Rapid Trends

03/07/2025

𝐖𝐢𝐤𝐞 𝐝𝐚𝐫𝐞𝐬 𝐜𝐨𝐚𝐥𝐢𝐭𝐢𝐨𝐧, 𝐬𝐚𝐲𝐬 𝐨𝐧𝐥𝐲 𝐏𝐃𝐏 𝐜𝐚𝐧 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐓𝐢𝐧𝐮𝐛𝐮 𝐢𝐧 𝟐𝟎𝟐𝟕

Me: which PDP? ...or he intended to say PDAPC?

20/06/2025

Things we hear

PEOPLE WITH PHDS ARE NOT NORMAL

The belief that persons with PhDs are not normal is widely held in some communities. Hence, terms such as Permanent Head Damage (PhD) or Permanently Hidden Damage (PhD).

This idea stems from the idea that people with PhDs are not only out of touch with everyday reality, but also socially awkward. But this misconception ignores the variety of experiences and personalities that exist within the PhD community in addition to being unfair.

In the first place, it is critical to acknowledge that earning a PhD is a noteworthy accomplishment that calls for years of commitment, sacrifice, and hard work. It is evidence of their intelligence, tenacity, and enthusiasm for the subject matter they have chosen to study. Because of this, it is unjust to write off PhD holders as not normal for having an academically developed and sharpened intellect than the general population.

While it is true that some PhD holders might display characteristics like introversion and intense concentration on their work that are often linked to high intelligence, it is crucial to keep in mind that these characteristics do not necessarily characterise a person as a whole, to be stereotyped or prejudiced as not being normal.
In addition to their academic endeavours, a large number of PhD holders are outgoing, socially adept, and involved in a variety of extracurricular activities.

The notion that individuals holding PhDs are not normal ignores the range of backgrounds and experiences that exist within the PhD community. Doctorate holders are diverse individuals with a wide range of personal, cultural, and socioeconomic experiences that have shaped who they are.

Therefore, it would be unfair to generalise about all PhD holders and assume that they are all awkward around people or disconnected from reality or simply not normal.

In short, the belief that people with PhDs are not normal is unfair and unfounded. Instead of assuming that those with PhDs are not normal, we should celebrate their achievements and recognize the value they bring to society.

04/06/2025

DATA PRESENTATION, ANALYSIS AND INTERPRETATION/DISCUSSION

In postgraduate research, understanding the differences between data presentation, data analysis, and data interpretation or discussion is essential.

These three concepts, while interconnected, serve distinct purposes in transforming raw data into meaningful insights.

Data presentation refers to the method of visually displaying data to make it understandable and accessible. Students often use charts, graphs, and tables to showcase their findings clearly.

For instance, in quantitative study, a student studying climate change might present temperature trends over decades using a line graph, allowing viewers to quickly grasp the changes over time. In a qualitative study a student may present word clouds, word trees, hybrid visualizations, network diagrams, direct quotes, summary diagrams, and trend diagrams.

Effective data presentation is crucial, as it forms the first impression of the research and can influence how the audience understands the data. Without proper presentation, even the most significant findings can be overlooked or misinterpreted.

Data analysis, on the other hand, in quantitative research, involves applying statistical methods to extract patterns and relationships from the presented data. This step goes beyond merely showing what the data looks like. For example, a student may calculate the correlation between carbon emissions and global temperatures using statistical software. Data analysis helps identify trends, make comparisons, and discern variations in the data. It is a more technical process that requires expertise in statistical techniques, as well as knowledge of the subject matter.

In quantitative research, data analysis is making sense of non-numerical data to uncover patterns, themes, and insights. It is a process of understanding the rich, detailed information gathered through methods like interviews, focus groups, or observations.

Good data analysis ensures that findings are valid and reliable, and it lays the groundwork for sound conclusions.

Lastly, data interpretation or discussion involves explaining the significance of the analyzed data. This is where students provide context to their findings and relate them to the existing body of knowledge or literature. For instance, after identifying a correlation between carbon emissions and rising temperatures, a student might discuss how these findings align with previous studies and their implications. Data interpretation is vital, as it allows students to communicate the relevance of their work and suggest recommendations or highlight areas for further investigation.

We argue that, data presentation, analysis, and interpretation or discussion are interconnected steps in postgraduate research, each playing a crucial role. Data presentation focuses on how information is visually communicated, data analysis involves extracting patterns and insights, and data interpretation or discussion provides context and meaning to the findings.

These differences are essential for students, as each step builds on the previous one, leading to meaningful conclusions that can inform future studies and practical applications.

©The Research Methodologist

29/05/2025

DESCRIPTIVE AND INFERRENTIAL STATISTICS

Statistics plays a critical role in postgraduate research, providing students with tools to analyze data and draw meaningful conclusions.

Within the realm of statistics, two primary categories emerge: descriptive statistics and inferential statistics.

Descriptive statistics refers to methods that summarize and present data in a meaningful way. It provides a snapshot of the data set through measures such as mean, median, mode, range, and standard deviation.

For instance, if a student is studying the academic performance of students in a university, descriptive statistics can summarize the students' scores, showing the average score (mean) alongside the highest and lowest scores (range). This allows students to easily comprehend the overall performance of the group without making any assumptions beyond the data presented.

Descriptive statistics is especially useful for initial data exploration, as it provides clear insights into the characteristics of the data set.

In contrast, inferential statistics enables students to make predictions or generalizations about a population based on a sample of data. It involves using probability theory to estimate population parameters and test hypotheses.

For example, if the same student uses a sample of 100 students from a larger population of 1,000 to determine if the average score significantly differs from the national average, they would employ inferential statistical methods. Techniques such as t-tests or regression analysis help infer conclusions about the entire population based on the sample data, allowing students to make broader generalizations about academic performance.

The key distinction between these two types of statistics lies in their purpose.

Descriptive statistics focuses on describing and summarizing data, while inferential statistics aims to draw conclusions and make predictions beyond the immediate data set. Both play vital roles in research; however, the choice between them depends on the research questions, data availability, and the level of analysis required.

We argue that, descriptive and inferential statistics serve distinct but complementary purposes in postgraduate research.

Descriptive statistics provides clarity and context for data, whereas inferential statistics allows students to extend their findings and make predictions about larger populations.

©The Research Methodologist

01/05/2025

THERE IS NO PERFECT DISSERTATION OR THESIS

The process of writing a dissertation or thesis is often daunting for many students, representing a culmination of years of academic work and research.

It involves extensive reading, writing, and revising, with the aim of producing a piece of work that contributes new knowledge to a field.

However, the reality is that no dissertation or thesis is ever truly perfect.

Students often find themselves in a cycle of constant correction and improvement, which can lead to never-ending revisions.

The complexity of academia means that new findings and perspectives continually emerge. When a student believes they have finished their dissertation or thesis, they might discover new literature or data that encourages them to rethink their work. This fluidity in research can create an environment where one feels there is always something more to include or correct.

Moreover, perfectionism is a common hurdle in the postgraduate world. Many students hold a belief that their work must meet the highest standards of quality, often driven by personal pride or the desire for recognition.

While striving for excellence is beneficial, it can paralyze progress. It is important to work on your dissertation or thesis and quickly submit to contribute to knowledge because waiting for perfection could mean missing critical opportunities.

Therefore, it is essential to recognize the necessity of submission deadlines in academia. Deadlines force students to draw a line under their work, highlighting the practical side of academic writing.

Submitting a thesis or dissertation does not mean abandonment of quality; rather, it reflects a moment of culmination where one believes the work is adequate for examination. This is an important lesson in balancing rigor with the pragmatic necessities of academic life.

In short, the pursuit of a perfect dissertation or thesis is unachievable and can lead to perpetual frustration. Finding a balance between thoroughness and timely submission ultimately leads to a more productive academic experience, empowering students to move forward rather than remain stuck in an endless cycle of revisions.

Let it go to the examiners; there is no such thing as a perfect or completed dissertation or thesis. They all have flaws, but they are adequate enough to pass.

©The Research Methodologist

30/04/2025

GREY LITERATURE

Grey literature encompasses documents that are not formally published through peer review or widely distributed.

This category includes reports, theses, dissertations, conference proceedings, government documents, and publications from non-profit organizations.

The importance of grey literature lies in its ability to provide valuable insights and data that are not typically found in conventional sources, thus enriching the landscape of knowledge.

Students use grey literature because of its immediacy and relevance. In rapidly evolving fields like healthcare and technology, timely access to information can significantly influence decision-making processes.

This demonstrates how grey literature can fill the gap between research findings and real-world applications, offering stakeholders current information that might not yet be available in peer-reviewed formats.

Moreover, grey literature often represents a broad spectrum of perspectives and voices. Many organizations provide reports that capture community experiences and data not typically represented in mainstream research.

By integrating these perspectives, grey literature enriches research and enhances its applicability in real-world contexts.

However, the use of grey literature comes with its own challenges. Quality and credibility can vary significantly, as these documents might not undergo rigorous peer review processes. Therefore, it is crucial for students to critically evaluate grey literature sources.

Tools like the International Grey Literature Network provide guidelines for assessing the reliability of these materials, ensuring that students can confidently utilize them in their dissertations and theses.

©The Research Methodologist

29/04/2025

FORMS OF MIXED METHOD RESEARCH DESIGNS

Students often struggle to put mixed methods research into practice, as it is challenging.

Mixed methods research designs can be put into four groups:

𝘾𝙤𝙣𝙫𝙚𝙧𝙜𝙚𝙣𝙩 𝙥𝙖𝙧𝙖𝙡𝙡𝙚𝙡 𝙙𝙚𝙨𝙞𝙜𝙣: when data collection and analysis of both quantitative and qualitative data occur at the same time but analyzed separately. This design aims to create mutually exclusive sets of data that inform each other. For example: 𝘢 𝘱𝘰𝘴𝘵𝘨𝘳𝘢𝘥𝘶𝘢𝘵𝘦 𝘴𝘵𝘶𝘥𝘦𝘯𝘵 𝘤𝘢𝘯 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸 𝘧𝘦𝘭𝘭𝘰𝘸 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴 𝘰𝘯 𝘤𝘢𝘮𝘱𝘶𝘴 𝘸𝘩𝘪𝘭𝘦 𝘢𝘭𝘴𝘰 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘶𝘴𝘪𝘯𝘨 𝘢 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘯𝘢𝘪𝘳𝘦 𝘵𝘰 𝘥𝘦𝘵𝘦𝘳𝘮𝘪𝘯𝘦 𝘵𝘩𝘦𝘪𝘳 𝘴𝘢𝘵𝘪𝘴𝘧𝘢𝘤𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘪𝘵𝘺 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴.

𝙀𝙢𝙗𝙚𝙙𝙙𝙚𝙙 𝙙𝙚𝙨𝙞𝙜𝙣: this is when the quantitative and qualitative data are collected simultaneously, but the qualitative data is rooted within the quantitative data. This design is best used when the student wants to focus on the quantitative data but still need to understand how the qualitative data further explains it. For instance: 𝘵𝘩𝘦 𝘴𝘵𝘶𝘥𝘦𝘯𝘵 𝘮𝘢𝘺 𝘤𝘰𝘭𝘭𝘦𝘤𝘵 𝘲𝘶𝘢𝘯𝘵𝘪𝘵𝘢𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢 𝘰𝘯 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴’ 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘰𝘧 𝘢𝘯 𝘰𝘯𝘭𝘪𝘯𝘦 𝘳𝘦𝘨𝘪𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯 𝘴𝘺𝘴𝘵𝘦𝘮 𝘢𝘯𝘥 𝘤𝘰𝘯𝘥𝘶𝘤𝘵 𝘪𝘯𝘥𝘪𝘷𝘪𝘥𝘶𝘢𝘭 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘴 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘧𝘶𝘳𝘵𝘩𝘦𝘳 𝘪𝘯𝘴𝘪𝘨𝘩𝘵 𝘪𝘯𝘵𝘰 𝘵𝘩𝘦𝘪𝘳 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦𝘴.

𝙀𝙭𝙥𝙡𝙖𝙣𝙖𝙩𝙤𝙧𝙮 𝙨𝙚𝙦𝙪𝙚𝙣𝙩𝙞𝙖𝙡 𝙙𝙚𝙨𝙞𝙜𝙣: here quantitative data is collected first, followed by qualitative data. This design is used when the student wants to further explain a set of quantitative data with additional qualitative information. For example: 𝘵𝘩𝘪𝘴 𝘸𝘰𝘶𝘭𝘥 𝘣𝘦 𝘪𝘧 𝘵𝘩𝘦 𝘴𝘵𝘶𝘥𝘦𝘯𝘵 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘦𝘥 𝘲𝘶𝘢𝘯𝘵𝘪𝘵𝘢𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢 𝘰𝘯 𝘜𝘯𝘪𝘷𝘦𝘳𝘴𝘪𝘵𝘺 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘦𝘴 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦𝘪𝘳 𝘴𝘢𝘵𝘪𝘴𝘧𝘢𝘤𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦𝘪𝘳 𝘫𝘰𝘣 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘤𝘰𝘯𝘥𝘶𝘤𝘵𝘦𝘥 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘴 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘮𝘰𝘳𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘢𝘣𝘰𝘶𝘵 𝘸𝘩𝘺 𝘵𝘩𝘦𝘺 𝘳𝘦𝘴𝘱𝘰𝘯𝘥𝘦𝘥 𝘵𝘩𝘦 𝘸𝘢𝘺 𝘵𝘩𝘦𝘺 𝘥𝘪𝘥.

𝙀𝙭𝙥𝙡𝙤𝙧𝙖𝙩𝙤𝙧𝙮 𝙨𝙚𝙦𝙪𝙚𝙣𝙩𝙞𝙖𝙡 𝙙𝙚𝙨𝙞𝙜𝙣: this design allows the student to collect qualitative data first, followed by quantitative data. This type of mixed methods research is used when the goal is to explore a topic before collecting any quantitative data. An example: 𝘵𝘩𝘪𝘴 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘢 𝘴𝘵𝘶𝘥𝘦𝘯𝘵 𝘴𝘵𝘶𝘥𝘺𝘪𝘯𝘨 𝘩𝘰𝘸 𝘭𝘦𝘤𝘵𝘶𝘳𝘦𝘳𝘴 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦𝘪𝘳 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴 𝘣𝘺 𝘤𝘰𝘯𝘥𝘶𝘤𝘵𝘪𝘯𝘨 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘶𝘴𝘪𝘯𝘨 𝘢 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘯𝘢𝘪𝘳𝘦 𝘵𝘰 𝘧𝘶𝘳𝘵𝘩𝘦𝘳 𝘦𝘹𝘱𝘭𝘰𝘳𝘦 𝘢𝘯𝘥 𝘮𝘦𝘢𝘴𝘶𝘳𝘦 𝘵𝘩𝘦𝘴𝘦 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯𝘴.

In summary, it is challenging to integrate data in mixed methods studies, but it can be done successfully with careful planning. Therefore, no matter which type of design the student chooses, understanding and applying these methods can help the student draw meaningful conclusions from his or her study.

©The Research Methodologist

26/04/2025

LIMITATIONS AND DELIMITATIONS OF THE STUDY

Research challenges can be categorized into two; limitations and delimitations. Both refer to “limits” within a study. However, they are different.

Limitations explain methodological shortcomings of a study. For example, a student sets a date with participants when to conduct in-depth interviews with them in their respective homes. On the material date, 3 out of the 10 participants decide to drop-out of the study because they are preparing to relocate to another city. Or the student happens not to have enough research funding to go and conduct interviews with all 10 participants or goes there but finds that the bridge has been washed away by heavy rains therefore has no access to the participants.

These limitations will affect the validity and reliability of the study because they compromise the research methodology which will affect the credibility of the findings.

Therefore, limitations are methodological challenges that most of the time are out of the researcher’s control but influence research findings. As a result, they determine both internal validity; extent to which results represent the truth in the population studied and external validity; the extent findings can be generalized to other contexts.

Both internal and external validity of the study are considered potential weaknesses. Therefore, measures should be put in place to address these weaknesses so that results are credible despite limitations.

In contrast, delimitations deal with the challenges of the study in terms of the focus and scope of research aims and research questions. For example, a student can set boundaries of his or her study to include and exclude some variables, literature, and population as long as convincing justification is provided. By doing so, the student narrows the study to make it more manageable and relevant to what he or she is trying to understand or solve through the study.

In short, limitations are shortcomings or influences that a postgraduate student cannot control that place restrictions on his or her methodology and conclusions. Any limitations that might influence the results should be mentioned and how they will be addressed. Delimitations are choices made by the student in the study which should be mentioned. They describe the focus and scope of the study as set by the student.

© The Research Methodologist

26/04/2025

THEMATIC ANALYSIS AND CONTENT ANALYSIS

One of the often asked topics by students is about: 𝘛𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘛𝘩𝘦𝘮𝘢𝘵𝘪𝘤 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘊𝘰𝘯𝘵𝘦𝘯𝘵 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘪𝘯 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩.

Thematic Analysis focuses on extracting high-level themes from within data, while Content Analysis focuses on the recurrences of concepts or keywords at a more surface-level of analysis, to be exact, their frequency.

To all intents and purposes, the main difference between the two methods lies in the possibility of quantification of data in Content Analysis by measuring the frequency of different categories and themes. While frequency is generally a core tenet of qualitative Content Analysis where statistical findings are tabulated or visualized in the final write-up, it is not a focus of Thematic Analysis.

Instead, in contrast to tallying concepts or keywords to extrapolate meaning as a student would in Content Analysis, a theme is not necessarily reflective of the frequency of its appearance within the data in a Thematic Analysis.

In short, statistical data is core to most Content Analysis but is not typically cited in Thematic Analysis. And while the former tends to focus on more manifest data that is apparent through surface-level analysis, neither method is inherently more beneficial or astute than the other.

Therefore, for clarity purposes, the main differences between Thematic Analysis and Content Analysis are:

(1). Thematic Analysis is a qualitative method used to uncover themes in textual data, while Content Analysis is either a quantitative or a qualitative approach that also involves some quantification of data.

(2) Content Analysis generally counts the occurrence of concepts or keywords to infer meaning, while Thematic Analysis assigns meaning by extracting high-level ideas.

(3) Thematic Analysis focuses on the overarching themes in the data and how those themes relate to one another, while in Content Analysis students count instances of coded concepts and keywords within large amounts of textual data with less focus on comparing or contrasting those codes.

What is important for a student to understand is how each qualitative research method works to confidently decide which one best suits his or her research needs bearing in mind that these methods have a lot of similarities.

©The Research Methodologist

25/04/2025

RESEARCH PROPOSAL OUTLINE - A GOOD EXAMPLE

A good research proposal should contain some or all of the following elements:

➢Working title
➢Statement of the problem or gap in the current research in your field of study
➢Context in which the problem arises; historical and current research in the field
➢Reasons why the problem exists and justification for addressing it
➢Methodology you will use to address the problem
➢Key research questions
➢Expected contribution to knowledge
➢Limitations
➢Time-frame and (special) resources required
➢ References/bibliography.

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