#data analysis in qualitative studies

[flowersio]

Data Analysis of Qualitative Research: A Comprehensive Overview

Data analysis in qualitative research is a critical process that turns raw data into meaningful insights. Unlike quantitative research, which focuses on numbers and statistical relationships, qualitative research centers around understanding phenomena from a subjective and in-depth perspective. This method is especially valuable for exploring complex human experiences, behaviors, and perceptions. Whether you’re a beginner or an experienced researcher, understanding how to analyze qualitative data effectively can significantly impact the quality and depth of your research outcomes.

What is Qualitative Research?

Qualitative research is a method that seeks to understand the meaning and context of human experiences. It often focuses on non-numerical data such as interviews, observations, open-ended surveys, and text-based materials. The aim is to uncover patterns, themes, and deeper insights into the phenomena being studied, whether it’s social behaviors, cultural trends, or personal experiences. The richness of qualitative data offers flexibility and depth, but it also presents a unique set of challenges when it comes to data analysis.

The Importance of Data Analysis in Qualitative Research

Data analysis is where the magic happens in qualitative research. Without it, the data remains just a collection of unorganized information. The analysis process enables researchers to organize, interpret, and synthesize the data to draw meaningful conclusions. Through careful analysis, researchers can identify patterns, connections, and nuances that might not be immediately apparent.

Moreover, qualitative data analysis allows researchers to address the research questions from various perspectives. Whether through thematic analysis, grounded theory, or narrative analysis, each approach offers a way to dive deeper into the subject matter, ensuring the research findings are robust, reliable, and meaningful.

Steps in Qualitative Data Analysis

Analyzing qualitative data typically follows a series of steps. While the specific process may vary based on the research design and methodology, the core stages remain consistent across most qualitative research projects.

1. Data Preparation

Before analysis can even begin, the first step is to prepare the data. In qualitative research, data often comes in the form of transcripts from interviews, focus groups, field notes, audio or video recordings, or survey responses. This data must be transcribed, if necessary, and organized in a way that facilitates easier analysis. Depending on the method used, this might involve categorizing data into manageable units or simply ensuring everything is accessible and ready for review.

2. Data Familiarization

Once the data is organized, researchers need to become familiar with it. This step involves immersing oneself in the data by reading through transcripts, listening to interviews, or watching videos repeatedly. This helps researchers begin to get a sense of the key ideas, recurring themes, and notable observations.

3. Coding the Data

Coding is one of the most important steps in qualitative data analysis. It involves labeling sections of data with short phrases or “codes” that represent key concepts or ideas. These codes can be predefined based on the research questions or can emerge organically during the analysis process. Researchers might use open coding (identifying themes as they arise) or a more structured approach, depending on the research design.

Coding makes the data manageable and helps highlight specific patterns or recurring themes across the dataset. For example, in a study exploring patient experiences in healthcare, common codes might include “communication,” “empathy,” or “wait times.” Codes are then grouped into broader categories or themes that align with the research objectives.

4. Identifying Themes and Patterns

After coding, the next step is to identify patterns and themes in the data. This process is often referred to as thematic analysis, where researchers group codes into overarching themes. The goal is to see how individual pieces of data connect to form a broader narrative or insight into the research question.

Researchers might identify both explicit themes (e.g., specific topics discussed by participants) and implicit themes (e.g., underlying values or beliefs that emerge from the data). This stage involves a lot of interpretation and reflection on the meaning behind the data. The analysis might involve comparing data across different groups, time periods, or settings to find any consistencies or discrepancies.

5. Interpreting the Findings

Once the themes have been identified, the next task is interpretation. This involves making sense of the patterns, relationships, and insights uncovered through coding and thematic analysis. Researchers must ask: What do these findings mean in the context of the research question? How do the identified themes relate to the literature or theory? What new insights have emerged, and what are their implications?

Interpretation requires a deep understanding of the research context, as well as the researcher’s ability to think critically and reflexively about the data. It’s important to recognize the researcher’s role in shaping the interpretation and to consider how personal biases may influence the analysis.

6. Reporting the Results

Finally, the results of the qualitative analysis are compiled and reported. This might include presenting the themes and patterns in the form of a narrative, often supported by direct quotes from participants to illustrate the findings. In qualitative research, it’s crucial to present findings in a way that conveys the richness and complexity of the data while remaining clear and concise.

The final report might also discuss the limitations of the study, areas for future research, and the broader implications of the findings. The goal is to convey a thorough understanding of the research topic, backed by solid data and analysis.

Challenges in Qualitative Data Analysis

Qualitative data analysis can be a challenging and time-consuming process, but it is also incredibly rewarding. One of the main challenges is dealing with the sheer volume of data. Since qualitative data is often unstructured, researchers must work with large amounts of text, audio, or visual materials, which can be overwhelming.

Another challenge is ensuring that the analysis is consistent and unbiased. Given the subjective nature of qualitative data, it’s crucial for researchers to approach their analysis with a clear, systematic methodology to minimize personal bias and ensure the results are credible and reliable.

Conclusion

In conclusion, data analysis in qualitative research is a nuanced and multi-step process that requires careful attention to detail, patience, and a deep understanding of the subject matter. By coding the data, identifying themes, and interpreting the findings, researchers can transform raw data into valuable insights that contribute to our understanding of human experiences and behaviors. Despite its challenges, qualitative data analysis remains an essential tool for researchers who want to explore the complexities of the world around us.

[probablyasocialecologist]

While the Cass Review has been presented by the U.K. media, politicians and some prominent doctors as a triumph of objective inquiry, its most controversial recommendations are based on prejudice rather than evidence. Instead of helping young people, the review has caused enormous harm to children and their families, to democratic discourse and to wider principles of scientific endeavour. There is an urgent need to critically examine the actual context and findings of the report. Since its 2020 inception, the Cass Review’s anti-trans credentials have been clear. It explicitly excluded trans people from key roles in research, analysis and oversight of the project, while sidelining most practitioners with experience in trans health care. The project centered and sympathized with anti-trans voices, including professionals who deny the very existence of trans children. Former U.K. minister for women and equalities Kemi Badenoch, who has a history of hostility toward trans people even though her role was to promote equality within the government, boasted that the Cass Review was only possible because of her active involvement. The methodology underpinning the Cass Review has been extensively criticized by medical experts and academics from a range of disciplines. Criticism has focused especially on the effect of bias on the Cass approach, double standards in the interpretation of data, substandard scientific rigor, methodological flaws and a failure to properly substantiate claims. For example, although the existing literature reports a wide range of important benefits of social transition and no credible evidence of harm, the Cass Review cautions against it. The review also dismisses substantial documented benefits of adolescent medical transition as underevidenced while highlighting risks based on evidence of significantly worse quality. A warning about impaired brain maturation, for instance, cites a single, very short speculative paper that in turn rests on one experimental study with female mice. Meanwhile extensive qualitative data and clinical consensus are almost entirely ignored. These issues help explain why the Cass recommendations differ from previous academic reviews and expert guidance from major medical organisations such as the World Professional Association for Transgender Health (WPATH) and the American Academy of Pediatrics. WPATH’s experts themselves highlight the Cass report’s “selective and inconsistent use of evidence,” with recommendations that “often do not follow from the data presented in the systematic reviews.” Leading specialists in transgender medical care from the U.S. and Australia emphasize that “the Review obscures key findings, misrepresents its own data, and is rife with misapplications of the scientific method.” For instance, the Cass report warns that an “exponential change in referrals” to England’s child and adolescent gender clinic during the 2010s is “very much faster than would be expected.” But this increase has not been exponential, and the maximum 5,000 referrals it notes in 2021 represents a very small proportion of the 44,000 trans adolescents in the U.K. estimated from 2021 census data.

7 August 2024

[rlyehtaxidermist]

social sciences [respectable]: we understand the empirical limitations inherent in our fields and have constructed frameworks that allow us to still perform meaningful analysis on both qualitative and quantitative data in light of the complexities of what we study

economics [wretched]: look at our MATH and NUMBERS. we put an INTEGRAL on here. you won't BELIEVE how many ways we use the word EQUILIBRIUM. by ignoring all the complexities of this world spanning problem that do not fit our theory we have reduced it to a calculation you can do on the back of an envelope. did you know if you suck his dick from the right angle you can read "all the most significant theories have significant departures from reality" embroidered in milton friedman's pubes. there should be a nobel prize for this.

[am-i-the-asshole-official]

AITA for blackmailing someone and then snitching to the feds anyway? Okay, so I work for a contract medical research lab generating quantitative image data, working closely with veterinary pathologists who provide the qualitative data. Together, we put together a report like "okay, here's what the medicine/medical device did and here's why we think it happened", and that report usually gets sent to the FDA if it looks promising enough that the sponsor wants to push for clinical trials and eventual market release. So we get a study in and we've got (fake numbers here) 400 sections, but the quote says they only want 300 measurements done. I'm confused and go "wait, which 300 out of the 400? which 100 should I ignore?" and go to the pathologist. She also thinks it's weird and reaches out to the client, hoping it's a typo and we're about to get paid for the bonus 100. Nope! He pressures us for it to be a phone call (no paper trail) and then not-so-subtly hints that he wants the... uglier-looking sections dropped. In other words, he wants to cherry-pick data that makes him look good. This is not only dangerous but The Most Illegal Shit. People's lives hang in the balance and they have to be able to trust the research that tells them medicines and medical devices are safe. We take that responsibility seriously. So the pathologist gathers data and emails him like "I'm taking a REPRESENTATIVE 300 samples for analysis, my report will include scoring of the ones that make you look bad, and if I find out you doctored the reports behind my back, I'm sending everything I have directly to the FDA." (this is not how data is normally submitted in the industry. normally the report is commissioned, and then all dealings with the FDA are done by the client) He grouses, but agrees. And then says "if the FDA reaches out to you, don't respond." .....What? But that's already industry standard? Why would he say that? Why would he expect the FDA to reach out to us? Anyway the pathologist and I discuss it, and both assume he's definitely about to doctor these reports behind our back once it's submitted. So at my suggestion... the pathologist sends the communications to the FDA anyway. Here's the thing: we don't actually know that this guy meant to do some ethics violations. We just assumed he was suspicious without real proof. Even unproven accusations in this industry can get you blacklisted for life, if not facing criminal charges. Did we risk destroying some random guy's life over bad vibes and nothing else?

What are these acronyms?

[studentessa-socit]

second year, second semester, sociology classes:

political science

This is a basic course on political science which will touch subjects as the definition and historical evolution of democracy, voting systems, etc.

cultural sociology

A very intersting class on the production of culture and the role of cultural symbols in the development of society. It's the other face of the medal of economic sociology, because while one focuses on how the material condition of people build the cultural products, this one focuses on the impact of the cultural product themselves.

gender studies

This is a class about the history of the concept of gender and on the nature of gender roles, I know for sure we will take a deep dive into the impact of gender roles in sport, and on the history of gender studies in our local (north Italian) context because those are the expertese of my teachers.

quantitative laboratory

A laboratory on the quantitative methods of social research during wich we will learn data analysis on R and at the end of which I will submit my first actual social research (which will be a secondary data analysis, probably on the impact of scholarships on the life course of poor people but it has to be better defined with the classmates I will make the project with).

qualitative laboratory

A laboratory on the qualitative methods of social research during wich we will learn to structure and codify qualitative projects such as in depth interviews, focus groups, and participant observation.

[darkmaga-returns]

by Nicolas Hulscher, MPH

A new study by Chaufan et al titled, “It isn’t about health, and it sure doesn’t care”: a qualitative exploration of healthcare workers’ lived experience of the policy of vaccination mandates in Ontario, Canada, was just published in the Journal of Public Health and Emergency:

Background: When coronavirus disease 2019 (COVID-19) vaccines became available, healthcare workers (HCWs) were prioritized for vaccination. Despite controversy, vaccine mandates were implemented in most healthcare settings across Canada, with many still in effect. Many studies have examined the perceived problem of vaccine hesitancy within the healthcare labour force. However, few have investigated the lived experience of mandated vaccination from the perspective of HCWs themselves. In this study, we examine this experience in a purposive sample of HCWs in the province of Ontario, including their decision-making processes, the mandates’ impact on their lives and livelihoods, and their views on the effects of mandates on patient care. The study is part of a mixed methods study reassessing the COVID-19 policy response in Canada. Methods: We performed a reflexive thematic analysis of qualitative data of responses to one open ended question and open-ended entries to closed questions, offered by 245 HCWs in a published survey of a purposive sample of 468 HCWs in Ontario, of diverse vaccination status, professions, ages, socioeconomic status, races/ethnicities, and genders. Respondents were recruited through snowball sampling via social media and professional networks of the research team.

[evidence-based-activism]

Men Underestimate and Women Overestimate Their Own Sexual Violence

Time for an excellent new (2024) article "Gender Differences in Sexual Violence Perpetration Behaviors and Validity of Perpetration Reports: A Mixed-Method Study".

What this study did:

This study asked 23 men and 31 women to "think out loud while privately completing [the Sexual Experiences Survey-Short Form Perpetration (SES-SFP) survey] and to describe (typed response) behaviors that they reported having engaged in on the SES". The researchers asked anyone who "reported no such behavior ... to describe any similar behaviors they may have engaged in". They then analyzed differences in the quantitative responses (numerical values on the SES) and the qualitative responses (written descriptions and think-aloud audio).

What this study found (broad strokes):

Men’s sexual violence (SV) perpetration was more frequent and severe than women’s

Men’s verbal coercion was often harsher in tone and men more often than women used physical force (including in events only reported as verbal coercion on the SES)

Women often reported that their response to a refusal was not intended to pressure their partner or obtain the sexual activity*

Two women also mistakenly reported experiences of their own victimization or compliance (giving in to unwanted sex) on SES perpetration items, which inflated women’s SV perpetration rate

Quantitative measurement can miss important qualitative differences in women and men’s behaviors and may underestimate men’s and overestimate women’s SV perpetration

*This phrasing is poor (in my opinion) the authors are emphasizing genuine differences in men and women's reported behavior for ambiguous situations (not just their internal intent). Specifically, women would endorse responses for behaviors that (most) people would not actually consider a form of sexual violence. For example, women often indicated that the behaviors they were reporting were all pre-refusal (i.e., the women stopped and respected when their partner said no/told them to stop). Other "seducing" behaviors (e.g., kissing/touching) were also reported by women because their partner ultimately refused. Men did not report these types of behaviors, which the authors suggest is possibly because women may be more likely to remember experiences where they wanted to engage in sex with someone who did not because this violates social norms. It's also possible that men are more likely to consider these behaviors acceptable provided they stop when refused. (Ironically this suggests that the anti-feminist hyperbole that people will start recording "normal sexual interactions" as violence ... has only affected women.)

Lots more details below the cut (I use a mix of - unmarked - quotes and paraphrasing):

Quantitative data

The overall prevalence of sexual perpetration of significantly inflated due to intentional over-sampling of likely perpetrators (particularly female perpetrators). This is reasonable because the authors are interested in examining differences among self-reported perpetrators, not in establishing incidence/prevalence rates.

Even without taking the qualitative aspects into consideration (i.e., looking only at the quantitative data), men reporting SV perpetration reported more frequent offenses than women (re-offended more often). Men were also more likely to report more severe acts of violence (per the original tactic-act, the tactic specific, and sexual act specific continua).

Differences in severity identified via qualitative analysis

Men’s verbal coercion was more often stronger; more deceptive, persistent, or intimidating; or otherwise harsher in tone (e.g., "She kept refusing to do anything with me. I remember saying to her “just cause you’re on your period doesn’t mean I can’t get head.” I then remember repeating my intentions with her and almost gaslighting her and making her feel that she must not love me."). Proportionally more men described continually asking or persisting after repeated refusals, getting angry, telling lies, making false promises, and trying to make their partner feel guilty.

Women’s verbal coercion was predominantly expressing disappointment or pouting after a single refusal (e.g., “I got upset and said whatever and rolled over the opposite way”)

Also a difference in intent that could only be identified in the qualitative data. 35% of women who perpetrated explicitly said they had not intended to pressure their partner, change their partner’s mind, or obtain the sexual activity after their partner refused (e.g., "I respected him not trying to do anything further, though, and did not attempt anything further."). No men explicitly said they had not intended to pressure their partner or obtain the sexual activity and [men] more often than women explicitly said that they had intended to (e.g., "I think it was one time where I just kept pressuring . . . Didn’t happen, but the pressure was there, that’s for sure. I definitely asked more than a couple times.")

A few of women’s SV perpetration behaviors appeared more like attempts to advocate for equity in their own sexual pleasure or to stick up for themselves in response to a partner’s coercion (e.g., "I really love receiving oral sex. But sometimes my partner ignores that and directly goes to the penetration. So, I stop him and make him do it because I also feel like being properly aroused to get a better sexual experience.")

False negatives

Some participants that did not mark any of the perpetration items still described similar experiences. Most were not coercive (e.g., asking and “respecting” a refusal, clarifying an unclear refusal) but a couple were clear false negatives. There appears to be an issue with some behaviors not clearly fitting into any of the described categories (e.g., Even the physical force SES items refer only to more extreme force (holding down, pinning arms, having a weapon).)

There were many more cases where a less severe offense was marked (i.e., coded as a true positive for perpetration but for incorrect offense in severity analysis). Specifically, men reported only verbal coercion but then described physical behaviors, so the tactic report was incorrect or incomplete (e.g., "We were experimenting with different things and I did not necessarily ask for their consent before putting my finger in their butt." was coded by one man as verbal coercion).

False negative may have occurred, in part, because behaviors that were themselves no different than those performed in consensual sex were not adequately captured. This is a problem given that previous qualitative research has also found that initiating or going ahead with penetration without asking or following a refusal is a common SV perpetration behavior used by men (i.e., this type of behavior may be recorded as either a false negative or a less severe offense in quantitative scales).

When women reported verbal coercion only, but then described initiating sexual acts without asking, they almost always initiated non-penetrative sexual acts in contrast to men who more often described penetrative sexual acts without asking.

The SES may underestimate use of physical force and, especially, men’s rape and attempted rape.

False positives

Some participants reported perpetration on the SES that their description showed was not forceful, coercive, or engaged in without consent or following a refusal. Men explained that they did not engage in the behavior, misread or misinterpreted the SES question, or clicked the wrong response. Some women reported these same problems, but two "were reports of victimization or giving in to unwanted sex" (i.e., mistakenly reported victimization as perpetration).

Notably, three out of the four men with false positives reported other instances of SV perpetration on the SES whereas two of the four women with false positives did not report other perpetration and, therefore, inflated women’s perpetration rate.

Taken together, our analysis of false negatives and false positives suggests that the SES likely underestimates men’s SV perpetration and overestimates women’s perpetration.

This doesn't even account for instances reporting no intent to perpetrate (as described above). But the fact that many women reported no intent may further support the conclusion that women overreport or are more likely to remember and report because their coercion violates social expectations

Verbalized thought processes

In general, most participants appeared to understand and interpret the SES as intended

But there was evidence that the distinction between attempted and completed acts on the SES may be unclear for some respondents (e.g, one woman said "I also don’t understand what they mean by “tried.” Like does this mean that . . . You simply spoke to them, and they said no? Does this mean that you were engaged in an act and they pushed you off? Or does this mean that something disrupted you? So, this question doesn’t seem very clear to me.")

Second, participants used different items on the SES to report having used a specific category of tactic that is not mentioned in the measure. For example, some participants described kissing and sexually touching their partner without asking to try to arouse them and reported this as verbal tactics to obtain non-penetrative sexual contact. This may have underestimated attempted and completed sexual coercion (because the intent was to engage in penetrative sex). It may also have overestimated non-consensual non-penetrative sexual contact category (the most frequent category for female offenders) since research also finds that partners often use nonverbal cues including kissing and touching to communicate about sexual interest.

There was also confusion about the meaning of “getting angry” or "showing displeasure". Some participants (particularly women) indicated these could refer to internal feeling as opposed to external expression or be a “normal human reaction to . . . feeling rejection” that does not necessarily include a purposeful attempt to manipulate.

Other problems: (1) confusion on if intoxication only applied to alcohol, (2) too many tactics listed in a single question resulting in confusion, (3) participant frequency estimates were rough estimates likely contributing to a significant underestimation problem, (4) participants wouldn't endorse items that specified "without consent" even if they later described coercive behaviors suggesting different phrasing may be needed, (5) participants reported shock at the severity of the tactics asked about, which may indicate SV is not normalized among non-perpetrators or may indicate that less severe tactics are not being captured

Concerning (4) above: Other research indicates that while conceptually narrower, asking about behaviors done after someone resisted or indicated “no” (i.e., post-refusal persistence) results in higher rates of self-reported SV perpetration than asking about behaviors done without consent or when the other person did not want to.

Citation: Jeffrey, Nicole K., and Charlene Y. Senn. “Gender Differences in Sexual Violence Perpetration Behaviors and Validity of Perpetration Reports: A Mixed-Method Study.” The Journal of Sex Research, Feb. 2024, pp. 1–16. DOI.org (Crossref), https://doi.org/10.1080/00224499.2024.2322591.

[vtellswhat]

Understanding the Types of Literature Review: A Comprehensive Guide

Understanding types of literature review: A comprehensive guide.

Literature reviews are critical components of academic research that give an overview of the available knowledge relating to a particular topic. This helps to identify gaps, forms a basis for further research, and grounds the study on established theory and evidence. Literature reviews, however, do not fit in one single type. Rather they are of different types. Each depends on the purpose and approach of the research. Let's have a detailed view of the types of literature reviews. ### 1. Narrative Review

A narrative review, sometimes known as the traditional one, gives a general overview of research regarding a particular topic. It is descriptive and focuses on summarizing and synthesizing findings without much depth analysis.

Key Features:

Focuses on storytelling and descriptive summary. - Majorly used in fields such as humanities and social sciences.

Lacks a systematic methodology for selecting studies, which can lead to bias.

Purpose:

Narrative reviews are ideal for understanding a topic broadly and identifying general trends or patterns in the literature.

2. Systematic Review

A systematic review is a rigorous and structured approach to synthesizing research. It follows a predefined protocol to ensure transparency, reproducibility, and comprehensiveness.

Key Features:

Has explicit inclusion and exclusion criteria.

Is planned in databases systematically to find studies.

Keeps bias at a minimum by having a clear methodology.

Purpose:

Systematic reviews are applied to answer particular research questions, especially in fields like healthcare, psychology, and social sciences. Systematic reviews come with immense value because of their reliability and objectivity.

3. Meta-Analysis

A meta-analysis is a type of systematic review that pools data from many studies together using statistical methods to make their own synthesis, which tries to produce a quantitative overview of research findings.

Key Features:

Assumes all studies share similarities in methodology to compare them. - Offers results with statistical significance by combining data. - Is considered a demanding statistical process.

Meta-analyses are commonly used in medicine and psychology to determine the effectiveness of interventions or treatments. ***

4. Scoping Review

Scoping reviews are exploratory and aim to map the breadth and scope of research on a topic. Less focused on answering specific questions and more on identifying research gaps, they are considered exploratory. #### Key Features:

Wide inclusion criteria, casting a net to encompass all aspects of a topic. Does not critically evaluate the quality of included studies in depth. Often a precursor to a systematic review. #### Purpose:

Scoping reviews are suitable for nascent research areas or subjects where there are a few published studies to date.

5. Integrative Review

An integrative review combines qualitative and quantitative research to achieve a holistic understanding of the topic under review. * Key Features:

It integrates data based on diverse methodologies.

This integration encourages innovation.

It is useful in the development of theories or models * Purpose:

It is common to find such reviews in nursing, education, and healthcare research where mixed methods are often employed.

6. Critical Review

A critical review evaluates and critiques existing literature, often proposing new frameworks or perspectives.

Key Features:

Involves in-depth analysis and interpretation.

Challenges existing assumptions or theories.

Requires a strong theoretical foundation.

Purpose:

Critical reviews are ideal for advanced academic writing, such as dissertations and theoretical papers.

7. Theoretical Review

Theoretical reviews focus on examining theories related to a topic rather than empirical research.

Key Features:

Compares and contrasts different theoretical frameworks.

Identifies theoretical gaps.

Explores the evolution of ideas over time.

Purpose:

These reviews are often used in disciplines like sociology, philosophy, and psychology to refine or propose theoretical models.

8. Annotated Bibliography

A much simpler form of literature review is the annotated bibliography-an overview and critique of each source.

Key Features:

Lists sources with brief descriptions and critiques. Not synthesizing findings from the studies. Serves as a precursor to further developed reviews.

Purpose:

This type is commonly used for coursework or preliminary research to organize sources.

Conclusion

Each type of literature review has a specific purpose and is appropriate for a range of research objectives. Whether the use is about embracing broad trends in a narrative review or diving deep in statistical relationships as in meta-analysis, awareness of the types can guide you towards choosing the right approach for your study. The right type chosen ensures that your research not only becomes more robust but also relevant and impactful in its field. Mastering the art of literature review will keep researchers conversing effectively in the academic arenas while paving a way to make further discoveries.

Need expert guidance for your PhD, Master’s thesis, or research writing journey? Click the link below to access resources and support tailored to help you excel every step of the way. Unlock your full research potential today!

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[destinationtoast]

hi toasty, i love your stats! i have a question for you that may be unanswerable, but do you have any insight into the phenomenon of x-reader fics on tumblr? i've noticed anecdotally for a while now that reader fics tend to have many more notes (like, thousands!) than their non-reader counterparts, and that they also seem to be mostly (?) posted in full here, rather than linking to ao3. (check the tags #wolverine fanfic vs #poolverine fanfic as an example). i know there is also reader fic posted on ao3, but i'm wondering whether anyone's done a qualitative or quantitative analysis of this (if that's even possible)? did tumblr just at some point become "the place to post reader fic"?

any insight welcome. blows my mind how there's like two entirely separate worlds of posting behavior/style happening here.

Hey! :) Thanks for the kind words and the interesting ask.

I don't have any data about xReader fic on tumblr (the only xReader-relevant data I think I have is from a 2019 analysis of Shipping on Wattpad vs. AO3 and FFN that looked at xReader prevalence in the archives at that time)..

However, I do have a couple recommendations for where to start finding out more about xReader fic (and where it gets posted):

Effie Sapurdis (interviewed by @fansplaining in Episode 221: Self-Inserts) is an Information & Media Studies researcher studying self-inserts. Effie has written a paper called Self-Insert Fanfiction as Digital Technology of the Self with this very promising abstract excerpt:

Then, drawing on a survey of self-insert fanfiction conducted across four platforms (Ao3, FF.net, Tumblr, and Wattpad), we explore how such works can be discovered, read, and engaged with, and we offer specific observations about self-insert subgenres, as drawn from a selection of these works.

I haven't read most of this paper, but a quick search for "Tumblr" revealed several passages that potentially relate to your question, including:

We observed that the keyword “Imagines” (i.e., with the -s) was most often appended to collections of “one-shot” stories, many of which were originally posted on the authors’ Tumblr accounts and were then “cross-posted” to Ao3 afterward.

(This is just one section -- there are other sections about other types of self-insert & xReader works). This passage highlights one possibility: perhaps many of the works you're seeing will eventually end up on an archive as well, possibly as part of one of those multi-chaptered fanwork that's a combination of many short works. But I don't know whether most people then go back and link to the archive version from the original Tumblr post.

Anyway, Effie's the expert here -- check out her work! :) I'll also throw this open to readers -- does anyone here have any other relevant data? (xReader writers and readers are also welcome to chime in and share their personal experiences and any patterns/trends they've noticed.)

[flowersio]

Data Analysis for Qualitative Research: A Comprehensive Guide

Data analysis for qualitative research plays a crucial role in helping researchers interpret, understand, and gain insights from non-numeric data. Unlike quantitative research, which deals with numerical data, qualitative research is focused on exploring meanings, concepts, experiences, and social phenomena. The goal is to uncover deeper understanding from subjective data such as interviews, focus groups, open-ended surveys, and even observations.

In this guide, we will explore the key aspects of qualitative data analysis, its techniques, and its importance in research. Let’s dive into it!

What is Qualitative Data Analysis?

Qualitative data analysis involves systematically examining and interpreting non-numeric data to understand patterns, themes, and meanings within the data set. The data analyzed is often text-based, though it can also include images, videos, or audio recordings. Unlike quantitative research, which seeks to test hypotheses and predict outcomes through statistical methods, qualitative research aims to explore the richness of human experience and the context in which it occurs.

The analysis process helps researchers identify recurring patterns or themes that provide insight into the subject being studied. For instance, in a study about customer satisfaction, qualitative data might reveal emotional responses, personal stories, and reasons behind specific opinions that cannot be captured through numerical data alone.

The Importance of Qualitative Data Analysis

Rich Insights into Human Behavior: Qualitative data analysis allows researchers to dive deeper into human emotions, motivations, and perspectives. By analyzing text-based data such as interviews or open-ended surveys, researchers can explore the nuances of how people think, feel, and behave in different situations.

Contextual Understanding: One of the strengths of qualitative analysis is that it allows for a more detailed understanding of the context surrounding a phenomenon. For instance, in social science research, it’s crucial to understand the social, cultural, and environmental factors that influence individuals’ actions or decisions.

Flexibility in Approach: Qualitative analysis is highly adaptable. Researchers can modify their approaches as they uncover new insights or refine their understanding of the data. This flexibility is key when working with complex, dynamic phenomena that are difficult to capture with rigid quantitative tools.

Methods of Qualitative Data Analysis

There are several methods researchers can use to analyze qualitative data, each suited to different research objectives. Let’s explore some common methods:

1. Thematic Analysis

Thematic analysis is one of the most commonly used methods of qualitative data analysis. It involves identifying, analyzing, and reporting patterns (or “themes”) within the data. Themes are recurring ideas, concepts, or phenomena that offer insight into the research question.

The process usually involves:

Familiarizing with the data: Reading and re-reading the data to get a good sense of the content.

Coding: Creating initial codes that summarize parts of the data.

Theme development: Grouping related codes into broader themes.

Reviewing themes: Refining the themes to ensure they accurately represent the data.

Defining and naming themes: Providing clear definitions and names for the identified themes.

2. Grounded Theory

Grounded theory is a method that aims to generate a theory from the data itself, rather than testing an existing theory. Researchers use grounded theory to explore phenomena in-depth and develop a conceptual framework grounded in the data. The process includes:

Open coding: Breaking the data down into manageable chunks and categorizing them.

Axial coding: Relating codes to one another to identify patterns.

Selective coding: Identifying core themes and concepts that explain the data.

Grounded theory is highly systematic and involves iterative rounds of data collection and analysis, allowing the theory to evolve as more data is gathered.

3. Content Analysis

Content analysis involves systematically analyzing the content of qualitative data, such as text or media, by categorizing it into predefined themes. This method is particularly useful when analyzing large volumes of textual data. It can be both qualitative and quantitative, depending on how researchers handle the coding process.

4. Narrative Analysis

Narrative analysis is focused on understanding the stories that people tell. Researchers analyze personal accounts, interviews, or life histories to understand how individuals make sense of their experiences. This method emphasizes the structure of the narrative and the social context in which it is told.

5. Discourse Analysis

Discourse analysis is the study of language use and communication. It explores how language shapes social reality and can help reveal power dynamics, social norms, and ideologies within the data. Researchers often use this method to analyze how people express opinions, construct meaning, and negotiate social relationships through language.

Steps in Qualitative Data Analysis

Data Preparation: The first step is transcribing or organizing the raw data. This may involve transcribing interviews or focus group discussions and ensuring the data is in a usable format.

Coding: Researchers begin coding the data by breaking it down into smaller units of meaning. This is often done manually or with the help of software like NVivo or ATLAS.ti. Codes are keywords or phrases that represent the content of the data.

Identifying Themes: Once the data is coded, researchers group the codes into themes or categories that provide insights into the research question.

Data Interpretation: The final step is interpreting the themes and drawing conclusions from the data. This might involve comparing themes, highlighting patterns, and linking the findings to the research questions or broader theory.

Reporting Findings: The results of qualitative analysis are typically presented in a narrative format, supported by direct quotes from the data. The researcher might also use visual aids, like diagrams or models, to present their findings.

Challenges in Qualitative Data Analysis

While qualitative data analysis offers rich insights, it comes with its challenges:

Subjectivity: Researchers must acknowledge and manage their own biases during analysis. Their interpretations may influence how the data is understood.

Complexity: Qualitative analysis can be time-consuming, especially when dealing with large volumes of data.

Lack of Standardization: Unlike quantitative methods, qualitative analysis doesn’t follow a set formula, which can make it difficult to replicate or compare studies.

Conclusion

Qualitative data analysis is an invaluable tool for understanding the complexities of human experiences and social phenomena. By employing various techniques such as thematic analysis, grounded theory, and content analysis, researchers can draw meaningful insights from rich, non-numeric data. Despite the challenges, such as subjectivity and complexity, qualitative research provides an in-depth and holistic view of the research topic, offering findings that quantitative methods often cannot.

For researchers and analysts, mastering qualitative data analysis is essential to uncover the deep, rich, and diverse insights that drive innovation and inform decision-making.

[fanhackers]

Fictophilia

If I were to simplify (okay, fine: oversimplify) the field of fan studies, I’d say that scholars typically take one of two broad disciplinary approaches: either they look at fan works (and come from fields like literary studies, media and film studies, etc.) or they look at fan cultures and social organizations (ethnography, anthropology.)  But other academic disciplines produce research that might be pertinent to fans and fan studies–for instance, psychology. 

I recently came across an article called  “Fictosexuality, Fictoromance, and Fictophilia: A Qualitative Study of Love and Desire for Fictional Characters,” (2020)  written by  Veli-Matti Karhulahti  and Tanja Välisalo in the journal Frontiers of Psychology.  The abstract explains:

Fictosexuality, fictoromance, and fictophilia are terms that have recently become popular in online environments as indicators of strong and lasting feelings of love, infatuation, or desire for one or more fictional characters. This article explores the phenomenon by qualitative thematic analysis of 71 relevant online discussions. Five central themes emerge from the data: (1) fictophilic paradox, (2) fictophilic stigma, (3) fictophilic behaviors, (4) fictophilic asexuality, and (5) fictophilic supernormal stimuli. The findings are further discussed and ultimately compared to the long-term debates on human sexuality in relation to fictional characters in Japanese media psychology. Contexts for future conversation and research are suggested.

The article is generally descriptive and nonjudgmental, and the authors note that “the present intention is not to propose fictophilia as a problem or a disorder,” but instead to assert that most people are “fully aware of the love-desire object’s fictional status and the parasocial nature of the relationship.” (In other words, we’re mostly pretty sane!) The essay also cites some interesting work that I’ve not seen typically referenced in literary or ethnographic fan studies works, including the proto-fan studies text Imaginary Social Worlds, by John L. Caughey (1984). While Caughey’s book (like many works of the 1980s) starts by evoking the figure of crazy or even homicidal fan (think Mark David Chapman or John Hinkley), his goal is to argue that ‘fantasy relationships’ are actually pretty normal.  The book looks at “fantasy relationships” across history, connecting fan crushes on characters and celebrities “to the lifelong bonds that people in different cultures have conventionally had with gods, monarchs, spirits, and other figures that they may never have had the chance to meet in person.”  While Caughey’s book is focused on Western history, Karhulahti and Välisalo’s “Fictosexuality” takes its examples primarily from Japan, examining numerous psychological studies of “Japan and its fiction-consuming ‘otaku’ cultures.” This gives it a global take not always seen in English-language fan studies texts (which tend to deal primarily with Western media.) “Fictosexuality” is also unusual for its interest in making connections between asexuality and fictophilia, asexuality also being underrepresented (and under-theorized) in fan studies texts.  

Fans have historically been wary of any attempt to psychoanalyse them–and fair enough: after all, it was only recently that people stopped assuming that all fans were out-of-control “fanatics,” and there’s been a lot of creepy and misleading work on fandom done by outsiders. (If you want agita, look up SurveyFail on Fanlore.)  But psychology and related fields may also have methods which allow us to understand fans and fandom in new ways.

–Francesca Coppa, Fanhackers volunteer

[marketxcel]

Primary Research vs Secondary Research: Definitions, Differences, and Examples

Both primary and secondary research holds a significant place in the researcher’s toolkit. Primary research facilitates the collection of fresh, original data, while secondary research leverages existing information to provide context and insights.

[halfagonyandhope]

ignite the stars │ch. 4

first chapter (x); previous chapter (x)

Satine Kryze is an internationally-recognized scholar in genocide studies who recently resigned from the Department of State over her concerns regarding the agency's ethics. Ben Kenobi is a tenured professor at Georgetown University studying the use of religion to justify military conflicts. Once high school sweethearts, the two haven't spoken since parting ways for university. That is, until Satine accepts a research fellowship - at Georgetown.

---

The rest of the week moves quickly, full of meetings - of both the dreaded office kind and the personal encounter kind - and Satine spends free moments at work making progress on her second book. Her free moments at home are spent in a mix of professional and personal: she reads Ben’s dissertation cover to cover.

Most dissertations, she knows, are not the author’s best work. They are the result of sleepless nights, the culmination of years of eighty-hour work weeks, attention split between planning lectures, grading coursework, submitting one’s own coursework, and - between it all - managing to conduct research. Most scholars cringe when forced to interact with their dissertation after they’ve graduated, finding various typos and illogical arguments not initially caught by tired eyes or disinterested committee members.

But Ben’s dissertation - 

It’s a work of art. His writing is academic poetry, each word chosen for maximum emotional impact. And it’s not just the writing but also the subject matter that he appears to have approached with reverence, with respect. 

He’s a storyteller, just like Satine. He’d conducted dozens of key informant interviews, performed qualitative content analysis - all to highlight marginalized voices. 

Again, Satine is astounded by the parallel tracks of their lives. 

He, of course, had noticed the similarities well before she’d been aware of them; he hadn’t been lying when he said he’d cited her dissertation within his own.

Kryze (2015) eloquently argued for increasing emphasis on qualitative data in the realm of conflict prevention studies, which historically has prioritized quantitative modeling to predict conflict. While giving credit to the importance of such algorithms, Kryze noted the dangers of overlooking the voices and stories of those most affected in favor of discrete data points captured by scholars halfway across the world who do not understand the language or culture of those they are studying. She proposed that such conflict prediction algorithms could be improved and enriched by incorporating qualitative analyses that highlight the lived experiences of those most deeply harmed by these conflicts.

She’d had to pause reading after the first mention of her name, and then again later when he’d cited the postdoc paper she’d spent two years writing and honing. And when she’d finally finished reading Ben’s dissertation - all two hundred and fifty pages of it - she’d had to pour herself another glass of wine.

Satine hadn’t thought anyone had read her dissertation or her postdoc paper. She still isn’t convinced the reviewers at the journal where the latter was published had even read it, either.

But Ben had read them both. And not just read them; he’d acted on them, engaging with them in such a way as to build upon her argument and strengthen it.

He’d considered the qualitative model she’d proposed for predicting genocide, and he’d tweaked it to apply the framework to his field, adding his own critical theory and background to predict - and thus possibly prevent - attacks of religious terrorism against Muslims.

It is, Satine thinks, taking another sip of wine, incredible work for a doctoral dissertation. And it’s more than that, too, and she knows it. It’s an academic love letter.

Satine downs the rest of her wine.

But a feeling nags at her. Ben had followed her career, and closely. He’d had to have known when she accepted the postdoc at Northwestern. She was in Evanston, IL, for two years while he was in Wisconsin’s capital. They’d been fewer than 150 miles apart during that time, and such a distance would have been a trifle if they’d mutually agreed to rekindle things.

And yet he hadn’t reached out.

Satine thinks of their last words to each other.

“We’ll see each other again; I know we will.”

“Promise me.”

“I promise you.”

After they'd walked away, even thinking of him was agony incarnate. Had he felt the same? It seemed unlikely, given how well he knew her work. But if it didn’t bring him as much pain as it did her, if he’d truly engaged with her work and had been open to the possibility of giving things with her another go…why had he not contacted her when they were both in the Midwest?

And by Satine’s calculations, their time in DC has overlapped for the past five years. If Ben was open to a relationship with her, surely he would have initiated contact in that time.

Frustrated, Satine wipes at the moisture welling in her eyes. Maybe she’s misreading everything. Maybe she’s reading between lines that don’t even exist.

She shuts her laptop and heads to the kitchen in search of a pint of Ben and Jerry’s.

---

Early on Friday morning, she runs into Ben in the hallway. He takes one look at her expression and says, “You read my dissertation.”

Satine nods, face still warming up after her trek across campus in the cold. She removes her hat and fumbles in her coat pocket for her keys. She’d forgotten her mittens at home, and her frozen fingers do nothing to help her locate the stubbornly missing keychain.

Ben, who appears to have arrived well before - his coat hangs in his office, and his eyes aren’t watering from the cold - notices the way her fingers clench and unclench, and he reaches for her free hand, rubbing it between his much larger ones to generate heat. When glorious feeling finally returns, he grabs her other hand and repeats the process. “What did you think?” he asks, his voice low.

“I…” starts Satine, but she’s having trouble remembering which words go in what order to form a proper sentence with the touch of his skin against hers.

He takes a step closer, no longer trying to warm up her fingers, but he doesn’t release her hand.

She looks up at him, glances at his lips, and then meets his eyes again.

“Ben, there you are!”

They jerk apart and turn to meet the new arrival, a man in his mid-twenties, taller than Ben by an inch or two with long, wavy, dirty blond hair. His right hand is covered by a leather glove but his left is not.

“I was meaning to ask you…” But the man trails off as he notices Satine and Ben, and even though they’ve moved apart, Satine realizes they’re still standing too close to be entirely appropriate.

“Uh, hey,” says the man, with a look from Satine to Ben. “Ben, you going to introduce me to your girlfriend?”

Ben rolls his eyes. “Anakin,” he groans, voice low. “This is Satine Kryze, and she’s not my girlfriend.”

The man, Anakin, steps to Satine and extends his right hand. It’s a prosthetic hand, and a good one - she wouldn’t have clocked it without the handshake. “Nice to meet you, Doc,” says Anakin. “Would you like to be Ben’s girlfriend?”

“Anakin,” says Ben in a warning tone, and Anakin backs up, hands up in a pacifying manner. Ben turns to Satine. “The insolent youth here is my postdoc, Anakin Skywalker.”

Satine had known Ben had a postdoc, but seeing him in person is something different entirely. She blinks at Ben. “They gave you a child? You?”

Ben rolls his eyes again. “Even worse: this child has a child, for all intents and purposes. He’s largely in charge of mentoring my master’s student, Ahsoka.”

“Hey! I’m right here, you know,” Anakin interjects.

Ben sighs at him. “You and Ahsoka can contribute to this conversation when the number of postgraduate degrees I have doesn’t outnumber the number of postgraduate degrees you two have combined.”

Satine tries to keep a straight face and fails miserably.

“Look, I didn’t fail my master’s,” says Anakin, and by his tone, this is a discussion they’ve had before. “I just wasn’t invited to continue at that program.”

Satine finally manages to locate her keys. “Excuse me, gentlemen,” she says with a smirk, and she lets herself into her office, listening to the two men continue to banter as she shuts the door behind her.

---

That afternoon, Satine departs her office a bit early in order to head to the seminar room. As she locks up, she feels more than hears Ben beside her.

“Confident in your ability to find the room?” he asks, and she can hear the wry smile in his voice.

Satine turns and leans her shoulder against the wall separating their offices. “Ducking out of office hours early to attend the seminar?” she shoots back.

He chuckles. “Guilty. But I have a good reason.”

“Escorting me so I won’t get lost isn’t a ‘good reason,'” says Satine.

Ben locks his door. “It is good reason, but I actually meant that I’m giving the seminar talk today,” he says. “And no matter how many times I present, I still have the inevitable nightmare that my slides don’t work or that the screens won't turn on.”

Satine nods, immediately empathetic. “I stand corrected.”

Ben smiles at her. “Walk with me?”

So they fall into step.

The silence is companionable, but Satine’s nerves are not. “Your dissertation was good,” she offers. “Very good.”

She steals a glance at him, thrilled to see the softness of his gaze. 

“High praise from you, Madam Secretary.”

He opens the door to the stairs for her, and they descend together, the sound of her heels echoing in the stairwell. “Yes, well,” says Satine, hand on the rail - she does not need to tumble down in front of him - as she glances behind her. “I can hardly say differently if I inspired parts of it, could I not?” But when they clear the landing, she turns to face him so that her expression makes clear she is joking. 

Ben, however, doesn’t look like he is being facetious as he says, “You inspired all of it.”

And then he opens the door and exits to the first floor, and she has no choice but to follow wordlessly after him.

The seminar room is, mercifully, next door to the stairwell, and Satine watches Ben walk down the aisle, past the rows of - as of yet - empty seats, and log into the computer at the lectern. Satine ponders where to sit, wondering if Ben would think it rude for her to choose a seat in the back or too distracting for her to be in the front. She decides to sit toward the middle but to the side, hoping it is an acceptable compromise.

At that moment, the door opens again and Anakin barges in, his long legs skipping steps as he makes his way down the stairs. “Thanks again, Ben,” he says, handing Ben a small device.

Ben just looks at him, amused. “Not a problem,” he says eventually. “I know I’m not technically your doctoral advisor any longer, but I’d be remiss if I didn’t tell you to purchase a spare PowerPoint remote. Is this the second time you’ve had to borrow mine before one of your lectures?” And he plugs the USB component of the remote into the computer.

Anakin grumbles, "This would be the fourth.”

Ben gives him a withering look. “I think I’ve made my point.”

As Ben loads his slide deck, Anakin notices Satine, and he heads over to her. “Mind if I join you?” he asks.

“I’d prefer it, actually,” says Satine.

Anakin grins. “You want details on Ben?” he says, not bothering to lower his voice.

“Anakin,” warns Ben, not even bothering to look up from the screen.

But Satine just laughs. “I’ll settle for insider gossip on the department. Any intel you have on Ben would just be icing on the proverbial cake.”

So Anakin takes the empty seat next to hers, and he begins to point out the names of the people who are trickling into the lecture hall in anticipation of the seminar. “Dooku Serenno,” says Anakin, nodding toward a man with graying hair who chooses to sit in the first row. “I assume you already know him.”

Satine nods. “Yes, he was on my interview panel. He’s the chair, right?”

“A relatively recent change, but, yes - he’s department chair. It was basically a successful coup attempt on his end. The previous chair had been in that position for something like twenty years. It was time for Dr. Yoda to retire, which is the only reason the rest of the faculty allowed it.”

Satine frowns. “Twenty years is a long time to serve as chair.”

Anakin sighs. “Academics,” he says in frustration. “Once they get a little taste of power, they won't give it up. It’s why I’ve told Ben to never let me apply for something like that. It fucks with you.” He taps his temple.

“Indeed,” says Satine.

Anakin leans over slightly, lowering his voice. “But you wanted gossip? Dr. Yoda was Serenno’s advisor, back in the day. And Serenno advised a man by the name of Quigon, and Dr. Quigon advised…” He waves his hand to Ben.

Satine raises a brow. “Ben?” She breathes in. “I’ve never heard of Dr. Quigon. Where does he work now?”

Darkness flashes across Anakin’s face for an instant, but it’s gone as soon as it appears. The din of more people filing into the hall gives cover for Anakin’s next words. “He doesn’t,” Anakin says eventually, making sure no one gets close enough to hear him. “He was found dead when he was doing fieldwork - " Anakin gives her a loaded look at the word. " - abroad, just before Ben defended his dissertation. There were no leads, and no one was ever arrested.”

Satine turns to him, horrified. “God,” she says, feeling suddenly nauseated. “Why wasn’t it widely reported? Surely that kind of news would have made the rounds in academia?”

Anakin shrugs. “Quigon seemed to deliberately keep a low profile. I think Ben suspects he worked for the Agency. They might also have killed any stories about his death.” He winces. "Poor choice of words, but you get my meaning."

Satine turns over his words in her mind. It’s not unheard of for scholars in international relations to have security clearances. And among those who do have access to classified information, it’s also not unlikely to be recruited for more sensitive work. From her undergrad coursework, Satine knows that anthropologists in particular were recruited as spies during World War II and during the Cold War. And the Agency, she knows, is shorthand for the Central Intelligence Agency, so Anakin is suggesting that Ben’s doctoral advisor was indeed an intelligence operative - and that he was killed on assignment.

This, actually, is the only explanation that makes any plausible sense to Satine, given that academics are the worst gossips she’s ever had the misfortune to work with.

"Was he killed in Russia?" murmurs Satine, feeling cold.

Anakin's eyes flash up to hers. "Yeah," he says. "How did you know?"

Ben’s sudden interest in learning a new language after landing a tenure-track position now makes a lot more sense.

"Lucky guess."

Anakin doesn't press for further explanation. Like her, he's aware of the growing size of the crowd around them. It's for the best, Satine realizes, for if her suspicions - and Anakin’s - are accurate, then any information they’re discussing could be classified. He really shouldn’t have revealed as much as he already has, especially in such a public setting. But Satine gets the feeling that Anakin has never cared enough about rules to follow them, or even to acknowledge they exist.

The moment is gone, however, as Anakin raises his hand and yells, “Snips! Over here!”

Satine’s eyes follow his own, landing on a young woman - Desi, Satine would guess, based on the style of her traditional clothes - whose dark hair is highlighted with streaks of blue and split into two braids. She’s wearing a gold nose ring and gold bangles on her wrists, and a bindi rests above her eyebrows. The young woman smiles when she catches sight of Anakin and heads in his direction. When she reaches them, Anakin says, “Snips, meet Ben’s girlfriend. Satine, this is Ahsoka.”

Ahsoka’s jaw nearly drops to the floor. “I always thought Dr. Ben was ace,” she says. “Or aromantic maybe?”

Satine glances at Ben, who is leaning against the lectern, ready to be introduced to the room; however, his eyes are on her, his expression telling her he knows exactly how Anakin has introduced her and exactly how Ahsoka has responded.

She nearly laughs at the panic in his eyes.

Satine offers her hand to Ahsoka. “I am Satine, but I am not Ben’s girlfriend,” she says. “I’m the new fellowship hire,” she elaborates. “I’ll be here for a year as I write my next book.”

“Oh,” says Ahsoka, reaching to shake Satine’s hand. “Nice to meet you, then. I’m Dr. Ben’s master’s student. It’s my second semester.”

"Congratulations on surviving your first," says Satine with a grin.

Satine would dearly love to ask Ahsoka at least ten questions, but at that moment, Serenno rises and heads to the center of the room, and the packed lecture hall falls silent. Ahsoka sits beside Anakin, and Serenno begins to speak.

The introduction is hardly needed for those in the department who know Ben already, but Satine knows the seminar is open to the entire school, and not everyone in attendance is already familiar with Ben’s work. It’s an impressive introduction, on account solely of Ben’s accolades and not at all due to any warmth Serenno exudes. Satine bites her lip, wondering why the man introducing his academic grandson doesn’t appear to be more fond of him.

But then Serenno gives the floor to Ben, and Satine’s attention is captured.

He’s a masterful speaker, she notices immediately. He has the air of someone who’s practiced and knows his arguments like he could give them in his sleep, but he’s not over-practiced or rehearsed. His cadence is not too slow nor too fast, and he smiles and makes jokes only at appropriate moments.

It’s breathtaking.

And a breath later, forty minutes have gone by, and Ben lands on a slide that says only:

Thank you! Questions?

The audience politely claps, and Satine waits for the inevitable awkward few minutes of people wracking their brains to grasp onto any question they can think of to make it seem as though they’d been paying attention. But to her surprise, several hands fly up immediately, and she smiles.

Ben fields the questions with ease, like he was born to be exactly where he ended up.

Finally, Satine raises her hand, and Ben nods at her.

She raises her voice and asks if he can re-explain a small detail. He listens to her, eyes on her like they are the only people in the room, and launches into a clear explanation.

She smiles again.

She’d understood what he said the first time, of course, but she knew that others hadn’t grasped it yet. And this piece is important, so important that she wants him to have a chance to explain it again, to make sure everyone in the room knows how incredible his findings are.

He catches her eye as he finishes his answer, and his expression tells her that he knows exactly what she’d been doing. He sends her a half smile.

She gives him the other half.

[soggypotatoes]

a glimpse into my room + Reggie + Windowsill

just finished studying for my final exam tomorrow, I am loving my degree soooo much. this sem was 'boring' research/stats which I loved bc both quantitative and qualitative are total OCD fodder (in opposite ways) that I am allowed to do!!! data analysis and spreadsheets and linguistics and being finicky about words??? gimme

[the-agent-of-blight]

Aromanticism in Academic Papers (day 4)

It's day 4 of my ASAW 2024 project to summarize a new paper that represents the few works in the body of literature that focus on aromanticism!

Today's paper is: Aromanticism, asexuality, and relationship (non-) formation: How a-spec singles challenge romantic norms and reimagine family life by Hannah Tessler (2023) [stable link]

This paper is another by the amazing Hannah Tessler, who has written 3 papers (at least) about aromanticism within the past year or two. So like. Thank you Hannah Tessler for writing about aromantic people your papers make a great impact on us at least, let alone the area of research aromantic people represent.

This study is a mixed methods study using a combination of data from the ace community survey and aromantic census as well as interviews with aromantic and asexual people. From a quantitative angle, the study finds that alloromantic asexuals are most likely to express interest in romantic relationships, followed by allosexual aromantics, and then aro aces are the least interested. Again, a seemingly common sense claim, but it simply had not been proven in the academic literature until this paper was published.

The results of the qualitative portions of this study are, in my opinon, its highlights. 75% of the interviewees alluded to heteronormative assumptions of marriage and nuclear family in their upbringing. Some people, have had the experience of viewing a set number of options for their future in terms of relationships, and experienced that number of options expand when they realized they were aromantic and felt more ownership over their own future.

Others mentioned how untangling the concept of love from sex and romance was difficult for them when they first had experiences in romantic relationships, and couldn't navigate the differences between platonic and romantic love. interviewees created new ways to engage with other people, removing romantic feelings from activities usually reserved for romantic relationships.

Tessler highlights how aro and/or ace singles challenge notions of family and change their goals from building a future around romance to a future around friendship. Several interviews mentioned themes of creating families in non-traditional ways. To use the words Tessler does, "They are figuring out how to create connections beyond a nuclear family to best prepare for the future."

This paper is amazing. I love it. It accurately and respectfully presents the lived experiences of several aromantic people across many age groups. It's some powerful stuff to read the first time. Though its analysis may seem fairly surface level, i'm going to stress again that academic literature needs to play catch-up. Things that those of us in the community have known for a long time are things that academia has no source for, and thus needs to do a whole study to prove something that to us, seems like common sense. This paper is proof that those of us in the aromantic community need to get more involved in research. If you're reading this, are aromantic, and ever see the chance to be interviewed by a researcher, I beg of you take it. At least fill out the Aromantic Census put out by AUREA if you dont already do that. Every bit we can do to help contribute to research will help academia catch up.

[link to day 5]

[conclave-030]

The Ultimate Data Collection Handbook: Exploring Methods, Types, and Advantages

Data collection is a fundamental part of any research, business strategy, or decision-making process. Whether you're a student, a professional, or just curious about how data is gathered and used, understanding the basics of data collection can be incredibly useful. In this guide, we'll explore the methods, types, and benefits of data collection in a way that’s easy to understand.

What is Data Collection?

Data collection is the process of gathering information to answer specific questions or to support decision-making. This information, or data, can come from various sources and can be used to make informed decisions, conduct research, or solve problems.

Methods of Data Collection

Surveys and Questionnaires

What Are They? Surveys and questionnaires are tools used to gather information from people. They can be distributed in person, by mail, or online.

How Do They Work? Respondents answer a series of questions that provide insights into their opinions, behaviors, or experiences.

When to Use Them? Use surveys and questionnaires when you need to gather opinions or experiences from a large group of people.

Interviews

What Are They? Interviews involve asking questions to individuals in a one-on-one setting or in a group discussion.

How Do They Work? The interviewer asks questions and records the responses, which can be either structured (with set questions) or unstructured (more conversational).

When to Use Them? Use interviews when you need detailed, qualitative insights or when you want to explore a topic in depth.

Observations

What Are They? Observations involve watching and recording behaviors or events as they happen.

How Do They Work? The observer notes what is happening without interfering or influencing the situation.

When to Use Them? Use observations when you need to see actual behavior or events in their natural setting.

Experiments

What Are They? Experiments involve manipulating variables to see how changes affect outcomes.

How Do They Work? Researchers control certain variables and observe the effects on other variables to establish cause-and-effect relationships.

When to Use Them? Use experiments when you need to test hypotheses and understand the relationships between variables.

Secondary Data Analysis

What Is It? This method involves analyzing data that has already been collected by someone else.

How Does It Work? Researchers use existing data from sources like government reports, research studies, or company records.

When to Use It? Use secondary data analysis when you need historical data or when primary data collection is not feasible.

Types of Data

Quantitative Data

What Is It? Quantitative data is numerical and can be measured or counted.

Examples: Age, income, number of products sold.

Use It When: You need to quantify information and perform statistical analysis.

Qualitative Data

What Is It? Qualitative data is descriptive and involves characteristics that can be observed but not measured numerically.

Examples: Customer feedback, interview responses, descriptions of behavior.

Use It When: You need to understand concepts, opinions, or experiences.

Benefits of Data Collection

Informed Decision-Making

Data provides insights that help individuals and organizations make informed decisions based on evidence rather than guesswork.

Identifying Trends and Patterns

Collecting data allows you to identify trends and patterns that can inform future actions or strategies.

Improving Services and Products

By understanding customer needs and preferences through data, businesses can improve their products and services to better meet those needs.

Supporting Research and Development

Data is crucial for researchers to test hypotheses, validate theories, and advance knowledge in various fields.

Enhancing Efficiency

Data helps in streamlining processes and improving operational efficiency by highlighting areas that need attention or improvement.

Conclusion

Understanding the methods, types, and benefits of data collection can greatly enhance your ability to gather useful information and make informed decisions. Whether you're conducting research, running a business, or just curious about the world around you, mastering data collection is a valuable skill. Use this guide to get started and explore the many ways data can help you achieve your goals.

To know more: A Guide to Data Collection: Methods, Types, and Benefits

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