Category: Analytic approaches


A few thoughts from Morgan Jones, master of analysis. Why is an analytical approach any better than what we are doing now? Can you prove an analytical approach is the only reliable way that can work? A more formal proof it's the only reliable way forward:. Proposition 1 - Analytical approach is only approach that works on difficult problems. Proposition 2 - The global environmental sustainability problem is a difficult problem.

Be careful. This article doesn't teach you what to think. It teaches you how to think. More than anything else, an analytical approach is the use of an appropriate process to break a problem down into the smaller pieces necessary to solve it. Each piece becomes a smaller and easier problem to solve. Problem solving is puzzle solving. Each smaller problem is a smaller piece of the puzzle to find and solve. Putting the pieces of the puzzle together involves understanding the relevant parts of the system.

Once all the key pieces are found and understood, the puzzle as a whole "snaps" together, sometimes in a final flash of insight. The key word in the above definition is "appropriate. This is the reason most people fail to solve difficult problems. They're using an inappropriate approach without realizing it.

The process doesn't fit the problem. You can look high and low, and under every bush in plain sight, but unless you're using an appropriate analytical approach you will never find enough pieces of the puzzle to solve a difficult problem. Even the most brilliant and heroic effort will lead to naught if you're using a problem solving process that doesn't fit the problem.

Lack of a process that fit the problem is why the alchemists failed to turn lead into gold. It's also why so many people and organizations, as well as entire social movements, are failing to turn opportunities into successes.

An analytical approach is also known as "structuring one's analysis. The book contains 14 powerful analytical techniques for solving difficult problems:. Exactly what does structuring one's analysis mean? The word analysis means separating a problem into its constituent elements. Doing so reduces complex issues to their simplest terms.

analytic approaches

If we are to solve problems, from those confined to a single individual to those affecting whole nations, we must learn how to identify and break out of restrictive mindsets and give full, serious consideration to alternative solutions. We must learn how to deal with the compulsions of the human mind that, by defeating objective analysis, close the mind to alternatives.

Failure to consider alternatives fully is the most common cause of flawed or incomplete analysis. As a result [of taking an instinctive, intuitive approach] we unwittingly, repeatedly, habitually commit a variety of analytic sins.An analytical approach is the use of analysis to break a problem down into the elements necessary to solve it. It's the same as formal analysis.

The approach one takes to solving a problem determines the probability of solving it. Difficult social problems require an analytical approach because they do not yield to any other approach.

NASA took an analytical approach when it had to solve the problem of how to put a man on the moon in less than ten years. Below is an overview of their approach:. Click on the image to see the full size diagram. Every item on the diagram is a smaller problem to solve. NASA broke the problem down into dozens of subproblems and then broke them down into further subproblems, and so on.

The result was thousands of smaller problems, each of which was easy to solve. The beauty of NASA's approach was it didn't just fit one program. It fits them all. This is a reusable process. Which problem is more difficult: flying Spaceship Earth into the Age of Sustainability or flying a Space Shuttle from the earth, into orbit, and back?

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Spaceship Earth contains seven billion people, while a Space Shuttle contains eleven people at the most. Spaceship Earth carries a payload of 2 trillion tons of biomass alone, while a Shuttle carries a maximum payload of 50, pounds. Spaceship Earth contains an estimated 7 to million species, whose members run into the trillions. Each is a living part. A Shuttle contains a little over 2. How does the approach the typical environmental organization is using to solve the sustainability problem compare to NASA's approach?

The goal of Thwink. Many such tools are presented on the site. When you put them all together, what you have is the right analytical approach. The problem is use of these tools is limited to science and business, like the NASA example. These tools are seldom to be found in public interest activism. As a result, activists are limited in what problems they can solve, since without these tools they are forced to fall back on intuition. That is exactly where scientists were before they discovered their own analytical approach.

This became known as the Scientific Method. Thus the higher level goal of Thwink. For much more see What Is an Analytical Approach?

analytic approaches

An intuitive approach requires no serious analysis. It thus goes much faster. It works fine on everyday problems, the sort we have encountered many times. But it fails spectacularly on difficult problems, because these are so different from what our intuition has been trained to handle.

The other side of the coin is an analytical approach. This uses analysis to look much deeper into the problem, find its root causesand then find out how to resolve the root causes.

This is so different from what most people are used to that it's a little hard to grasp at first. Most activists believe they are already are taking an analytical approach.

But this is a rather shallow analysis because it stops at the intermediate causes. An analytical approach would go all the way down to the root causes.The big data revolution has given birth to different kinds, types and stages of data analysis. Boardrooms across companies are buzzing around with data analytics - offering enterprise wide solutions for business success. However, what do these really mean to businesses?

The key to companies successfully using Big Datais by gaining the right information which delivers knowledge, that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.

Common Analytical Approaches

Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what distinguishes a best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged to benefit the business - at an optimum. The three dominant types of analytics —Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have.

Each of these analytic types offers a different insight. A lioness hired a data scientist fox to help find her prey. Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey.

Finally, based on above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. Big data analytics helps a business understand the requirements and preferences of a customer, so that businesses can increase their customer base and retain the existing ones with personalized and relevant offerings of their products or services.

The big data industry is growing at a rapid pace due to various applications like smart power grid management, sentiment analysis, fraud detection, personalized offerings, traffic management, etc. After the organizations collect big data, the next important step is to get started with analytics.

Many organizations do not know where to begin, what kind of analytics can nurture business growth and what these different types of analytics mean.

To help release your Data Science projects faster we have put together a library of solved code example.

Types of Analytics: descriptive, predictive, prescriptive analytics

Click here to get free access. This type of analytics, analyses the data coming in real-time and historical data for insights on how to approach the future.

The main objective of descriptive analytics is to find out the reasons behind precious success or failure in the past. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

A business learns from past behaviours to understand how they will impact future outcomes. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects.There is a wide range of interpretive or analytical approaches employed in qualitative research.

Here, we briefly describe a few interpretive approaches commonly used in health research:. Because of the diversity in analytical approaches that can be employed in qualitative research, when preparing a report or manuscript it is useful to:.

5 Text Analytics Approaches: A Comprehensive Review

These include:. If is fairly common in healthcare research to find study designs that merge qualitative and quantitative data. Below we provide several resources that discuss how to conduct studies that use multiple methods. Here, we overview a few key points. Some authors have noted that study designs that link qualitative and quantitative methods for purposes of confirmation or convergence of methods can be problematic.

Note that the concurrent design described by Miller and Crabtree above does not seek convergence among qualitative and quantitative data sources, but complementarity.

When analyzing qualitative and quantitative data, consider approaches for translating qualitative data into a quantitative form e. This will allow analysts to look across datasets and may foster more creative analyses. Creswell, JW. Greene, JC. San Francisco: Jossey-Bass. Miles, MB. Particularly pp. Miller, WL.

Handbook of Qualitative Research pp. Thousand oaks, CA: Sage Publications. Morgan, DL. Rossman, GB. Sandelowski, M. Stange, KC. Steckler, A. Miller and Crabtree highlight 4 broad approaches: Concurrent design - two independent studies are conducted on the same study population and the results are converged.

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For example, interventions might be enhanced if the researchers concurrently conduct an interpretive study to examine the process of implementing the intervention or improvement. Nested design - qualitative and quantitative methods can be integrated into a single research study.

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For example, qualitative studies can be used to understand and operationalize key variables at the same time outcomes are evaluated. Combination design - case study design that combines multiple methods in order to understand the complexity of a setting.

For example, a researcher may combine field methods sequentially with survey techniques, interviewing and record or chart review. Resources Creswell, JW. Citation: Cohen D, Crabtree B.Are you receiving more feedback than you could ever read, let alone summarize?

These methods range from simple techniques like word matching in Excel to neural networks trained on millions of data points. Here is my summary to break down these methods into 5 key approaches that are commonly used today.

Text analytics is the process of extracting meaning out of text. For example, this can be analyzing text written by customers in a customer survey, with the focus on finding common themes and trends. The idea is to be able to examine the customer feedback to inform the business on taking strategic action, in order to improve customer experience.

To make text analytics the most efficient, organisations can use text analytics software, leveraging machine learning and natural language processing algorithms to find meaning in enormous amounts of text. To take Thematic as an example, we analyze the free-text feedback submitted in customer feedback forms, which was previously difficult to analyze, as companies spend time and resource struggling to do this manually. Subsequently, we use text analytics to help companies find hidden customer insights and be able to easily answer questions about their existing customer data.

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In addition, with the help of text analytics software such as Thematic, companies can find recurrent and emerging themes, tracking trends and issues, and create visual reports for managers to track whether they are closing the loop with the end customer.

Happy to discuss this with anyone who is interested in providing feedback. The academic Natural Language Processing community does not register such an approach, and rightly so. In fact, in the academic world, word spotting refers to handwriting recognition spotting which word a person, a doctor perhaps, has written. There is also keyword spottingwhich focuses on speech processing. It probably took me less than 10 minutes to create this, and the result is so encouraging!

But wait…. Out of 7 comments, here only 3 were categorized correctly. You can imagine that the formula above can be tweaked further. If you have a dataset with a couple of hundred responses that you only need to analyze once or twice, you can use this approach. If the dataset is small, you can review the results and ensure high accuracy very quickly.

If you wish to build your own Text Analytics solution, check out our in-depth guide: How to build your own feedback analysis solution. The Manual Rules approach is closely related to word spotting. Both approaches operate on the same principle of creating a match pattern, but these patterns can also get quite complex. Majority of Text Analytics providers as well as many other smaller players, who sell Text Analytics as an add-on to their main offering, provide an interface that makes it easy to create and manage such rules.

They also sometimes offer professional services to help with the creation of these rules. The best thing about Manual Rules is that they can be understood by a person.

analytic approaches

They are explainable, and therefore can be tweaked and adjusted when needed. But the bottom line is that creating these rules takes a lot of effort. You also need to ensure that they are accurate and maintain them over time.

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To get you started, some companies come with pre-packaged rules, already organized into a taxonomy. They may also have specific categories setup for certain industries, e. The benefit of this approach is that once set up, you can run millions of feedback pieces and get a good overview of the core categories mentioned in the text. But, there are plenty of disadvantages for this approach, and in fact any manual rules and word spotting technique:. The most common reason why rules fail stems from polysemywhen the same word can have different meanings:.

Capturing sentiment with manually pre-set rules is impossible. People often do not realize how diverse and varied our language is. This is particularly problematic for the software industry, where each product is unique and the customer feedback talks about very specific issues.

analytic approaches

In any industry, even if you have a working rule-based taxonomy, someone with good linguistic knowledge would need to constantly maintain the rules to make sure all of the feedback is categorized accurately. And yet, despite these disadvantages, this approach is the most widely used commercial application of Text Analytics, with its roots in the 90s, and no clear path for fixing these issues.I need help getting started with at least words original only please.

If you can help me get started answering the questions below from one of the works listed I can finish it. Using one of the following 4 four works: 1. The welcome Table by Alice Walker 2. Country Lovers by Nadine Gordimer 3. Constantly Risking Absurdity by Lawrence Ferlinghetti. Answer the following questions 1. Explain why the literary work captured your interest, using terms and concepts such as ambiguity; genre; farce; imagination, satire 2.

Describe one of the analytical approaches. You may use of the the following analytical approaches: 1. Formalist Approach; 2.

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Response Approach; 3. Archetypal Approach. Evaluate the meaning of the selected literary work, using the analytical approach you described. The full response is in the attachment. I have copied and pasted what will show here in this text box, but it does not show hyperlinks or formatting, and the attachment does. Please open it. You have the option of choosing from among several analysis focuses, theories or styles.

I have modeled your response upon the Response analysis method, and refer you to what some Web-based resources have to say about this method below. The reader's reaction, how the piece affects the reader, is taken into consideration when interpreting a piece. Essentially what this theory means is that the text itself has meaning and the interaction between the reader and the text also carries meaning. The interpretation in Reader-Response Criticism lies between the intersection of these two sets of meaning when evaluating a text.

In a sense, this moves the text from existing on its own - on, for example, the physical pages of a book - and instead assumes that the text exists only when it is read. This theory makes literary works more like performance art where the reader's act of reading and interpreting the text is the performance.Analytic philosophyalso called linguistic philosophya loosely related set of approaches to philosophical problems, dominant in Anglo-American philosophy from the early 20th century, that emphasizes the study of language and the logical analysis of concepts.

Although most work in analytic philosophy has been done in Great Britain and the United Statessignificant contributions also have been made in other countries, notably AustraliaNew Zealandand the countries of Scandinavia.

Analytic philosophers conduct conceptual investigations that characteristically, though not invariably, involve studies of the language in which the concepts in question are, or can be, expressed. A perspicuous representation of these structures in the language of modern symbolic logic, so the formalists thought, would make clear the logically permissible inferences to and from such sentences and thereby establish the logical boundaries of the concept under study.

Another tradition, sometimes referred to as informalismsimilarly turned to the sentences in which the concept was expressed but instead emphasized their diverse uses in ordinary language and everyday situations, the idea being to elucidate the concept by noting how its various features are reflected in how people actually talk and act.

Even among analytic philosophers whose approaches were not essentially either formalist or informalist, philosophical problems were often conceived of as problems about the nature of language. An influential debate in analytic ethicsfor example, concerned the question of whether sentences that express moral judgments e.

Thus, in this debate the philosophical problem of the nature of right and wrong was treated as a problem about the logical or grammatical status of moral statements. In spirit, style, and focus, analytic philosophy has strong ties to the tradition of empiricismwhich has characterized philosophy in Britain for some centuries, distinguishing it from the rationalism of Continental European philosophy.

In fact, the beginning of modern analytic philosophy is usually dated from the time when two of its major figures, Bertrand Russell — and G. Moore —rebelled against an antiempiricist idealism that had temporarily captured the English philosophical scene.

The most renowned of the British empiricists— John LockeGeorge BerkeleyDavid Humeand John Stuart Mill —have many interests and methods in common with contemporary analytic philosophers.

Most empiricists, though admitting that the senses fail to yield the certainty requisite for knowledge, hold nonetheless that it is only through observation and experimentation that justified beliefs about the world can be gained—in other words, a priori reasoning from self-evident premises cannot reveal how the world is.

Accordingly, many empiricists insist on a sharp dichotomy between the physical sciences, which ultimately must verify their theories by observation, and the deductive or a priori sciences—e. This conclusion was a cornerstone of two important early movements in analytic philosophy, logical atomism and logical positivism. The question then arises whether philosophy itself is to be assimilated to the empirical or to the a priori sciences. Early empiricists assimilated it to the empirical sciences.

Moreover, they were less self-reflective about the methods of philosophy than are contemporary analytic philosophers. Preoccupied with epistemology the theory of knowledge and the philosophy of mindand holding that fundamental facts can be learned about these subjects from individual introspectionearly empiricists took their work to be a kind of introspective psychology.

Analytic philosophers in the 20th century, on the other hand, were less inclined to appeal ultimately to direct introspection. More important, the development of modern symbolic logic seemed to promise help in solving philosophical problems—and logic is as a priori as science can be. It seemed, then, that philosophy must be classified with mathematics and logic.

The exact nature and proper methodology of philosophy, however, remained in dispute. For philosophers oriented toward formalism, the advent of modern symbolic logic in the late 19th century was a watershed in the history of philosophy, because it added greatly to the class of statements and inferences that could be represented in formal i.

The formal representation of these statements provided insight into their underlying logical structures; at the same time, it helped to dispel certain philosophical puzzles that had been created, in the view of the formalists, through the tendency of earlier philosophers to mistake surface grammatical form for logical form.