NEW YEAR’S MESSAGE FROM THE OWNER OF MENTORNET (PTY) LTD

It was close – COVID-19 nearly sank Mentornet. At one stage I even tried to sell the company for whatever I could get. Fortunately, nobody was interested. At least I received some positive and encouraging emails from loyal friends of Mentornet.

Evette was adamant from the word go – she would revive what she calls her baby. And her tenacity and perseverance did the trick. Roald sacrificed his position as MD of Mentornet and accepted an appointment as a computer programmer for a larger company, thereby saving Mentornet his salary. He still manages and maintains the Mentornet online system in his free time, though.

All the important business graphs are developing in the right direction – turnover, committed invoices, payments by clients, online learning enrolments and contact learning enrolments are on the way up.

Granted, the improvements are from a rather low base. We are now facing a new reality and, therefore, we are also measuring progress and growth as if from scratch. Developments are measured with the beginning of the COVID-19 pandemic as the starting point.

The following are some decisions that we took that might be of interest to you:

  1. Evette is now the MD. In her capacity as MD she will structure and manage the Mentornet premises in such a way that contact learning students will enjoy maximum protection against COVID-19.
  2. I will still be the CEO of Mentornet and will do pretty much what I did for the last twenty years. In addition to this I will path the way in articulating our strategy to the “new reality”.
  3. Riétte will do administration and marketing.
  4. Onika will continue with her responsibilities as in the past (office admin, OHS, facilitation, assessment, moderation).
  5. Ashlea will still be the receptionist.
  6. We will still use part-time facilitators, assessors and moderators.

Our focus in the future will be the following:

  1. Providing contact and online learning of the short courses and qualifications for which Mentornet is accredited.
  2. Development and offering of non-accredited courses to cater for specific management and economic needs, for example courses in research methodology, entrepreneurship, etc.
  3. Aiding other private learning institutions with course administration, quality assurance, training materials, facilitation, assessment, moderation, verification, etc.
  4. We will market the training, services and learning materials that Mentornet offer world-wide.

Thank you for your wonderful support the past 20 years. All the best for 2021 and all the years to follow.

Dr. Hannes Nel, CEO and Owner of Metornet (Pty) Ltd

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ARTICLE 102: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation: Typing Format

Written by Dr. Hannes Nel

The attention that a student pays to the layout of the thesis or dissertation is often an indication of what the quality of the work is.

Why would a post-graduate student pay careful attention to the quality of his or her research and the contents of the thesis or dissertation if they do not care about the quality of the reporting?

You will save yourself lots of time if you study the university policy on the layout of the thesis or dissertation before you start writing your thesis or dissertation.

The hints that I share in this article should be accepted by most universities.

However, you should also consult the university policy, even if your study leader or lecturer suggests that you use the suggestions given here.

General layout rules

Universities mostly require either double or one and a half spacing, font size 12 on one side only of A4 paper. Quotations and footnotes should be single spacing and either in Italics or a smaller font size, for example font size 10. The preferred font type is Times New Roman although some universities might also accept other font types that are easy to read and sufficiently formal, such as Ariel.

The following are examples of such requirements taken from the policies and procedures of different universities.

Typing format, text structure and general prescriptions

You need to format your thesis or dissertation exactly according to the university’s instructions. A good thesis or dissertation should be extensive and precise. Use a good yet simple and concise language (UK version of English) and keep sentences short without damaging the flow of your arguments.

A-4 size bond paper of a good quality should be used. The thesis or dissertation must be printed in at least one and a half spacing with one and a half spacing between paragraphs. Paragraphs are justified against the left-hand margin. Take care to be consistent and follow the general typing rules.

The margins at the top, bottom and right-hand side should be 25 mm, while the left-hand margins should be at least 30 mm.

Some universities have no set rules for how long a thesis or dissertation should be. A thesis does not normally exceed 50 000 words of text (approximately 150 pages). It is often accepted that dissertation on PhD level should not be more than 70 000 words.

Language and spelling

If English is your second language it is most important that help is sought during the initial draft reading process already. Faultless language, good style and correct spelling are a prerequisite for the writing of a thesis or dissertation. It is difficult to learn technical writing skills as well as concentrate on a second language.

It is advisable to have your thesis or dissertation language edited by an expert if English is your first language and a must if English is your second or third language.

Check your writing for spelling and grammatical errors. Also pay attention to minor details such as punctuation and when to use a full stop or comma. Use the spell-check facility of your computer but keep in mind that it is not foolproof.

Don’t try to make your writing look more academic by using highfalutin words and concepts that you do not really understand.

Sentences

Always write full sentences.

All sentences should have a verb.

Keep your sentences simple and as short as possible without damaging the flow of your arguments.

Use adverbs such as ‘however’ to link sentences. This will often improve the flow of your argument or narrative.

Sentences and paragraphs should be linked to one another. Avoid jumping around between unrelated arguments, topics or discussions.

Tenses

Reports of work done are usually written in the past tense except for discussions and conclusions, which are written in the present tense.

The present tense is used where universal truths such as natural laws are stated.

Do not change tenses in a sentence unless there is a good reason for doing so.

Paragraphing

Paragraphs will help you organise your writing by breaking the text up into manageable sections.

Short paragraphs are better than long ones. Variation in paragraph lengths reduces the monotony and renders the work easier to read.

Paragraphs should be linked to one another to ensure the flow from one paragraph to the next.

Each paragraph should deal with one aspect only. A conclusion or summary should be a next paragraph.

Capitals

Lower-case letters are used, except in cases where grammatical rules require capital letters.

The first word in a sentence and in a direct quotation is capitalised.

Proper nouns are capitalised and common nouns such as mountain and business are capitalised if they form part of a name. For example, ‘the mountain is dark and covered in mist’ versus ‘Table Mountain is dark and covered in mist’.

Common nouns are capitalised when they are used with a number or letter to designate a specific thing, for example ‘Room 101’.

Acronyms and abbreviations

An acronym is a word formed from the initial letters of a name or by combining initial letters, or parts of a series of words. The full word, name or concept that can have an acronym must be written out in full, followed by the acronym in brackets where they are used for the first time in your thesis or dissertation.

Certain acronyms, like ‘radar’ and ‘scuba’ have become accepted words and can be used without any explanation.

Abbreviations are short representations of words or terms, for example e.g. for ‘exempli gratia’ or ‘for example’. They should be used with discretion because they can make the text difficult to read. Unfamiliar abbreviations must be written out in full, followed by the abbreviation in brackets where they are used for the first time in your thesis or dissertation.

You should have a list of acronyms and abbreviations as an addendum at the end of your thesis or dissertation.

The word ‘percentage’ is written out in the text and written as a symbol (%) if it is used with a number, e.g. ‘15%’.

Numbering

Pages are numbered from 1 in sequence throughout the thesis or dissertation, not chapter by chapter. The first (cover) page is page number one, but the page number is not shown. Page numbers are shown from page two.

Page numbers should run consecutively through the thesis or dissertation with all pages numbered.

Number items or paragraphs that need to be numbered in numerals and sub-paragraphs in further numbers. The paragraph number “7” would, for example, be followed by sub-paragraph number “7.1.”, and sub-paragraph “7.1.” would be followed by sub-paragraph “7.2.”) or, if the sub-paragraph is further divided, by sub-sub-paragraph “7.1.1.”. Some universities prefer that the full stop after the last number presenting a sub-paragraph or lower be omitted.

Bullets are used if you have three or less points to number.

Figures are numbered numerically and go with the figure heading under the figure. Figures should fit onto not more than one page.

Tables are also numbered numerically, and table numbers go with the table heading above the table. This is because tables can stretch over more than one page.

Headings

Chapters and sections should have headings. Headings should be selected carefully and should be short and to the point. It should be a ‘summary’ of the contents of the chapter or section.

Headings that do not constitute full sentences do not end with a full stop, unless the contents of the section follow directly after the heading on the same line.

Summary

You need to consult the university policy for the layout of a thesis or dissertation.

Make sure that the layout that you use will be acceptable to the university.

Issues that you should check include the following:

  • Line spacing.
  • Font size and type.
  • Paper size and type.
  • Page numbering.
  • The numbering of tables and figures.
  • Headings.
  • Margins.
  • Language usage and spelling.
  • Paragraph length, spacing, numbering and justification.
  • The use of capital letters.
  • Punctuation.
  • Sentences.
  • Tenses.
  • The use of acronyms and abbreviations.

Close

Some universities will allow you some leeway in the layout of your thesis or dissertation.

This, however, will mostly be when you add to or enhance the prescribed format and playout.

You might, for example, add an illustration representing the context of each chapter directly after the chapter heading.

Or you may include an electronic copy of your thesis or dissertation if you are required to submit a typed and professionally bound report.

Enjoy your studies.

Thank you.

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ARTICLE 101: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation Part 5 of 9 Parts: Ethics Part 3 of 3 Parts

Written by Dr. Hannes Nel

I discuss the following issues on ethics in this article:

  1. Trust.
  2. Deception.
  3. Analysis, reporting and publishing.
  4. Plagiarism.
  5. Legality.
  6. Professionalism.
  7. Research ethics and society.
  8. Copyright and intellectual property right.
  9. The originality of your research.
  10. Promulgation of results.

Trust. Trust is the classic key to good research relations. Even so, trust is a constant challenge in any research process. All participants and stakeholders in a research project must have a healthy trust relationship. This includes knowing that you, as the researcher, can be trusted not to erode the relationship between participants to the extent that they would be reluctant or unwilling to co-operate. Trust also applies to the report or the discursive practices defining the standards for presenting both you and the work as trustworthy.

Deception. We have seen that the handling of subjects’ identities is an important ethical consideration. Handling your own identity as a researcher can also be tricky. You must have a good reason for not revealing yourself as a researcher to those you want to study. Most of the time, however, you will benefit from conducting transparent research. Even when you must conceal your research identity you need to keep in mind that, because deceiving people is unethical, deception within research needs to be justified by compelling scientific or administrative concerns. Even then, the justification will probably be arguable.

There is no excuse for providing members of your target group any false information about your own identity, whom you represent, what the purpose of your research is or what the research findings will be used for. It is, therefore, advisable to promise to send the participants copies of your research report before you submit it or use it. If time allows you should also provide the respondents time to appeal against the contents or findings of your report. Some people will set this as a precondition for their participation.

Analysis, reporting and publishing. In addition to your ethical obligations to subjects, you also have ethical obligations to your colleagues in the scientific community. In any rigorous research, you as the researcher should be more familiar than anyone else with the technical shortcomings and failures of the study. You have an obligation to make such shortcomings and failures known to your readers. Even though you may feel foolish admitting mistakes, you should do it anyway.

Negative findings should be reported if your respondents point them out, provided you can confirm them, of course. In science it is often as important to know that two variables are not related as to know that they are. Similarly, you must avoid the temptation to gain recognition and praise by describing your findings as the product of a carefully pre-planned analytical strategy when that is not the case. Findings are sometimes unexpected, even though they may seem obvious in retrospect.

You should always strive to maintain objectivity and integrity in the conduct of scientific research. This implies the following:

  1. You should always adhere to the highest possible technical standards in your research.
  2. You should always indicate – at the conclusion of a research study – the limits of your findings and the methodological constraints that determine the validity of such findings.
  3. You should not under any circumstances manipulate your data or observations.
  4. You must adhere to the public nature of scientific practice. One implication of this is that you should always be prepared to disclose your methodology and techniques of analysis.

In addition to the ethics of analysis and reporting, it is also imperative that you must maintain the same standard of ethical work when publishing your findings. The ethics of publishing involve the following issues:

  1. Appropriate ascription of authorship to a publication.
  2. Rejection of any form of plagiarism.
  3. No simultaneous submission of manuscripts.

Plagiarism. Somebody once wrote that nothing that is now written has not been written before. I would have given credit to the original writer of this insightful statement if I knew who it was. No doubt there are many who claimed credit for being the first. It might be true, but you should not use it as an excuse for claiming authorship of someone else’s work. You will know when you are plagiarising someone else’s work. For example, if you are rewriting what is written in a book that is open right next to you on your desk, then you know that you should acknowledge your source. It is even worse if your readers can see that what you wrote is not your own because of subtle tell-tale signs, for example a sudden change in writing style, a cliché, switching from first to third person, etc.

It takes a good measure of honesty, maturity and a healthy self-image to always give credit for good work by others. It is, furthermore, not necessary to be paranoid about being honest. You will have ample opportunity to show your cognitive thinking and creative writing skills in a thesis or dissertation of more than a hundred pages. Besides, giving credit where credit is due lends validity, authenticity and quality to your work.

While examining the research literature, particularly when photocopying and taking notes, you may copy extracts from sources verbatim (exactly as it was written in the original document) with the intention of incorporating these extracts into your final written report. Although it is common practice to accumulate an abundance of quotations in the initial information collection stage, it is essential when writing the final report that quotations be selected judiciously and used sparingly. Over-quoting can damage the flow of your arguments. The essential selection criterion to follow is relevance, whereas the basic mechanical consideration is the length of quotation. Long quotations are rarely justified and may cause readers to wonder whose ideas they are assessing.

The ability to cite the work of others appropriately is a major indication of your ability to interpret data and to generate your own ideas and arguments on a particular topic from the data that you collected.

You commit plagiarism in your thesis, dissertation, or any other document that you write, when you use words, ideas or opinions that you obtained from the written work of somebody else without giving credit to the original writer. Strictly speaking it is still plagiarism even if you break the original argument down into its component parts (deconstruct) or change the original meaning or level of the original message or argument (reconstruct). This can become confusing when doing research because, after all, you must consult other sources of information. Furthermore, most research is to some extent a reconstruction or deconstruction of existing knowledge with the aim of adding value or providing a new perspective on existing knowledge and philosophy.

The obvious solution would be to acknowledge your sources. You must provide references whenever you quote (use the exact words), paraphrase (use the ideas of another person, in your own words) or summarise (use the main points of another’s opinions, theories or data).

The number of sentences or pages of somebody else’s work that you use are not relevant. Whether it is one sentence, a whole section or perhaps even an entire chapter or assignment, it is still plagiarism. You will know when you are guilty of plagiarism, therefore you cannot argue that you did it accidently or unintentionally. If you use somebody else’s work as if it is your own, you are guilty of plagiarism.

Plagiarism can lead to you failing your studies and perhaps even being expelled from the university.

Legality. You must always ensure that your conduct of the research and reporting your research findings are done within the boundaries of legislation. Legality relates strongly to ‘informed consent’. Although you should guarantee confidentiality, participants in your research need to understand and accept the potential risks of participating. Cruelty to animals, damage to the environment, etc. may be illegal and you need to avoid such transgressions.

Professionalism. Regardless of whether you belong to a professional body or not, you are always expected to conduct your research in a professional manner. This includes making use of scientific methodology and acknowledging any sources that you consult and use. It also implies accuracy in collecting data and reporting analysis of data collected. Research must always be of benefit to the research participants and society at large.

Research ethics and society. The most important principle that guides the relationship between science and the rest of society is that of accountability. Although we sometimes refer to the scientific community as a distinct and relatively autonomous sector of society, this does not mean that the scientific community can do what it wants without regard for the rights of the rest of society. This accountability refers to a general obligation to conduct research in a socially responsive and responsible manner. Accountability in research is manifested in the following:

  1. A rejection of secret and clandestine research.
  2. An obligation to the free and open dissemination of research results.
  3. A responsibility to funders and sponsors of the research.

Coypright and intellectual property right. In the academic context, copyright is primarily about getting the most from your hard work rather than legal complications and plagiarism. Legislation largely protects your copyright. However, some universities have a precondition for embarking on master’s or doctoral studies that the copyright belongs to them. This is mostly specified in your enrolment application, but you need to make sure what the regulations are and that they are acceptable to you.

Intellectual property right describes a class of several different legal regimes that generally concern creations of the human mind. Copyright can be regarded as a subsection of intellectual property right (together with trademarks and patent laws).

The originality of your research. It is not only the identity of individuals that needs to be protected. Especially in online research the challenge to protect data is rather daunting. There is so much information available on the internet that it is almost impossible to protect and ensure the validity of information. Computer programs can store information passively or incidentally. It is almost impossible to book a hotel room without the hotel or accommodation service provider capturing substantial personal information belonging to you. Some electronic watches not only tell you the time and date, they also measure and store your heart rate, blood pressure, weight gain or loss, running times, etc.

The ease with which electronic devices can collect and store personal information is becoming a challenge and opportunity for researchers. Because of this, ethical issues continue to grow more complicated as new technologies and capacities develop.

Electronics make it increasingly difficult to protect and prove the originality of your research. It is already difficult to create original ideas, philosophies and theories – to prove that the results of your research are your own and original is even more difficult. Acknowledging the sources that you consulted is a good start – at least you will show that you respect the intellectual property of others. With such literature and field study as foundation, you should demonstrate sound arguing and thinking skills. This will already count in your favour when your work is evaluated for originality.

Promulgation of results. The worst scenario imaginable for an individual who completed a thesis or dissertation is the report becoming a dust-collector on a library shelf. To avoid this, you should make the results of your research available in a format usable by people who may benefit from it (with prior permission, of course). You can, for example, have all or some of your findings published, act as a speaker for symposiums, etc.

Summary

Trust is the foundation of cooperation.

Even though research on master’s and doctoral level is mostly an individual project, you will need the assistance of many other role players.

Therefore, the success of your study largely depends on mutual trust between you and others who are involved in your work.

You should not deceive people about your identity, whom you represent, what the purpose of your research is or what your research findings will be used for.

You owe it to the academic fraternity and society to make any shortcomings and failures of your study known.

Transparency is key.

Always give credit for writing and other forms of research by others that you use in your thesis or dissertation.

Do not transgress legislation, rules, regulations on any level when conducting research. However, it is possible that legislation might obstruct progress or be wrong for a variety of reasons. Therefore, it might sometimes be necessary to follow you own good judgement. Just keep in mind that we are all subjective.

You must always conduct your research in a professional manner.

Keep in mind that you are accountable to society for the research that you deliver. Therefore, your research should be to the benefit of society or at least part of society.

And preferably not at the expense of other sections of society.

Make sure what national legislation and the university’s policy regarding copyright and intellectual property right are before you enrol for post-graduate studies.

Obviously, you must be willing to accept and abide by such legislation and policies.

Proving the originality of your research is difficult to achieve.

It would be impossible to check the internet and other sources of information to ensure that your ideas and arguments are your own.

The best you can do is to acknowledge the sources that you use.

Do not use this situation as an excuse for committing plagiarism.

Share the result of your research by writing books and articles, making videos, acting as a speaker at conferences and lecturing.

Close

In my 98th article, dealing with deconstruction and empirical generalisation, I asked if deconstruction is not just a euphemism for plagiarism.

The answer is captured in my discussion of plagiarism in this video.

Almost all researchers need to use the work of others in their research.

Such work can serve as the foundation for your research and to corroborate and enrich your arguments.

However, you must always acknowledge the work of others that you use.

Enjoy your studies.

Thank you.

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ARTICLE 100: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation Part 4 of 9 Parts: Ethics Part 2 of 3 Parts

Written by Dr. Hannes Nel

What does ethics in research entail?

Some post-graduate students will think that not committing plagiarism is what it is all about.

And they will not be entirely wrong.

Not committing plagiarism is an element of ethics.

However, there are many other facets to the concept.

Ethics are not only important for writing your thesis or dissertation, but also for the safety and integrity of the participants.

Especially the target group for your research.

I discuss the following issues on ethics in this article:

  1. Axiology.
  2. Codes of consent.
  3. No harm to participants.

Axiology. The quality of our research will be judged according to the criteria of validity and authenticity. This brings us to the concept “axiology”. Axiology addresses the nature of ethical behaviour. In research axiology refers to what you belief to be ethical. Basic beliefs about what is ethical are embedded in research paradigms and guide the researcher’s decision making. The purpose of the research needs to be balanced with what you value as well as other ethical considerations in the conduct of research, notably validity and authenticity.

Validity and authenticity are prerequisites for understanding. It is in this that epistemology and ethics are brought together. It is also a meeting point between epistemology and ontology because what we know (ontology) is tied up with what we understand (epistemology).

Ontological and educative authenticity, on the other hand, were designated as criteria for determining a raised level of awareness; in the first instance, by individual research participants and, in the second, by individuals who share a particular value system and, therefore, maintain contact for some social or organisational purpose. That is why the validity of your epistemological approach starts with ontology. It is rather difficult and mostly unnecessary, to separate epistemology from ontology, because they form a unified system and are highly interdependent. Epistemology is the declarative extension of ontology and often includes additional ontological statements.

It is, however, important that you do not confuse ontology and epistemology. As a matter of routine, it helps to mention ontology first, and then epistemology, since it enables you to base your study on a statement of “fact” (which can include your target group, world or society) before you do any explaining and theorising.

It is, sometimes, necessary and useful to develop models of real-life situations or artefacts for research purposes. Choice of representation (i.e., the way in which models must be articulable) does, in fact, have real implications for what aspects of the research target receive the most attention – what the model handles well, and what gets minimised or left out. On the other hand, models of what there is (ontology) need to be explained by what can be known and how it can be known (epistemology). We know that the shortest distance between two points is a straight line – this is what we know, or the ontology. How we know this, that is. the evidence that the shortest route between two points is a straight line, is the epistemology.

Epistemology is not just a way of knowing. It is also a system of knowing through cognitive reasoning based on internal logic (contextualising information gathered to your research purpose) and the wider applicability of the knowledge, that is external validity (ensuring that findings are in line with the general environment and that they will be acceptable to other stakeholders).

Epistemology is intimately linked to a world-view. People from different continents, countries and even regions will often not have the same outlook or frame of reference towards the world around them. Thus, the conditions under which people live and learn, shape both their knowledge and their world-views.

Codes of consent. Codes of consent deal with if the target group for the research participates voluntarily or not. Qualitative research can be an intrusion into people’s lives, especially if it is social research. The interviewer’s knock on the door or the arrival of a questionnaire in the post or by email signals the beginning of an activity that the respondent has not requested, and one that may require a significant portion of his or her time and energy. Participation in a social experiment disrupts the subject’s personal and work schedule.

What is needed is informed consent, meaning that the research subjects need to know that they are being researched and what the nature and purpose of the research are. Participants in research should base their voluntary participation on a full understanding of the nature of the research and possible risks involved. When obtaining their consent, you need to appreciate that the participants may be under subtle pressure to co-operate, and you should take this possibility into account.

Consent is considered ‘informed’ when, in a language that the participants understand, you explain to them the nature of the research, their right to refuse to participate or withdraw from participation at any time, factors that may influence their willingness to participate, and the data collection methods to be used. The participants must have a complete understanding of the nature, aims and processes of the research, its intended outcomes, as well as any consequences that may follow from participation and publication.

Participants in research are often required to provide personal information about themselves, such as their age, weight, eating habits, drinking habits, smoking habits, etc. Such information may be unknown to their friends and associates and they might not want people close to them to know. Furthermore, research on human activities often requires that such information be revealed to strangers. Other professionals, such as physicians, and lawyers, also require such information. Their requests, however, may be justified because the information is required for them to serve the personal interests of the respondent. Social researchers can seldom make this claim. Like medical scientists, they can only argue that the research effort may ultimately help all of humanity.

No one should be forced to participate in research. This norm, however, is far easier to accept in theory than it is to apply in practice. It is unlikely that people will participate voluntarily if they do not believe that they will, somehow, benefit from participating. That is probably the most important reason why the response rate to questionnaires is often low, and you should plan on receiving only a fraction of the questionnaires back that you send out. Any response rate higher than 10% is good, unless you take special steps, like delivering and collecting the questionnaires personally.

No harm to the participants. Research should never physically, psychologically or financially injure the people involved, regardless of whether they volunteer for the study. Questions that would embarrass people or endanger their home life, friendship, career, etc. should not be asked or, if asked, be done with the consent of the participants. Sometimes subjects are asked to reveal deviant behaviour, attitudes they feel are unpopular, or demeaning personal characteristics, such as low income, the receipt of welfare payments, etc. You, as the researcher, should agree not to reveal such information and you must keep your undertaking. You must look for the subtlest dangers that information might end up in the wrong hands and guard against them.

The ethical norms of voluntary participation and no harm to participants have become formalised in the concept of informed consent, which we touched on under the sub-heading “codes of consent”. 

To avoid harm to respondents, you as the researcher should have the firmest of scientific grounds for asking questions that may cause injury to others. The objective of informed consent may be rather difficult to achieve and maintain in the case of internet or other electronic research contexts. You might not even have physical contact with the participants in the research. The challenge is exacerbated if the maintenance of anonymity is also needed. With this as background, informed consent can sometimes cause harm, be counterproductive or simply impossible to achieve.

Qualitative research projects may also force participants to face aspects of themselves that they do not normally consider. The project can be a source of continuing, personal agony for the subject. If the study concerns codes of ethical conduct, for example, the subject may begin questioning his or her own morality, and that personal concern may last long after the research has been completed and reported.

Subjects can also be harmed by the analysis and reporting of data. If the respondent reads the research report it might happen that he or she may find themselves characterised in an index, table or description. Having done so, they may find themselves portrayed – though not identified by name – as bigoted, unpatriotic, irregular, etc. 

An obvious and generally applicable concern in the protection of the participants’ interests and well-being is the protection of their identity, especially in survey research. Two techniques – anonymity and confidentiality – can be used in this regard.

Anonymity. A respondent may be considered anonymous when you cannot link a given response with a given respondent. This means an interview survey respondent can never be considered anonymous, since an interviewer collects the information from an identifiable respondent. Assuring anonymity makes it difficult to keep track of who has or has not returned the questionnaires.

Anonymity relates to the issue of privacy and is especially difficult to maintain on the internet. Privacy is regarded as the right to withhold information from public consumption. People often use publicly accessible information spaces, like Facebook, but maintain strong expectations of privacy. Because of this, privacy often refers to the way information is used rather than how easy or difficult it is for people to gain access to such information.

Confidentiality. Confidentiality means that you, as the researcher, should protect your participant’s identity, places of work and stay, and the location of the research. In a confidential survey, the researcher can identify a given person’s responses but essentially promises not to do so publicly.

You can use several techniques to ensure the maintenance of confidentiality. All stakeholders in the research team who might need to maintain confidentiality and who will have access to data and findings should be trained in their ethical responsibilities. All names and addresses should be removed from the questionnaires as soon as they are no longer needed and replaced by special identification numbers, not their national identification numbers. A file should be prepared linking special identification numbers or codes with real identification numbers. This file should be kept in a safe or lockable filing cabinet to which only people who need to know have access.  

It is your responsibility to inform the respondent if a survey is confidential rather than anonymous. Do not use the term anonymous if you mean confidential.  

Summary

Axiology addresses the nature of ethical behaviour.

Basic beliefs about what is ethical are embedded in research paradigms.

You need to achieve a balance in your research between ethics, your values, validity and authenticity.

Validity and authenticity are prerequisites for understanding.

Ethics is based on the ontology and epistemology of your research topic.

Codes of consent deal with if the target group for your research participates voluntarily or not.

Participants in research need to be informed about the purpose and nature of the research, how they will be involved and possible risks.

Participants are sometimes asked to share personal information with the researcher.

No one should be forced to participate in research.

You should keep in mind that the response rate to especially questionnaires is often low.

Research should never physically, psychologically or financially injure participants in the research.

Participants must not be harmed by the collection, analysis or reporting of data.

Questions asked to participants must be relevant and necessary for the research.

Anonymity and confidentiality should be maintained if necessary.

Anonymity is difficult to maintain.

Confidentiality means that the participant’s identity, places of work and stay and where the research took place must only be revealed on a need-to-know basis.

Close

Maintaining sound ethical standards is important for the protection of the interests of others who participate in your research.

However, most importantly, you should protect your own interests.

It is in your interest not to cause damage to other people.

And it is in your interest to submit good quality work.

Because gaining higher qualifications is supposed to prepare you for a career and quality life.

Enjoy your studies.

Thank you.   

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ARTICLE 99: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation, Part 3 of 9: Ethics in Research Part 1 of 3

Written by Dr. Hannes Nel

Do people still care about the truth?

Did people ever care about the truth?

Are opinions more important than facts?

And what will the implications be if the truth is no longer important, and opinions are more important than facts?

I discuss the principles of ethics in research in this article.

Ethics are typically associated with morality, that is matters of right and wrong. You need to know, understand and accept the general consensus amongst academic researchers about what is acceptable and not acceptable in the conduct of scientific inquiry. The following principles are fundamental to an ethical approach to research:

  1. Research should always respect and protect the dignity of participants in research. This requires sensitivity, empathy, and accountability towards the target group for your research. The greater the vulnerability of the participants in the research (community, author, expert, etc.), the greater the obligation of the researcher to protect the participant. To this end, you as the researcher should:
    1. Ensure that you know and understand the values, cultures and protocols of your target group. It might be necessary to be academically or culturally qualified to work with some communities.
    1. Consult experts on communities if you lack the qualifications, knowledge and cultural background to work with them.
    1. Share your findings honestly, clearly, comprehensively and accountably with only those who are entitled to have access to the findings.
    1. Report your findings, and the limitations thereof, openly and honestly so that peers and the public in general may scrutinise and evaluate them, keeping in mind that your findings may probably only be shared with certain people.
    1. Acknowledge and point out the possibility of alternative interpretations.
    1. Respect the right of fellow researchers to work with different paradigms and research methods and accept it if they disagree with your finding and interpretation.
    1. Agree to disagree rather than to defend your point of view fanatically in an effort to sway others.
    1. Honour the authority of professional codes in specific disciplines.
    1. Refrain from using your position for undeserved, corrupt or otherwise dishonest personal gain.
  2. Because ‘harm’ is defined contextually, ethical principles are more likely to be understood inductively rather than applied universally. That is, rather than a one-size-fits-all approach, ethical decision-making is best approached through the application of practical judgement related to the specific context.
  3. When making ethical decisions, you should balance the rights of participants with the social benefits of the research and your right to conduct the research. In different contexts the rights of subjects may outweigh the benefits of research.
  4. The importance of adhering to ethical requirements is equally important regardless of which stage of the research process is involved.
  5. Ethical decision-making is a deliberate process, and you should consult as many people and resources as possible in the process, including fellow researchers, people participating in or familiar with the contexts or sites being studied, research review boards, ethic guidelines, published scholarships and where applicable, legal precedent.

With the above principles in mind, the ethical issues that impact the most on research are:

  1. The notion of truth.
  2. Axiology.
  3. Codes of consent.
  4. No harm to the participants.
  5. Trust.
  6. Deception.
  7. Analysis and reporting.
  8. Plagiarism.
  9. Legality.
  10. Professionalism.
  11. Research ethics and society.
  12. Copyright and intellectual property right.
  13. The originality of your research.
  14. Promulgation of results.

The notion of truth. Truth is largely governed by critical epistemology. Critical epistemology is an understanding of the relationship between power, cognitive reasoning and truth. This implies that the way we think about concepts, theory, philosophy and phenomena determines what we would regard as truth. You should uphold the epistemological principles that apply to all researchers, meaning that truth should be a product of logical reasoning and evidence. In terms of critical epistemology, however, we need to be careful – it is easy to twist your arguments to fit your preferences by describing them in terms of an unfounded epistemology. The need for and availability of power can erode logical truth. Sometimes writers and researchers work with a predetermined political agenda in mind, for example to gain support from a particular group or to promote a political objective, rather than to strive for scientific validity. You will only truly develop new knowledge or add to existing knowledge, that is, make a positive epistemological contribution to science, if you are objective and honest in your interpretation and analysis of information. This brings us to the epistemic imperative.

In the world of science our aim is to generate truthful (valid/plausible) descriptions and explanations of the world. This is called the epistemic intent of science. “Epistemic” is derived from episteme, the Greek word for “truthful knowledge”. We use “truthful” as a synonym for “valid” or “close approximation of the truth”. We accept knowledge to be accurate and true when we have sufficient reason to believe that it is a logical and motivated representation or explanation of a phenomenon, event or process. There needs to be enough evidence to support such claims. It mostly takes time to accumulate evidence and claims of truth must withstand repeated testing under various conditions in order to be accepted as valid or, at least, plausible.

“Instant verification” of a hypothesis or theory is largely impossible to achieve. Research takes place all the time, and scientific communities accept certain points of view, hypotheses or theories as valid and plausible, based on the best available evidence at a given point in time. However, new empirical evidence contradicting current “truth” can be revealed by new research at any time in the future. The obvious thing to do when this happens would be for scientists to revise their opinions and change their theories.

Commitment to “truth” is not the same as the search for certainty or infallible knowledge. Neither does it imply holding truth as absolute without any concern for time and space. The notions of “certainty” and “infallibility” would suggest that we can never be wrong. If we are to accept a particular point of view as “certain” or “infallible” we are in fact saying that no amount of new evidence can ever lead us to change our beliefs. This would obviously be a false stance, making a mockery of scientific enterprise. Life and the environment are dynamic concepts – not only do they change because of internal and external forces impacting on them, but we also discover flaws in our beliefs and perceptions. None of the paradigms that we discussed already go so far as to claim that truth is exact and perfectly final. Pre-modernism might be regarded as an exception by some. The commitment to true and valid knowledge is, therefore, not a search for infallible and absolute knowledge.

Even though we know that “truth” is a rather volatile concept, the “epistemic imperative” demands that researchers commit themselves to the pursuit of the most truthful claims about the world and the phenomena and events that have an impact on human beings. This has at least three implications:

  • The idea of an imperative implies that a type of “moral contract” has been entered into. It is neither optional nor negotiable. This “contract” is intrinsic to scientific inquiry. Every researcher and scientist should commit themselves to this contract. When you embark on a scientific project, or undertake any scientific enquiry, you tacitly agree to the epistemic imperative – to the search for truth. But the epistemic imperative is not merely an ideal or regulative principle. It has real consequences. This is evident in the way that the scientific community deals with any attempt to circumvent or violate the imperative.
  • The “epistemic imperative” is a commitment to an ideal. Its goal is to generate results and findings which are as valid or truthful as possible. The fact that it is first and foremost an ideal means that it might not always be attained in practice. All research, however, should represent steps closer to accuracy and truth. It seems to be unlikely, if not impossible, to achieve perfect accuracy and truth, amongst other things because of methodological problems, practical constraints (such as lack of resources) and a dynamic environment. We are often required to settle for results that are, at best, approximations to the truth.
  • The meaning that we attach to the concept “truth” presupposes a loose, somewhat metaphorical relationship between our scientific proposition and the world. Contrary to the classical notion that “truth” means that what we regard as reality, and what reality actually is, as being the same, we accept that this relationship is not that simple. The notion of “fit”, “articulation” or “modelling” is a more appropriate term for two reasons: Firstly, it suggests that a point of view can be relatively true. Articulation is not an absolute notion but allows for degrees of accuracy. Secondly, the term “articulation” can refer to the relationship between our points of view and the world (the traditional notions of “representation” or “correspondence”), or to the relationships between our points of view. In the latter’s case, we would use the term “coherence”. This means that “articulation”, “fit” or “modelling” is used to refer to both empirical and conceptual correspondence. When our conceptual system exhibits a high degree of internal coherence, we could also speak of the concepts as “fitting”, “being articulated” or “being modelled” well.

Summary

Ethics deal with matters of right and wrong.

The principles of an ethical approach to research are:

  1. Respect and protect the dignity of participants in research.
    1. Base ethical decision-making on the application of practical judgement in a specific context.
    1. Balance the rights of participants with the social benefits of the research and your right to conduct the research.
    1. Maintain and apply sound ethics throughout the research process.
    1. Treat all participants and stakeholders in your research ethically.

Truth is largely governed by critical epistemology.

It should be the product of logical reasoning and evidence.

The need for and availability of power can erode logical truth.

Always keep the epistemic imperative in mind when conducting research.

The implications of the epistemic imperative are:

  1. A moral contract is intrinsic to scientific inquiry.
  2. All research should represent steps closer to accuracy and truth.
  3. Truth is not always absolute or timeless.

Close

On the questions that I posed in my introduction –

All people do not care about the truth.

But, as you know, this is nothing new.

Not all people seem to have the ability to foresee the consequences of dishonesty for individuals, families, communities, cities, countries, the world.

Ironically lack of visionary thinking has this nasty way of causing great damage to the myopic in the end.

Enjoy your studies.

Thank you.

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ARTICLE 98: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation Part 2 of 9 Parts

Written by Dr. Hannes Nel

I discuss deconstruction and empirical generalisation in this article.

Is deconstruction just a euphemism for plagiarism?

After all, what we do when we deconstruct a concept, argument, knowledge or philosophy, is to take what somebody else said or wrote and change it to serve our own purpose.

You be the judge if deconstruction is theft or progression.

Deconstruction

Deconstruction is not an independent research method as such, but rather a way in which data that you collected for your research is ‘unpacked’ into more useful chunks that belong together and that can be articulated to the purpose of your research. To rearrange the data, you need to identify the right meanings for terminology and concepts.

Constructivism as a paradigm addresses deconstruction. Some academics are of the opinion that deconstruction belongs with post-structuralism. However, it is important to also discuss it separately as part of the process of research methodology seeing that it is necessary, regardless of your paradigmatic preference.

To clarify the difference between constructivism as a paradigm and deconstruction as a research method – constructivism deals with the way in which people perceive their research environment; deconstruction deals with the way in which you, as a researcher, will contextualise and articulate the research data that you collect to convey the ‘message’ of your investigation.

When deconstructing data that you collected, you will group them under headings and sub-headings that will enable you to offer the data in harmony with the purpose of your research, hopefully on a higher level of abstraction or at least in a more creative manner. When studying towards a doctoral degree you will need to ‘create’ new data, which will probably include some deconstruction.

When doing research for a master’s degree and even more so for a doctoral degree, you will need to group your data into a set of categories and transform the groupings into abstract types of philosophies and knowledge which you need to analyse further. Dedicated computer software enables you to code your data so that deconstruction is much easier to accomplish by just grouping pieces of information under specific codes and then analysing and recombining the information into new messages. In this manner you can reconstruct the data that you collected into a logical, accurate and authentic thesis or dissertation.

Deconstructing data is not about disclosing an already established, underlying or privileged truth, thereby committing plagiarism. Rather, it is about synthesising existing data in such a manner that the inherent truth of the data is extracted and offered as an alternative, higher level construction of reality. In the case of doctoral studies such deconstruction should lead to alternative meanings, aligned with the problem statement, problem question or hypothesis of the research.

It stands to reason that the products of a research deconstruction need to be tested by checking with readers, and by exploring with especially your study leader, the extent to which the set of deconstructed components as captured in your thesis or dissertation, is in line with the general usage and meaning of the components, while being articulated to the purpose and requirements of your research and contextualised to the scope and range of your research target group.

As is often the case with master’s and doctoral studies, the deconstructed information may apply more widely than just the target group for the study. The deconstructed data may not be limited to component meanings associated with only your abstracted categories as defined in your thesis or dissertation. How you group your data is up to you and you may test new concepts and their technical or academic definitions. The dominant logic of the process of deconstruction is abduction, although induction plays a part in testing the scope and range of the constructed concepts and their meaning in terms of a variety of related everyday meanings.

For the sake of efficiency, you will start with meaningful components that you already deconstructed previously. By linking subsets of components, according to plausible themes, which should be the problem statement or hypothesis of your research broken down into abstracted categories, you can produce a compact set of concepts and associated academic meanings articulated to the purpose of your research. These ‘sets of concepts’ are the typologies through which you communicate your arguments in a thesis or dissertation.

Typologies not only provide descriptions but also enable a clear exchange of deeper understanding about the meanings of words and concepts with which you work in your thesis or dissertation. Hence, typologies answer ‘what’ questions but not ‘why’ questions. Stated differently, your typologies reflect the ontology of your research, which you will need as the foundation for the epistemology, which would be your discussion, analysis and explanations of your arguments.

The epistemology of your thesis or dissertation proposes and tests discriminating insights about associations between elements of the regulatory and the primary ‘why’ questions. Because a theory or argument should at least hold across the same for your research, the testing should be applied to each unit of a selected sample to ensure validity with a reasonable probability of being accurate. You will not statistically calculate the probability that your sample is large enough to provide a good measure of accuracy when conducting qualitative research. However, you should take great pains in ensuring accuracy of your findings, for example by making your sample as large as possible, consulting as many different sources of information as you can reasonably obtain, asking readers for comment, arranging focus groups, etc.

Empirical generalisation

Empirical generalisation should not be confused with empiricism, which is a paradigm, as you should know by now. Empirical generalisation is studies based on the collection and presentation of evidence to prove a hypothesis or claim in the form of a problem statement or question. The evidence needs to be shown to be accurate, valid and credible. As such it represents the most basic requirements for qualitative research.

Empirical research mostly refers to evidence that can be observed and measured, which implies quantitative research. It can be directed at the ontology of a phenomenon, requiring you to focus on “what”, as well as the epistemology of phenomena, requiring answers to questions like “how many?”; “why?”; “what are the results?”; “what is the effect?”; and “what caused it?”.

Summary

Deconstruction:

  1. Is not an independent research method.
  2. Is used to group and articulate data to the purpose of research.
  3. Fits in well with constructivism.
  4. Can be rendered efficient through coding.
  5. Synthesises existing data to identify the inherent truth in the data.
  6. Needs to be checked by other stakeholders in the research.

On doctoral level, you will:

  1. Create new data from existing data.
  2. Escalate data to a higher level of abstraction.
  3. Develop or identify alternative meanings for words and concepts aligned with the problem statement, research question or hypothesis for your research.
  4. Mostly use induction.

On master’s degree level, you will:

  1. Deconstruct data to make it more creative.
  2. Mostly use deduction.

On doctoral and master’s degree level:

  1. Data need to be grouped into a set of categories and transformed into abstract types of philosophies and knowledge.
  2. You should aim at generalisation of your findings.
  3. You must ensure that your findings are logical, accurate and authentic.
  4. Typologies can be used:
    1. To communicate arguments in your thesis or dissertation.
    1. To describe concepts relevant to your research.
    1. To enable a clear exchange and deeper understanding of the meanings of concepts and words.
    1. To serve as an ontology upon which the epistemology for your research can be developed.

Empirical generalisation means providing solutions to a research problem, answers to a research question or evidence to prove or disprove a hypothesis.

Evidence must be accurate, valid and credible.

Close

So, what do you think?

Is deconstruction just a euphemism for plagiarism?

Let’s put this question on ice for the time being.

The three articles following on this one deal with ethics.

Perhaps we will be in a better position to answer the question after we have taken a closer look at ethics and what it means.

Enjoy your studies.

Thank you.

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ARTICLE 97: Research Methods for Ph. D. and Master’s Degree Studies: The Layout of the Thesis or Dissertation Part 1 of 9 Parts

Written by Dr. J.P. Nel

This article is an introduction to eight more articles on how to structure your thesis or dissertation.

I already pointed out in my initial articles that a thesis for master’s degree studies and a dissertation for doctoral studies are not the same.

Even so, there are enough similarities so that we can discuss them together.

Besides, it is a good idea to use the thesis that you write for your master’s degree as a learning opportunity for when you embark on doctoral studies.

And most universities will not object if you write and approach your thesis as you would a dissertation.

I will point out salient differences between a thesis and a dissertation.

Research without writing is of little purpose. There are, of course, other ways of communicating your research findings, most notably through oral presentation, but putting them on paper remains of paramount importance. The thesis or dissertation remains the major means by which you should communicate your findings.

It is something of a paradox, therefore, that many researchers are reluctant to commit their ideas to paper. Then again, not all people like writing and some might claim that it requires writing talent. For those who enjoy writing, this can be the most enjoyable part of the research process, because when compiling your research findings, you need to take what you wrote in the body of your document and create something new from it. Even though you have your research data to fall back on, you still need to think creatively. This takes some courage, hard work and lots of self-discipline.

It is always important to do immaculate and professional research. However, your biggest challenge is to develop an interesting and well-structured thesis or dissertation from the research data. Any research paper is based upon a four-step process. Firstly, you need to gather lots of general, though relevant, information. Secondly, you need to evaluate, analyse and condense the information into what is specifically relevant to a hypothesis, problem statement or problem question. Thirdly, you need to come to conclusions about the information that you analysed, and formulate findings based on your conclusions. Finally, your thesis or dissertation should again become more general as you try to apply your findings to the world in general or at least more widely than the target group for the research.

Different disciplines will use slightly different thesis or dissertation structures, so the structure described in the following nine articles is based on some basic principles. The steps given here are the building blocks of constructing a good thesis or dissertation.

A thesis or dissertation should clearly and thoroughly indicate what you have done to solve a problem that you identified. In addition, it should be factual, logical and readable. A good thesis or dissertation should be comprehensive and precise. Most importantly, though, it must be professionally researched.

Some of the contents of your research proposal will not have changed and should be included in your dissertation, as should some of the information that did change, but in the improved format or content. Your problem statement, question or hypothesis, for example, might have changed. The literature and other sources of information that you consulted will have changed and should include many more sources than the original list.

You should ensure that the time set aside for writing sessions is sufficient, as constant restarting and trying to find out where you left off when you last worked on the thesis wastes time and interferes with your thinking processes. If you are fully employed you should write after hours at least one hour per day, five days a week. Even then you will need to catch up by working over weekends, long weekends and holidays.

It is when writing a thesis or dissertation that you will really come to appreciate your desktop or laptop computer. When writing a thesis or dissertation, you should:

  1. Manage your time well.
  2. Make electronic backups of your work as often as possible.
  3. Plan each chapter in detail and structure your thesis or dissertation before you start writing. The layout of your thesis or dissertation may change over your period of study. Even so, good preparation is still important.
  4. First write your draft, then edit it critically and eliminate unnecessary material. Do not expect to get it right the first time around. Review is part of post graduate studies.
  5. Motivate the necessity of the study and explain the goal clearly.
  6. Give your study leader and anybody else who might read your thesis or dissertation a clear understanding of the research problem. The implications should be explained in such a way that everyone reading the thesis or dissertation has the same orientation towards the problem.
  7. Provide sufficient theoretical background to base the study on.
  8. Clearly describe the data collection methods and aids used.
  9. Provide sufficient and accurate data and indicate exactly how the data was used to solve the research problem.
  10. Conform to the university’s requirements for typing, printing and binding, and also meet the requirements set out formally in the learning institution’s post graduate policy and procedure.

We have come full circle from discussing the research process, all the concepts that you should apply and the tools that are available, to unpacking the research in the form of a thesis or dissertation. The following nine articles, therefore, return to the beginning of the research process and deal with the entire process, the only difference being that now we focus on putting the thesis or dissertation on paper.

Summary

The thesis or dissertation is the major means by which to communicate research findings.

Writing a thesis or dissertation requires creative thinking, some courage, hard work and lots of self-discipline.

You must find out in advance what the university’s requirements, rules, regulations and procedures for master’s or doctoral studies are and abide by them.

And you must manage time well.

Conducting research and writing a thesis or dissertation mostly consist of four main steps:

  1. Gather information.
  2. Evaluate, analyse and condense the information.
  3. Come to conclusions and findings.
  4. Apply your findings in practise.

The requirements for a thesis or dissertation are:

  1. It must clearly and thoroughly indicate what you have done to solve a problem.
  2. It must be comprehensive and precise.
  3. You must research the topic of your research professionally.
  4. In the case of doctoral studies your dissertation must align with your initial study proposal.
  5. You should continually make electronic backups of your work.
  6. You must plan and structure your thesis or dissertation before you start writing.
  7. You should review your work regularly.
  8. You should do enough literature study.
  9. You must clearly motivate the importance and value of your research.
  10. You must explain the research problem.
  11. You must clearly describe how you will collect and analyse data.
  12. You must show how you use the data that you collect in your thesis or dissertation.

Close

The eight articles following on this one are critically important for your further studies.

You can use them to guide your research process.

You can also use them to do a self-evaluation of your work before you submit the final manuscript for your thesis or dissertation.

Enjoy our studies.

Thank you.

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ARTICLE 96: Research Methods for Ph. D. and Master’s Degree Studies: Methods for Organising and Analysing Data: Part 2 of 2 Parts

Written by Dr. Hannes Nel

Research has shown that most people seek for excuses to fail rather than for ways in which to achieve success.

Nobody throws in the towel without rationalising about why their decision is justified.

And that is the difference between a winner and a loser.

Success always requires perseverance.

The most important decision that you must make before embarking on master’s degree or doctoral studies is that you will succeed.

Do not even think of failure as an option.

I discuss memoing and reflection on the analysis process in this article.

Memoing. Memos are an extremely versatile tool that can be used for many different purposes. It refers to any writing that you do in relation to the research other than your field notes, transcription or coding. A memo can be a brief comment that you type or write in the margin of your notes on an interview, notes on observations that you made during field work, your own impressions or ideas inspired by field work or literature study, an essay on your analysis of data, provisional conclusions and even possible findings. The basic idea behind memoing is to get ideas, observations and impressions down on paper to serve as the foundation for reflection, analytical insight and remembering spur of the moment ideas. Memos can also be coded in order to save them as part of the other data that you collected for further analysis.

Memos capture your thoughts on the main information that you recorded and can be most useful for creating new knowledge and findings. In dedicated computer software that uses it, memos are similar to codes, but usually contain longer passages of text. They, furthermore, differ from quotations in that quotations are extracts from primary documents, while memos represent your personal observations and impressions.

Although mostly recorded independently, a memo may refer to other memos, quotations, and codes. They can be grouped according to types (method, theoretical, descriptive, etc.), which is helpful in organizing and sorting them. Memos may also be assigned to primary documents so that they can be analysed with associated other coded data.

Memos are one of the most important techniques you have for recording and developing your ideas. You should, therefore, think of memos as a way of recording or presenting an understanding you have already reached. Memos should include reflections and conclusions on your reading and ideas as well as your fieldwork. They can be analytical, conceptual, theoretical or philosophical in nature. Memos can be written on almost anything that might have a positive impact on your research findings, including methodological issues, ethics, personal reactions, sudden understanding of previously complex concepts, misconceptions, etc. Memos should, therefore, be written in narrative format, including logical reasoning about the elements of your research. 

Writing memos by means of dedicated computer software is an important task in every phase of the qualitative research process. The ideas captured in memos are often the “pieces of the puzzle” that are later put together when you make conclusions and compile findings. Memos might be rather short in the beginning and become more elaborate as you gain more clarity on your arguments and the nature of the data or observations that you are investigating.

Memos can stand alone, in the event of which they would explain data that deals with a particular and important issue relevant to the purpose of the research. Memos can also be linked to other memos, quotations, or codes, in the event of which linked objects should refer to associated data and arguments to form a new, reconstructed or deconstructed narrative. Such associated memos, quotations and codes can contain methodological notes; they can be used as a bulletin board to exchange information between team members; they can be used to write notes about the analytical process, keeping a journal of to-dos; conclusions and findings can be deduced from them. Memos may also serve as a repository for symbols, text templates, and embedded objects (photos, figures, diagrams, graphs, etc.) that you may want to insert into primary documents or other memos.

The difference between memos and codes. A code can be just one word or a heading, forming a succinct, dense descriptor for a concept or argument emerging when you study data closely with the intent of identifying data elements relative to the purpose and topic of your research. Complex findings can be reduced to markers of important and relevant data.

A memo is normally longer than a code. A memo is a record of the process of cognitive thinking that you would go through when collecting data through observation, literature study, interviewing, etc. Words and short sections of a memo can be coded. Like codes, memos have short and concise names. These names, or titles, are used for displaying memos in browsers, and help to find specific memos.

The similarity and difference between memos and comments. The best way in which to compare memos and comments is probably to compare them with codes. Codes should be seen as “headings” for concepts. Memos and comments both refer to lengthy texts and both are generated by you as the researcher.

However, comments belong with just one entity or argument. You can, for example comment on a particular primary data source, such as a book, a report, minutes of a focus group meeting, etc. Memos, on the other hand, can be associated with more than one object or source of information. Memos, furthermore, can contribute to your collection of data in more than one way, for example as theoretical data, philosophical data, descriptions of methods, general comments, etc. Memos can be free-standing while comments must always be linked to other data. Memos can be associated with more than one object and be used for a variety of purposes, for example to discuss, analyse and process theoretical data, to describe methods, to comment, to inform, etc.

Reflection. The last step in data analysis is reflection. Reflection has to do with the ability to stand back from and think carefully about what you have done or are doing. The following questions will help you develop your ability to reflect on your analysis:

1.         What was your role in the research?

2.         Did you feel comfortable or uncomfortable? Why?

3.         What action did you take? How did you and others react?

4.         Was it appropriate? How could you have improved the situation for yourself, and others?

5.         What could you change in the future?

6.         Do you feel as if you have learnt anything new about yourself or your research?

7.         Has it changed your way of thinking in any way?

8.         What knowledge, from theories, practices and other aspects of your own and other’s research, can you apply to this situation?

9.         What broader issues – for example ethical, political or social – arise from this situation?

10.       Have you recorded your thoughts in your research diary?

Summary

Memos are versatile tools that you can use in the analysis of data. You can use memos to do the following:

  1. Integrate data in your thesis or dissertation.
  2. Consolidate your impressions and ideas into provisional conclusions and possible findings.
  3. To serve as the foundation for reflection, analytical insight and to remember spur of the moment ideas.
  4. To store interrelated ideas as codes.
  5. To capture your thoughts on the main information that you recorded.
  6. To develop new knowledge and findings.

Memos:

  1. May refer to other memos, quotations and codes.
  2. Can be grouped according to type.
  3. May be assigned to primary documents.
  4. Is a way of recording or presenting an understanding that you have already reached.
  5. Should include reflections and conclusions on your reading, ideas and fieldwork.
  6. Can be analytical, conceptual, theoretical or philosophical.
  7. Can be written on almost anything that might add value to your research.
  8. Should be written in a narrative format.
  9. May serve as a repository for symbols, text templates and embedded objects.

Memos are similar to codes, but usually contain longer passages of text.

Memos differ from comments in that comments belong with just one entity or argument, while memos can be associated with more than one object or source of information.

Also, memos can be free standing while comments must always be linked to other data.

The last step in data analysis is reflection.

Close

With this article we cross the bridge from data analysis to the layout of the thesis or dissertation.

Once you know how to structure a thesis or dissertation, you should be able to write and submit it.

There is one more step before you submit your thesis or dissertation for assessment, and that is to review your work.

The people who successfully completed a thesis or dissertation in the past are pretty much the same as you.

They are intelligent, creative and willing to work hard.

But they are not super human beings.

And there is no reason why you cannot achieve what they did.

Enjoy your studies.

Thank you.

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ARTICLE 95: Research Methods for Ph. D. and Master’s Degree Studies: Methods for Organising and Analysing Data Part 1 of 2 Parts

Written by Dr. Hannes Nel

Data needs to be organised before it can be analysed.

Depending on whether a qualitative or quantitative approach is followed, the data needs to be arranged in a logical sequence or quantified.

This can be done by quantifying, sequencing, coding or memoing the data.

I discuss quantifying, sequencing and coding data in this article.

I will discuss memoing data in my second video on methods for organising and analysing data.

Quantifying data. Most data analysis today is conducted with computers, ranging from large, mainframe computers to small, personal laptops. Many computer programs are dedicated to analysing social science data, and it would be worth your while obtaining and learning to use such software if you need to write a thesis or dissertation, even if you do not exclusively use quantitative research methodology, because you might need to interpret some statistics or you might use some quantitative methods to enhance, support or corroborate your qualitative findings. However, you will probably need not much more than office software if you need to do largely qualitative research.

Almost all research software requires some form of coding. This can differ substantially from one software program to the next, so you will need to find out exactly how it works even before you purchase the software. Your study leader will probably know which software will be the most suitable for your research and give you advice on this. You will only quantify data if statistical analysis is necessary, so do not do this unless you know that you will need it in your thesis or dissertation.

Many people are intimidated by empirical research because they feel uncomfortable with mathematics and statistics. And indeed, many research reports are filled with unspecified computations. The role of statistics in research is quite important, but unless you write an assignment or thesis on statistics or mathematics you will not be assessed on your statistical or mathematical proficiency. That is why most universities offer statistical services. There are several private and public universities also offering such services, so use them. There is also nothing wrong with purchasing dedicated software to do your statistical analysis with, although it might be necessary to do a course on the software before you will be able to utilise it properly.

Sequencing the data. Many researchers are of the opinion that organising the data in a specific sequence offers the clearest available picture of the logic of causal analysis in research. This is called the elaboration model. Especially using contingency tables, this method portrays the logical process of scientific analysis.

When collecting material for interpretive analysis, you experience events, or the things people say in a linear, chronological order. When you then immerse yourself in field notes or transcripts, the material is again viewed in a linear sequence. This sequence can be broken down by inducing themes and coding concepts so that events or remarks that were far away from each other in a document, or perhaps even different documents, are now brought close together. This gives you a fresh view on the data and allows you to carefully compare sections of text that appear to belong together. At this stage, you are likely to find that there are all sorts of ways in which extracts that you grouped together under a single theme, differ, or that there are all kinds of sub-issues and themes that come to light.

Exploring themes more closely in this way is called elaboration. The purpose is to capture the finer nuances of meaning not captured by your original, possibly crude, coding system. This is also an opportunity to revise the coding system – either in small ways or drastically.  If you use software it might even be necessary to start your coding all over again. This can be extremely time-consuming, but at least every time you start over you end up with a much better structured research report.  

Coding. In most qualitative research, the original text is a set of field notes, data obtained through literature study, interviews, and focus groups. One of the first steps that you will need to take before studying and analysing data is to code the information. You can use cards for this, but dedicated computer software can save you time, effort and costs. Codes are typically short pieces of text referencing other pieces of text, graphical, audio, or video data. From a methodological standpoint, codes serve a variety of purposes. They capture meaning in the data. They also serve as tools for finding specific occurrences in the data that cannot be found by simple text-based search techniques. Codes also help you organise and structure the data that you collected.

Their main purpose is to classify many textual or other data units in such a manner that the data that belongs together can be grouped as such for easy analysis and structuring. One can, perhaps, think of coding as “indexing” your data. You can also see it as a way to mark keywords so that you can find, retrieve and group them more easily at a later stage. The length of a code should be restricted and should not be too long-winded.

Codes can also be used to classify data at different levels of abstraction, to group sets of related information units together for the purpose of comparison. This is what you would often use to consider and compare related arguments to make conclusions that can be the motivation for new knowledge. Dedicated computer software does not create new knowledge; it only helps you as the researcher to structure existing knowledge and experiences in such a manner that it will be easier for you to think creatively, that is to create new knowledge.

Formal coding will be necessary if you make use of dedicated research software. Even if you do not use research software you probably will need a method of coding to arrange your data according to the structure of your thesis or a dissertation. Your original data will probably include additional data, such as the time, date and place where the data was collected.

It is also a purpose of coding data to move to a higher conceptual level. The codes will inevitably represent the meanings that you infer from the original data, thereby moving closer towards the solution of your problem statement, or confirmation or rejection of your null hypothesis. By coding data, you will, of course, rearrange the data that you collected under different headings representing steps in the research process.

Five coding procedures are popularly used: open coding, in vivo coding, coding by list, quick coding and free coding.

With most qualitative research software, you can create codes first and then link them to sections in your data. Creating new codes is called open coding. The nature of the initial codes, which can be referred to as Level 1 codes or open codes, can vary and might change as you progress with your research. You should give a name for each new code that you open, and you can usually create one or more codes in a single step. These codes can stick closely to the original data, perhaps even reusing the exact words in the original data. Such codes can be deduced from research questions. In vivo coding is mostly used for this purpose. 

In vivo coding means creating a code for selected text as and when you come across text, or just a word in the text, that can and should serve as a code. This would normally be a word or short piece of text that would probably appear in other pieces of data that should be linked and grouped with the data in which you identified the code.

If you know where you are going with your study, you will probably create codes first (up front), then link them to sections of data. This would be coding by list. Coding by list allows you to select existing codes from a code list that you prepared in advance. You would typically select one or more codes associated with the current data selection.

You can also create codes as you work through your data, which would then be quick coding. In the case of quick coding, you will continue with the selected code that you are working with. This is an efficient method for the consecutive coding of segments using the most recently used code.

You can create codes that have not yet been used for coding or creating networks. Such codes are called free codes and they are a form of quick coding, although they can be prepared in advance. The reasons why you would create free codes can be:

  1. To prepare a stock of predefined codes in the framework of a given theory. This is especially useful in the context of teamwork when creating a base project.
  2. To code in a “top-down” (or deductive) way with all necessary concepts already at hand. This complements the “bottom-up” (or inductive) open coding stage, in which concepts emerge from the data.
  3. To create codes that come to mind during normal coding work and that cannot be applied to the current segment but will be useful later.

It will be easier to code data if you already have a good idea of what you are trying to achieve with your research. Sometimes the data will actually “steer” you towards codes that you did not even think of in the beginning. This is typical of a grounded theory approach, although you should always keep an open mind about your research, regardless of which approach you follow. Coding also helps to develop a schematic diagram of the structure of your thesis or dissertation. This can be based on your initial study proposal. A mindmap can, for example be used to structure your research process and to identify initial codes to start with.

A code may contain more than a single word but should be concise. There should be a comment area on your screen that you can use to write a definition for each code, if you need one. As you progress in doing the first level coding, you may start to understand how your data might relate to broader conceptual issues. Some of your field experiences may in fact be sufficiently similar so that you might be able to group different coded data together on a higher conceptual level. Your coding has then proceeded to a higher set of codes, referred to as Level 2 or category codes.

After a code has been created, it appears as a new entry in several locations (drop-down list, code manager). In this respect the following are important to remember:

  1. Groundedness: Groundedness refers to the number of quotations associated with the code. Large numbers indicate strong evidence already found for this code.
  2. Density: The number of codes connected to this code is indicated as the density. Large numbers can be interpreted as a high degree of theoretical density.
  3. Comment: The tilde character “~” can, as an example, be used to flag commented codes. It is not used for codes only but for all commented objects.

It is not only text that can be coded. You can also code graphic documents, audio and video material. There are many other ways in which codes can be utilised, for example they can be sorted, modified, renamed, deleted, merged and of course reported.

Axial coding. Axial coding is the process of putting data back together after it has been restructured by means of open coding. Open coding allows you to select data that belong together (under a certain code or sub-code) taken from a variety of sources containing the original or primary data. Categories of data are, thus, systematically developed and linked with subcategories. You can then develop a new narrative through a process of reconstruction. The new narrative might apply to a different context and should be articulated to the purpose of your research.

The articulation of selected data can typically relate to a condition, strategy or consequences. Data relating to a condition or strategy should address conditions that lead to the achievement of the purpose of the study. The purpose of the study will always be to solve a problem statement or question or to prove or disprove a null hypothesis. Consequential data include all outcomes of action or interaction.

Selective coding. Selective coding refers to the process of selecting a core category, systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development. Categories are, thus, integrated and refined. The core category would be the central phenomenon to which all the other categories are linked. To use a romantic example, in a novel you will identify the plot first, then the storyline, which you should analyse to identify the elements of the storyline that relate to the plot. From this you should be able to deduce lessons learned or a moral for the story.

Summary

Data is mostly organised by making use of dedicated computer programmes.

Most such computer programmes require some form of coding.

Data can be sequenced by following an elaboration model.

Contingency tables are mostly used to achieve logic in scientific analysis.

Data is often analysed in a linear, chronological order.

Codes are typically short pieces of text referencing other pieces of text, graphical, audio or video data.

Codes:

  1. Capture meaning.
  2. Serve as tools for finding specific occurrences in the data.
  3. Help you to organise and structure the data.
  4. Classifies textual or other data units in related groups and at different levels of abstraction.

Dedicated computer software does not create new knowledge.

Five coding procedures are popularly used.

They are open coding, in vivo coding, coding by list, quick coding and free coding.

Open coding means creating new codes.

In vivo coding means creating a code for elected text as and when you come across text, or just a word in text, that can and should serve as a code.

Coding by list is used when you know where you are going with your study so that you can create the codes even before collecting data.

Quick coding means creating codes as you work through your data.

Free codes are codes that have not been used yet. They can be the result of coding by list or quick coding.

To the five coding procedures should be added axial coding and selective coding.

Axial coding is the process of putting data back together after it has been restructured by means of open coding.

Selective coding refers to the process of electing a core category, systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development.

You should always keep an open mind about your research and the codes that you create.

Close

If what I discussed here sounds confusing and alien, then it is probably because of what we discussed under schema analysis in my previous video.

It is unlikely that the level of language used here is beyond you.

If that were the case, you would not have watched this video.

No doubt you will understand everything if you watch this video again after having tried out one or two of the computer programmes that deal with especially qualitative research.

Enjoy your studies.

Thank you.

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ARTICLE 94: Research Methods for Ph. D. and Master’s Degree Studies: Data Analysis Part 7 of 7 Parts

Written by Dr. Hannes Nel

What, do you think, is the biggest challenge for somebody who embarks on doctoral or master’s degree studies?

Well, the answer to this question will probably be different for different people, depending on their circumstances, perceptions, value systems and culture.

If we were to combine all the possible challenges, we will probably arrive at “to understand”.

In my opinion that is the biggest challenge facing any post-graduate student.

Not only do you need to understand endless concepts, phenomena, theories and principles, you also must explain them in your thesis or dissertation.

And on doctoral level you will be required to define and explain new concepts, phenomena, theories and principles.

Data analysis is necessary for such elucidation.

I discuss the following data analysis methods in this article:

  1. Schema analysis.
  2. Situational analysis.
  3. Textual analysis.
  4. Thematic analysis.

Schema analysis

Schema analysis requires that you simplify cognitive processes to understand complex concepts and narrative information more readily. In this manner a narrative that might otherwise be difficult to understand because of the level of language used, cultural differences or any other reason, is made easier to understand for those who might find the language challenging or the cultural context alien.

Schema analysis might require additional explanation, interpretation and reconstruction of the message. An individual who grew up in the city might not know how to milk a cow and a farmer might not know how to obtain food from a street vending machine. 

Today schema analysis is also used in computer programming, where a schema is the organisation or structure for a database. A schema is developed by modelling data.  The purpose remains the same as when you would have done schema analysis manually – it is a process of rendering data more user-friendly.

Situational analysis

As opposed to comparative analysis, situational analysis focuses more on non-human elements. It implies the analysis of the broad context or environment in which an event takes place. It can include an analysis of the state and condition of people and the ecosystem, including the identification of trends; the identification of major issues related to people and ecosystems that require attention and an analysis of key stakeholders.

Textual analysis

Textual analysis, also called ‘content analysis’, is a data collection technique as well as a data analysis technique. It helps us to understand information on symbolic phenomena. It is used to investigate symbolic content such as words that appear in, for example, newspaper articles, comments on a blog, political speeches, etc. It is a qualitative technique in which the researcher attempts to describe the denotative meaning of content in an objective way.  

There are two levels of meaning, namely denotative and connotative meaning. The denotative meaning of a word refers to the literal meaning that you will find in a dictionary. This meaning is free from any form of interpretation. The connotative meaning of a word refers to the connotation that we ascribe to a particular word, based on the feeling or idea that the word invokes in us, which is often based on our prior experiences.

For example, the denotative meaning of the word ‘host’ is ‘one who lodges or entertains a stranger or guest at his or her house’. However, a woman who was abused by a host in whose guest house she stayed in her youth might conjure up in her mind a host as being a dangerous and sly human being who takes advantage of vulnerable people. The connotative meaning of ‘host’ is, therefore, largely the opposite of what the word is supposed to mean. In textual analysis we only work with the denotative meaning of words to make valid and reliable assumptions of the data within context.

You can only work with what was reported when doing qualitative research and you should not make any assumptions about the originator’s intended meaning. The context in which the information was used, however, also needs to be taken into consideration.

Textual analysis can be subjective because its interpretation is done by fallible people. It can include the analysis of freshly collected data as well as transcribed data. You should transcribe all the raw data that you collected from the written and verbal responses of participants during conversations, interviews, focus groups, meetings, etc. Electronically recorded interviews will need to be retyped word for word to facilitate textual analysis.

Thematic analysis

Also known as concept analysis or conceptual analysis, it is actually a coding regime, according to which data is reduced by means of identifying certain themes. Thematic analysis uses deductive coding by grouping concepts under one of a prepared list of themes.

In thematic analysis you first need to familiarise yourself with the data before you can even select themes. You should list the themes that you would like to cover in your research when you do your literature review. After having listed themes, the next step would be to generate codes. Codes serve as an important foundation for the structuring and arrangement of data by means of qualitative computer software. Even though one might not call it coding, capturing information on cards is also a form of coding, even though rather simple and limited in usability.

You can also search for themes now if you did not do so as a first step already. This is done by collating the codes that you identified into potential themes. Themes are actually “headings” under which related or linked codes are grouped, or clustered. Most qualitative research computer software allows you to review and edit your codes and themes when necessary, which will inevitably happen as you progress with your research.

Summary

Schema analysis:

  1. Requires that you simplify cognitive processes.
  2. Might require additional explanation, interpretation and reconstruction of selected data.
  3. Is also used in computer programming.

Situational analysis:

  1. Focuses on non-human elements.
  2. Analysis the broad context or environment for the research.
  3. Can include an analysis of the state and condition of people and the ecosystem.

Textual analysis

  1. Combines data collection and analysis.
  2. Helps to understand information on symbolic phenomena.
  3. Attempts to objectively describe the denotative meaning of content.
  4. Takes the context in which information was used into consideration.
  5. Can be subjective.
  6. Can include the analysis of freshly collected as well as transcribed data.

Thematic analysis

  1. Is a coding regime.
  2. Reduces data in terms of certain themes.
  3. Requires the identification of themes before coding can be done.

Close

That concludes my articles on data analysis and all the other concepts and theories behind doctoral and master’s degree studies.

In the remaining 14 articles I will focus more on the structure and layout of a thesis or dissertation.

Enjoy your studies.

Thank you.

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