conduct research in this area?

If so, I invite you to cooperation.

I am asking for comments

The Business Intelligence systems currently being developed by IT technology companies and sold for companies support business management processes or a specific part of it. These systems, if they are connected to the Big Data database system and integrated with logistics, accounting, production, sales departments, inventories, relationships, contractors, financial institutions, etc., can significantly improve business management systems. If the whole system of obtaining and processing collected in Big Data information concerns all external and internal determinations of the company's operations and takes place in real time, the company has an excellent tool supporting decision-making processes. This type of tool allows you to make better decisions in the strategic and ongoing management of a company, corporation, financial institution or other type of business entity. However, in this area there is constant progress determined primarily by the creation of new ICT solutions, new data processing technologies, etc. Therefore, the following question arises: When will artificial intelligence be implemented into the analytical Business Intelligence systems and what are the consequences?

Will business management become even easier after implementing artificial intelligence in Business Intelligence business intelligence systems? And if so, for what kind of business entities? or only for large corporations and financial institutions, in which millions of different categories of information are being processed on a daily basis, or such new-generation systems supporting decision making, will also be available for smaller companies and enterprises from the SME sector?

I invite you to the discussion.

Are commercial banks already introducing sentiment analysis conducted on Big Data data collected from social media portals to the standard of customer verification procedures?

Inclusion of the sentiment analysis conducted on Big Data collected from social media portals could be an important additional information on the economic and financial situation of the potential client. This type of information can be a significant additional factor of full verification, eg creditworthiness of a potential borrower. I know that some service companies, marketing companies, insurance companies and banks include the sentiment analysis carried out on data collected in Big Data database systems collected from social media portals for verification of potential customers. But is it already becoming a standard or is it only at the design stage for now?

I invite you to the discussion.

Will Big Data database technologies be available in the future also for companies from the SME sector?

Advanced technologies of digitalization and automation of data processing first find their application in business. Then also in public institutions can be introduced including in the field of e-governance. This also applies to Big Data database technologies, which is applicable in various sectors of the economy, but due to the high investment costs of implementing this technology in the business processes of business entities, so far only large corporations and larger enterprises can afford such technologies. However, in the future, investment costs of implementing tech technologies into business processes should decrease and processing technologies and data collection in Big Data database systems should be available also for smaller companies, including business entities of the SME sector.

I invite you to the discussion.

What tools for social media marketing will be developed in the future?

Marketing in social media is still a very developing field in the field of marketing techniques used on the Internet. On the one hand, some of the largest online technology companies have built their business concept on social media marketing or are entering this field. On the other hand, there are startups of technology companies acquiring data from the Internet and processing information in Big Data database systems for the purpose of providing information services to other entities as support for strategic and operational management, including planning advertising campaigns. Therefore, the question arises: What tools for social media marketing will be developed in the future?

I invite you to the discussion.

Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?

In my opinion, information obtained from various research centers, meteorological centers, satellites, etc., then collected and processed in Big Data database systems should help in more and more precise prediction of new, unfavorable weather phenomena, including climatic cataclysms and others. In this way, earlier and in a more planned way, crisis management systems can be organized in the situation when the predetermined flood disaster becomes real and will happen. Gradually increasing computing power of powerful computer servers managing platforms of Big Data database systems, implemented artificial intelligence, increasing number of verified historical data on the overall climate phenomena on Earth will allow in the future more precisely, more accurately determine the level of threats, risk value, predict time, place and scale adverse weather events, i.e. also cataclysms that threaten people's lives.

Therefore, the question is still valid:

Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?

I invite you to the discussion.

Will the artificial intelligence for Business Intelligence application be implemented as part of the ongoing computerization of analytical processes?

Business entity management processes are more and more often supported by computerized Business Intelligence platforms that facilitate multi-criteria analysis and reporting.

Complex, multi-criteria analyzes regarding the verification of large companies' operations require aggregation and analytical processing of large data sets in Big Data database systems.

Specialized IT companies produce applications that help in conducting economic analyzes, i.e. the Business Intelligence platform.

More and more often, large and medium-sized companies use these platforms to adapt them to the specifics of their business.

However, in what direction will technological progress be realized in this field?

In view of the above, the current question is: Will the artificial intelligence for Business Intelligence application be implemented as part of the progressing computerization of analytical processes?

I invite you to the discussion.