I think this issue is more common among students of natural and physical sciences. And the reason is that they are taught one, not even one I some cases, course related to research methods during their studies (for example, masters).  However, when they start working on their research (master thesis or even doctoral) where they have to use empirical research methods, things start going side ways for them. They have more than one "evils" (on lighter node) to deal with: "finding the problem and generating research question(s)" and "how to find answer to the questions(method)" and "analysis (statistical or qualitative)" and "interpretation". Here absence of mentorship is the biggest problem for students from natural and physical sciences.

I  myself had the  same issue. Being background in IT, when I decided to go for human element in information security related studies, I was lacking in required skills such as which method to use. There was no guidance available in my department either, as there was no-one having expertise in empirical or behavioral research methods except for basics. I had to look for other sources, for example researchers from social sciences department, Google, RG, literature etc. However, learning from my experience, now I always suggest juniors to focus on research design, from RQs to what statistics will be used, before starting data collection.

So, in my opinion, lack of relevant training and poor mentorship lead to this trouble. 

I think most of people have a very limited knowledge of data analysis. People dont face the analysis as part of empirical science, statistics is something people ask others to do. I dont understand also how people are able to read and judge the literature to support their research if they dont have knowledge to judge if that statistics is appropriate or not. 

In medical research we have these Systematic reviews that I am sure most authors had no idea how to judge the statistical analysis. Where is the source of all these? Quality of graduate courses? advisors who had no knowledge of statistics or even methods and their example contaminates their students and so on. 

Also, I am not sure also if most of people do really know how to write a questionnaire! 

 I think this issue is more common among students of natural and physical sciences. And the reason is that they are taught one, not even one I some cases, course related to research methods during their studies (for example, masters).  However, when they start working on their research (master thesis or even doctoral) where they have to use empirical research methods, things start going side ways for them. They have more than one "evils" (on lighter node) to deal with: "finding the problem and generating research question(s)" and "how to find answer to the questions(method)" and "analysis (statistical or qualitative)" and "interpretation". Here absence of mentorship is the biggest problem for students from natural and physical sciences.

I  myself had the  same issue. Being background in IT, when I decided to go for human element in information security related studies, I was lacking in required skills such as which method to use. There was no guidance available in my department either, as there was no-one having expertise in empirical or behavioral research methods except for basics. I had to look for other sources, for example researchers from social sciences department, Google, RG, literature etc. However, learning from my experience, now I always suggest juniors to focus on research design, from RQs to what statistics will be used, before starting data collection.

So, in my opinion, lack of relevant training and poor mentorship lead to this trouble. 

I think they will have started by collecting data before writing a proposal or methodology section.

This is a common problem among novices (i.e., students). It is more than poor mentorship however. It is poor learning of research methods (quantitative and qualitative). I agree with your assumption that he governing notion is that a way to analyze the data will be found, and hence (due to laziness perhaps?) no thought is given to this issue in advance. Someone once told me the following: "Students are the population who wants to get the least for the worth of the money they pay." This seems to be true often. Students often want the diploma but do not seek the knowledge. It is this process which makes them happy when material is not going to be in the exam (and hence is not to be learned), and it is this that results in sloppy research designs.

While I may be partially echoing other suggestions, I do believe that most investigators do not maintain a proper mental framework for asking/developing a scientific inquiry. I would add that the issue isn't purely methods or analytical skill but a lack of cognizance for the process associated with exploring phenomenon (in a general sense). 

As far as the question surrounding the function of Research Gate, I have observed that leagues of junior scholars (and, even a number of tenure-seeking professors) simply are not doing the intellectual gymnastics and are searching for easy answers - which, as someone who strives to promote the role of scientific inquiry in applied settings, I am made both frustrated and incredulous. 

I think understanding the subject that the researcher is working on can solve this problem. The researcher must work on identifying the information that he is targeting. Therefore, it must be a basic idea for each question.

Although I ensure enough preparation for data analyses before data collection yet, I think that the people generally don't prepare due to a common misconception that statistical analyses will be done by the statistician.  

This problem arises as a result of lack of courses in Statistics in the undergraduate and the postgraduate. In engineering some academic researchers believe that statistics is not a science nor it is important to research articles ,and this is a completely wrong thinking. 

Dear David L Morgan, Thank you very much for making very important inquiries on the Research Gate portal and inspiring important discussions on particularly important issues.

The topics that are the subject of discussion are particularly important and scientifically important and in the context of human life.

Question:

Why don't people do a better job of preparing for data analysis before they collect their data?

this is a very important issue that requires scientific research.

I wish you all good and best regards

The issue of the methodology of conducting data analysis is the most important issue of scientific research.

In my opinion, it is good that there was a discussion in the question of this query.

This is a very interesting and scientifically important topic.

Because the topic is very important so I still read the answers, I follow the interesting discussion.

Well-conducted data analysis is the most important issue of scientific research. Research methods should be tailored to the specifics of research, the nature of the analytical process and substantive research issues.

I invite you to the discussion

In the field of data analysis, the interesting and developing in recent years, the issue of processing and analysis of large collections of information is the analysis of data in the cloud computing on Big Data database platforms.

To what extent the Big Data data sentiment analysis from social media portals can be used in forecasting the company's development?

Large amounts of information downloaded from comments, entries, posts from social media portals are processed in Big Data database systems to determine, for example, consumers' awareness of the offer of products and services of specific companies. This type of information is of great importance for the planning of advertising campaigns informing about the mission, idea, product offer, and the usability features of the company's offer. This type of data may be important for forecasting the changing preferences of consumers regarding the offer of specific companies.

In view of the above, I am asking you: To what extent the Big Data data sentiment analysis from social media portals can be used in forecasting the company's development?

Please reply. I invite you to the discussion

It seems to me that the analysis of large collections of information collected in Big Data database systems will have a significant impact on the development of entrepreneurship research. First of all, the analysis of large data sets Big Data will improve data analytics on cloud computing platforms, the use of computerized Business Intelligence applications as supporting business management processes.

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 dis

Will future Big Data database systems supported by artificial intelligence be used in precise forecasting in order to verify futurological projections?

Currently, it is difficult to define this type of analytic problem. The key issue is forecasting future global problems. It is necessary to collect additional analytical data over the next years, and perhaps in the 21st century, in huge Big Data database systems supported by another generation of artificial intelligence, it will be possible to predict what may happen to the planet Earth in the future.

In view of the above, the current question is: Will future Big Data database systems supported by artificial intelligence be used in precise forecasting for the verification of futurological projections?

I invite you to the discussion.

Currently, intensive development of analytics and information processing is carried out in large data sets for the needs of scientific works.

Dear David L Morgan, In my opinion, the question concerns particularly important issues for people. It's good that there was a discussion in this topic. I believe that research on this topic should be continued. There was an interesting discussion.

Because the topic is very important so I still read the answers, I follow the interesting discussion.

In my opinion, because not all people conducting research use advanced information processing instruments such as data processing in the cloud, analysis of information collected from the Internet using sentiment analysis in Big Data database systems, analysis of large data sets using probabilitic, stochastic analytical models and other advanced statistics in computerized platforms such as Business Intelligence.

Currently, techniques for analyzing large data sets in Big Data database systems are developing rapidly. The costs of these analytical processes and necessary IT tools are gradually falling and can now be available to any entity with relatively low financial resources.

I invite you to the discussion.

Mentors in research work really need to make adequate efforts to ground their mentees in research design which is supposed to lay solid foundation for any research project. This normally will include the types and characteristics of the variables to be collected and the valid statistical analysis techniques that will be employed so as to produce really valid results. This is to avoid the common " garbage in garbage out " results we occasionally see in some publications. Any research results that emanate from faulty data or analysis is as good as the input garbage.

The problem to me seems more common is educational research than any other field. The source can be traced to recent decline interest in quantitative research methodology and associated fear of statistics in the field. People find succor under the pretense of 'qualitative research' in conducting interviews, transcription, 'coding' and interpreting results based on statements. This practice has dented the quality of quantative research studies by leaving it in the hands of struggling educational research students. Hence, the problem emanated as mentioned by David L Morgan . It is my hope that educationists realise quantative methods as potential and indispensable factor in conducting a thorough qualitative research study.

Little or no training in quantitative methods and data analysis at the undergraduate level.

Data analysis is not taken sufficiently seriously.

Data analysis is not taught well in universities.

This is a serious deficiency in the era of big data.

The big reason is inadequate research design and deep planning. Students propose, supervisors accept without putting hard questions in context of data analysis to the students. Students never dare to ask for it, if some odd dares the usual response is 'We'll see later'. So, its not one way traffic, it's comprehensive one where all stakeholders are equally responsible.