#Research methods

[weekendviking]

Researching stuff.

Adding links here to methods for finding out things, because on the modern internet, actually finding out accurate information is now uniformly obfuscated by the relentless enshitification of search functions, proliferation of search engine optimised content mills, nation state level intentional misinformation and propaganda programs, and of course, these days, all the crap sources above are endlessly enriched by the output of generic Large Language Model plagiarism statistical bullshit engines, both image and text (And video and Bot and so forth).

Finding academic and peer reviewed Journal Articles - they haven't quite fucked google scholar yet, so it's better than the enshittified google or bing or <insert enshittified search bar embed here> whatever.

So I generally hit up Google Scholar for whatever subject, author name, paper title or similar that I've gleaned from whatever article or mention or wikpedia page sparked my interest. Often that gets me what I want, as there's often a link to a pdf of what I need within those search results. Yay.

If that doesn't work, then I start escalating, usually via the methodology here described at Logic of Science's blog:

They wrote it down so I don't have to. Excellent. Although some of the links in there have degraded. So the main ones I'll put here:

And then there's the pirate nuclear option, Sci Hub. Because it makes the big publishers and corporates really angry, don't use sci hub from a work or academic 'net access environment. Also it necessarily moves around a lot, so I generally search up where is sci hub now, to avoid going to a link that's expired or may now be a honeypot/trap:

Also, look out for content mill generated fake journals. I usually check here:

The other thing that's getting harder is finding out whether an image is misattributed or just plain fake. So right click and save the image, and then go to images dot google dot com, which is nowhere near as good as it used to be, but still not entirely enshittified, and click on the wee camera icon to the right and upload the image, and look through the results. What you find is _All_ the places that have posted that image, page after page of them. Scroll through - click on the ones that seem to be the oldest, check who's posting them. What you often find with viral outrage images is that they are _not_ what you think, especially if the image is a bit old, a bit bitrotted, or there's something else wrong - the clothing isn't right for the country/culture/time being outraged about, or something like that. Sometimes you find out that it's true, but most of the time you find out that it's wrong, that someone has just done a quick search for an image that roughly matches the outrage or the politics they want to push, added some outragey comments, and shared it, and enjoyed their flamey fire. I've been doing this for decades, ever since I started using a browser capable of image searching, mainly because I was outraged at people posting fake geology memes. But it works just as well on finding anything else.

And of course, if it's to a website, see if the wayback machine still has it cached:

[ancientroyalblood]

The Role of Research in Non-Fiction Writing

Research is the cornerstone of any well-written non-fiction work. Whether you’re writing a biography, a historical analysis, a scientific report, or even a personal essay, research grounds your writing in truth and credibility. Unlike fiction, which relies on the imagination, non-fiction demands accuracy, facts, and a deep understanding of the subject matter. In this post, we’ll explore the…

[ineedfairypee]

3.5k over the wordcount 🫠

[academicelephant]

Things are going well atm. Mostly, anyway

I bought new sneakers, the same as my old ones, but just in a different colour

I've nearly done with the practice paper for the advanced course in quantitative research methods, and I think I'll be able to hand it in this week + I'm confident in having done things correctly (probably not 100% but like, 90%?)

The essay for the Education and Globalisation course is also almost finished, and I should be able to hand it in this week as well, or early next week at the latest

I've applied for another internship post, and sent a bunch of open apllications, but haven't heard back from any of them yet (which is not surprising since it's been just a few days)

I've got the data for my Master's thesis collected and transferred into a format that can be run in the software. I will probably start the analysis next week

Now I just need to somehow get a grasp of both the evolutionary psychology and radiation course content so that I can go take the exams. The radiation course stresses me more because you can't choose when to take the exam, and the first exam is in two weeks! I mean, I've learned something, but there's still a lot of stuff I haven't yet

[gothicquery]

TLDR; Research Methods is officially over!!!

Just took my final for the course and got an 83%. I'm happy with my score; I won't know my overall grade for the course for a couple weeks if not more but I have maintained an 86% average for the majority of the course. I feel like this was one of the few courses I have taken in my three years of college experience where I actually will use what I learned outside of the course. This course taught me how to conduct literature reviews, how to find credible sources, and how to really dive deep into topics. I gained a lot from this course overall and I honestly would not mind having to take it again which I cannot say for a lot of my courses.

[psychwritings]

Stephen Olejnik ends his paper summarizing the methodological steps necessary in multivariate analysis of variance with this quote:

"...good research introduces at least as many questions as it answers."

For those of us currently doing psychological research, I thought this was an important point for us to keep in mind. We can only do so much in one paper, but what we leave for readers to uncover next is also of extreme importance, if not equally important to the results of the current study.

If any followers are looking for more statistical guidance, let me know. I am happy to provide resources for different methods commonly used across psychological research.

Citation:

Olejnik, S. (2010). Multivariate analysis of variance. The reviewer’s guide to quantitative methods in the social sciences, 315-328.

[outstanding-quotes]

We must become undisciplined.

Christina Sharpe, In the Wake: On Blackness and Being

[revisesociology]

Evaluate the strengths and limitations of using non-participant observation to investigate pupil behaviour in schools (20)

This is a 20 mark methods in context questions from the 2020 A-level Sociology Education with Theory and Methods exam paper (7192/1). Link to the Mark Scheme for the Paper here. Below I include the Item and Question, a full answer, and some hints and tips. If you like this sort of thing then you might also like this post: pages and posts: Methods in Context Questions: How to Answer…

[untypicable]

Surviving the Methodology Chapter: A Guide for the Emotionally Shattered

Ah, the Methodology chapter: that noble, soul-crushing rite of passage every thesis writer must endure. If you’re currently drafting yours, congratulations! You’re somewhere between hopeful optimism and complete emotional devastation — and that’s perfectly normal. At untypicable, we don’t believe in sugar-coating academic trauma. Writing your methodology isn’t a logical step-by-step process;…

[phoenixyfriend]

I would guess it's more than 25%, but it's also not unreasonable for it to be that, officially. I'm not a statistics specialist, but I do a lot of reading on survey methods and the like, so here are a few options:

I'm sure a lot of users, especially teenagers (which make up a greater portion of the site's userbase than most of us veterans would like to admit), would reply with "prefer not to answer" or "don't know" to a survey on the topic that was less anonymous than the above style of poll, and that wouldn't get lumped in with LGBTQ or cishet. Since it's not explicitly LGBTQ, it's not counted in the 25% stat that's cited, so it 'looks' like they're cishet when the info is reported as just "this is queer, and the rest is undefined not-queer" as opposed to "this is queer, this is cishet, and this is undefined."

There's also the sampling bias (which you mentioned in your tags), but I'd like to posit that there would also be a sampling bias in which people responded to an official poll from tumblr. It wouldn't just be a question of "who sees this," but also a matter of "who bothers to respond to official requests for data from the supposed authority, as opposed to fellow users." I'd expect that queer users are less likely to respond to tumblr's official attempts to gather such information, due to the cynicism and distrust of tumblr as a platform, born partly from Yahoo's past discrimination against queer users, the reputation for which stayed with tumblr even when it changed hands to more queer-friendly owners, automattic. In that case, the commentary in the notes and tags on this post that says "oh, it's obvious who they actually bother to listen to" is a bit on the nose by accident: they listen to people who bother to respond to those surveys and the like.

Another point would be that a general, informal poll like the above in English would be passed around to English speaking users, but probably wouldn't make as much distance among non-English speaking countries. If a tumblr user is located in a country with shaky data security and negative lgbtq protections, they may choose to respond with "decline to answer" or "no" even when the answer is yes, because that is how they feel safe answering at all. While the notes count on the above poll seems impressive, it's also unlikely to include anyone who is queer (and doesn't want to hit no) but cannot safely say something, especially in a poll that's more likely to circulate among queer circles in the first place. Part of why it's more likely to circulate among queer circles is that queer folk, in a bid for recognition among their peers, will reblog it, so the data skew is self-perpetuating.

The phrases 'lgbtq' and 'queer' are also more likely to be blacklisted by cishet users, especially terfs, so they also wouldn't see the poll to reply in the first place, but would respond to an official demographic survey by tumblr's official page in order to sway the site's development in their own favor.

The last point I have is that the 25% is still much higher than non-tumblr polls. The US national self-identification of LGBT folk is at 7.1% as of 2022, and tumblr is over three times more than that. 2020 was only 4.5%. A global survey claims 9%.

So tumblr is still acknowledging that the website's queer usership is significantly higher than the US National or Global averages. Several times higher, in fact!

The question is: what were their research methods, and how do we know if they were better or worse than the ones that we, as users, have available to us?

Thanks for all of the recent feedback around Community Labels being incorrectly applied to content. In particular, we appreciate the input we’ve received from the LGBTQIA+ community and understand the frustrations from folks who felt that their content was unfairly labeled. When we realized this was happening, we immediately investigated and are taking steps to prevent this from happening again.

The LGBTQIA+ community makes up about a quarter of the Tumblr community. It is important for us to support all Tumblr users, especially those whose safe spaces are under threat in certain parts of the world.

As you know, alongside of the rollout of Community Labels we also expanded the types of content allowed on Tumblr as a way to welcome more creativity, art, and self-expression. Our goals remain the same today. Human error happens and we apologize to anyone who has been impacted by these mistakes.

We are working to better understand what happened and will follow up with more information soon.

[arinavidman]

Review on podcast #2, Bridging the Gaps

“Learning How to Learn”: Techniques to Help You Learn with Dr Barbra Oakley

Key questions of discussion

The way how learning occurs in the brain. There is interaction of neurons in the base of learning, when you learn something new, you create new connections between neurons. When you practice, these connections become stronger.

There are 2 fundamental parts of memory – working memory (what are you conscious about at the moment), long-term memory (when you remember something, you are connecting the links that you have previously built). Working memory is really small, it can’t hold a lot.

The whole challenge of learning is to transfer your knowledge from working memory to long-term memory.

Tips for learning:

practice the material;

use metaphors for learning (use something you know as a bridge to learn something new, even if it doesn’t look close to each other as metaphors are sets of connections in brain that have already been created);

use effectively 2 modes of thinking (the first mode is when a person is focused on something, the second is a “default mode network”, so connections happen when person is not focused - that is time when ideas come to you. You can’t be in 2 modes one time), when people are stuck on something and can’t find decision the best idea is to take a step away and take a break for it;

use pomodoro technique, specify time for learning and then take a break, so the brain can work unconsciously;

try to understand the basis, simple and key idea;

discuss with others what you have learnt (by talking to others we are helping to create neural chunks and we speak about what is really important there), speaking with others is a greatest enhancement for learning;

do challenging stuff, push yourself to do hard things;

interleave, try not to do things in the same way while learning;

It is also possible to learn by chunks (the word has different meanings), splitting information in sets. There would be sets of neural links that form one complex set.

The brain can be changed by what we learn. Students learn differently, those who do it slower and longer, probably learn something deeply and might be more persistent and flexible. Flexibility that comes from not finding things easy to learn is valuable for real creativity and learning.

Reasons of popularity of online course Learning How To Learn: foundations of neuroscience and a lot of metaphors that help people to understand things properly.

Future of online courses. Online learning will definitely grow and increase in importance as you can do it wherever you are and it is cheaper, but it can never replace the on-campus education. Only self-motivated learner can go through online-program.

There is addictive nature of online and social media which are designed to attract one’s attention, but people should be able to distract themselves from the impact of innovation. Nothing new is “ground-shaking”, social innovations may have minor effect, but we can change our brain and decide what is important.

Who is the researcher?

Dr Barbara Oakley:

a professor of Engineering at Oakland University in Rochester, Michigan (Probability and statistics, Neuroscience, Bioengineering, Electrical circuits, Thermodynamics and electromagnetics);

educator, writer, engineer;

studied in the University of Washington, B.A. in Slavic Languages and Literature, a B.S. in Electrical Engineering;

served in the US Army;

worked as a Russian translator in the Bering Sea;

worked as the radio operator at the South Pole Station in Antarctic;

studied in the Oakland University, M.S. degree in Electrical and Computer Engineering, and a Ph.D. in Systems Engineering;

an inaugural “Innovation Instructor” at Coursera, where she co-taught one of the world’s most popular massive open online course “Learning How to Learn”.

She has won numerous teaching awards, including Oakland University's top award for tenured faculty. She is a winner of the McGraw Prize--the colloquial "Nobel Prize for Education".

Her work focuses on the complex relationship between neuroscience and social behavior. Some of her works were published in the Proceedings of the National Academy of Sciences, the Wall Street Journal, and The New York Times.

She has written many books including “Learning How to Learn: How to Succeed in School Without Spending All Your Time Studying; A Guide for Kids and Teens”, “Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential”. Her book A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra), (Penguin, 2014) is a New York Times best-selling science book.

Links used:

https://www.linkedin.com/in/barbaraoakley/

https://www.oakland.edu/secs/directory/oakley/

https://barbaraoakley.com/about-me/

https://www.coursera.org/instructor/barboakley

What do I think about this topic?

My interest in the topic of learning began when I was pursuing my B.A. in Marketing. I had to do my final graduation qualification work which took a significant amount of time not only writing, but also reflecting and analyzing my past four years. The key insight I gained was that the process of getting higher education is primarily about learning how to learn, rather than acquiring knowledge. University lecturers can provide students with various materials in any format how we approach and process this information ultimately depends on our choice and individual learning style.

When I was doing assignments or projects, I often tried to speed up the process by using “hacks” of learning. For instance, I would remove any distractions, repeat everything I read out loud and schedule specific times for studying and relaxing. After several exam periods I realized that these hacks were very helpful and I began to experiment with different ways of learning each time I prepared for an exam or worked on a project. Over the course of my four years as a bachelor's student, I “invented” my own formula of studying that is still relevant for me as a student. Now when I see any information about learning, I read it and compare with my own experience.

Regarding the content of podcast, I was not surprised by Dr Oakley words as the information was not new for me. However, I really enjoyed listening to her as all mentioned ideas appeared in my mind unconsciously during studying, and now she helped me clarify and better understand how my brain works during the learning process.

I support all the tips for learning that she mentions, such as the Pomodoro technique, practicing what you’ve learned, repeating information to your friends and etc. I would also like to comment on the question about the future of online learning. I totally agree with the fact that on-campus education will never be replaced by online courses. From my perspective, learning on-campus may even enhance the chances of becoming a specialist in one’s field of studying. The key is that people need to socialize and interact face-to-face, as this is what helps them to grow mentally and become more intelligent.

The full discussion can be found on this link:

[digitalxonixblogs]

Top 5 Ways AI is Revolutionizing Historical Research Methods

The study of the past has always relied on careful research, critical analysis in addition to the mixing of many sources. But, with the emergence technology such as Artificial Intelligence (AI), the methods of conducting historical research are experiencing a dramatic change. Through the integration of AI techniques, researchers are able to look over huge datasets, find the hidden patterns and increase their knowledge of the historical context. Photon Insights is at the forefront of this new technology by providing AI-powered tools to help historians increase their research capabilities. This article outlines 5 ways AI is changing the way we conduct the methods of historical research.

1. Enhanced Data Analysis

One of the biggest advantages of AI to research in the past is its capacity in analyzing and processing massive quantities of data quickly and effectively. Traditional research in the field of history typically requires laborious sorting through documents, archives, and other sources. With AI researchers are able to automate these steps which allows for more complete studies.

Keyword Focus: Big Data, Historical Documents

AI algorithms are able to scan thousands of old documents in just a fraction of the time it takes human researchers. Natural Language Processing (NLP) techniques allow AI to comprehend and interpret the context of texts, which allows researchers to spot patterns, emotions and themes that might not immediately be apparent. This improved analysis of data results in deeper understanding and more granular understanding of the historical events.

Photon Insights offers advanced data analytics tools that simplify documents analysis. This helps historians discover significant information faster and with greater accuracy.

2. Improved Access to Archives

The digitization of historic documents has created the vast amount of information accessible than ever. However, the sheer amount in digital archive can seem overwhelming. AI will allow easier the accessibility of these archives through using sophisticated search and retrieval algorithms.

Keyword Focus: Digital Archives, AI Search Engines

AI-driven search engines can analyse the contents of documents and produce pertinent results that are based upon context, rather than just matching keywords. This means that users are able to find relevant information faster and efficiently, even within huge digital repository.

Utilizing AI, Photon Insights helps historians navigate through the maze of digital archives. It ensures that important documents are searchable and accessible, thus increasing the efficiency of research.

3. Automating Transcription and Translation

The process of transcribing and translating documents from the past is a time-consuming and laborious process particularly in the case of old documents or manuscripts in languages other than English. AI technology, like optical character recognition (OCR) and machine translation, are able to significantly reduce the time and effort required for these tasks.

Keyword Focus: OCR, Machine Translation

AI-powered OCR tools are able to convert images of printed or handwritten text into machine-readable formats making it possible for researchers to digitize archives documents quickly. In the same way, machine translation software allow for document translation different languages, removing barriers that traditionally prevented historical research.

Photon Insights employs cutting-edge OCR and translation technology that allows historians to focus on their interpretation instead of the complexities that translate and transcription.

4. Predictive Modeling and Trend Analysis

AI isn’t only concerned with processing historical data, it also lets researchers apply predictive methods for trend analysis and modeling. Through the analysis of historical patterns, AI can help historians make educated predictions about the future developments or trends based on the past data.

Keyword Focus: Predictive Analytics, Historical Trends

By using machine learning algorithms researchers are able to create models that recreate the past or predict possible outcomes based upon existing information. This method allows historians to investigate “what-if” scenarios and gain more understanding of elements that have influenced the historical development.

Photon Insights provides tools for predictive analytics that allow historians to apply advanced models to their studies and make better informed judgments regarding the historical context.

5. Enhancing Collaboration and Interdisciplinary Research

The complexity of research in historical studies typically benefits from interdisciplinarity methods. AI facilitates collaboration among researchers from a variety of disciplines, such as the fields of linguistics, data science, as well as history itself. This cooperation enhances the process of research by incorporating different perspectives and methods.

Keyword Focus: Interdisciplinary Collaboration, Research Networks

AI platforms allow collaboration and communication between researchers, allowing researchers to share their findings methods, resources, and methodologies in a seamless manner. These networks facilitate the exchange of information which can enrich research and leading to more complete studies.

Photon Insights is designed to encourage collaboration across disciplines, and provide historians with the opportunity to meet experts from related fields, thereby fostering an interdisciplinary approach to research in the field of historical.

Conclusion

AI is changing the way we conduct techniques for historical research, providing tools and techniques that improve the analysis of data, facilitate accessibility to archive collections, simplify routine tasks, allow predictive modeling and encourage collaboration between researchers. As historians adopt these innovations and technologies, the possibility of deeper insights and deeper understanding of our past grows dramatically.

Photon Insights is leading the lead in integrating AI in historical research. It offers new solutions that enable historians to increase the capabilities of their studies. Through the use of AI researchers are able to not only improve their research processes but also discover new levels of knowledge previously unattainable.

The field of research in historical studies is evolving and evolve, the introduction of AI will certainly alter how historians work and make it more productive as well as collaborative and informative. The future of research in the field of history is now upon us and AI is on the cutting edge and ready to unravel all the mystery of our history.

[ineedfairypee]

Anything but collinearity

[academicelephant]

I think I've made a new friend! We've been sitting at the same table in the practice sessions of the advanced course in quantitative research methods and lately we've been starting to chat outside the practice sessions. Also, she's now moved to sit next to me in the lectures. We did sit in the same table in the basic course too, but back then we didn't really talk as she had her friend group there and I wasn't part of it, I just sat at the table with them. Now, though, we're both alone which I think is why this happened

[uistudioz]

UX Research Methods

Discovering UX Research Methods for Better Design 😃

Hope you like this❤️

Let us know if we missed to add any!👇