Qualitative Data Quotes

If you are going to use the results of market research to make a big business decision, then it’s a good idea to do quantitative research rather than qualitative.

Qualitative research is about collecting and understanding non-numerical data. It’s about how humans think, perceive, or give meaning to a thing based on various stimuli.

Interviews are a qualitative form of collecting data. The reason it generates good responses is because it’s way more personal than other forms of data gathering techniques.

The worse thing that contemporary qualitative research can imply is that, in this post-modern age, anything goes. The trick is to produce intelligent, disciplined work on the very edge of the abyss.

All so-called ‘quantitative’ data, when scrutinized, turn out to be composites of ‘qualitative’ – i.e., contextually located and indexical – interpretations produced by situated researchers, coders, government officials and others. The

Digital analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).

Web Analytics 2.0 is: the analysis of qualitative and quantitative data from your website and the competition, to drive a continual improvement of the online experience that your customers, and potential customers have, which translates into your desired outcomes (online and offline).

Empirically, things are poignant, tragic, beautiful, humorous, settled, disturbed, comfortable, annoying, barren, harsh, consoling, splendid, fearful; are such immediately and in their own right and behalf.... These traits stand in themselves on precisely the same level as colours, sounds, qualities of contact, taste and smell. Any criterion that finds the latter to be ultimate and "hard" data will, impartially applied, come to the same conclusion about the former. -Any- quality as such is final; it is at once initial and terminal; just what it is as it exists. it may be referred to other things, it may be treated as an effect or a sign. But this involves an extraneous extension and use. It takes us beyond quality in its immediate qualitativeness.... The surrender of immediate qualities, sensory and significant, as objects of science, and as proper forms of classification and understanding, left in reality these immediate qualities just as they were; since they are -had- there is no need to -know- them. But... the traditional view that the object of knowledge is reality par excellence led to the conclusion that the object of science was preeminently metaphysically real. Hence, immediate qualities, being extended from the object of science, were left thereby hanging loose from the "real" object. Since their -existence- could not be denied, they were gathered together into a psychic realm of being, set over against the object of physics. Given this premise, all the problems regarding the relation of mind and matter, the psychic and the bodily, necessarily follow. Change the metaphysical premise; restore, that is to say, immediate qualities to their rightful position as qualities of inclusive situations, and the problems in question cease to be epistemological problems. They become specifiable scientific problems; questions, that is to say, of how such and such an event having such and such qualities actually occurs.

Being able to spot bullshit based on data is a critical skill. Decades ago, fancy language and superfluous detail might have served a bullshitter’s needs. Today, we are accustomed to receiving information in quantitative form, but hesitant to question that information once we receive it. Quantitative evidence generally seems to carry more weight than qualitative arguments. This weight is largely undeserved—only modest skill is required to construct specious quantitative arguments. But we defer to such arguments nonetheless. Consequently, numbers offer the biggest bang for the bullshitting buck.

Based on the findings of a recent qualitative survey carried out in Switzerland, in fact, most of us have up to ten discreet interdependent social identities—identities, the study concludes, which are often in conflict.16 Let’s imagine a middle-aged bank teller living in Pensacola, Florida. He is a father, a son and a husband. He is a Floridian. He is a bank employee. He is also a bicyclist and a recreational runner, and at night, drinking with his friends, he is “the funny one.” He is also a vegetarian, an amateur guitarist, and on weekends he helps coach soccer at his daughter’s high school. Then there are his online identities, including his Facebook, Twitter and Instagram selves. Most surprising is that the man’s ethical mind-set, honesty, sociability and even level of social engagement changes from personality to personality. Imagine that in his professional role, for example, he may be primed to dissembling, or outright deceit, while simultaneously, as a dad, he finds dishonesty repellent.

Each tribe’s solution to its central problem is a brilliant, hard-won advance. But the true Master Algorithm must solve all five problems, not just one. For example, to cure cancer we need to understand the metabolic networks in the cell: which genes regulate which others, which chemical reactions the resulting proteins control, and how adding a new molecule to the mix would affect the network. It would be silly to try to learn all of this from scratch, ignoring all the knowledge that biologists have painstakingly accumulated over the decades. Symbolists know how to combine this knowledge with data from DNA sequencers, gene expression microarrays, and so on, to produce results that you couldn’t get with either alone. But the knowledge we obtain by inverse deduction is purely qualitative; we need to learn not just who interacts with whom, but how much, and backpropagation can do that. Nevertheless, both inverse deduction and backpropagation would be lost in space without some basic structure on which to hang the interactions and parameters they find, and genetic programming can discover it. At this point, if we had complete knowledge of the metabolism and all the data relevant to a given patient, we could figure out a treatment for her. But in reality the information we have is always very incomplete, and even incorrect in places; we need to make headway despite that, and that’s what probabilistic inference is for. In the hardest cases, the patient’s cancer looks very different from previous ones, and all our learned knowledge fails. Similarity-based algorithms can save the day by seeing analogies between superficially very different situations, zeroing in on their essential similarities and ignoring the rest. In this book we will synthesize a single algorithm will all these capabilities:

you will see this framework at work when ethnographers employ both quantitative (e.g., surveys) and qualitative data collection (LeCompte & Schensul, 1999) and when case study researchers use both quantitative and qualitative data (Luck, Jackson, & Usher, 2006; Yin, 2009).

Human beings are complex information consumers: they have active needs, passions, and preferences. They lead different lifestyles — some that you will never be able to empathize with unless you dive deep in qualitative and quantitative data. And that is precisely the point of persona research.

And while the details of how it is implemented vary somewhat from company to company, the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

More recently, physicist Edwin May, who directed the ESP research at SRI after 1986 and then headed the program researching “anomalous cognition” (May’s preferred term) after it was transferred to SAIC, and psychologist Sonali Bhatt Marwaha have also argued that all forms of ESP are likely precognition misinterpreted or misidentified.29 Unlike Feinberg, they do not assume precognition is solely an “inside the head” phenomenon30; but reducing anomalous cognition to precognition is a bold step that may move the field of parapsychology forward by, as they say, “collaps[ing] the problem space”31 of these phenomena. What has always seemed like several small piles of interesting but perhaps not overwhelming data supporting various diverse forms of psi or anomalous cognition may really be a single, impressively large pile of evidence for the much more singular, astonishing, and as I hope to show, physically plausible ability of people to access information arriving from their own future. In Part Two, where I address the possible “nuts and bolts” of this ability, I will be making a case for precognition being something close to Feinberg’s “memory of things future”—an all-in-the-head information storage and retrieval process, but one that is not limited to short-term memory. Evidence from life and laboratory suggests it may be possible, within limits, to “premember” experiences days, months, and years in our future, albeit dimly and obliquely, in a manner not all that different from how we remember experiences in our past. The main qualitative difference would be that, unlike memory for past experiences, we have no context for recognizing information from our future, let alone interpreting or evaluating it, and thus will seldom even notice its existence. We would also have little ability to directly search our memory for things future, the way we can rummage in our mental attic for information we know we acquired earlier in life. Yet things we will learn in our future may “inform” us in many non-conscious ways, and this information may be accessed in dreams and art and tasks like ESP experiments that draw on ill-defined intuitive abilities.

Continuous variables can take on almost any numeric value and can be meaningfully divided into smaller increments, including fractional and decimal values. You often measure a continuous variable on a scale. For example, when you measure height, weight, and temperature, you have continuous data. Categorical variables have values that you can put into a countable number of distinct groups based on a characteristic. Categorical variables are also called qualitative variables or attribute variables. For example, college major is a categorical variable that can have values such as psychology, political science, engineering, biology, etc.

Reproducibility is also hampered by qualitative issues that include such intangibles as the “culture of science,” the “scientific reward system,” and cognitive biases, as well as matters of scientific practice governing how we do experiments and publish our reports. Even if identifying the remedies for some of these problems were straightforward—and it isn’t—putting them into place would require the concerted effort of the scientific community.

Reproducibility is not a binary condition: a scientific report may not be all right or all wrong. One part of a multipart report may not replicate, while other parts do. Or a replicated result might match the original result qualitatively though not quantitatively, as was the case in many of the RPP experiments. It is often hard to classify reports as “reproducible” or “irreproducible.

He turns his head to look down at Haruka sitting on the ornate rug in the middle of the floor. A disarray of books and notes spirals outward from his position—he is the eye of an academic hurricane. Haruka holds a black mug of steaming coffee in one hand while with the other he casually flips through a reference book on qualitative data analysis.

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Third Wave technology will also change how we measure success in the classroom. What good is an annual standardized test, after all, once teachers and parents can get detailed reports with a wide range of metrics, comparing their students on a regular basis to others in their class or school or state? In this way, big data on individual students will do for education what standardized testing never quite could: bring quantitative precision to a qualitative learning process.

Remember that codes are more than a filing system. Every project needs a systematic way to store coded field data and a way to retrieve them easily during analysis.

Quantitative data abhors emotion; qualitative data marinates in it.

What makes a good metric What vanity metrics are and how to avoid them The difference between qualitative and quantitative metrics, between exploratory and reporting metrics, between leading and lagging metrics, and between correlated and causal metrics What A/B testing is, and why multivariate testing is more common The difference between segments and cohorts

Collection Methods in Qualitative Studies Where was the setting of the study? What was the rationale for choosing the setting? Who were the participants and what were their roles and characteristics? Why were they chosen? What data collection methods were used? What role did the researcher adopt within the setting? Who collected the data and were they qualified for their roles? How were data collectors trained? Was the training adequate? Was the process of the fieldwork adequately reported? How did the event unfold? Was data collection continued until saturation was achieved? Were the researchers’ assumptions or biases acknowledged?

Similar to the value of personal data that could be obtained in the OPM breach, medical records also offer an attractive bounty to criminals looking to commit more targeted fraud or steal someone's identity.  “When someone has your clinical information, your bank account information, and your Social Security number, they can commit fraud that lasts a long time,” Pam Dixon, executive director of the World Privacy Forum, told Monitor correspondent Jaikumar Vijayan in March after the Premera Blue Cross breach. “The kind of identity theft that is on the table here is qualitatively and quantitatively different than what is typically possible when you lose your credit card or Social Security number.” What's more, it often takes longer for victims to discover that medical data has been stolen than to realize that his or her credit card is being used. Consequently, medical data theft can lead to a variety of long-term problems including damaged credit, misdiagnosed illnesses, and unwarranted medical charges. Personal data has become such a valuable commodity that it's outpacing stolen credit cards on the black markets. 

it," John Hultquist, the senior manager of cyberespionage threat intelligence at iSight, told The New York Times. Similar to the value of personal data that could be obtained in the OPM breach, medical records also offer an attractive bounty to criminals looking to commit more targeted fraud or steal someone's identity.  “When someone has your clinical information, your bank account information, and your Social Security number, they can commit fraud that lasts a long time,” Pam Dixon, executive director of the World Privacy Forum, told Monitor correspondent Jaikumar Vijayan in March after the Premera Blue Cross breach. “The kind of identity theft that is on the table here is qualitatively and quantitatively different than what is typically possible when you lose your credit card or Social Security number.

2. Abstract concepts. It is extremely difficult to explain how any set of purely physical actions and interactions could possibly invest consciousness with the immaterial—which is to say, purely abstract—concepts by which all experience is necessarily interpreted and known. It is almost impossible to say how a purely material system of stimulus and response could generate universal categories of understanding, especially if (and one hopes that most materialists would grant this much) those categories are not mere idiosyncratic personal inflections of experience, but real forms of knowledge about reality. In fact, they are the very substance of our knowledge of reality. As Hegel argued perhaps more persuasively than any other philosopher, simple sense-knowledge of particular things, in itself, would be utterly vacuous. My understanding of anything, even something as humbly particular as that insistently red rose in my garden, is composed not just of a collection of physical data but of the conceptual abstractions that my mind imposes upon them: I know the rose as a discrete object, as a flower, as a particular kind of flower, as a kind of vegetation, as a horticultural achievement, as a biological system, as a feature of an ecology, as an object of artistic interest, as a venerable and multi-faceted symbol, and so on; some of the concepts by which I know it are eidetic, some taxonomic, some aesthetic, some personal, and so on. All of these abstractions belong to various kinds of category and allow me, according to my interests and intentions, to situate the rose in a vast number of different sets: I can associate it eidetically not only with other flowers, but also with pictures of flowers; I can associate it biologically not only with other flowers, but also with non-floriferous sorts of vegetation; and so on. It is excruciatingly hard to see how any mechanical material system could create these categories, or how any purely physical system of interactions, however precisely coordinated, could produce an abstract concept. Surely no sequence of gradual or particulate steps, physiological or evolutionary, could by itself overcome the qualitative abyss between sense experience and mental abstractions.

Correlations made by big data are likely to reinforce negative bias. Because big data often relies on historical data or at least the status quo, it can easily reproduce discrimination against disadvantaged racial and ethnic minorities. The propensity models used in many algorithms can bake in a bias against someone who lived in the zip code of a low-income neighborhood at any point in his or her life. If an algorithm used by human resources companies queries your social graph and positively weighs candidates with the most existing connections to a workforce, it makes it more difficult to break in in the first place. In effect, these algorithms can hide bias behind a curtain of code. Big data is, by its nature, soulless and uncreative. It nudges us this way and that for reasons we are not meant to understand. It strips us of our privacy and puts our mistakes, secrets, and scandals on public display. It reinforces stereotypes and historical bias. And it is largely unregulated because we need it for economic growth and because efforts to try to regulate it have tended not to work; the technologies are too far-reaching and are not built to recognize the national boundaries of our world’s 196 sovereign nation-states. Yet would it be best to try to shut down these technologies entirely if we could? No. Big data simultaneously helps solve global challenges while creating an entirely new set of challenges. It’s our best chance at feeding 9 billion people, and it will help solve the problem of linguistic division that is so old its explanation dates back to the Old Testament and the Tower of Babel. Big data technologies will enable us to discover cancerous cells at 1 percent the size of what can be detected using today’s technologies, saving tens of millions of lives. The best approach to big data might be one put forward by the Obama campaign’s chief technology officer, Michael Slaby, who said, “There’s going to be a constant mix between your qualitative experience and your quantitative experience. And at times, they’re going to be at odds with each other, and at times they’re going to be in line. And I think it’s all about the blend. It’s kind of like you have a mixing board, and you have to turn one up sometimes, and turn down the other. And you never want to be just one or the other, because if it’s just one, then you lose some of the soul.” Slaby has made an impressive career out of developing big data tools, but even he recognizes that these tools work best when governed by human judgment. The choices we make about how we manage data will be as important as the decisions about managing land during the agricultural age and managing industry during the industrial age. We have a short window of time—just a few years, I think—before a set of norms set in that will be nearly impossible to reverse. Let’s hope humans accept the responsibility for making these decisions and don’t leave it to the machines.

Creativity—that is, inventing your way out of a problem—is definitely the better stance.

 The biggest enemy of your learning is the gnawing worry that you’re not “doing it right.” Dissertation work tends to encourage that.

After you analyze each company independently, you’ll run a third analysis of the two companies combined, which will provide you with qualitative insights about the benefits of such a transaction. Once you run all the analyses, you would use the qualitative data to refine your hypothesis and then analyze the potential benefits quantitatively (e.g., estimate the magnitude of financial benefit).

Ramakrishna Paramhans Ward, PO mangal nagar, Katni, [M.P.] 2nd Floor, Above KBZ Pay Centre, between 65 & 66 street, Manawhari Road Mandalay, Myanmar Phone +95 9972107002 Market research plays a pivotal role in shaping business strategies and facilitating growth in dynamic markets like Myanmar. As businesses navigate through the complexities of the Myanmar market landscape, the expertise and insights provided by market research agencies become invaluable. One such prominent player in the field is AMT Market Research Agency, known for its comprehensive approach and tailored solutions. This article delves into the significance of market research for businesses in Myanmar, explores the services offered by AMT, showcases success stories, analyzes emerging trends in the industry, and presents client testimonials, providing a holistic view of the market research agency in Myanmar # 1. Introduction to Market Research in Myanmar ## Understanding the Market Landscape Market research in Myanmar is like exploring a hidden gem - full of potential but requiring a keen eye to uncover the treasures within. As one of the fastest-growing economies in Southeast Asia, Myanmar presents a unique blend of traditional values and modern aspirations that make it a fascinating market to study. ## Challenges and Opportunities in Myanmar Navigating the market in Myanmar can be akin to a thrilling adventure, with challenges and opportunities around every corner. From infrastructural limitations to cultural nuances, businesses face hurdles that require insightful market research to overcome. However, with the right approach, the untapped potential of Myanmar's market can lead to significant growth and success. # 2. Overview of AMT Market Research Agency ## Background and History of AMT AMT Market Research Agency is not your average player in the market research scene. With a rich history rooted in a passion for uncovering insights and a commitment to excellence, AMT has established itself as a trusted partner for businesses looking to navigate Myanmar's complex market landscape. ## Key Differentiators of AMT What sets AMT apart from the rest of the pack? It's not just their cutting-edge methodologies or their team of expert researchers, but their genuine enthusiasm for understanding the intricacies of the Myanmar market. AMT doesn't just deliver data - they offer valuable insights that drive strategic decision-making. # 3. Importance of Market Research for Businesses in Myanmar ## Driving Informed Decision-Making In a market as dynamic as Myanmar, making informed decisions is crucial for business success. Market research provides the necessary data and insights that empower businesses to make strategic choices with confidence. With AMT by your side, you can trust that your decisions are backed by solid research and analysis. ## Mitigating Risks in a Dynamic Market The only constant in the Myanmar market is change. With shifting consumer behaviors, regulatory landscapes, and competitive pressures, businesses face a myriad of risks. Market research acts as a compass, guiding businesses through the uncertainties and helping them navigate the market with clarity and foresight. # 4. Services Offered by AMT Market Research Agency ## Quantitative Research Solutions Numbers don't lie, and neither does quantitative research. AMT offers a range of quantitative research solutions that provide businesses with statistically sound data to make informed decisions. From surveys to data analysis, AMT ensures that your business is equipped with the numbers it needs to succeed. ## Qualitative Research Approaches Sometimes, it's not just about the numbers - it's about understanding the why behind the what. Qualitative research approaches offered by AMT delve deep into consumer insights, behaviors, and motivations, providing businesses with a rich understanding of the market landscape.

AMT Market Research Agency in Myanmar stands as a pivotal player in the dynamic landscape of market research within the country. With a commitment to delivering unparalleled insights and strategic solutions, AMT Market Research has established itself as a trusted partner for businesses seeking to thrive in Myanmar's evolving market. This article delves into the core aspects of AMT Market Research, exploring its services, methodologies, and the significance of market research in Myanmar. Through case studies, client testimonials, and a glimpse into future trends, we uncover the depth and impact of AMT Market Research in driving success for businesses in Myanmar. Introduction to AMT Market Research Agency in Myanmar AMT Market Research is not your run-of-the-mill agency in Myanmar. With a knack for unraveling the mysteries of consumer behavior, they're the Sherlock Holmes of the market research world. Let's delve into what makes them tick. Background of AMT Market Research Founded with a passion for decoding the pulse of the market, AMT Market Research has been shaking up the industry in Myanmar. Their team of savvy researchers leaves no stone unturned in uncovering insights that drive business success. Mission and Vision of the Agency AMT Market Research's mission is simple yet powerful: to empower businesses with data-driven decisions that spark growth and innovation. Their vision? To be the go-to partner for companies looking to navigate the ever-evolving market landscape in Myanmar. Services Offered by AMT Market Research When it comes to services, AMT Market Research doesn't just dip their toes in the water—they dive in headfirst, armed with a treasure trove of strategic insights. Market Entry Strategy From market sizing to competitor analysis, AMT Market Research crafts bespoke market entry strategies that pave the way for success in Myanmar's dynamic business environment. Consumer Behavior Analysis Curious about what makes your target audience tick? AMT Market Research digs deep into the minds of consumers, decoding their preferences, habits, and aspirations to help you tailor your offerings with precision. Competitor Analysis In a market as competitive as Myanmar, staying ahead of the game is crucial. AMT Market Research's competitor analysis services provide a roadmap for outshining the competition and carving out your niche. Importance of Market Research Agency in Myanmar In the bustling landscape of Myanmar, market research isn't just a luxury—it's a necessity. Understanding the economic terrain and growth opportunities is key to thriving amidst the challenges that lie ahead. Economic Landscape of Myanmar Myanmar's economic landscape is a canvas of untapped potential and burgeoning opportunities. Market research serves as the compass that guides businesses through this vibrant yet complex terrain. Growth Opportunities and Challenges With growth opportunities aplenty, Myanmar beckons businesses with promises of success. However, navigating the challenges, be it regulatory hurdles or shifting consumer trends, requires a keen understanding of the market—enter AMT Market Research. Methodologies Utilized by AMT Market Research When it comes to research methodologies, AMT Market Research doesn't settle for the ordinary. Their toolbox is brimming with innovative techniques that paint a comprehensive picture of the market landscape. Quantitative Research Techniques Numbers don't lie, and neither do AMT Market Research's quantitative research techniques. From surveys to data analysis, they crunch the numbers to unearth patterns and trends that inform strategic decision-making. Qualitative Research Approaches

Ramakrishna Paramhans Ward, PO mangal nagar, Katni, [M.P.] 2nd Floor, Above KBZ Pay Centre, between 65 & 66 street, Manawhari Road Mandalay, Myanmar Phone +95 9972107002 1. Study Organizations in Myanmar: A Growing Demand for survey companies in Myanmar is a Southeast Asian nation steeped in culture and history. Over the past ten years, it has undergone rapid economic growth and modernization. This development has made an expanding market for different administrations, including statistical surveying. Businesses in Myanmar benefit greatly from the assistance of survey firms in comprehending consumer behavior, market trends, and the landscape of competition. Among the main players in this field is AMT Statistical surveying, an organization known for its complete administrations and neighborhood skill. The Role of survey companies in Myanmar Businesses wishing to establish or expand their presence in this dynamic market must conduct market research in Myanmar. Myanmar, which has a population of over 54 million people, presents significant opportunities for businesses operating in a variety of industries, including tourism, finance, consumer goods, and telecommunications. However, the market also faces unique obstacles like a diverse ethnic landscape, varying degrees of economic development across regions, and a regulatory environment that is constantly shifting. By providing insights into consumer preferences, purchasing patterns, and market dynamics, survey companies assist businesses in navigating these complexities. To get accurate and relevant data, they use a variety of methods, such as observational studies, qualitative interviews, focus groups, and quantitative surveys. Driving Overview Organizations in Myanmar A few overview organizations work in Myanmar, each offering a scope of administrations custom-made to address the issues of various clients. AMT Market Research stands out among these due to its extensive experience and thorough comprehension of the local market. AMT Statistical surveying AMT Statistical surveying is a noticeable player in Myanmar's statistical surveying industry. Surveys of customers' satisfaction, market research, brand health monitoring, and other services are all offered by the business. AMT's group of experienced scientists and examiners influence their neighborhood information and skill to convey noteworthy bits of knowledge for organizations. AMT Statistical surveying uses a blend of customary and current information assortment techniques. Depending on the research objectives and target audience, they conduct in-person interviews, telephone surveys, and online surveys. Their methodology guarantees top notch information assortment, even in remote and difficult to-arrive at areas of Myanmar. Myanmar Advertising Exploration and Advancement (MMRD) Laid out in 1992, MMRD is one of the most established statistical surveying firms in Myanmar. The organization offers an extensive variety of examination administrations, including market passage studies, contender investigation, and financial investigations. MMRD has gained notoriety for its intensive and solid exploration, making it a confided in accomplice for both neighborhood and worldwide organizations. Boondocks Myanmar Exploration Boondocks Myanmar Exploration is one more outstanding player on the lookout. The organization represents considerable authority in giving experiences into Myanmar's advancing business sector scene. Their administrations incorporate area explicit exploration, purchaser conduct studies, and effect evaluations. Wilderness Myanmar Exploration is known for its inventive philosophies and capacity to adjust to the quickly changing economic situations. Understanding Myanmar Understanding Myanmar is a somewhat new participant in the statistical surveying industry however has rapidly earned respect for its excellent exploration and client-driven approach.

the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

Quantitative data tell us what people are doing. Qualitative data tell us why people are doing it. The best kind of research combines the two kinds of data.

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria Attitude toward the future Passive or reactive (the future will be) Proactive and creative (we create or shape the future) Way of thinking Generally deduction Greater use of induction

11.2.3 Quantitative and Qualitative Data Data is often described as being either quantitative or qualitative. Quantitative data is data that can be measured, written down

The cause-effect information is defined as the smaller (minimum) of the cause-information and the effect-information. If either one is zero, the cause-effect information is likewise zero. That is, the mechanism's past must be able to determine its present, which, in turn, must be able to determine its future. The more the past and the future are specified by the present state, the higher the mechanism's cause-effect power. Note that this usage of 'information' is very different from its customary meaning in engineering and science introduced by Claude Shannon. Shannon information, which is always assessed from the external perspective of an observer, quantifies how accurately signals transmitted over some noisy communication channel, such as a radio link or an optical cable, can be decoded. Data that distinguishes between two possibilities, OFF and ON, carries 1 bit of information. What that information is, though - the result of a critical blood test or the least significant bit in a pixel in the corner of a holiday photo - completely depends on the context. The meaning of Shannon information is in the eye of the beholder, not in the signals themselves. Shannon information is observational and extrinsic. Information in the sense of integrated information theory reflects a much older Aristotelian usage, derived from the Latin in-formare, 'to give form or shape to.' Integrated information gives rise to the cause-effect structure, a form. Integrated information is causal, intrinsic, and qualitative: it is assessed from the inner perspective of a system, based on how its mechanisms and its present state shape its own past and future. How the system constrains its past and future states determines whether the experience feels like azure blue or the smell of wet dog.

Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative approaches in many phases of the research process (J. W. Cresswell & Plano Clark, 2007, p. 5).

Hammersley (2014) suggests that qualitative researchers aim to describe causation in one of two ways: 1. Create plausible models of causation using imaginative tools like metaphors literary devices, and thought experiments to explain how a thing might work in many different contexts, in abstract language 2. Conduct a deep analysis of observed, empirical data to generate

The typical practical reasons why you might choose to mix methods include: 1. Time does not permit in-depth qualitative research 2. Time does not permit in-depth quantitative research 3. Negotiating access to participants is challenging 4. Finding a large data set is challenging

The bureaucratic method can be defined as one that (a) administers human beings as if they were things and (b) administers things in quantitative rather than qualitative terms, in order to make quantification and control easier and cheaper. The bureaucratic method is governed by statistical data: the bureaucrats base their decisions on fixed rules arrived at from statistical data, rather than on response to the living beings who stand before them; they decide issues according to what is statistically most likely to be the case, at the risk of hurting the 5 or 10 percent of those who do not fit into that pattern. Bureaucrats fear personal responsibility and seek refuge behind their rules; their security and pride lie in their loyalty to rules, not in their loyalty to the laws of the human heart.

Note that decisions driven by data are different from decisions informed by data. Data paints only half of the picture: what people are doing. You still need a qualitative picture to understand why people do things. Get both perspectives and you’re well positioned to experiment and try new things.

This is why you may need to blend quantitative and qualitative analysis. Our friend John Lilly likes to distinguish between “genius-driven design” (e.g., Apple) and “data-driven design” (e.g., Google). Both approaches have their strengths and weaknesses. Data-driven design is great at optimizing products with incremental changes, but it could steer you to the top of a local hill rather than the highest peak. Genius-driven design may be the only way to build a revolutionary product, but it usually needs to be supplemented with data-driven refinement

In chess one realises that all education is ultimately self education. This idea is a timely consideration in our data driven world. Chess lends itself to structural information and quantitive analysis in a range of ways. For instance the numerical value of the pieces, databases of millions of games, computerised evaluation functions and the international rating system. However, the value of the experience of playing the game is more qualitative than quantitive. Like any competitive pursuit or sport, chess is an elaborate pretext for the production of stories. The benign conceit of rules and points and tournaments generates a narrative experience in which you are at once co-director, actor and spectator. Chess is education in the literal sense of bringing forth, and it is self education because our stories about a game emerge as we play it, as we try to achieve our goals, just as they do in real life. Chess stories are of our own making and they are often about challenges we overcame or failed to overcome. Every chess player knows the experience of encountering a vexed colleague whose desperate to share their tragic tale in which they were “completely winning!” until they screwed up and lost. And yet we also know tougher characters who recognise that taking resolute responsibility for your mistakes, no matter how painful, is the way to grow as a person and a player. As the child psychologist Bruno Bettelheim says: "we grow, we find meaning in life and security in ourselves by having understood and solved personal problems on our own, not by having them explained to us by others”.

Here’s the problem: The future isn’t made up of predetermined, structured data alone. It changes as a result of people, and what we are learning, breaking, achieving, feeling, saying, thinking, and building in the present. Algorithms can’t account for the introduction of new qualitative variables, such as hardheaded CEOs, temperamental developers, or the eruption of mob justice within online communities.

Information is only as useful how is collected an interpreted. Algorithms are only as good as the scope and reliability of the data sets to which they are applied. So too, the findings of a qualitative researcher are only as good as that individual's neutrality, perceptiveness, and skill at eliciting anecdote and emotion. In other words, how well the qualitative researcher listens. At best, a quant can give you broad brushstrokes while a qual can provide finer detail. Both approaches are valid, and when used in concert, can be extremely revealing.  But when it comes to human interactions, and divining individual's unique motivations, proclivities and potentials, listing is so far the best and most accurate tool.

Information is only as useful as how it is collected and interpreted. Algorithms are only as good as the scope and reliability of the data sets to which they are applied. So too, the findings of a qualitative researcher are only as good as that individual's neutrality, perceptiveness, and skill at eliciting anecdote and emotion. In other words, how well the qualitative researcher listens. At best, a quant can give you broad brushstrokes while a qual can provide finer detail. Both approaches are valid, and when used in concert, can be extremely revealing.  But when it comes to human interactions, and divining individual's unique motivations, proclivities and potentials, listing is so far the best and most accurate tool.

Qualitative versus quantitative metrics Qualitative metrics are unstructured, anecdotal, revealing, and hard to aggregate; quantitative metrics involve numbers and statistics, and provide hard numbers but less insight. Vanity versus actionable metrics

Whether it’s anthropology or sociology or geography, social scientists are often asked – no, required – early in their careers, to choose between humanistic and scientific approaches to the subject matter of their discipline and between collecting and analyzing qualitative or quantitative data. Even worse, they are taught to equate science with quantitative data and quantitative analysis and humanism with qualitative data and qualitative analysis. This denies the grand tradition of qualitative approaches in all of science, from astronomy to zoology. When Galileo first trained his then-brand-new telescope on the moon, he noticed what he called lighter and darker areas. The large dark spots had, Galileo said, been seen from time immemorial and so he said, “These I shall call the ‘large’ or ‘ancient’ spots.” He also wrote that the moon was “not smooth, uniform, and precisely spherical” as commonly believed, but “uneven, rough, and full of cavities and prominences,” much like the Earth. No more qualitative description was ever penned

Amazon’s approach to metrics embodies the Customer Obsession leadership principle. The relevance of Customer Obsession becomes evident in the company’s focus on input versus output metrics. If you look at the input metrics for Amazon, they often describe things customers care about, such as low prices, lots of available products, fast shipping, few customer service contacts, and a speedy website or app. A lot of the output metrics, such as revenue and free cash flow, are what you’d typically see in a company’s financial report. Customers don’t care about those. But as we stated at the beginning of this book, Amazon has an unshakable conviction that the long-term interests of shareowners are perfectly aligned with the interests of customers. Controllable input metrics are a quantitative (diving deep with data) and qualitative (anecdotes) way of measuring how well the organization is satisfying these customer interests so that the output metrics trend the way the company desires.