#qualitativeanalysis

As a sociologist the brigading campaign has been disquieting and I’ve been working on thinking out why. It took me a while to figure out, and it's the interview coding sense I cultivated in grad school. English is too chaotic a language and its users too diverse for 85% of the comments on your posts having the same complaints (as opposed to informed critiques of aspects) in the same sentence structure with about 5 thesaurus variants. I’d like to know if I’m alone in noticing that once the brigading attack began when Star Trek: SFA's first episode aired, there's like a weekly or 1.5 week refresh on the agitprop brigaders trying to drown out any fan who enjoys Trek made after Enterprise? The newest one being Bad Writing/It's [Dren]" "I don't have to justify any of this, by saying I do, you are the bigot!"

The sheer pride from the accounts in not being able to elucidate WHY these accounts (just to cover the organics and bots) yell about considering something objectively bad when it is an expression of personal taste, and their insistence on yucking other fan's yum, is horrifying. It reminds me of Harlan Ellison's statement that Stupidity or Arrogance can be worked around, but the combination is Immobile. Not saying arrogant stupidity of the Harlan Ellison kind applies to folks who find aspects of Starfleet Academy not to their tastes, but rather those who insist that one or two aspects they outright hate to an irrational level means the whole thing is trash and so are the folks who watch it, even though they keep insisting that "EVERYONE HATES IT".

There is also how many of these accounts demand one accept their subjective view of reality as inherently objective and authoritative, instead of maturely accepting that there are fans of the show, and that doesn't make them morally reprehensible, or artistically stupid. They get meaner and meaner, if you ask them to simply explain why something is "derogatorylowestadjectiveo'choice" without resorting to similarly unspecific adjectival critiques. The best I've gotten out of them is complaining that the dialogue sounds like someone thinks Gen Z sounds like. (As if we weren't a few years away from the full aging into adulthood for the oldest Alphas). Though more typically it’s complaints, as opposed to critiques, about undergrads acting like undergrads, and demanding truly unrealstic levels of “Professionalism” that not even IRL militaries or any other Trek series has displayed. While things like Prank Wars are true to a historically accurate tradition between both civilian and institutional undergraduate colleges that often have the implicit approval of each school’s head. 

The sheer pride from the accounts  (just to cover the organics and bots) in not being able to elucidate WHY they yell about considering something objectively bad, combined with the proud/loud insistence on yucking other fan's yum, is horrifying. It reminds me of Harlan Ellison's statement that Stupidity or Arrogance can be worked around, but the combination is Immobile. Not saying arrogant stupidity of the Harlan Ellison kind applies to folks who find aspects of Starfleet Academy not to their tastes, but rather those who insist that one or two aspects they outright hate to an irrational level means the whole thing is trash and so are the folks who watch it, even though they keep insisting that "EVERYONE HATES IT". Fascinating definition of “everybody/everyone”.

Many demand you accept their subjective view of reality as inherently objective. Instead of accepting that there are fans of the show and that doesn't make them morally reprehensible or artistically stupid. Takes me back to the early days online in the New Atheist days of the late '00s. They get meaner and meaner the more you ask them to simply explain why something is "derogatorylowestadjectiveo'choice" without resorting to similarly unspecific adjectival complaints. The best I've gotten out of them is complaining that the dialogue sounds like someone thinks Gen Z sounds like. (As if we weren't a few years away from the full aging into adulthood for the oldest Alphas).

For a while the dog whistles were:

~"UNPROFESSIONAL"

These are 18-20 year olds, they don't have the experience to inform them how not to be dumbasses. That's the point of their education.

~"GAY KLINGON" "PROM DRESS"

I invited these to go to their local Highland games and tell those in the Caber Toss they were wearing prom skirts

~"THEY ARE DUMBASSES"

Again, no sh*t Sherlock. Please, the first person who got through their 18-30 range without being a massive dumbass, A LOT, due to not having experience backing your decisions, and hopefully having learned to control for your own biases, throw the first stone! There is also a strong pattern of general intolerance for disrespect of folks who have time spent learning what it is that they do or are speaking about. Which gets worse the longer you ask them to deal in something other than Sith Absolutes. IDIC after all.

~Constant THIS SUCKS, IT JUST SUCKS, IT'S OBVIOUS, JUST LOOK AT THE AUDIENCE REVIEWS

When a RT Audience review aggregate, or any other data set, contains that high a number of one star reviews with little to nothing in each comment other than PvP multiplayer insults, the dataset is poisoned. It's not a reliable source. That is not me deriding RT Audience Scores, it's me pointing out that Data Sanitization is a thing. It’s a shame that the brigade decided to destroy the usefulness of that metric..

~The current one is either BAD WRITING or FINANCIAL DISASTER

From a artistic perspective Starfleet Academy is rather well written and, speaking as someone who grew up with CW soaps, is so much better in production and writing. It has a few oddities, but so does every other incarnation of Trek. 

For one thing, Star Trek Starfleet Academy doesn't drag out the drama of being an adult in undergrad for the sake of eyeballs, but actually resolves those dramas in realistic, but quick, ways that are designed to show us instances of character development into better adults and actual officers.

We can't say if it was a financial disaster yet because we don't have the accountancy department's numbers.

However, we can say that finances were not the only considerations, given the political and cultural environment in the United States right now and the r/TheDonald style of lowest tier trolls brigading Starfleet Academy netwide . In fact, the tactics strongly remind me of what Reddit experienced during the r/theDonald brigading time period that led to them getting fully banned instead of locked behind a wall.

(Media and Cinematography/Film Analysis has shown me so much of what Trek has done with some truly spectacular cinematography quite often through the entire franchise. I love that folks bitched about the tilted camera on S1 Discovery, only to noticed that only happens when Lorca is on screen, and is a film tactic to indicate something is OFF that is as old as Night of the Hunter , and its frelling FORESHADOWING VIA CAMERA ANGLE THAT IS SO FRELLING COOL!!!!)

Gif for meme tax

The basin of attraction in a dynamic manifold constitutes all possible initial conditions from which trajectories can start to explore the attractor in a coordinate phase space. These trajectories do not cross themselves usually and continue to trace dense orbits in the attractor inside the manifold. In certain manifolds, such as chaotic ones, they are characterized by sensitivity to their formation initial conditions so that they diverge rapidly from each other since that formation.

This process represented in essence a qualitative description of how dynamical systems evolve where classical descriptions, that included continuous mathematics such as calculus, provided the quantitative description most of the time in the form of analytical solutions. Then the need to conduct machine-based analysis brought about numerical techniques with varied degrees of accuracy but that relied on discrete, rather than continuous, mathematics.

Chaos theory was reborn again through such numerical analyses to provide not only qualitative treatment, but it came with an added set of mathematical tools that described natural phenomena and reproduced some of the important solutions found using classical mathematics. In addition, this numerical formulation of chaos theory allowed for toggling from continuous mathematics to discrete one, thereby giving an advantage when communicating in machine language to carry out computing jobs.

However, such communicating requires first the realization that the continuous form of chaotic attractors in their phase space must be discretized. So, these trajectories can by "discretized" into numerics before communicating their data to the computer. Many techniques exist to achieve this including the "collapse" of such continuous formulation into either a "fixed-point" or a "limit cycle". The first type is just one data point that does not change with mapping but reveals system stability, whereas the second formation is a confined regular attractor vs. the original so-called "strange attractor".

Strange attractor - it is a complex, fractal-shaped set of numerical values in a chaotic dynamical system that attracts nearby trajectories over time. Unlike simple attractors (points or loops), they exhibit extreme sensitivity to initial conditions leading to unpredictable, non-repeating behavior.

Nowadays, the dual utilization of qualitative and computational analyses of chaos theory gave it an advantage to understand many concepts over diverse disciplines that includes traditionally non-technological topics such as literary texts and societal sciences. In these contexts, in particularly, the terminology used in describing chaos theory concepts give direct access to further analyze them technically.

Figure: fixed points of functions in the complex plane commonly lead to patterned chaos fractal structures. The plots on the left color the value of the fixed point and on the right, they present the number of iterations to reach a fixed point for the Sine function.

I Spent Three Months Coding 80 Interviews by Hand. Then I Found Out There Was Another Way. I Am Still Angry.

Nobody warned me about the coding phase.

My supervisor mentioned it. My methods textbook described it. My PhD cohort talked about it in the vague way people talk about something unpleasant that is still far away.

None of that prepared me for sitting at my desk at 11pm on a Wednesday in October, on interview number 34 of 80, realising I had been doing this for six weeks and I was not even halfway through.

I had 46 interviews left to code. I had a chapter draft due in eight weeks. I had a supervisor who was kind but firm about deadlines. And I had a growing suspicion that the thing I had spent four years training to do, which was think carefully and rigorously about complex human experience, was being almost entirely consumed by a mechanical, repetitive, mind-numbing process that had nothing to do with thinking.

I was not analysing. I was sorting.

What Manual Coding Actually Feels Like

If you have never done qualitative research, here is what manual coding in NVivo or ATLAS.ti actually looks like at two in the morning six weeks in.

You read a sentence. You decide what it means. You assign it a code. You read the next sentence. You decide what it means. You assign it a code or create a new one. You read the next sentence.

You do this for every sentence in every transcript. Eighty interviews. Each one forty-five to sixty minutes long. Each transcript fifteen to twenty pages. You are making thousands of individual decisions, each one feeling consequential, each one feeling like it might be the wrong call, each one sitting in a growing structure of categories that you built in week two and are no longer sure accurately represents what the data is actually saying.

By week six you are not a researcher. You are a very slow, very tired, very expensive text-sorting machine.

The thinking part, the part you did a PhD to do, the part where you look at what human beings said and work out what it means and why it matters, that part is scheduled for later. After the coding. After the months of organising. After the process that was supposed to support the analysis has consumed the time and energy that was supposed to go into it.

I finished my 80 interviews. It took eleven weeks. I submitted the chapter. My supervisor said the analysis was good but she wished I had gone deeper on three of the themes.

I did not have the energy to go deeper. I had spent it all on the coding.

The Message That Changed Everything

Four months after I submitted that chapter, a friend in my cohort sent me a message at 9am on a Monday morning.

It just said: "have you tried qinsights. genuinely asking"

I had not. I had heard the name somewhere, maybe in a methods forum, maybe in a conference side conversation. I had not tried it because I had assumed it was another software tool with a steep learning curve and a free trial that would take two weeks to set up properly and still not solve the actual problem.

I was wrong about all of that.

What Qinsights Actually Is

Qinsights is an AI qualitative research platform built by Dr. Susanne Friese. If you are in qualitative research you may know her name. She wrote the definitive methodological guide to ATLAS.ti. She has been training researchers in qualitative data analysis software since 1992. She is one of the most recognised methodologists in the field.

She built Qinsights because she spent thirty years watching brilliant researchers spend most of their time doing something a machine could do better, faster, and without getting tired at 2am.

The platform is built around what she calls Conversational Analysis with AI. You bring your transcripts. You talk to them. You ask questions. What are the dominant themes across these interviews? Where do participants contradict each other? What is the tension between what people say they believe and how they describe their actual behaviour? What am I missing?

The AI reads across your entire dataset simultaneously. It surfaces patterns. It brings evidence. It links every single insight back to the exact line in the transcript, the exact timestamp in the recording, the exact speaker in the focus group. Every claim is traceable. Nothing is invented. Nothing is inferred without a source.

And you stay in charge. That is the part I want to emphasise because it is the part I was most sceptical about before I tried it. Dr. Friese built this platform with thirty years of understanding of exactly where the line is between what AI can genuinely do and what only a trained researcher with disciplinary knowledge and interpretive judgment can do. Qinsights handles the volume and the structure. You handle the meaning.

What Happened When I Used It

My next project after that chapter was a set of 45 semi-structured interviews for a collaborative research project. I uploaded the transcripts to Qinsights on a Tuesday morning.

By Thursday afternoon I had a thematic framework, a set of sub-themes, supporting evidence for every claim, and a synthesis document that my co-investigator described as the most clearly structured analysis she had seen from any project we had worked on together.

I had not coded a single line manually.

I want to be precise about what I mean when I say the analysis was good. I do not mean it was fast and acceptable. I mean it was rigorous. Every theme was grounded in the data. Every claim had a traceable evidence chain. My co-investigator, who had not been involved in the data collection, said she felt she understood the dataset from reading the analysis in a way she rarely felt from reading traditionally coded outputs.

The themes I found were not the themes I would have found after eleven weeks of manual coding with my analytical capacity running on empty. They were the themes I found when I had the cognitive space to actually think about what I was seeing.

Why I Am Still Angry

I am angry because nobody told me earlier.

I am angry because I spent eleven weeks doing something that took two days. I am angry because the energy I spent sorting text in the early hours of October and November was energy I could have spent on the interpretive work I trained for years to do. I am angry because my chapter could have been deeper and I would have had the capacity to make it deeper if I had not been so depleted by the time I got there.

I am not angry at NVivo. I am not angry at ATLAS.ti or MAXQDA. They are tools that were built for a world where what Qinsights does was not possible. They were the best option available. They were not good enough.

I am angry at the gap between what qualitative research could have been for researchers like me and what it was, for years, because the field kept accepting a bottleneck as a permanent feature of the work instead of a solvable problem.

The bottleneck is solved. Has been for a while now. And too many researchers are still sitting at their desks at 2am in October, on interview number 34 of 80, not knowing that.

What I Want You to Do With This

If you are a qualitative researcher and you are in the coding phase right now, or dreading the coding phase of your next project, or avoiding starting a project because you know what the coding phase costs you, I want you to try Qinsights before you spend another week doing it manually.

Not because it is faster. Because when you are not exhausted from eleven weeks of sorting, you do better research. You go deeper on the themes that matter. You notice the tensions your manual process would have flattened. You bring your actual expertise to the analysis instead of bringing whatever is left of it after the process has taken the rest.

Dr. Susanne Friese built this platform because she believed qualitative researchers deserved better than spending most of their time on the part of the job that should require them the least.

She was right.

Professional Qualitative Analysis Research Services for Healthcare, Clinical & Academic Studies

Unlock Strategic Insights with Secondary Qualitative Data Research

"What just happened can be explained by any number of things." It is safe to say now that a few years into preaching the use of qualitative analysis methods of research first before starting an R&D project, I am comfortable with finding that QA is filtering to popular practice, but this is the fun part when doing R&D: getting the quick and initial valid results that may justify further support to carry out the remainder of analyses items.

But returning again to my favorite qualitative analysis practice, much remains to be done to convince the upper echelon of science that this is a valid discipline on its own even though its other synonym, research methods, constitutes volumes of textbooks and massive body of research. This may seem like a bizarre situation. In fact, it is.

You can barely find one academic journal that is explicitly titled 'qualitative analysis" or something of this variant. Academics still consider this as "not sufficient" research on its own to merit the peer review process of its publications, so one continues to use workarounds such as publishing instantly in preprints depositories that seem to exist to bridge such gaps. Once a few qualitative analysis preprints are published augmented by some "nice looking" quantitative or even numerical analysis results that include some artefacts, peers will start honoring such production with a review.

I have mentioned the simple reason behind this standoffish stance before, but I will repeat it again here: good qualitative analysis research supported by even high value computational analysis on any science and technology topic can be done today virtually for free assisted by AI tools. However, there is a catch: no science project, especially in the natural sciences, is complete without real world experimental analysis as evident in the major science disciplines such as biology, physics and chemistry.

Having said all of the above, the landscape for global R&D funding is getting more competitive particularly from public funding agencies. Aside from the fact that prioritizing any topic for funding support by such agencies can be independent of the funding budget if it addresses a strategic project for example, the realization by both public and private research supporting entities that the streamlined qualitative and computational analyses tools is the reason behind the more competing concepts research is allowing for the reducing of the amount of funding overall to support more and more promising and diversified projects.

Image is due to ChatGPT AI search engine.

Analysis? What analysis, fine go ahead and analyze this or that, for we have been caught up in this trap for the last few years almost forgetting when to carry out ordered tasks to accomplish missions.

Even by adopting data analytics as a profession, we failed to recognize the most important type of analysis which is qualitative analysis (QA). A few of us dedicate enough time to subsume narratives, or observe a phenomenon let alone formulating a grounded theory. What about ethnographic analysis? It is still a no no, but we jump immediately to tackle case studies with great appetite. All of the aforementioned means of analyses are categorized under qualitive analysis, or research methods, that is largely an abandoned trove of knowledge, but it will get worse before it gets better in this regard.

Further, it used to be that researchers were fearful of giving away their "lifelong" research work for pennies and not being able to eventually capitalize on them, until similar research is becoming a "day long" exercise with the advent of smart tools on the web and access to immense resources of good knowledge everywhere.

This should be a junction where all should compete to give their research career a much-needed facelift by adopting QA, lest they are left behind. Having said all of this, posing as who you are really is a no brainer. If you are a chemist, for example, and wanting to convince me that you will design and build for me a skyscraper, I will take you for a con artist.

Image is due to Google's AI Gemini Imagen 3 model.

Yes, many people find mathematics easier to understand qualitatively (conceptually) rather than just quantitatively (computationally). This approach focuses on grasping underlying principles, patterns, and properties, which often leads to a deeper, more intuitive understanding.

Why Qualitative Understanding is Preferred:

Intuition First: Many learners prefer to understand the "why" or the conceptual framework (e.g., visualizing a vector on a unit circle) before memorizing formulas, which makes the mathematics easier to recall and apply.

Contextualization: It helps in understanding the relationships between concepts, such as recognizing that multiplication is repeated addition or that calculus describes rates of change.

Overcoming Calculation Fatigue: Some students struggle with the heavy computation in subjects like statistics or calculus, but excel when focusing on the abstract, logical, or structural side of math.

Long-Term Retention: A focus on the "big picture" and the logical reasoning behind algorithms rather than just rote memorization often leads to better long-term mastery.

Key Findings:

Mathematical Thinking: Qualitative reasoning enhances mathematical ability by allowing individuals to detect patterns and make inferences based on properties rather than just numbers.

Not Just for Geniuses: A qualitative approach can make math more accessible to a wider range of people, as it focuses on structural understanding rather than just speed or rote computation.

Developmental Shift: As individuals get older, they may find it easier to grasp the purpose and connections in math, moving away from purely numerical manipulation to a more holistic, conceptual, or "qualitative" view.

Would you like to explore how to apply this qualitative approach to a specific topic like calculus or algebra?