#Quantitative data interpretation

[marketxcel]

5 Methods of Data Collection for Quantitative Research

Discover five powerful techniques for gathering quantitative data in research, essential for uncovering trends, patterns, and correlations. Explore proven methodologies that empower researchers to collect and analyze data effectively.

[literaryvein-reblogs]

Writing Notes: Case Study

Case Study - a highly detailed analysis of a particular subject, usually involving multiple sets of quantitative data observed over a period of time that allow researchers to draw conclusions in the context of the real world.

Throughout the years, the results of case study research have given us a greater and more holistic understanding in fields such as medicine, political and social sciences, and economics.

Researchers have used case studies to explore relationships between variables and a central subject, whether that subject be a human's reaction to medication, a country’s reaction to an economic crisis, or the effect of pesticides on crops over a period of time.

This methodology relies heavily on data collection and qualitative research to answer hypotheses in multiple fields.

Types of Case Studies

There are several different kinds of case studies. Here are a few:

Illustrative case study: Researchers use observations on every angle of a specific case, generally resulting in a thorough and deep data analysis.

Exploratory case study: Primarily used to identify research questions and qualitative methods to explore in subsequent studies, this type of case study is frequently in use in the field of political science.

Cumulative case study: This type relies on the analysis of qualitative data gathered over a range of timelines, which can draw new conclusions from old research methodology or studies.

Critical instance case study: Used to answer questions about the cause and effects of a particular event, critical instance case studies are helpful in cases that pose unique perspectives on otherwise established truths.

Marketing case study: This type of case study evaluates the quantifiable results of a marketing strategy, new product, or other business decision.

Examples of Case Studies

Here are a three examples of case studies in different fields:

Content marketing: In the marketing context, case studies typically explain how the business responded to the needs of a certain client, and whether or not the response was effective. Since these types of case studies are a tool to attract new customers rather than to merely share information, they should contain clear headings, attractive fonts, and infographic data that is easy to interpret.

Neuroscience: The tragic case of Phineas Gage allowed researchers to observe the changes in behavior and personality he experienced after surviving a horrific railroad accident that damaged parts of his brain. This led to a better understanding of the relationship between our frontal lobe and emotional functioning. This type of research is an example of a case study that would be impossible to ethically replicate in a laboratory, but nonetheless was a breakthrough in neuroscience and health care.

Psychoanalysis: Modern talk therapy owes much to the individual case of Anna O, otherwise known as Bertha Pappenheim. While living in Vienna in 1880, she began experiencing severe hallucinations and mood swings. Joseph Bruer, a pioneer in psychoanalysis, took Bertha under his care, and after multiple sessions where she discussed her inner emotional state and fears with Bruer, her symptoms waned. This case study is often seen as the first successful example of psychoanalysis.

Benefits of a Case Study

A case study can allow you to:

Collect wide-reaching data: Using a case study is an excellent way to gather large amounts of data on your subject, generally resulting in research that is more grounded in reality. For example, a case study approach focused on business research could have dozens of different data sources such as expense reports, profit and loss statements, and information on customer retention. This collected data provides different angles you can use to draw conclusions in a real-life context.

Conduct studies in an accessible way: You do not need to work in a lab to conduct a case study. In a number of cases, researchers use case study methodology to study things that cannot be replicated in a laboratory setting, such as observing the spending habits of a group of people over a period of months.

Reduce bias: Since case studies can capture a variety of perspectives, researchers’ own preconceptions on a subjects have less of an influence.

See connections more clearly: Through case studies, you can track paths of positive or negative development, which makes specific results repeatable, verifiable, and explainable.

Source ⚜ More: Notes & References ⚜ Writing Resources PDFs

[evidence-based-activism]

I know you’ve found before that intimate partner violence against men isn’t underreported to police in comparison to violence against women, but has there been any research on police responses to domestic violence calls specifically? MRAs and misogynists always claim the man is automatically arrested just because police assume only men can be aggressors even if he is the victim (which is very rare in the first place even though they insist otherwise). But a lot of anecdotal evidence I’ve heard from female victims is that the police either did nothing or in some cases threatened to arrest her instead of her abusive partner, which makes sense considering the low conviction rate for domestic violence. Is there any data to debunk this idea that women are automatically taken seriously while men’s allegations are ignored?

There has been some work on this (and on tangentially related topics)!

Police response to domestic violence victims

First, a 2015 survey was conducted by the National Domestic Violence Hotline. It interviewed women who either had or had not had contact with the police about their victimization [1].

Of the women who had interacted with the police:

~80% felt either no safer or less safe than before they contacted them.

~67% felt somewhat or extremely afraid to call the police in the future.

~25% had been arrested or threatened with arrest during a partner abuse incident or while reporting a sexual assault incident.

This report had the advantage of being detailed, but was unfortunately not nationally representative. This special report from the Bureau of Justice Statistics [2] helps fill in that gap.

However, it does report on domestic violence by both an intimate partner and "other relations", which is an important caveat when interpreting the results.

According to this report:

Male and female victimizations were equally likely to be reported to the police, except that male victimizations involving "serious injury" were more likely to be reported than female victimizations involving "serious injury".

The offender was similarly likely to be arrested during the initial report for incidents involving a female or male victim across incident categories.

Women were more likely than men to sign a complaint (or have one signed on their behalf).

Police were more likely to follow-up on incidents or arrest an offender at follow up when there is a signed complaint. This is relevant because women were more likely to sign a complaint, which makes it appear as though police follow up on female victims more often. This is an example of a factor that can correlate with sex, resulting in an illusory gender difference.

Notably, despite the fact that men were less likely to sign a complaint for serious injury than women, the police were still equally likely to follow up with male and female victims with serious injury.

Together, these reports suggest that police response to male and female victims in domestic incidents is similar. Further, women report many of the same negative police reactions as men do, including being arrested or threatened with arrest. In other words, men are not uniquely discriminated against by the police in domestic violence incidents; police officers are just disturbingly unqualified to deal with domestic violence victims.

Male police officers

And, to be completely clear about the problem, 73% of American police officers are male [3]. Furthermore, other work [4] states, "female officers were more likely than male officers to provide support to citizens involved in domestic violence".

Together, this indicates that the poor police response to victims – both male and female – is primarily the result of other men's actions.

Differences in men's and women's domestic violence

As you indicated, I've discussed this many times. However, it stands repeating that violence from male and female perpetrators is both quantitatively and qualitatively different [5-7].

For example, this review [5] describes how "women’s physical violence appears to be more in response to violence initiated against them". (In other words, women's physical violence is responsive or defensive.) They also describe how "the type and quality [of emotional abuse tactics] appears to differ between the sexes", where men "threaten life and inhibit partner autonomy" and women's tactics "consist of yelling and shouting". They also note that men are the "predominant perpetrators of sexual abuse”.

Other work [6] indicates "men generally did not consider physical violence to be threatening when it was perpetrated by women" and men were "not subjected to the multiple control tactics".

In this study [7], men are more frequent perpetrators of domestic abuse and commit more severe acts, and yet women were "three times more likely to be arrested per incident".

I mention all of this to highlight how it would be completely reasonable for police officers to assume the woman is the primary victim. (More data on this can be found in this post, this post, and this post, among many others in my #male violence tag and this website [8].) Despite this, police do not appear to be making this assumption. Instead, they take a “gender-neutral" approach to domestic violence. This is, therefore, evidence of police discrimination against women (i.e., unfairly treating them as equally likely to be the aggressor).

The impact of DARVO

Why does all of that matter? Because we have substantial evidence that perpetrators of domestic violence employ the principles of DARVO (i.e., Defend, Attack, Reverse Victim and Offender) [9-10].

According to this principle, some perpetrators of domestic violence claim to be the "actual" victim of their partner. Often, they convince critical people (e.g., police officers, juries, etc.) for long enough to cause severe harm (e.g., convince the police to arrest the victim, find the abusive defendant not guilty).

This is of particular relevance to this discussion, given the frequency with which women who employ self-defensive or responsive violence are misidentified as the primary aggressor [11-13]. (Also see this post for a hypothetical description of the issues involved.)

This facilitates their abuser's use of the legal system to extend their coercive control of these women [9-11, 14-15].

What I find most striking about this work is that it shows how the abuses Men's Rights Activists claim are frequently and uniquely happening to male victims actually feature prominently in many women's abuse histories and have for decades.

Perhaps this is best described as the employment of DARVO on a group level. Either way, it clearly indicates that there is no systemic bias against men in the legal system. Quite the opposite, research indicates a substantial bias against women.

Conclusion

In conclusion, research suggests:

Men are not "automatically" arrested in domestic violence cases

Police response is similarly poor to both male and female victims

Most police officers are male, and male police officers tend to be less supportive in domestic violence incidents

Men's and women's violence in relationships is very different both in quantitative and qualitative terms

Women are more often misidentified as the primary aggressor than men and the principles of DARVO play a significant role in this (and other) legal abuse of female victims

References under the cut:

National Domestic Violence Hotline, Who Will Help Me? Domestic Violence Survivors Speak Out About Law Enforcement Responses. Washington, DC (2015). http://www.thehotline.org/resources/law-enforcement-responses

Reaves, B. A. (2017). Police response to domestic violence, 2006-2015. Washington, DC: US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

FBI. (2019). Table 74 – Full-time Law Enforcement Employees . https://ucr.fbi.gov/crime-in-the-u.s/2019/crime-in-the-u.s.-2019/topic-pages/tables/table-74

Sun, I. Y. (2007). Policing domestic violence: Does officer gender matter?. Journal of Criminal Justice, 35(6), 581-595.

Hamberger, L. K., & Larsen, S. E. (2015). Men’s and women’s experience of intimate partner violence: A review of ten years of comparative studies in clinical samples; Part I. Journal of Family Violence, 30, 699-717.

Nybergh, L., Enander, V., & Krantz, G. (2016). Theoretical considerations on men’s experiences of intimate partner violence: An interview-based study. Journal of family violence, 31, 191-202.

Hester, M. (2013). Who does what to whom? Gender and domestic violence perpetrators in English police records. European Journal of criminology, 10(5), 623-637.

Domestic abuse is a gendered crime. (n.d.). Women’s Aid. Retrieved from https://www.womensaid.org.uk/information-support/what-is-domestic-abuse/domestic-abuse-is-a-gendered-crime/

Quinonez, A. A., & Kuennen, T. (2023). Turning the Tables: How Those Who Are Accused Deny, Attack, and Reverse. Wis. JL Gender, & Soc'y, 38, 64.

Harsey, S. J., & Freyd, J. J. (2022). Defamation and DARVO. Journal of Trauma & Dissociation, 23(5), 481-489.

Reeves, E. (2020). Family violence, protection orders and systems abuse: views of legal practitioners. Current issues in criminal justice, 32(1), 91-110.

Henning, K., Renauer, B., & Holdford, R. (2006). Victim or offender? Heterogeneity among women arrested for intimate partner violence. Journal of family Violence, 21, 351-368.

Nancarrow, H., Thomas, K., Ringland, V., & Modini, T. (2020). Accurately identifying the “person most in need of protection” in domestic and family violence law. Australia's National Research Organisation for Women's Safety.

Douglas, H. (2018). Legal systems abuse and coercive control. Criminology & criminal justice, 18(1), 84-99.

Dichter, M. E. (2013). “They arrested me—and I was the victim”: Women’s experiences with getting arrested in the context of domestic violence. Women & Criminal Justice, 23(2), 81-98.

[max1461]

I've always thought philosophy and math were kinda similar with regards to "necessary to understand the world" it seems like I'm going to upset some people by saying but it seems to me like dealing with a lot of large scale issues requires you to understand and interpret quantitative data and also an ability to formulate meaningful arguments.

Yeah I agree, contra kircheis, that philosophy is quite important. But I will note that math does teach you how to formulate a meaningful argument, "formulating arguments" is basically all that pure math is, in contrast to applied math where you are perhaps also likely to find yourself manipulating real-world data in some kind of "rote" way.

Look into mathematical proofs; 90% of math is writing proofs.

[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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[grandhotelabyss]

Now that the shining “digital humanities future” envisaged in the 2000s and early 2010s is now (probably) over, what do you think were the effects on the discipline of English Literature of trying to reduce everything to “graphs, maps, and trees,” visualisations, data sets, and pattern matching across corpora? Is there even a there there anymore?

I don't know. Did they ever find anything new, anything truly paradigm-shattering, anything we didn't understand before, anything we really need to know about anything that matters? I'm not talking about the strictly historical-sociological work, the how-many-English-novels-were-imported-into-Meiji-era-Japan or how-many-Bibles-were-printed-in-colonial-America or how-many-books-did-the-precursor-to-the-National-Library-of-Moldova-contain-in-1889, which is fine as far as it goes, but does belong to history and sociology rather than to literary criticism, though it may of course inform literary criticism. As far as anything claiming to help us with the hermeneutics, though: did anything come of it? Did they ever answer Elif Batuman's objection from 2005—

In 2001, I enrolled in a seminar taught by Moretti on this subject, entitled “Lost Bestsellers of Victorian Britain.” The idea was to identify the formal devices that might have been responsible for the initial success and subsequent failure of these books. It was a great seminar, although not one distinguished by its humanness, or by the quality of the reading. Coelebs in Search of a Wife, The Mysteries of London, The Woman Who Did: these proved to be the kind of mirthless and dismal productions that made you want to take your copy of the Princesse de Clèves and go hide in the Rhine. There was, however, one exception: Edward Bulwer-Lytton’s Pelham, or Adventures of a Gentleman, from the lost genre of the “silver fork novel.”

“On entering Paris,” Pelham relates, “I had resolved to set up ‘a character’: for I was always of an ambitious nature. . . . After various cogitations as to the particular one I should assume, I thought nothing appeared more likely to be obnoxious to men, and therefore pleasing to women, than an egregious coxcomb.” A young man goes to Paris determined to be “an egregious coxcomb”—now that’s a premise with some life to it.

One day I mentioned Pelham in self-defense to my advisor in the Slavic department, who was forever making fun of me for taking this class on Lost Victorian Bestsellers. “There’s this one book,” I said. “It’s really good.”

To my surprise, my advisor knew all about it. “Oh, well, Pelham,” she said. “Sure. Pushkin loved Pelham.”

As it turns out, Pushkin’s love for Pelham actually resulted in his writing one and a half chapters of a novel called The Russian Pelham—and incorporating certain features of Lytton’s “egregious coxcomb” into Eugene Onegin, an antihero who gets up at dawn, takes an ice-water bath, and spends the rest of the morning practicing his pool shots. Pelham was, in a manner of speaking, big in Japan; it wasn’t lost at all, at least not to Pushkinists. This discovery made me reconsider the theoretical project of reading the 30,000 lost Victorian novels. After all, it had taken all my willpower to get through just six of them—and then, the only one I liked turned out to be one that I might have encountered anyway, through Pushkin—i.e., through the canon.

—or Nan Z. Da's critique of 2019—

Not only has this branch of the digital humanities generated bad literary criticism, but it tends to lack quantitative rigor. Its findings are either banal or, if interesting, not statistically robust. The problem appears to be structural. In order to produce nuanced and sophisticated literary criticism, CLS must interpret statistical analysis against its true purpose; conversely, to stay true to the capacities of quantitative analysis, practitioners of CLS must treat literary data in vastly reductive ways, ignoring everything we know about interpretation, culture, and history. Literary objects are too few, and too complex, to respond interestingly to computational interpretation — not mathematically complex, but complex with respect to meaning, which is in turn activated by the quality of thought, experience, and writing that attends it.

—in a way that would convince a layperson, autodidact, reader-to-save-one's-soul?

When I used to teach an intro-to-the-English-major course, I would start with J. Hillis Miller's On Literature as a theoretically informed but less cynical alternative to Eagleton's intolerably smirking Literary Theory, which was popular with other instructors of the same course. Miller historicizes literature qua imaginative writing as beginning in part not only with print culture, Protestant individualism, the rise of the nation-state, the rise of the middle class, etc. but also its institutionalization in the modern research university. Divided as it is between Wissenschaft and Bildung, roughly science and humanities, the university does designate a "scientific" element to literary studies (philology, textual criticism, stylistics, the history of the book), but the broader point of such studies was always Bildung, acculturation, the training of the citizen. The ideological basis of this training, and the desiderated citizen supposed to emerge from it, has altered over time, and with it the concept of "the best that has been thought and said" assigned to effect said citizen's acculturation. But if we lose this telos then we lose the rationale for literary study in the first place. The reduction of literature in the normative sense—the best that has been thought and said, for all the socially contested definitions of "best"—to the whole body of written fictions, and the reduction of this body to a system of Darwinian combat accessible in its pattern only to computational analysis, as if the results of such notional combat were themselves quantitative and not qualitative in nature, as if one could do a value-free analysis of novels in the same way one can do a value-free analysis of butterflies, is to cede the authority of literary studies, which can only be premised on the aesthetic charisma of its object, to the social and finally to the natural sciences, and to an outdated mechanistic 19th-century idea of the natural sciences at that. No, as Batuman points out, the value of the canon, revise it how we will, and there would be no point in revising it if it did not matter to individual and collective Bildung, remains the only satisfying warrant for the continuation of academic literary study. A science-envying reductionism, like a baptism guest who tactlessly proposes that the infant is only a bundle of meat and bone, only destroys the occasion.

[sapphicscholar]

Fic by Numbers Tag Game

Tagged by @lilolilyr - thanks!!

Rules: give us the links to your fics with the most hits, second most kudos, third most comments, fourth most bookmarks, fifth most words, and with the least words (feel free to interpret however you would like; if not on AO3, can be on Tumblr or FFNET!).

1) most hits: stronger together (supergirl femslash prompt fill collection) - this fic is literally never gonna be displaced from the top of my lists 😂 and here it rings in with 263,720 hits

2) second most kudos: welcome to the gayborhood, danvers (supergirl - Sanvers and supercorp academia au) - my first ever fic!! Ringing in with 1,653 kudos

3) third most comments: Supercat Sanvers 2020 (supergirl political au) sneaks in with a fool 793 comment threads

4) fourth most by bookmarks: I don’t think I’ve ever really looked at this stat but apparently it’s a supposedly fun thing (Hacks Ava/Deborah cruise ship post-s1 fic) look at a non supergirl fandom making the list with 186 bookmarks! Also I really did love writing this fic and the many fandom friends I found in the process

5) fifth by length is, drum roll please, Folie à Deux (Hacks again! Ava/Deborah X-Files au) clocking in at 74,319 words!

Good work team! Bout to go put quantitative data skills on my CV

[teamarcstechnologies]

7 Key Principles to Drive Success in Market Research

Market research plays a crucial role in guiding business strategies and decision-making. Here are seven key principles to ensure success in your market research efforts:

1. Define Clear Objectives

Start with well-defined goals. Understand what insights you need and how they will support your business decisions.

2. Know Your Audience

Identify and segment your target audience effectively. Tailor your research methods to align with their preferences and behaviors.

3. Choose the Right Methodology

Select the most suitable research approach, whether qualitative, quantitative, or a hybrid model, to ensure meaningful results.

4. Leverage Advanced Tools and Technology

Incorporate AI, big data, and analytics tools to enhance data accuracy and speed. Modern technology can streamline data collection and interpretation.

5. Ensure Data Quality

Prioritize data accuracy, relevance, and reliability. Scrutinize data sources and methodologies to avoid biased or incomplete insights.

6. Adhere to Ethical Standards

Respect privacy and comply with regulations like GDPR. Ethical practices build trust and credibility with your audience.

7. Translate Insights into Action

Insights are valuable only when applied. Create actionable recommendations and integrate them into your strategy to drive results.

By following these keys, businesses can elevate their market research practices and gain a competitive edge in their industry.

To know more: online market research platforms

online panel management platform

fraud detection and reporting tool

[rise2research]

The Biggest Hurdles in Market Research Today

The market research industry has been undergoing significant changes, driven by technological advancements, shifting consumer behaviors, and the increasing demand for real-time insights. Below are the key challenges transforming this dynamic industry:

1. Data Overload and Management

With the proliferation of digital platforms, organizations have access to vast amounts of data. While this presents opportunities, managing and making sense of this data remains a major challenge.

2. Evolving Consumer Behavior

Consumer preferences are changing rapidly due to societal, economic, and technological factors.

3. Integration of Advanced Technologies

The adoption of artificial intelligence (AI), machine learning (ML), and big data analytics has revolutionized market research.

4. Data Privacy and Ethical Concerns

Stringent data privacy regulations, such as GDPR and CCPA, have introduced complexities in data collection and usage.

5. Declining Response Rates

As consumers become increasingly wary of surveys and data collection methods, response rates have dropped.

6. Demand for Real-Time Insights

Businesses now require faster and more actionable insights to stay competitive.

7. Globalization and Cultural Nuances

Conducting market research across diverse geographies and cultures introduces complexities in interpreting data.

8. Budget Constraints and ROI Pressures

Clients increasingly demand more insights at lower costs, challenging research firms to demonstrate the ROI of their services while managing operational expenses.

9. Adapting to Hybrid Research Models

The industry is shifting towards hybrid research methods that combine qualitative and quantitative techniques, as well as traditional and digital tools.

Conclusion

The challenges transforming the market research industry are reshaping its landscape. Companies that proactively address these hurdles through innovation, adaptability, and ethical practices will be better positioned to thrive in this evolving market. Staying ahead of these changes is not just an option—it's a necessity for sustained success.

To know more: data analytics services company

healthcare market research services

[sarkariresultdude]

Management Aptitude Test Results Explained: Insights and Next Steps

 The Management Aptitude Test (MAT) is a standardized evaluation designed to evaluate the managerial skills of applicants intending to leadership roles. The outcomes provide insights into an person’s competencies in areas along with vital questioning, decision-making, communique, problem-fixing, and management capability. This report will delve into the shape of the MAT, interpret its scoring gadget, and spotlight its implications for career development.

Detailed explanation of MAT test results

1. Overview of the MAT

The MAT assesses several critical regions important for effective control. These regions normally consist of:

Quantitative Skills: Evaluates numerical reasoning, statistics interpretation, and monetary acumen.

Verbal Ability: Measures conversation talents, reading comprehension, and language proficiency.

Logical Reasoning: Assesses the potential to analyze conditions, identify patterns, and make sound judgments.

Data Analysis: Tests skillability in interpreting and manipulating facts for strategic choice-making.

Managerial Aptitude: Gauges leadership skills, team management, and the capability to deal with place of business demanding situations.

Each section contributes to a holistic expertise of the candidate’s ability, with scores reflecting strengths and areas for development.

2. Scoring and Interpretation

The MAT scoring gadget is generally divided into percentile ranks, scaled scores, and sectional breakups. Each offers precise insights into the candidate's overall performance:

Percentile Rank

The percentile rank shows how a candidate's overall performance compares to others who took the check. For instance, a ninetieth percentile rank approach the candidate scored better than ninety% of test-takers.

Scaled Scores

Scaled ratings standardize uncooked scores across exceptional take a look at variations, ensuring fairness and comparability. Each section has a most score, often contributing to an mixture score.

 Sectional Analysis

A distinct sectional evaluation highlights overall performance in unique domain names, such as quantitative ability or verbal competencies. For example, sturdy overall performance in logical reasoning may suggest strategic questioning capabilities, while decrease ratings in statistics analysis may want to recommend areas requiring similarly schooling.

Three. Detailed Analysis of Results

Below is a hypothetical example of MAT consequences to demonstrate interpretation:

Candidate Name: Alex Morgan

Test Date: December 2024

Sectional Scores:

Quantitative Skills: 85/a hundred

Verbal Ability: seventy eight/a hundred

Logical Reasoning: 92/one hundred

Data Analysis: 70/one hundred

Managerial Aptitude: 88/100

Percentile Rank: 94th Percentile

Interpretation:

Alex demonstrates splendid logical reasoning skills, which align nicely with roles requiring strategic planning and essential decision-making.

A strong managerial flair score suggests a herbal inclination closer to leadership and group management.

Performance in facts evaluation suggests a want for development, specifically in roles requiring superior data-pushed choice-making.

4. Insights and Implications

Strengths

Alex's effects highlight strengths in logical reasoning and managerial flair. These attributes are valuable for roles regarding undertaking control, strategic management, or consultancy. Effective communique skills, as contemplated in verbal capacity scores, in addition enhance Alex’s suitability for positions that require frequent interplay with stakeholders.

 Areas for Improvement

While typical performance is commendable, the lower rating in data analysis shows a want for talent enhancement in this area. Pursuing specialized schooling in analytics, gear like Excel, or information visualization software which include Tableau will be beneficial.

 Career Recommendations

Based on these outcomes, Alex is nicely-suitable for mid-stage managerial roles with growth capability. Potential profession paths include operations management, group leadership, or strategic making plans roles. With centered upskilling in data evaluation, Alex ought to excel in facts-driven positions, along with enterprise analytics or market research.

Comparative Benchmarking

A evaluation with industry averages offers further insights. For example, the common MAT rating for applicants applying to pinnacle management packages is often within the 80th percentile. Alex’s 94th percentile rank positions them most of the pinnacle applicants, showcasing a competitive edge.

 Leveraging MAT Results

Candidates can use their MAT effects in numerous ways:

Personal Development

Understanding strengths and weaknesses allows for centered talent improvement. For example, Alex may want to enhance their information evaluation skills thru workshops, on line guides, or practical tasks.

Professional Growth

Employers cost MAT rankings as signs of potential. Candidates can include their rankings in resumes or interviews to spotlight their managerial aptitude.

 Further Education

High MAT rankings can enhance programs to MBA applications or different control-focused instructional interests.

7. Common Challenges and Solutions

Balancing Strengths and Weaknesses

Candidates regularly excel in some regions even as struggling in others. Addressing those gaps through continuous mastering is prime.

 Test Anxiety

Performance on standardized checks may be inspired by means of pressure. Practicing mock tests and adopting rest techniques can mitigate this difficulty.

 Interpreting Results

Understanding what ratings imply in practical terms is critical. Consultation with career advisors or mentors can help translate results into actionable insights.

8. Real-Life Applications of MAT Results

Many businesses use MAT results to perceive skills for managerial roles. For example, a enterprise may also prioritize applicants with excessive logical reasoning and managerial aptitude rankings for management positions.

Similarly, instructional establishments regularly use MAT effects as a part of their admissions standards, making sure that incoming students have the foundational skills wanted for advanced studies in management.

Nine. Final Thoughts

The Management Aptitude Test is a powerful device for assessing an man or woman’s capacity as a manager. Its results offer an in depth roadmap for private and professional growth. By leveraging those insights, candidates can align their career trajectories with their strengths while addressing areas for improvement.

Candidates like Alex, who score inside the higher percentiles, have widespread possibilities to excel in management roles. With focused development, they are able to gain their profession aspirations and contribute effectively to organizational success.

[dropsofsciencenews]

What’s Left on the Neolithic...plate?

ITA version ESP version

Sometimes, not having soap to wash the dishes thoroughly can be useful... especially if we want to uncover our past activities. It is thanks to the residues found in ancient ceramic vessels that a research team, composed of members from the Universitat Autònoma de Barcelona (UAB), the University of Zaragoza, and the University of Strasbourg, has revealed the first direct evidence of the consumption and processing of dairy products since the beginning of the Neolithic period, around 7,500 years ago.

The study utilised the remains of about thirty ceramic vessels obtained from two archaeological sites found in the caves of Chaves and Puyascada, located in the province of Huesca, Spain, to understand the usage and preservation habits of food. The researchers organised and classified the vessels according to various criteria, such as the type of profile, shape, cooking conditions, surface treatment, type of decoration, depth, and volume. Subsequently, lipids were extracted from the ceramic remains using a technique that employs acidified methanol, then analysed through gas chromatography and mass spectrometry.

Thanks to the morphological profiles, the researchers classified the vessels based on their function: preparation with or without heating, serving, and storage. Indeed, a group of ceramic pots was classified as suitable for food preparation, particularly for prolonged boiling thanks to the closed rims that prevent excessive evaporation. Others were suitable for pounding or stirring, as they have thick walls, which would be more resistant to heavy impacts. Regarding organic matter, the residues found in the vessels include ruminant fats, pork, plant products, and dairy, suggesting intentional mixing or subsequent uses.

Another group of small vessels was interpreted as serving containers, used for individual consumption of food and liquids. They could have been easily handled with one hand, so they were likely intended for individual use. The identified ingredients range from animal fats to edible plants and resins. Finally, a last group of vessels, with deep and closed necks, was considered ideal for storing liquids and low-fat foods, such as cereals and legumes.

The analysis of organic residues from the Chaves ceramics indicates that these were mainly used for processing ruminant meat, representing 50% of the residues, and dairy products, which constitute 28%. This is consistent with the mortality profiles of the animals at Chaves, which show that goats, cattle, and pigs were slaughtered young, during the so-called "optimum of meat". At Puyascada, on the other hand, the ceramics were mainly used for dairy products, which account for 54% of the organic residues, while ruminant fats only make up 27%, and pork fat is well represented at 36%. The low percentage of ruminant fats could be due to different preparation and consumption methods compared to Chaves.

The high percentage of dairy fats at Puyascada suggests a priority in the use of ceramics for milk processing. Additionally, the importance of pork fat, despite the low quantitative presence of the species, could indicate specific preparation of pork or its use as a fat reserve. The preparation and consumption of pork fat were widespread practices in Neolithic Iberia, with evidence in many archaeological sites. The data suggest that, while milk was processed and consumed at both sites, ruminant and pork fats were managed differently, reflecting distinct production and consumption strategies.

From the shape of the vessels and the organic residues, scientists have managed to offer a valuable window into the social dynamics and agricultural and livestock practices of Neolithic communities in the Iberian Peninsula.

Other previous studies confirm the production of dairy products in Europe during the Neolithic, but this is the first study that makes a direct comparison between neighbours, between caves located about 100 km apart, describing the diversity of lifestyles.

source: https://link.springer.com/article/10.1007/s12520-024-02001-9#Sec14

examples of ceramic vessels:

[woso-dreamzzz]

i’m a psych a crim student too omg!

apparently there’s maths in psych at some point and i havent reached that yet but can you pls confirm or deny for my own sanity🙏

also how hard is the maths (if there is any)? i got an a* at gcse and i didnt take it for alevel so like will i struggle with it? do i need a refresher? or is it chill?

Yeah, there's Maths in Psych!

It's in research methods though and you don't have to do it by hand. I presume other unis use it too but there's a programme that we use at mine called SPSS that runs all your calculations for you. All you need to do is put in your data and decide what you want SPSS to run for you.

The hard bit is making sure your data is inputted correctly because certain variables and tests have to be run in a very specific way.

It's only for quantitative data though because qualitative is more interpretation.

First year is pretty chill with SPSS, it's fairly simple. Second year is where it really kicks in but that's a gradual thing so it isn't too much of a leap

[allthebrazilianpolitics]

Re-emergence of Oropouche virus between 2023 and 2024 in Brazil: an observational epidemiological study

Background

Oropouche virus is an arthropod-borne virus that has caused outbreaks of Oropouche fever in central and South America since the 1950s. This study investigates virological factors contributing to the re-emergence of Oropouche fever in Brazil between 2023 and 2024.

Methods

In this observational epidemiological study, we combined multiple data sources for Oropouche virus infections in Brazil and conducted in-vitro and in-vivo characterisation. We collected serum samples obtained in Manaus City, Amazonas state, Brazil, from patients with acute febrile illnesses aged 18 years or older who tested negative for malaria and samples from people with previous Oropouche virus infection from Coari municipality, Amazonas state, Brazil. Basic clinical and demographic data were collected from the Brazilian Laboratory Environment Management System. We calculated the incidence of Oropouche fever cases with data from the Brazilian Ministry of Health and the 2022 Brazilian population census and conducted age–sex analyses. We used reverse transcription quantitative PCR to test for Oropouche virus RNA in samples and subsequently performed sequencing and phylogenetic analysis of viral isolates. We compared the phenotype of the 2023–24 epidemic isolate (AM0088) with the historical prototype strain BeAn19991 through assessment of titre, plaque number, and plaque size. We used a plaque reduction neutralisation test (PRNT50) to assess the susceptibility of the novel isolate and BeAn19991 isolate to antibody neutralisation, both in serum samples from people previously infected with Oropouche virus and in blood collected from mice that were inoculated with either of the strains.

Findings

8639 (81·8%) of 10 557 laboratory-confirmed Oropouche fever cases from Jan 4, 2015, to Aug 10, 2024, occurred in 2024, which is 58·8 times the annual median of 147 cases (IQR 73–325). Oropouche virus infections were reported in all 27 federal units, with 8182 (77·5%) of 10 557 infections occurring in North Brazil. We detected Oropouche virus RNA in ten (11%) of 93 patients with acute febrile illness between Jan 1 and Feb 4, 2024, in Amazonas state. AM0088 had a significantly higher replication at 12 h and 24 h after infection in mammalian cells than the prototype strain. AM0088 had a more virulent phenotype than the prototype in mammalian cells, characterised by earlier plaque formation, between 27% and 65% increase in plaque number, and plaques between 2·4-times and 2·6-times larger. Furthermore, serum collected on May 2 and May 20, 2016, from individuals previously infected with Oropouche virus showed at least a 32-fold reduction in neutralising capacity (ie, median PRNT50 titre of 640 [IQR 320–640] for BeAn19991 vs <20 [ie, below the limit of detection] for AM0088) against the reassortant strain compared with the prototype.

Interpretation

These findings provide a comprehensive assessment of Oropouche fever in Brazil and contribute to an improved understanding of the 2023–24 Oropouche virus re-emergence. Our exploratory in-vitro data suggest that the increased incidence might be related to a higher replication efficiency of a new Oropouche virus reassortant for which previous immunity shows lower neutralising capacity.

Read the paper.

[stagnate-03]

Your Guide to Success in Quantitative Research: 8 Practical Tips

Quantitative research plays a crucial role in fields like social sciences, business, healthcare, and education. It provides numerical data that can be analyzed statistically to identify patterns, relationships, and trends. However, excelling in quantitative research requires more than just crunching numbers.

1. Start with a Clear Research Question

The foundation of any successful research is a well-defined research question. This question guides the entire study, determining your methodology, data collection, and analysis. Ensure that your research question is specific, measurable, and aligned with the purpose of your study.

For example, instead of asking, "How do students perform in school?" a clearer question might be, "What is the relationship between study hours and academic performance in high school students?"

Tip: Before starting, spend time refining your question. This will save you time and effort during the research process.

2. Choose the Right Research Design

Quantitative research can take many forms, including experiments, surveys, and observational studies. Choosing the right design depends on your research objectives and the type of data you need. Are you testing a hypothesis?

Tip: Match your research design with your objectives to ensure you’re collecting the right kind of data.

3. Use Valid and Reliable Instruments

The tools you use to gather data—whether they’re questionnaires, tests, or measuring devices—must be both valid (measuring what you intend to measure) and reliable (producing consistent results over time).

Tip: If you’re developing your own instrument, pilot it first with a small group to check its validity and reliability. If using an existing tool, review past studies to confirm it works well for your research population.

4. Select an Appropriate Sample Size

A common mistake in quantitative research is working with a sample size that’s too small, which can lead to unreliable or inconclusive results. On the other hand, excessively large samples can waste resources. To avoid these pitfalls, conduct a power analysis to determine the optimal sample size for your study.

Tip: Use tools like G*Power to calculate the right sample size based on your research goals and the expected effect size. This ensures your findings are statistically significant and applicable to a larger population.

5. Ensure Random Sampling for Representativeness

Your findings will only be meaningful if your sample represents the broader population you’re studying. Random sampling ensures that every individual in the population has an equal chance of being selected, reducing bias and increasing the generalizability of your results.

Tip: Use random sampling methods (e.g., simple random sampling, stratified random sampling) to ensure your data is as representative as possible.

6. Minimize Bias in Data Collection

Bias can creep into any research process, affecting the accuracy and fairness of your results. To reduce bias, carefully design your data collection process. For example, avoid leading questions in surveys and standardize how data is collected across all participants to prevent interviewer or observer bias.

Tip: Blind or double-blind studies can help minimize bias, especially in experiments where participants or researchers might be influenced by expectations.

7. Analyze Data Properly with the Right Statistical Tools

Once you’ve collected your data, the next step is analysis. Choosing the right statistical tests is essential to interpret your findings correctly. Descriptive statistics (like means and frequencies) give a broad overview, while inferential statistics (like t-tests, chi-squares, or regression analyses) help determine whether your findings are statistically significant.

Tip: If you’re unsure which test to use, consult a statistician or use resources like statistical decision trees to guide your choice based on your data type and research questions.

8. Interpret Results with Context and Caution

After analyzing your data, it’s tempting to jump to conclusions. However, quantitative research is not just about the numbers; it’s about what those numbers mean in context. Always interpret your results in relation to your research question and the existing body of knowledge.

Be cautious when generalizing your findings, especially if your sample size is small or non-representative. Additionally, consider the limitations of your study—were there any confounding variables, measurement errors, or external factors that might have influenced your results?

Tip: Be transparent about the limitations of your study. Acknowledging them strengthens the credibility of your research.

Conclusion

Mastering quantitative research requires attention to detail, a solid understanding of statistical methods, and a commitment to rigor throughout the process. By following these 8 practical tips—starting with a clear question, choosing the right design, using valid instruments, selecting the appropriate sample, minimizing bias, analyzing correctly, and interpreting results carefully—you’ll be well on your way to conducting successful and impactful quantitative research.

Read more: https://stagnateresearch.com/blog/how-to-excel-in-quantitative-research-8-essential-tips-for-success/

Also read: Project Management Service Company

data processing in research services

[shamira22]

1. **Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.Here is an example using Python:```pythonimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport scipy.stats as stats# Load your datasetdf = pd.read_csv('your_dataset.csv')# Display the first few rows of the datasetprint(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variablemodel = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc testfrom statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B."7. **Submit Your Assignment**: - Ensure you follow all submission guidelines provided by Coursera.If you need specific help with your dataset or any part of the code, feel free to ask!

[careermantradotorg]

IIMs in India: A Comprehensive Guide to the Indian Institutes of Management

The Indian Institutes of Management (IIMs) in India are world-renowned for producing some of the most successful business leaders, entrepreneurs, and professionals. These prestigious institutions offer top-tier management education and are considered the pinnacle of business schools in India. Whether you're an aspiring MBA candidate or curious about the legacy of IIMs, this guide will help you explore all the essential details about IIMs in India.

What are IIMs?

IIMs, or Indian Institutes of Management, are autonomous public institutions offering management education programs such as the Post Graduate Program (PGP) in Management (equivalent to an MBA) and doctoral-level Fellow Programs in Management (FPM). They were established by the Indian government to enhance the quality of management education in India. The first IIM was set up in Calcutta (now Kolkata) in 1961, and today, there are 20 IIMs spread across the country.

List of Top IIMs in India

While all IIMs provide excellent education, some are considered the cream of the crop due to their history, faculty, and placement records. Here's a list of the top IIMs in India:

IIM Ahmedabad (IIMA) IIM Ahmedabad is arguably the most prestigious business school in India. It has consistently ranked as the top IIM due to its world-class faculty, rigorous curriculum, and excellent placement records. Its flagship program, the PGP, is highly sought after by students across the country.

IIM Bangalore (IIMB) Known for its lush green campus and innovative teaching methods, IIM Bangalore is another top-tier management institute. It has strong industry connections, and its focus on entrepreneurship and innovation makes it a standout choice for MBA aspirants.

IIM Calcutta (IIMC) The oldest of the IIMs, IIM Calcutta has maintained its leadership in management education for over six decades. It is known for its finance and consulting placements, making it a favorite among students who aim for careers in these sectors.

IIM Lucknow (IIML) IIM Lucknow offers a robust curriculum, and its programs in agribusiness management are unique in the IIM ecosystem. Its strategic location and strong alumni network make it a go-to institution for many MBA candidates.

IIM Indore (IIMI) Known for its integrated five-year management program (IPM) that caters to undergraduate students, IIM Indore has gained popularity among young aspirants. Its diverse course offerings and industry exposure make it one of the top choices for MBA programs in India.

IIM Kozhikode (IIMK) Situated in the scenic hills of Kerala, IIM Kozhikode combines a serene environment with cutting-edge management education. Its strong focus on technology and business analytics has made it a preferred choice for tech-savvy management students.

Admission Process for IIMs in India

The process of getting into IIMs in India is highly competitive, with the Common Admission Test (CAT) serving as the primary gateway. Here’s an overview of the admission process:

CAT Exam: Conducted annually, the CAT is a computer-based test that evaluates candidates on their quantitative aptitude, verbal ability, reading comprehension, data interpretation, and logical reasoning. Candidates must score exceptionally well to secure a spot at the top IIMs.

Shortlisting Process: After the CAT results are announced, IIMs shortlist candidates based on their CAT scores, academic performance, work experience, and diversity factors like gender and academic discipline.

Personal Interview (PI) & Written Ability Test (WAT): Shortlisted candidates are invited for the second round, which includes a personal interview (PI) and a written ability test (WAT). These rounds assess a candidate's communication skills, clarity of thought, and ability to handle business scenarios.

Final Selection: The final selection is based on a composite score that includes CAT scores, PI/WAT performance, academic background, work experience, and other criteria specific to each IIM.

Programs Offered by IIMs in India

The IIMs in India offer a variety of programs tailored to meet the needs of students, working professionals, and even international candidates. Some of the key programs are:

Post Graduate Program (PGP): The flagship two-year program, equivalent to an MBA, offered by all IIMs. It prepares students for leadership roles in the corporate world.

Executive MBA (PGPX, EPGP): A one-year full-time MBA program designed for professionals with significant work experience. It offers a fast-tracked route to career advancement.

Fellow Program in Management (FPM): This is a doctoral-level program designed for those interested in research and academics. It is equivalent to a Ph.D. in management.

Integrated Program in Management (IPM): A five-year program offered by select IIMs, such as IIM Indore, that allows students to pursue an undergraduate and postgraduate degree in management.

Career Opportunities and Placements

One of the major reasons why students aspire to join IIMs in India is the excellent placement opportunities they offer. Each year, top companies from diverse sectors like consulting, finance, marketing, operations, and technology recruit IIM graduates with lucrative salary packages.

For instance, the average salary package for IIM Ahmedabad graduates in 2023 was approximately ₹34.5 lakhs per annum, with international placements reaching even higher figures. Companies like McKinsey, Goldman Sachs, Amazon, and the Boston Consulting Group are regular recruiters across various IIMs.

The Legacy of IIMs in India

The IIMs have played a crucial role in shaping India’s business landscape. Alumni from IIMs hold leadership positions in some of the largest corporations globally, and many have gone on to establish successful startups. The strong emphasis on analytical thinking, leadership development, and ethical business practices has helped IIM graduates stand out in the competitive global marketplace.

Conclusion

IIMs in India are much more than just business schools; they are institutions that mold the future leaders of the corporate world. With a rigorous admission process, a diverse range of programs, and excellent career opportunities, IIMs continue to be the dream destination for management aspirants across the country. If you're looking to make a mark in the world of business and management, IIMs offer the perfect platform to launch your career.

Whether you're just beginning your journey or already have years of work experience, there’s an IIM program designed to fit your needs. So, are you ready to take the first step toward an exciting future in management?