Assessment Data Quotes

When moral posturing is replaced by an honest assessment of the data, the result is often a new, surprising insight.

It is well and good to opine or theorize about a subject, as humankind is wont to do, but when moral posturing is replaced by an honest assessment of the data, the result is often a new, surprising insight.

Reaction is just that—an action you have taken before. When you “re-act,” what you do is assess the incoming data, search your memory bank for the same or nearly the same experience, and act the way you did before. This is all the work of the mind, not of your soul.

The important thing with Elon,” he says, “is that if you told him the risks and showed him the engineering data, he would make a quick assessment and let the responsibility shift from your shoulders to his.

CSIPP™ stresses the importance of data in informing risk assessments and crisis response strategies. By utilizing data effectively, organizations can make informed decisions that protect their reputation and stakeholder trust.

When the tragedies of others become for us diversions, sad stories with which to enthrall our friends, interesting bits of data to toss out at cocktail parties, a means of presenting a pose of political concern, or whatever…when this happens we commit the gravest of sins, condemn ourselves to ignominy, and consign the world to a dangerous course. We begin to justify our casual overview of pain and suffering by portraying ourselves as do-gooders incapacitated by the inexorable forces of poverty, famine, and war. “What can I do?” we say, “I’m only one person, and these things are beyond my control. I care about the world’s trouble, but there are no solutions.” Yet no matter how accurate this assessment, most of us are relying on it to be true, using it to mask our indulgence, our deep-seated lack of concern, our pathological self-involvement.

Maintaining good accounting records is vital to the successful management of a business. It's really good to be able to assess business-specific financial data to inform decisions. So every business should invest in good accounting software like Intuit, Quicken, or Freshbooks... Or any of the many apps out there.

But the history of science—by far the most successful claim to knowledge accessible to humans—teaches that the most we can hope for is successive improvement in our understanding, learning from our mistakes, an asymptotic approach to the Universe, but with the proviso that absolute certainty will always elude us. We will always be mired in error. The most each generation can hope for is to reduce the error bars a little, and to add to the body of data to which error bars apply. The error bar is a pervasive, visible self-assessment of the reliability of our knowledge.

During sustained stress, the amygdala processes emotional sensory information more rapidly and less accurately, dominates hippocampal function, and disrupts frontocortical function; we’re more fearful, our thinking is muddled, and we assess risks poorly and act impulsively out of habit, rather than incorporating new data.

We have noted that gut feelings are an important part of the body’s sensory apparatus, helping us to evaluate the environment and assess whether a situation is safe. Gut feelings magnify perceptions that the emotional centres of the brain find important and relay through the hypothalamus. Pain in the gut is one signal the body uses to send messages that are difficult for us to ignore. Thus, pain is also a mode of perception. Physiologically, the pain pathways channel information that we have blocked from reaching us by more direct routes. Pain is a powerful secondary mode of perception to alert us when our primary modes have shut down. It provides us with data that we ignore at our peril.

But why should we accept that the way men do things, the way men see themselves, is the correct way? Recent research has emerged showing that while women tend to assess their intelligence accurately, men of average intelligence think they are more intelligent than two-thirds of people. This being the case, perhaps it wasn’t that women’s rates of putting themselves up for promotion were too low. Perhaps it was that men’s were too high.

In fact, the greatest savings from wellness programs come from the penalties assessed on the workers. In other words, like scheduling algorithms, they provide corporations with yet another tool to raid their employees’ paychecks.

Uncertainty is an acid, corrosive to authority. Once the monopoly on information is lost, so too is our trust. Every presidential statement, every CIA assessment, every investigative report by a great newspaper, suddenly acquired an arbitrary aspect, and seemed grounded in moral predilection rather than intellectual rigor. When proof for and against approaches infinity, a cloud of suspicion about cherry-picking data will hang over every authoritative judgment.

impressive series of studies by Thomas Åstebro sheds light on what happens when optimists receive bad news. He drew his data from a Canadian organization—the Inventor’s Assistance Program—which collects a small fee to provide inventors with an objective assessment of the commercial prospects of their idea.

At the first trans health conference I ever attended, a parent asked about long-term health risks for people taking hormones. The doctor gave a full assessment of issues that trans men face; many of them mimic the risks that would be inherited from father to son if they'd been born male, now that testosterone is a factor. "What about trans women?" another parent asked. The doctor took a deep breath. "Those outcomes are murkier. Because trans women are so discriminated against, they're at far greater risk for issues like alcoholism, poverty, homelessness, and lack of access to good healthcare. All of these issues impact their overall health so much that it's hard to gather data on what their health outcomes would be if these issues weren't present." This was stunning-a group of people is treated so badly by our culture that we can't clearly study their health. The burden of this abuse is that substantial and pervasive. Your generation will be healthier. The signs are already there.

The witch-hunt narrative is now the conventional wisdom about these cases. That view is so widely endorsed and firmly entrenched that so widely endorsed and firmly entrenched that there would seem to be nothing left to say about these cases. But a close examination of the witch hunt canon leads to some unsettling questions: Why is there so little in the way of academic scholarship about these cases? Almost all of the major witch-hunt writings have been in magazines, often without any footnotes to verify or assess the claims made. Why hasn't anyone writing about these cases said anything about how difficult they are to research? There are so many roadblocks and limitations to researching these cases that it would seem incumbent on any serious writer to address the limitations of data sources. Many of these cases seem to have been researched in a manner of days or weeks. Nevertheless, the cases are described in a definitive way that belies their length and complexity, along with the inherent difficulty in researching original trial court documents. This book is based on the first systematic examination of court records in these cases.

Assessment can be either formal and/or informal measures that gather information. In education, meaningful assessment is data that guides and informs the teacher and/or stakeholders of students' abilities, strategies, performance, content knowledge, feelings and/or attitudes. Information obtained is used to make educational judgements or evaluative statements. Most useful assessment is data which is used to adjust curriculum in order to benefit the students. Assessment should be used to inform instruction. Diagnosis and assessment should document literacy in real-world contexts using data as performance indicators of students' growth and development.

Given the central place that technology holds in our lives, it is astonishing that technology companies have not put more resources into fixing this global problem. Advanced computer systems and artificial intelligence (AI) could play a much bigger role in shaping diagnosis and prescription. While the up-front costs of using such technology may be sizeable, the long-term benefits to the health-care system need to be factored into value assessments. We believe that AI platforms could improve on the empirical prescription approach. Physicians work long hours under stressful conditions and have to keep up to date on the latest medical research. To make this work more manageable, the health-care system encourages doctors to specialize. However, the vast majority of antibiotics are prescribed either by generalists (e.g., general practitioners or emergency physicians) or by specialists in fields other than infectious disease, largely because of the need to treat infections quickly. An AI system can process far more information than a single human, and, even more important, it can remember everything with perfect accuracy. Such a system could theoretically enable a generalist doctor to be as effective as, or even superior to, a specialist at prescribing. The system would guide doctors and patients to different treatment options, assigning each a probability of success based on real-world data. The physician could then consider which treatment was most appropriate.

The umbrella assertion made by Team B—and the most inflammatory—was that the previous National Intelligence Estimates “substantially misperceived the motivations behind Soviet strategic programs, and thereby tended consistently to underestimate their intensity, scope, and implicit threat.” Soviet military leaders weren’t simply trying to defend their territory and their people; they were readying a First Strike option, and the US intelligence community had missed it. What led to this “grave and dangerous flaw” in threat assessment, according to Team B, was an overreliance on hard technical facts, and a lamentable tendency to downplay “the large body of soft data.” This “soft” data, the ideological leader of Team B, Richard Pipes, would later say, included “his deep knowledge of the Russian soul.

I know that the consequences of scientific illiteracy are far more dangerous in our time than in any that has come before. It’s perilous and foolhardy for the average citizen to remain ignorant about global warming, say, or ozone depletion, air pollution, toxic and radioactive wastes, acid rain, topsoil erosion, tropical deforestation, exponential population growth. Jobs and wages depend on science and technology. If our nation can’t manufacture, at high quality and low price, products people want to buy, then industries will continue to drift away and transfer a little more prosperity to other parts of the world. Consider the social ramifications of fission and fusion power, supercomputers, data “highways,” abortion, radon, massive reductions in strategic weapons, addiction, government eavesdropping on the lives of its citizens, high-resolution TV, airline and airport safety, fetal tissue transplants, health costs, food additives, drugs to ameliorate mania or depression or schizophrenia, animal rights, superconductivity, morning-after pills, alleged hereditary antisocial predispositions, space stations, going to Mars, finding cures for AIDS and cancer. How can we affect national policy—or even make intelligent decisions in our own lives—if we don’t grasp the underlying issues? As I write, Congress is dissolving its own Office of Technology Assessment—the only organization specifically tasked to provide advice to the House and Senate on science and technology. Its competence and integrity over the years have been exemplary. Of the 535 members of the U.S. Congress, rarely in the twentieth century have as many as one percent had any significant background in science. The last scientifically literate President may have been Thomas Jefferson.* So how do Americans decide these matters? How do they instruct their representatives? Who in fact makes these decisions, and on what basis? —

Imagine you're sitting having dinner in a restaurant. At some point during the meal, your companion leans over and whispers that they've spotted Lady Gaga eating at the table opposite. Before having a look for yourself, you'll no doubt have some sense of how much you believe your friends theory. You'll take into account all of your prior knowledge: perhaps the quality of the establishment, the distance you are from Gaga's home in Malibu, your friend's eyesight. That sort of thing. If pushed, it's a belief that you could put a number on. A probability of sorts. As you turn to look at the woman, you'll automatically use each piece of evidence in front of you to update your belief in your friend's hypothesis Perhaps the platinum-blonde hair is consistent with what you would expect from Gaga, so your belief goes up. But the fact that she's sitting on her own with no bodyguards isn't, so your belief goes down. The point is, each new observations adds to your overall assessment. This is all Bayes' theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence. It accepts that you can't ever be completely certain about the theory you are considering, but allows you to make a best guess from the information available. So, once you realize the woman at the table opposite is wearing a dress made of meat -- a fashion choice that you're unlikely to chance up on in the non-Gaga population -- that might be enough to tip your belief over the threshold and lead you to conclude that it is indeed Lady Gaga in the restaurant. But Bayes' theorem isn't just an equation for the way humans already make decisions. It's much more important that that. To quote Sharon Bertsch McGrayne, author of The Theory That Would Not Die: 'Bayes runs counter to the deeply held conviction that modern science requires objectivity and precision. By providing a mechanism to measure your belief in something, Bayes allows you to draw sensible conclusions from sketchy observations, from messy, incomplete and approximate data -- even from ignorance.

The UN investigators point out many of the other issues we’d tried and failed to convince Facebook’s leaders to address: the woefully inadequate content moderation Facebook provided for Myanmar; the lack of moderators who “understand Myanmar language and its nuances, as well as the context within which comments are made”; the fact that the Burmese language isn’t rendered in Unicode; the lack of a clear system to report hate speech and alarming unresponsiveness when it is reported. The investigators noted with regret that Facebook said it was unable to provide country-specific data about the spread of hate speech on its platform, which was imperative to assess the problem and the adequacy of its response. This was surprising given that Facebook had been tracking hate speech. Community operations had written an internal report noting that forty-five of the one hundred most active hate speech accounts in Southeast Asia are in Myanmar. The truth here is inescapable. Myanmar would’ve been far better off if Facebook had never arrived there.

There was little effort to conceal this method of doing business. It was common knowledge, from senior managers and heads of research and development to the people responsible for formulation and the clinical people. Essentially, Ranbaxy’s manufacturing standards boiled down to whatever the company could get away with. As Thakur knew from his years of training, a well-made drug is not one that passes its final test. Its quality must be assessed at each step of production and lies in all the data that accompanies it. Each of those test results, recorded along the way, helps to create an essential roadmap of quality. But because Ranbaxy was fixated on results, regulations and requirements were viewed with indifference. Good manufacturing practices were stop signs and inconvenient detours. So Ranbaxy was driving any way it chose to arrive at favorable results, then moving around road signs, rearranging traffic lights, and adjusting mileage after the fact. As the company’s head of analytical research would later tell an auditor: “It is not in Indian culture to record the data while we conduct our experiments.

Though Hoover conceded that some might deem him a “fanatic,” he reacted with fury to any violations of the rules. In the spring of 1925, when White was still based in Houston, Hoover expressed outrage to him that several agents in the San Francisco field office were drinking liquor. He immediately fired these agents and ordered White—who, unlike his brother Doc and many of the other Cowboys, wasn’t much of a drinker—to inform all of his personnel that they would meet a similar fate if caught using intoxicants. He told White, “I believe that when a man becomes a part of the forces of this Bureau he must so conduct himself as to remove the slightest possibility of causing criticism or attack upon the Bureau.” The new policies, which were collected into a thick manual, the bible of Hoover’s bureau, went beyond codes of conduct. They dictated how agents gathered and processed information. In the past, agents had filed reports by phone or telegram, or by briefing a superior in person. As a result, critical information, including entire case files, was often lost. Before joining the Justice Department, Hoover had been a clerk at the Library of Congress—“ I’m sure he would be the Chief Librarian if he’d stayed with us,” a co-worker said—and Hoover had mastered how to classify reams of data using its Dewey decimal–like system. Hoover adopted a similar model, with its classifications and numbered subdivisions, to organize the bureau’s Central Files and General Indices. (Hoover’s “Personal File,” which included information that could be used to blackmail politicians, would be stored separately, in his secretary’s office.) Agents were now expected to standardize the way they filed their case reports, on single sheets of paper. This cut down not only on paperwork—another statistical measurement of efficiency—but also on the time it took for a prosecutor to assess whether a case should be pursued.

In 1942, Merton set out four scientific values, now known as the ‘Mertonian Norms’. None of them have snappy names, but all of them are good aspirations for scientists. First, universalism: scientific knowledge is scientific knowledge, no matter who comes up with it – so long as their methods for finding that knowledge are sound. The race, sex, age, gender, sexuality, income, social background, nationality, popularity, or any other status of a scientist should have no bearing on how their factual claims are assessed. You also can’t judge someone’s research based on what a pleasant or unpleasant person they are – which should come as a relief for some of my more disagreeable colleagues. Second, and relatedly, disinterestedness: scientists aren’t in it for the money, for political or ideological reasons, or to enhance their own ego or reputation (or the reputation of their university, country, or anything else). They’re in it to advance our understanding of the universe by discovering things and making things – full stop.20 As Charles Darwin once wrote, a scientist ‘ought to have no wishes, no affections, – a mere heart of stone.’ The next two norms remind us of the social nature of science. The third is communality: scientists should share knowledge with each other. This principle underlies the whole idea of publishing your results in a journal for others to see – we’re all in this together; we have to know the details of other scientists’ work so that we can assess and build on it. Lastly, there’s organised scepticism: nothing is sacred, and a scientific claim should never be accepted at face value. We should suspend judgement on any given finding until we’ve properly checked all the data and methodology. The most obvious embodiment of the norm of organised scepticism is peer review itself. 20. Robert K. Merton, ‘The Normative Structure of Science’ (1942), The Sociology of Science: Empirical and Theoretical Investigations (Chicago and London: University of Chicago Press, 1973): pp. 267–278.

As Graedon scrutinized the FDA’s standards for bioequivalence and the data that companies had to submit, he found that generics were much less equivalent than commonly assumed. The FDA’s statistical formula that defined bioequivalence as a range—a generic drug’s concentration in the blood could not fall below 80 percent or rise above 125 percent of the brand name’s concentration, using a 90 percent confidence interval—still allowed for a potential outside range of 45 percent among generics labeled as being the same. Patients getting switched from one generic to another might be on the low end one day, the high end the next. The FDA allowed drug companies to use different additional ingredients, known as excipients, that could be of lower quality. Those differences could affect a drug’s bioavailability, the amount of drug potentially absorbed into the bloodstream. But there was another problem that really drew Graedon’s attention. Generic drug companies submitted the results of patients’ blood tests in the form of bioequivalence curves. The graphs consisted of a vertical axis called Cmax, which mapped the maximum concentration of drug in the blood, and a horizontal axis called Tmax, the time to maximum concentration. The resulting curve looked like an upside-down U. The FDA was using the highest point on that curve, peak drug concentration, to assess the rate of absorption into the blood. But peak drug concentration, the point at which the blood had absorbed the largest amount of drug, was a single number at one point in time. The FDA was using that point as a stand-in for “rate of absorption.” So long as the generic hit a similar peak of drug concentration in the blood as the brand name, it could be deemed bioequivalent, even if the two curves reflecting the time to that peak looked totally different. Two different curves indicated two entirely different experiences in the body, Graedon realized. The measurement of time to maximum concentration, the horizontal axis, was crucial for time-release drugs, which had not been widely available when the FDA first created its bioequivalence standard in 1992. That standard had not been meaningfully updated since then. “The time to Tmax can vary all over the place and they don’t give a damn,” Graedon emailed a reporter. That “seems pretty bizarre to us.” Though the FDA asserted that it wouldn’t approve generics with “clinically significant” differences in release rates, the agency didn’t disclose data filed by the companies, so it was impossible to know how dramatic the differences were.

Henry, there’s something I would like to tell you, for what it’s worth, something I wish I had been told years ago. You’ve been a consultant for a long time, and you’ve dealt a great deal with top secret information. But you’re about to receive a whole slew of special clearances, maybe fifteen or twenty of them, that are higher than top secret. I’ve had a number of these myself, and I’ve known other people who have just acquired them, and I have a pretty good sense of what the effects of receiving these clearances are on a person who didn’t previously know they even existed. And the effects of reading the information that they will make available to you. First, you’ll be exhilarated by some of this new information, and by having it all—so much! incredible!—suddenly available to you. But second, almost as fast, you will feel like a fool for having studied, written, talked about these subjects, criticized and analyzed decisions made by presidents for years without having known of the existence of all this information, which presidents and others had and you didn’t, and which must have influenced their decisions in ways you couldn’t even guess. In particular, you’ll feel foolish for having literally rubbed shoulders for over a decade with some officials and consultants who did have access to all this information you didn’t know about and didn’t know they had, and you’ll be stunned that they kept that secret from you so well. You will feel like a fool, and that will last for about two weeks. Then, after you’ve started reading all this daily intelligence input and become used to using what amounts to whole libraries of hidden information, which is much more closely held than mere top secret data, you will forget there ever was a time when you didn’t have it, and you’ll be aware only of the fact that you have it now and most others don’t … and that all those other people are fools. Over a longer period of time—not too long, but a matter of two or three years—you’ll eventually become aware of the limitations of this information. There is a great deal that it doesn’t tell you, it’s often inaccurate, and it can lead you astray just as much as the New York Times can. But that takes a while to learn. In the meantime it will have become very hard for you to learn from anybody who doesn’t have these clearances. Because you’ll be thinking as you listen to them: “What would this man be telling me if he knew what I know? Would he be giving me the same advice, or would it totally change his predictions and recommendations?” And that mental exercise is so torturous that after a while you give it up and just stop listening. I’ve seen this with my superiors, my colleagues … and with myself. You will deal with a person who doesn’t have those clearances only from the point of view of what you want him to believe and what impression you want him to go away with, since you’ll have to lie carefully to him about what you know. In effect, you will have to manipulate him. You’ll give up trying to assess what he has to say. The danger is, you’ll become something like a moron. You’ll become incapable of learning from most people in the world, no matter how much experience they may have in their particular areas that may be much greater than yours.

Well before the end of the 20th century however print had lost its former dominance. This resulted in, among other things, a different kind of person getting elected as leader. One who can present himself and his programs in a polished way, as Lee Quan Yu you observed in 2000, adding, “Satellite television has allowed me to follow the American presidential campaign. I am amazed at the way media professionals can give a candidate a new image and transform him, at least superficially, into a different personality. Winning an election becomes, in large measure, a contest in packaging and advertising. Just as the benefits of the printed era were inextricable from its costs, so it is with the visual age. With screens in every home entertainment is omnipresent and boredom a rarity. More substantively, injustice visualized is more visceral than injustice described. Television played a crucial role in the American Civil rights movement, yet the costs of television are substantial, privileging emotional display over self-command, changing the kinds of people and arguments that are taken seriously in public life. The shift from print to visual culture continues with the contemporary entrenchment of the Internet and social media, which bring with them four biases that make it more difficult for leaders to develop their capabilities than in the age of print. These are immediacy, intensity, polarity, and conformity. Although the Internet makes news and data more immediately accessible than ever, this surfeit of information has hardly made us individually more knowledgeable, let alone wiser, as the cost of accessing information becomes negligible, as with the Internet, the incentives to remember it seem to weaken. While forgetting anyone fact may not matter, the systematic failure to internalize information brings about a change in perception, and a weakening of analytical ability. Facts are rarely self-explanatory; their significance and interpretation depend on context and relevance. For information to be transmuted into something approaching wisdom it must be placed within a broader context of history and experience. As a general rule, images speak at a more emotional register of intensity than do words. Television and social media rely on images that inflamed the passions, threatening to overwhelm leadership with the combination of personal and mass emotion. Social media, in particular, have encouraged users to become image conscious spin doctors. All this engenders a more populist politics that celebrates utterances perceived to be authentic over the polished sound bites of the television era, not to mention the more analytical output of print. The architects of the Internet thought of their invention as an ingenious means of connecting the world. In reality, it has also yielded a new way to divide humanity into warring tribes. Polarity and conformity rely upon, and reinforce, each other. One is shunted into a group, and then the group polices once thinking. Small wonder that on many contemporary social media platforms, users are divided into followers and influencers. There are no leaders. What are the consequences for leadership? In our present circumstances, Lee's gloomy assessment of visual media's effects is relevant. From such a process, I doubt if a Churchill or Roosevelt or a de Gaulle can emerge. It is not that changes in communications technology have made inspired leadership and deep thinking about world order impossible, but that in an age dominated by television and the Internet, thoughtful leaders must struggle against the tide.

Police recording of false allegations of rape: "The data on the pro formas limit the extent to which one can assess the police designations, but their internal rules on false complaints specify that this category should be limited to cases where either there is a clear and credible admission by the complainants, or where there are strong evidential grounds. On this basis, and bearing in mind the data limitations, for the cases where there is information (n=144) the designation of false complaint could be said to be probable (primarily those where the account by the complainant is referred to) in 44 cases, possible (primarily where there is some evidential basis) in a further 33 cases, and uncertain (including where victim characteristics are used to impute that they are inherently less believable) in 77 cases. If the proportion of false complaints on the basis of the probable and possible cases are recalculated, rates of three per cent are obtained, both of all reported cases (n=67 of 2,643), and of those where the outcome is known (n=67 of 2,284). Even if all those designated false by the police were accepted (a figure of approximately ten per cent), this is still much lower than the rate perceived by police officers interviewed in this study. A question asked of all of them was how they assessed truth and falsity in allegations and within this, 50 per cent (n=31) further discussed the issue of false allegations." A gap or a chasm?: attrition in reported rape cases.

The important point here is that with hindsight it is always possible to spot the most anomalous features of the data and build a favorable statistical analysis around them. However, a properly-trained scientist (or simply a wise person) avoids doing so because he or she recognizes that constructing a statistical analysis retrospectively capitalizes too much on chance and renders the analysis meaningless. To the scientist, such apparent anomalies merely suggest hypotheses that are subsequently tested on other, independent sets of data. Only if the anomaly persists is the hypothesis to be taken seriously. Unfortunately, the intuitive assessments of the average person are not bound by these constraints. Hypotheses that are formed on the basis of one set of results are considered to have been proven by those very same results. By retrospectively and selectively perusing the data in this way, people tend to make too much of apparent anomalies and too often end up detecting order where none exists.

We conducted research on the competencies and development requirements of each state. The required information was collected from the Planning Commission, government departments—both central and state—national and international assessments of the state and other relevant documents. The data was analysed and put in a presentable form using graphics and multimedia. At the meetings, PowerPoint presentations were made to the MPs with an emphasis on three areas: 1) the vision for a developed India; 2) the heritage of the particular states or union territory; and 3) their core competencies. The objective was to stress the point that to achieve the development of the nation, it was vital to achieve the development of each of these areas. Hence a fourth aspect was also prepared—selected development indicators for each of them. And what an enrichment I got by way of preparation and by the contributions of the members of Parliament, who hailed from all parties. Meeting them helped me to understand the richness of the diverse parts of the country.

When little-league baseball players are thought to be incompetent, they are only allowed to play where the ball is rarely hit (for little leaguers, in right field), and thus they have few opportunities to overcome their unfortunate reputation. The continued absence of any positive contributions can then easily be mistaken for an absence of talent rather than an absence of opportunity. This type of expectancy effect is obviously a special case of the hidden data problem described above. A perceiver’s expectation can cause him or her to behave in such a way that certain behaviors by the target person cannot be observed, making what is observed a biased and misleading indicator of what that person is like. The employers, college admissions officers, and grant review panelists discussed earlier are all potential victims of seemingly-fulfilled prophecies: Their own actions guarantee that they will rarely receive a challenge to their negative assessments of job applicants, potential students, and research proposals.

Even though there is no evidence of a direct relationship between Trump and these extremist groups, Fortune magazine assessed the impact of the interactions between them. Using social media analytics software, it tracked the campaign’s connections to white supremacists. Locating the white supremacists who were considered social media “influencers,” Fortune discovered that a significant number of Trump campaign workers followed the leading #WhiteGenocide influencers. The study concluded that “the data shows…that Donald Trump and his campaign have used social media to court support within the white supremacist community, whether intentionally or unintentionally.

In the course of our personal and professional lives, we often run into situations that appear puzzling at first blush. We cannot see for the life of us why Mr. X acted in a particular way, we cannot understand how the experimental results came out the way they did, etc. Typically, however, within a very short time we come up with an explanation, a hypothesis, or an interpretation of the facts that renders them understandable, coherent, or natural. The same phenomenon is observed in perception. People are very good at detecting patterns and trends even in random data. In contrast to our skill in inventing scenarios, explanations, and interpretations, our ability to assess their likelihood, or to evaluate them critically, is grossly inadequate. Once we have adopted a particular hypothesis or interpretation, we grossly exaggerate the likelihood of that hypothesis, and find it very difficult to see things any other way.

When scientific proposals are brought forward, they are not judged by hunches or gut feelings. Only one standard is relevant: a proposal's ability to explain or predict experimental data and astronomical observations. Therein lies the singular beauty of science. As we struggle toward deeper understanding, we must give our creative imagination ample room to explore. We must be willing to step outside conventional ideas and established frameworks. But unlike the wealth of other human activities through which the creative impulse is channeled, science supplies a final reckoning, a built-in assessment of what's right and what's not. A complication of scientific life in the late twentieth and early twenty-first centuries is that some of our theoretical ideas have soared past our ability to test or observe. String theory has for some time been the poster child for this situation; the possibility that we're part of a multiverse provides an even more sprawling example. I've laid out a general prescription for how a multiverse proposal might be testable, but at our current level of understanding none of the multiverse theories we've encountered yet meet the criteria. With ongoing research, this situation could greatly improve.

Passage Five: From Business Manager to Group Manager This is another leadership passage that at first glance doesn’t seem overly arduous. The assumption is that if you can run one business successfully, you can do the same with two or more businesses. The flaw in this reasoning begins with what is valued at each leadership level. A business manager values the success of his own business. A group manager values the success of other people’s businesses. This is a critical distinction because some people only derive satisfaction when they’re the ones receiving the lion’s share of the credit. As you might imagine, a group manager who doesn’t value the success of others will fail to inspire and support the performance of the business managers who report to him. Or his actions might be dictated by his frustration; he’s convinced he could operate the various businesses better than any of his managers and wishes he could be doing so. In either instance, the leadership pipeline becomes clogged with business managers who aren’t operating at peak capacity because they’re not being properly supported or their authority is being usurped. This level also requires a critical shift in four skill sets. First, group managers must become proficient at evaluating strategy for capital allocation and deployment purposes. This is a sophisticated business skill that involves learning to ask the right questions, analyze the right data, and apply the right corporate perspective to understand which strategy has the greatest probability of success and therefore should be funded. The second skill cluster involves development of business managers. As part of this development, group managers need to know which of the function managers are ready to become business managers. Coaching new business managers is also an important role for this level. The third skill set has to do with portfolio strategy. This is quite different from business strategy and demands a perceptual shift. This is the first time managers have to ask these questions: Do I have the right collection of businesses? What businesses should be added, subtracted, or changed to position us properly and ensure current and future earnings? Fourth, group managers must become astute about assessing whether they have the right core capabilities. This means avoiding wishful thinking and instead taking a hard, objective look at their range of resources and making a judgment based on analysis and experience. Leadership becomes more holistic at this level. People may master the required skills, but they won’t perform at full leadership capacity if they don’t begin to see themselves as broad-gauged executives. By broad-gauged, we mean that managers need to factor in the complexities of running multiple businesses, thinking in terms of community, industry, government,

Perhaps the most obvious difference between modern social and personality psychology is that the former is based almost exclusively on experiments, whereas the latter is usually based on correlational studies. […] In summary, over the past 50 years social psychology has concentrated on the perceptual and cognitive processes of person perceivers, with scant attention to the persons being perceived. Personality psychology has had the reverse orientation, closely examining self-reports of individuals for indications of their personality traits, but rarely examining how these people actually come off in social interaction. […] individuals trained in either social or personality psychology are often more ignorant of the other field than they should be. Personality psychologists sometimes reveal an imperfect understanding of the concerns and methods of their social psychological brethren, and they in particular fail to comprehend the way in which so much of the self-report data they gather fails to overcome the skepticism of those trained in other methods. For their part, social psychologists are often unfamiliar with basic findings and concepts of personality psychology, misunderstand common statistics such as correlation coefficients and other measures of effect size, and are sometimes breathtakingly ignorant of basic psychometric principles. This is revealed, for example, when social psychologists, assuring themselves that they would not deign to measure any entity so fictitious as a trait, proceed to construct their own self-report scales to measure individual difference constructs called schemas or strategies or construals (never a trait). But they often fail to perform the most elementary analyses to confirm the internal consistency or the convergent and discriminant validity of their new measures, probably because they do not know that they should. […] an astonishing number of research articles currently published in major journals demonstrate a complete innocence of psychometric principles. Social psychologists and cognitive behaviorists who overtly eschew any sympathy with the dreaded concept of ‘‘trait’’ freely report the use of self-report assessment instruments of completely unknown and unexamined reliability, convergent validity, or discriminant validity. It is almost as if they believe that as long as the individual difference construct is called a ‘‘strategy,’’ ‘‘schema,’’ or ‘‘implicit theory,’’ then none of these concepts is relevant. But I suspect the real cause of the omission is that many investigators are unfamiliar with these basic concepts, because through no fault of their own they were never taught them.

The Italian-owned Benetton label, for example, manufactures its entire clothing line in white. Once the clothes are delivered to distribution centers, Benneton’s analysts assess what color or length is in vogue, at which point workers dye and cut the company’s shirts, jackets, pants and infant apparel to replicate the style and color preferences popular at the time.

With 70 percent accuracy, my source tells me, software can assess how people feel based on the way they type, and the number of typos they make. With 79 percent precision, software can determine a user’s credit rating based on the degree to which they write in ALL CAPS.

There are three categories of criteria that an individual must meet in order to be diagnosed with ASD. The categories are listed below along with the typical traits, which may indicate whether the individual needs further assessment: 1.Persistent deficits in social communication and social interaction across contexts, not accounted for by general developmental delays: lack of friends and social life friends often much older or younger mumbling and not completing sentences issues with social rules (such as staring at other people) inability to understand jokes and the benefit of ‘small talk’ introverted (shy) and socially awkward inability to understand other people’s thoughts and feelings uncomfortable in large crowds and noisy places detached and emotionally inexpressive. 2.Restricted, repetitive patterns of behaviour, interests or activities: obsession with ‘special interests’ collecting objects (such as stamps and coins) attachment to routines and rituals ability to focus on a single task for long periods eccentric or unorthodox behaviour non-conformist and distrusting of authority difficulty following illogical conventions attracted to foreign cultures affinity with nature and animals support for victims of injustice, underdogs and scapegoats. 3.Restricted, repetitive patterns of behaviour, interests or activities: inappropriate emotional responses victimised or bullied at school, work and home overthinking and constant logical analysis spending much time alone strange laugh or cackle inability to make direct eye contact when talking highly sensitive to light, sound, taste, smell and touch uncoordinated and clumsy with poor posture difficulty coping with change adept at abstract thinking ability to process data sets logically and notice patterns or trends truthful, naïve and often gullible slow mental processing and vulnerable to mental exhaustion intellectual and ungrounded rather than intuitive and instinctive problems with anxiety and sleeping visual memory.

Narrative writing about the self is a chiral process, not a symmetrical, achiral process. An object or a system is chiral if a person cannot superpose the object upon its exhibited mirror image. Although narrative writing is an embodiment of the self, human beings’ predilection to simplify data through imposition of a narrative arc over complex sets of data typically leads to an imprecise replication. The only way to avoid the narrative fallacy is by examining the clarity of the story and methodically assessing the narrator’s reliability in collecting, analyzing, presenting facts and the validity of the narrative including the objective aspect, the emotional aspect, social aspect, and moral aspect.

American pragmatist philosopher Charles Sanders Peirce’s observation that no new idea in the history of the world has been proven in advance analytically, which means that if you insist on rigorous proof of the merits of an idea during its development, you will kill it if it is truly a breakthrough idea, because there will be no proof of its breakthrough characteristics in advance. If you are going to screen innovation projects, therefore, a better model is one that has you assess them on the strength of their logic—the theory of why the idea is a good one—not on the strength of the existing data. Then, as you get further into each project that passes the logic test, you need to look for ways to create data that enables you to test and adjust—or perhaps kill—the idea as you develop it.

Solvay Business School Professor Paul Verdin and I developed a perspective that frames an organization's strategy as a hypothesis rather than a plan.62 Like all hypotheses, it starts with situation assessment and analysis –strategy's classic tools. Also, like all hypotheses, it must be tested through action. When strategy is seen as a hypothesis to be continually tested, encounters with customers provide valuable data of ongoing interest to senior executives.

Imagine if Wells Fargo had adopted an agile approach to strategy: the company's top management would then have taken repeated instances of missed targets or false accounts as useful data to help it assess the efficacy of the original cross-selling strategy. This learning would then have triggered much-needed strategic adaptation.

This graph shows all the observations together with a line that represents the fitted relationship. As is traditional, the Y-axis displays the dependent variable, which is weight. The X-axis shows the independent variable, which is height. The line is the fitted line. If you enter the full range of height values that are on the X-axis into the regression equation that the chart displays, you will obtain the line shown on the graph. This line produces a smaller SSE than any other line you can draw through these observations. Visually, we see that that the fitted line has a positive slope that corresponds to the positive correlation we obtained earlier. The line follows the data points, which indicates that the model fits the data. The slope of the line equals the coefficient that I circled. This coefficient indicates how much mean weight tends to increase as we increase height. We can also enter a height value into the equation and obtain a prediction for the mean weight. Each point on the fitted line represents the mean weight for a given height. However, like any mean, there is variability around the mean. Notice how there is a spread of data points around the line. You can assess this variability by picking a spot on the line and observing the range of data points above and below that point. Finally, the vertical distance between each data point and the line is the residual for that observation.

Project evaluation is a critical task in the management of a project primarily to assess project maximizing outcomes. However systematic collection of data is integrated with the evaluation process that may be conducted at various project phases, and the task of the evaluation-the approach depends upon the type of project, the project vision, the provision, the timeline of the project, and the phase of the project.

As studies have shown, there’s a difference between data, information, and knowledge. Always assess what you're gaining.

Structured methods for learning Method Uses Useful for Organizational climate and employee satisfaction surveys Learning about culture and morale. Many organizations do such surveys regularly, and a database may already be available. If not, consider setting up a regular survey of employee perceptions. Useful for managers at all levels if the analysis is available specifically for your unit or group. Usefulness depends on the granularity of the collection and analysis. This also assumes the survey instrument is a good one and the data have been collected carefully and analyzed rigorously. Structured sets of interviews with slices of the organization or unit Identifying shared and divergent perceptions of opportunities and problems. You can interview people at the same level in different departments (a horizontal slice) or bore down through multiple levels (a vertical slice). Whichever dimension you choose, ask everybody the same questions, and look for similarities and differences in people’s responses. Most useful for managers leading groups of people from different functional backgrounds. Can be useful at lower levels if the unit is experiencing significant problems. Focus groups Probing issues that preoccupy key groups of employees, such as morale issues among frontline production or service workers. Gathering groups of people who work together also lets you see how they interact and identify who displays leadership. Fostering discussion promotes deeper insight. Most useful for managers of large groups of people who perform a similar function, such as sales managers or plant managers. Can be useful for senior managers as a way of getting quick insights into the perceptions of key employee constituencies. Analysis of critical past decisions Illuminating decision-making patterns and sources of power and influence. Select an important recent decision, and look into how it was made. Who exerted influence at each stage? Talk with the people involved, probe their perceptions, and note what is and is not said. Most useful for higher-level managers of business units or project groups. Process analysis Examining interactions among departments or functions and assessing the efficiency of a process. Select an important process, such as delivery of products to customers or distributors, and assign a cross-functional group to chart the process and identify bottlenecks and problems. Most useful for managers of units or groups in which the work of multiple functional specialties must be integrated. Can be useful for lower-level managers as a way of understanding how their groups fit into larger processes. Plant and market tours Learning firsthand from people close to the product. Plant tours let you meet production personnel informally and listen to their concerns. Meetings with sales and production staff help you assess technical capabilities. Market tours can introduce you to customers, whose comments can reveal problems and opportunities. Most useful for managers of business units. Pilot projects Gaining deep insight into technical capabilities, culture, and politics. Although these insights are not the primary purpose of pilot projects, you can learn a lot from how the organization or group responds to your pilot initiatives. Useful for managers at all levels. The size of the pilot projects and their impact will increase as you rise through the organization.

Learning Plan Template Before Entry Find out whatever you can about the organization’s strategy, structure, performance, and people. Look for external assessments of the performance of the organization. You will learn how knowledgeable, fairly unbiased people view it. If you are a manager at a lower level, talk to people who deal with your new group as suppliers or customers. Find external observers who know the organization well, including former employees, recent retirees, and people who have transacted business with the organization. Ask these people open-ended questions about history, politics, and culture. Talk with your predecessor if possible. Talk to your new boss. As you begin to learn about the organization, write down your first impressions and eventually some hypotheses. Compile an initial set of questions to guide your structured inquiry after you arrive. Soon After Entry Review detailed operating plans, performance data, and personnel data. Meet one-on-one with your direct reports and ask them the questions you compiled. You will learn about convergent and divergent views and about your reports as people. Assess how things are going at key interfaces. You will hear how salespeople, purchasing agents, customer service representatives, and others perceive your organization’s dealings with external constituencies. You will also learn about problems they see that others do not. Test strategic alignment from the top down. Ask people at the top what the company’s vision and strategy are. Then see how far down into the organizational hierarchy those beliefs penetrate. You will learn how well the previous leader drove vision and strategy down through the organization. Test awareness of challenges and opportunities from the bottom up. Start by asking frontline people how they view the company’s challenges and opportunities. Then work your way up. You will learn how well the people at the top check the pulse of the organization. Update your questions and hypotheses. Meet with your boss to discuss your hypotheses and findings. By the End of the First Month Gather your team to feed back to them your preliminary findings. You will elicit confirmation and challenges of your assessments and will learn more about the group and its dynamics. Now analyze key interfaces from the outside in. You will learn how people on the outside (suppliers, customers, distributors, and others) perceive your organization and its strengths and weaknesses. Analyze a couple of key processes. Convene representatives of the responsible groups to map out and evaluate the processes you selected. You will learn about productivity, quality, and reliability. Meet with key integrators. You will learn how things work at interfaces among functional areas. What problems do they perceive that others do not? Seek out the natural historians. They can fill you in on the history, culture, and politics of the organization, and they are also potential allies and influencers. Update your questions and hypotheses. Meet with your boss again to discuss your observations.

There are several good assessments available but for retirement or transition coaching, I use the Birkman as it will give you a new look at your usual or strengths behaviors, working style, and motivational needs. The Birkman assessment is non-judgmental, empowering and gives a “beyond the surface” look at ourselves with records and data on 8 million-plus people across six continents and 23 languages.

Research consistently shows that tougher individuals are able to perceive stressful situations as challenges instead of threats. A challenge is something that’s difficult, but manageable. On the other hand, a threat is something we’re just trying to survive, to get through. This difference in appraisals isn’t because of an unshakable confidence or because tougher individuals downplay the difficulty. Rather, those who can see situations as a challenge developed the ability to quickly and accurately assess the situation and their ability to cope with it. An honest appraisal is all about giving your mind better data to predict with.

But everything, absolutely all the library work, had also been data. Collectible information that could be assessed and analyzed, that inferences could be made from. Some might argue that information and data, numbers and charts and statistics, aren't concerned with what feels "good" or "bad" (or any number of things in between), but I disagree. All data is tied back to emotions - to some original question, concern, desire, hypothesis that can be traced back to the feelings of a researcher, or a scientist, or whoever formed a hypothesis, asked a question, became interested in measuring something, tried to solve a problem, or cure a virus, and so forth.

Are damaging data practices and systems capable of reform? Re-evaluate your relationship to data and assess whether existing practices and systems are capable of reform. If reform seems possible, question who is best placed undertake this work. When reform fails, or efforts to reform risk keeping a damaging system alive for longer, consider if an abolitionist approach might put data in the hands of those most in need.

Does your project create more good than harm? And for whom? Assess what your project intends to achieve and its potential to cause harm; only continue when the potential benefits outweigh the potential dangers. Disaggregate the differential impacts among LGBTQ people to ensure that the project does not only benefit the least marginalized individuals, for whom sexual orientation is the only characteristics that excludes them from full inclusion.

Educators’ lives are filled with opportunities to develop their own social awareness during student and adult interactions. They participate in work groups, such as co-teaching, professional learning programs, faculty meetings, team meetings, data analysis teams, developing common assessments, lesson-study groups, and curriculum development committees. The checklist in the figure below can be modified to fit any type of group activity. It can be reviewed by the supervisor or coach and the educator prior to the activity. After the activity, the educator can be asked to confidentially self-assess his or skills, thereby increasing self-awareness of his/her relationship skills and self-management skills.

Recent research has emerged showing that while women tend to assess their intelligence accurately, men of average intelligence think they are more intelligent than two-thirds of people.

The success of product development and launch strategies for businesses in all industries is greatly influenced by best market research companies in Myanmar. These businesses enable businesses to make informed decisions that drive innovation and market penetration by providing valuable insights into consumer behaviors, market trends, and competitive landscapes. In today's dynamic business environment, it is essential for organizations looking to create competitive products and launch them successfully to comprehend the significance of market research. This article looks at how market research companies play a variety of roles in product development and launch strategies. It emphasizes the importance of working together, making decisions based on data, and taking advantage of market research trends for long-term growth and market leadership. 1. Companies' Introduction to Market Research An Overview of Business Market Research Market research is essential for helping businesses comprehend their market, competition, and customers. To make decisions based on accurate information, it involves gathering and evaluating data. Contribution of best market research companies in Myanmar to Product Development Market research firms are specialized in collecting insights that can be used to inform product development strategies for businesses. They assist in determining the demand for new products, assessing consumer behavior, and identifying market opportunities. 2. Understanding Consumer Needs and Preferences is Critical to Product Development Market research provides businesses with in-depth insights into consumer preferences, needs, and actions. In order to create products that resonate with the intended audience, this understanding is essential. Research can be used to assess market demand and potential, allowing businesses to make decisions about product development based on data. New products' chances of success are boosted while risks are reduced. 3. Utilizing Market Research for Launch Strategies Market Segmentation and Targeting Market research enables businesses to divide their target market into subsets based on behavior, psychographics, and demographics. For a more successful product launch, this segmentation helps tailor marketing strategies to specific consumer groups. Pricing and Positioning Strategies Businesses can figure out the best pricing strategy for their products based on consumer perceptions, competitor pricing, and market trends by conducting market research. Additionally, research aids in effectively positioning products in the market to set them apart from rivals. 4. Collaborating with Market Research Firms Choosing the Right Market Research Partner It is essential to select the right market research partner in order to obtain insights that are both accurate and applicable. Expertise, industry experience, and the capacity to provide insights that are in line with the company's goals should all be taken into account. Creating Effective Research Briefs Businesses should provide their market research partner with concise and in-depth research briefs to maximize the value of the research. A clearly defined brief assists in ensuring that the research is focused, pertinent, and in line with the company's objectives. 5. Studies of Cases: Product Launches That Work: The Importance of Market Research Market research is essential to the success of product launches. We are able to see the direct impact of market research on product development strategies by analyzing real-world examples. Market research companies offer valuable insights that can make or break a product launch, from comprehending consumer preferences to determining market gaps. ### Impact of Market Research on Product Launches Market research has a significant impact on product launches because it ensures that businesses know what their target audience wants and needs. Companies are able to effectively f

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Arguing about the functions of mood can be challenging. Some hypothesized functions of mood play out over time and are nearly impossible to test decisively with a laboratory experiment. Take the hypotheses that (1) low mood helps people disengage from unattainable goals and (2) we end up better off as a result of letting go. Testing this hypothesized chain of events requires data about the real-world goals that people want to attain and the ability to measure people’s adjustment and well-being over the longer term. A nonexperimental study of adolescent girls in Canada did just this, collecting four waves of longitudinal data on the relationship between goals and depression over nineteen months. Consistent with the first hypothesis, those adolescents who had depressive symptoms reported a tendency to become more disengaged from goals over time. The stereotypical image of a disengaged adolescent sulking in her room with an iPod may not look like the process of rebuilding psychological health. Results were in fact consistent with the idea that letting go was a positive development: those adolescents who became more disengaged from goals ended up being better off, reporting lower levels of depression in the later assessments.

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Six decades of study, however, have revealed conflicting, confusing, and inconclusive data.17 That’s right: there has never been a human study that successfully links low serotonin levels and depression. Imaging studies, blood and urine tests, postmortem suicide assessments, and even animal research have never validated the link between neurotransmitter levels and depression.18 In other words, the serotonin theory of depression is a total myth that has been unjustly supported by the manipulation of data. Much to the contrary, high serotonin levels have been linked to a range of problems, including schizophrenia and autism.19

The three tenets of upstream data are: Data management Quantification of uncertainty Risk assessment

other and distinct from other groups. These techniques usually precede regression and other analyses. Factor analysis is a well-established technique that often aids in creating index variables. Earlier, Chapter 3 discussed the use of Cronbach alpha to empirically justify the selection of variables that make up an index. However, in that approach analysts must still justify that variables used in different index variables are indeed distinct. By contrast, factor analysis analyzes a large number of variables (often 20 to 30) and classifies them into groups based on empirical similarities and dissimilarities. This empirical assessment can aid analysts’ judgments regarding variables that might be grouped together. Factor analysis uses correlations among variables to identify subgroups. These subgroups (called factors) are characterized by relatively high within-group correlation among variables and low between-group correlation among variables. Most factor analysis consists of roughly four steps: (1) determining that the group of variables has enough correlation to allow for factor analysis, (2) determining how many factors should be used for classifying (or grouping) the variables, (3) improving the interpretation of correlations and factors (through a process called rotation), and (4) naming the factors and, possibly, creating index variables for subsequent analysis. Most factor analysis is used for grouping of variables (R-type factor analysis) rather than observations (Q-type). Often, discriminant analysis is used for grouping of observations, mentioned later in this chapter. The terminology of factor analysis differs greatly from that used elsewhere in this book, and the discussion that follows is offered as an aid in understanding tables that might be encountered in research that uses this technique. An important task in factor analysis is determining how many common factors should be identified. Theoretically, there are as many factors as variables, but only a few factors account for most of the variance in the data. The percentage of variation explained by each factor is defined as the eigenvalue divided by the number of variables, whereby the

Putting your house in order is fun! The process of assessing how you feel about the things you own, identifying those that have fulfilled their purpose, expressing your gratitude, and bidding them farewell, is really about examining your inner self, a rite of passage to a new life. The yardstick by which you judge is your intuitive sense of attraction, and therefore there’s no need for complex theories or numerical data. All you need to do is follow the right order.

researchers analyzed data on more than six thousand children in Hong Kong, where smoking is not confined to those in lower economic brackets and where most smokers are men. The children were assessed when they were seven years old and again when they were eleven. Those whose fathers smoked when the mothers were pregnant were more likely to be overweight or obese. It was the first evidence supporting the idea that childhood obesity could be affected by a mother’s exposure to her husband’s smoking while she was pregnant.

Remedies exist for correcting substantial departures from normality, but these remedies may make matters worse when departures from normality are minimal. The first course of action is to identify and remove any outliers that may affect the mean and standard deviation. The second course of action is variable transformation, which involves transforming the variable, often by taking log(x), of each observation, and then testing the transformed variable for normality. Variable transformation may address excessive skewness by adjusting the measurement scale, thereby helping variables to better approximate normality.8 Substantively, we strongly prefer to make conclusions that satisfy test assumptions, regardless of which measurement scale is chosen.9 Keep in mind that when variables are transformed, the units in which results are expressed are transformed, as well. An example of variable transformation is provided in the second working example. Typically, analysts have different ways to address test violations. Examination of the causes of assumption violations often helps analysts to better understand their data. Different approaches may be successful for addressing test assumptions. Analysts should not merely go by the result of one approach that supports their case, ignoring others that perhaps do not. Rather, analysts should rely on the weight of robust, converging results to support their final test conclusions. Working Example 1 Earlier we discussed efforts to reduce high school violence by enrolling violence-prone students into classes that address anger management. Now, after some time, administrators and managers want to know whether the program is effective. As part of this assessment, students are asked to report their perception of safety at school. An index variable is constructed from different items measuring safety (see Chapter 3). Each item is measured on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree), and the index is constructed such that a high value indicates that students feel safe.10 The survey was initially administered at the beginning of the program. Now, almost a year later, the survey is implemented again.11 Administrators want to know whether students who did not participate in the anger management program feel that the climate is now safer. The analysis included here focuses on 10th graders. For practical purposes, the samples of 10th graders at the beginning of the program and one year later are regarded as independent samples; the subjects are not matched. Descriptive analysis shows that the mean perception of

Simple Regression   CHAPTER OBJECTIVES After reading this chapter, you should be able to Use simple regression to test the statistical significance of a bivariate relationship involving one dependent and one independent variable Use Pearson’s correlation coefficient as a measure of association between two continuous variables Interpret statistics associated with regression analysis Write up the model of simple regression Assess assumptions of simple regression This chapter completes our discussion of statistical techniques for studying relationships between two variables by focusing on those that are continuous. Several approaches are examined: simple regression; the Pearson’s correlation coefficient; and a nonparametric alterative, Spearman’s rank correlation coefficient. Although all three techniques can be used, we focus particularly on simple regression. Regression allows us to predict outcomes based on knowledge of an independent variable. It is also the foundation for studying relationships among three or more variables, including control variables mentioned in Chapter 2 on research design (and also in Appendix 10.1). Regression can also be used in time series analysis, discussed in Chapter 17. We begin with simple regression. SIMPLE REGRESSION Let’s first look at an example. Say that you are a manager or analyst involved with a regional consortium of 15 local public agencies (in cities and counties) that provide low-income adults with health education about cardiovascular diseases, in an effort to reduce such diseases. The funding for this health education comes from a federal grant that requires annual analysis and performance outcome reporting. In Chapter 4, we used a logic model to specify that a performance outcome is the result of inputs, activities, and outputs. Following the development of such a model, you decide to conduct a survey among participants who attend such training events to collect data about the number of events they attended, their knowledge of cardiovascular disease, and a variety of habits such as smoking that are linked to cardiovascular disease. Some things that you might want to know are whether attending workshops increases

(e). Hence the expressions are equivalent, as is y = ŷ + e. Certain assumptions about e are important, such as that it is normally distributed. When error term assumptions are violated, incorrect conclusions may be made about the statistical significance of relationships. This important issue is discussed in greater detail in Chapter 15 and, for time series data, in Chapter 17. Hence, the above is a pertinent but incomplete list of assumptions. Getting Started Conduct a simple regression, and practice writing up your results. PEARSON’S CORRELATION COEFFICIENT Pearson’s correlation coefficient, r, measures the association (significance, direction, and strength) between two continuous variables; it is a measure of association for two continuous variables. Also called the Pearson’s product-moment correlation coefficient, it does not assume a causal relationship, as does simple regression. The correlation coefficient indicates the extent to which the observations lie closely or loosely clustered around the regression line. The coefficient r ranges from –1 to +1. The sign indicates the direction of the relationship, which, in simple regression, is always the same as the slope coefficient. A “–1” indicates a perfect negative relationship, that is, that all observations lie exactly on a downward-sloping regression line; a “+1” indicates a perfect positive relationship, whereby all observations lie exactly on an upward-sloping regression line. Of course, such values are rarely obtained in practice because observations seldom lie exactly on a line. An r value of zero indicates that observations are so widely scattered that it is impossible to draw any well-fitting line. Figure 14.2 illustrates some values of r. Key Point Pearson’s correlation coefficient, r, ranges from –1 to +1. It is important to avoid confusion between Pearson’s correlation coefficient and the coefficient of determination. For the two-variable, simple regression model, r2 = R2, but whereas 0 ≤ R ≤ 1, r ranges from –1 to +1. Hence, the sign of r tells us whether a relationship is positive or negative, but the sign of R, in regression output tables such as Table 14.1, is always positive and cannot inform us about the direction of the relationship. In simple regression, the regression coefficient, b, informs us about the direction of the relationship. Statistical software programs usually show r rather than r2. Note also that the Pearson’s correlation coefficient can be used only to assess the association between two continuous variables, whereas regression can be extended to deal with more than two variables, as discussed in Chapter 15. Pearson’s correlation coefficient assumes that both variables are normally distributed. When Pearson’s correlation coefficients are calculated, a standard error of r can be determined, which then allows us to test the statistical significance of the bivariate correlation. For bivariate relationships, this is the same level of significance as shown for the slope of the regression coefficient. For the variables given earlier in this chapter, the value of r is .272 and the statistical significance of r is p ≤ .01. Use of the Pearson’s correlation coefficient assumes that the variables are normally distributed and that there are no significant departures from linearity.7 It is important not to confuse the correlation coefficient, r, with the regression coefficient, b. Comparing the measures r and b (the slope) sometimes causes confusion. The key point is that r does not indicate the regression slope but rather the extent to which observations lie close to it. A steep regression line (large b) can have observations scattered loosely or closely around it, as can a shallow (more horizontal) regression line. The purposes of these two statistics are very different.8 SPEARMAN’S RANK CORRELATION

to the measures described earlier. Hence, 90 percent of the variation in one variable can be explained by the other. For the variables given earlier, the Spearman’s rank correlation coefficient is .274 (p < .01), which is comparable to r reported in preceding sections. Box 14.1 illustrates another use of the statistics described in this chapter, in a study of the relationship between crime and poverty. SUMMARY When analysts examine relationships between two continuous variables, they can use simple regression or the Pearson’s correlation coefficient. Both measures show (1) the statistical significance of the relationship, (2) the direction of the relationship (that is, whether it is positive or negative), and (3) the strength of the relationship. Simple regression assumes a causal and linear relationship between the continuous variables. The statistical significance and direction of the slope coefficient is used to assess the statistical significance and direction of the relationship. The coefficient of determination, R2, is used to assess the strength of relationships; R2 is interpreted as the percent variation explained. Regression is a foundation for studying relationships involving three or more variables, such as control variables. The Pearson’s correlation coefficient does not assume causality between two continuous variables. A nonparametric alternative to testing the relationship between two continuous variables is the Spearman’s rank correlation coefficient, which examines correlation among the ranks of the data rather than among the values themselves. As such, this measure can also be used to study relationships in which one or both variables are ordinal. KEY TERMS   Coefficient of determination, R2 Error term Observed value of y Pearson’s correlation coefficient, r Predicted value of the dependent variable y, ŷ Regression coefficient Regression line Scatterplot Simple regression assumptions Spearman’s rank correlation coefficient Standard error of the estimate Test of significance of the regression coefficient Notes   1. See Chapter 3 for a definition of continuous variables. Although the distinction between ordinal and continuous is theoretical (namely, whether or not the distance between categories can be measured), in practice ordinal-level variables with seven or more categories (including Likert variables) are sometimes analyzed using statistics appropriate for interval-level variables. This practice has many critics because it violates an assumption of regression (interval data), but it is often

12.2. The transformed variable has equal variances across the two groups (Levene’s test, p = .119), and the t-test statistic is –1.308 (df = 85, p = .194). Thus, the differences in pollution between watersheds in the East and Midwest are not significant. (The negative sign of the t-test statistic, –1.308, merely reflects the order of the groups for calculating the difference: the testing variable has a larger value in the Midwest than in the East. Reversing the order of the groups results in a positive sign.) Table 12.2 Independent-Samples T-Test: Output For comparison, results for the untransformed variable are shown as well. The untransformed variable has unequal variances across the two groups (Levene’s test, p = .036), and the t-test statistic is –1.801 (df = 80.6, p =.075). Although this result also shows that differences are insignificant, the level of significance is higher; there are instances in which using nonnormal variables could lead to rejecting the null hypothesis. While our finding of insignificant differences is indeed robust, analysts cannot know this in advance. Thus, analysts will need to deal with nonnormality. Variable transformation is one approach to the problem of nonnormality, but transforming variables can be a time-intensive and somewhat artful activity. The search for alternatives has led many analysts to consider nonparametric methods. TWO T-TEST VARIATIONS Paired-Samples T-Test Analysts often use the paired t-test when applying before and after tests to assess student or client progress. Paired t-tests are used when analysts have a dependent rather than an independent sample (see the third t-test assumption, described earlier in this chapter). The paired-samples t-test tests the null hypothesis that the mean difference between the before and after test scores is zero. Consider the following data from Table 12.3. Table 12.3 Paired-Samples Data The mean “before” score is 3.39, and the mean “after” score is 3.87; the mean difference is 0.54. The paired t-test tests the null hypothesis by testing whether the mean of the difference variable (“difference”) is zero. The paired t-test test statistic is calculated as where D is the difference between before and after measurements, and sD is the standard deviation of these differences. Regarding t-test assumptions, the variables are continuous, and the issue of heterogeneity (unequal variances) is moot because this test involves only one variable, D; no Levene’s test statistics are produced. We do test the normality of D and find that it is normally distributed (Shapiro-Wilk = .925, p = .402). Thus, the assumptions are satisfied. We proceed with testing whether the difference between before and after scores is statistically significant. We find that the paired t-test yields a t-test statistic of 2.43, which is significant at the 5 percent level (df = 9, p = .038 < .05).17 Hence, we conclude that the increase between the before and after scores is significant at the 5 percent level.18 One-Sample T-Test Finally, the one-sample t-test tests whether the mean of a single variable is different from a prespecified value (norm). For example, suppose we want to know whether the mean of the before group in Table 12.3 is different from the value of, say, 3.5? Testing against a norm is akin to the purpose of the chi-square goodness-of-fit test described in Chapter 11, but here we are dealing with a continuous variable rather than a categorical one, and we are testing the mean rather than its distribution. The one-sample t-test assumes that the single variable is continuous and normally distributed. As with the paired t-test, the issue of heterogeneity is moot because there is only one variable. The Shapiro-Wilk test shows that the variable “before” is normal (.917, p = .336). The one-sample t-test statistic for testing against the test value of 3.5 is –0.515 (df = 9, p = .619 > .05). Hence, the mean of 3.39 is not significantly

The answer to information asymmetry is not always the provision of more information, especially when most of this ‘information’ is simply noise, or boilerplate (standardised documentation bolted on to every report). Companies justifiably complain about the ever-increasing volume of data they are required to produce, while users of accounting find less and less of relevance in them. The notion that all investors have, or could have, identical access to corporate data is a fantasy, but the attempt to make it a reality generates a raft of regulation which inhibits engagement between companies and their investors and impedes the collection of substantive information that is helpful in assessing the fundamental value of securities. In the terms popularised by the American computer scientist Clifford Stoll, ‘data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom’.9 In

Staff will need to receive adequate training from IT staff on ways to effectively use databases. Beyond the ability to navigate a database system, staff will need additional skills such as developing queries or using spreadsheets to analyze and present data. In other words, rather than simply concentrating on the business functions that technology supports, student affairs staff should integrate assessment functions into their understanding of technology tools.

it is not uncommon for experts in DNA analysis to testify at a criminal trial that a DNA sample taken from a crime scene matches that taken from a suspect. How certain are such matches? When DNA evidence was first introduced, a number of experts testified that false positives are impossible in DNA testing. Today DNA experts regularly testify that the odds of a random person’s matching the crime sample are less than 1 in 1 million or 1 in 1 billion. With those odds one could hardly blame a juror for thinking, throw away the key. But there is another statistic that is often not presented to the jury, one having to do with the fact that labs make errors, for instance, in collecting or handling a sample, by accidentally mixing or swapping samples, or by misinterpreting or incorrectly reporting results. Each of these errors is rare but not nearly as rare as a random match. The Philadelphia City Crime Laboratory, for instance, admitted that it had swapped the reference sample of the defendant and the victim in a rape case, and a testing firm called Cellmark Diagnostics admitted a similar error.20 Unfortunately, the power of statistics relating to DNA presented in court is such that in Oklahoma a court sentenced a man named Timothy Durham to more than 3,100 years in prison even though eleven witnesses had placed him in another state at the time of the crime. It turned out that in the initial analysis the lab had failed to completely separate the DNA of the rapist and that of the victim in the fluid they tested, and the combination of the victim’s and the rapist’s DNA produced a positive result when compared with Durham’s. A later retest turned up the error, and Durham was released after spending nearly four years in prison.21 Estimates of the error rate due to human causes vary, but many experts put it at around 1 percent. However, since the error rate of many labs has never been measured, courts often do not allow testimony on this overall statistic. Even if courts did allow testimony regarding false positives, how would jurors assess it? Most jurors assume that given the two types of error—the 1 in 1 billion accidental match and the 1 in 100 lab-error match—the overall error rate must be somewhere in between, say 1 in 500 million, which is still for most jurors beyond a reasonable doubt. But employing the laws of probability, we find a much different answer. The way to think of it is this: Since both errors are very unlikely, we can ignore the possibility that there is both an accidental match and a lab error. Therefore, we seek the probability that one error or the other occurred. That is given by our sum rule: it is the probability of a lab error (1 in 100) + the probability of an accidental match (1 in 1 billion). Since the latter is 10 million times smaller than the former, to a very good approximation the chance of both errors is the same as the chance of the more probable error—that is, the chances are 1 in 100. Given both possible causes, therefore, we should ignore the fancy expert testimony about the odds of accidental matches and focus instead on the much higher laboratory error rate—the very data courts often do not allow attorneys to present! And so the oft-repeated claims of DNA infallibility are exaggerated.

In the first part of this work, we examined the impact of using a dump or slice style entry on officer performance. We found that, compared to the slice conditions, officers took approximately twice as long to respond to a second gunman in the dump conditions. Once the officers in the dump conditions detected the second gunman in the room, they were almost 5 times more likely to violate the universal firearms safety rules and commit a priority of fire violation. The first officer also momentarily stalled in the doorway during 18% of the dump entries but never stalled during a slice entry. We did observe more instances of the officers in the slice entry shooting at the innocent suspect in the room, but this difference was not large enough to be confident that it was not the product of chance assignment error. Taken together, we argued that the data suggested that the slice was a better entry style than the dump to teach patrol officers.

Unexpected emergency plumbers Unexpected emergency plumber is? If your own group, but probably the same dress isn’t in the middle, where they start imitating the pool, the owner most likely to smoke. This is certainly a task that will require a qualified plumber, clean bathrooms and sinks in each backup, and even the simple addition of a new line of right tubes. Unfortunately, there are elements that do not require any old plumber, but a situation of sudden emergency, like H2O uncontrolled always works with tap water and start flooding the marsh peace. However, they are high quality. How can I tell if other service providers should be, or not? Are you sure you need a plumber crisis? Shortly before speaking to the installer should complete the water supply or the probability that the water line, the rack provides back. It is in order to avoid problems with the drinking water. He is not only very welcome to complete the water flow. After the arrest of H2O oneself've, evaluates the circumstances. If the problem is a bathroom fully equipped, bathroom once, until dawn, so the long-term wear’s each washing. He is a very potential and are reluctant to get up early in the morning when you are ready for self-determination, these solutions makes the kitchen sink, toilet and a lounge. In fact, you can get away from high fire call 24 hours a plumber at night for a few hours or during holidays or weekends to stay. In an interview with an unexpected emergency plumbers Unfortunately, when the time of the suspension of H2O and objective analysis and emergency may not be present, created only for contacting unexpected emergency sanitary and easy and to take concerns in writing to the other include some content his hands to keep the person. Preliminary interviews hydraulic range is trying to understand a lot of the other Box difficulties. Other personal data and many other facts themselves can be better able to assess the management of the crisis and the calculation of the payments change. Is a great addition to the amount pipeline management principle affects many, if not yet in a plumber decision. In fact, bought a lot of contact carrier price quotes can also sometimes significant price differences. Also check out the views of the services is in his hands. Some of the costs only in the room, even if they, after maintenance. Well, the result have, as it in this area before the season and it is surprising simply be a monthly bill. Please ask to get the price of maintenance. 24 hours plumber not calculates the direction of providing greater than a cell phone, and requires separate installation scenario earlier selection. But it can be equipped with a direction to select difficulty of defining and thinking about the cost, if he succeeded in presenting the sewage system in unforeseen emergencies. Ask will differ plumber state and talk about their own crisis normal or common prices. If you need to contact the unexpected rescue tend to check an unexpected emergency plumber to the self to take us in the direction of first, so that you can be your own ready to talk to the plumber, one after another, much better, then you determine the value.

I recommend you do a detailed time study for yourself to see where you spend your time. Make an estimate of how many hours each week you take for the major activities of your life: work, school, rest, entertainment, hobbies, spouse, children, commuting, church, God, friends, and so on. Then, over a typical period of your life, take two weeks and do a detailed time study. Keep track of how you spend your time, using fifteen- to thirty-minute increments. After you have gathered the raw data, categorize them carefully into the major groups: rest, work/school, church/God, family, and recreation. Create subcategories as appropriate for anything that might consume multiple hours per week, like listing commuting under work or TV under recreation. Finally, with the summary in hand, make the difficult assessments about how you are using your time. Ask yourself: • Any surprises? Areas where I just couldn’t imagine I was wasting—er, uh, um, spending—so much of my time? • Is this where I want my time to go? • Am I putting as much time as I’d like into the areas I want as the priorities in my life? • How much time am I really spending with my spouse? Children? Friends? • Did I realize how much time I was spending at work? • If I wanted to spend more time on XYZ or ABC, in what areas would I consciously choose to spend less time?

A computational procedure is said to have a top-down organization if it has been constructed according to some well-defined and clearly understood fixed computational procedure (which may include some preassigned store of knowledge), where this procedure specifically provides a clear-cut solution to some problem at hand. (Euclid's algorithm for finding the highest common factor of two natural numbers, as described in ENM, p. 31, is a simple example of a top-down algorithm.) This is to be contrasted with a bottom-up organization, where such clearly defined rules of operation and knowledge store are not specified in advance, but instead there is a procedure laid down for the way that the system is to 'learn' and to improve its performance according to its 'experience'. Thus, with a bottom-up system, these rules of operation are subject to continual modification. One must allow that the system is to be run many times, performing its actions upon a continuing input of data. On each run, an assessment is made-perhaps by the system itself-and it modifies its operations, in the lifht of this assessment, with a view to improving this quality of output. For example, the input data for the system might be a number of photographs of human faces, appropriately digitized, and the system's task is to decide which photographs represent the same individuals and which do not. After each run, the system's performance is compared with the correct answers. Its rules of operation are then modified in such a way as to lead to a probable improvement in its performance on the next run.

At best, hiring is a 50/50 crap shoot. Hiring great folks is critical to building the team, and it’s not easy. Many folks put on a great interview, but the excellence ends there. Others get solid references and you wonder if you hired the same person. And, in other cases, you get a start from somebody you never expected. My best interview question is, “What would your worst reference say about you?” It’s hard to give the answer to that question, “I work too hard,” with a straight face. I have also found that having key candidates take an assessment test gives you more data. Lastly, back-channel references are the best. It’s hard for candidates to game them.

One of the reasons for its success is that science has built-in, error-correcting machinery at its very heart. Some may consider this an overbroad characterization, but to me every time we exercise self-criticism, every time we test our ideas against the outside world, we are doing science. When we are self-indulgent and uncritical, when we confuse hopes and facts, we slide into pseudoscience and superstition. Every time a scientific paper presents a bit of data, it's accompanied by an error bar - a quiet but insistent reminder that no knowledge is complete or perfect. It's a calibration of how much we trust what we think we know. If the error bars are small, the accuracy of our empirical knowledge is high; if the error bars are large, then so is the uncertainty in our knowledge. Except in pure mathematics nothing is known for certain (although much is certainly false). Moreover, scientists are usually careful to characterize the veridical status of.their attempts to understand the world - ranging from conjectures and hypotheses, which are highly tentative, all the way up to laws of Nature which are repeatedly and systemati­cally confirmed through many interrogations of how the world works. But even laws of Nature are not absolutely certain. There may be new circumstances never before examined - inside black holes, say, or within the electron, or close to the speed of light -where even our vaunted laws of Nature break down and, however valid they may be in ordinary circumstances, need correction. Humans may crave absolute certainty; they may aspire to it; they may pretend, as partisans of certain religions do, to have attained it. But the history of science - by far the most successful claim to knowledge accessible to humans - teaches that the most we can hope for is successive improvement in our understanding, learning from our mistakes, an asymptotic approach to the Universe, but with the proviso that absolute certainty will always elude us. We will always be mired in error. The most each generation can hope for is to reduce the error bars a little, and to add to the body of data to which error bars apply. The error bar is a pervasive, visible self-assessment of the reliability of our knowledge.

Unless a school has clearly identified the essential standards that every student must master, as well as unwrapped the standards into specific student learning targets, it would be nearly impossible to have the curricular focus and targeted assessment data necessary to target interventions to this level.

To assess a nation through its economic data is a little like re-envisaging oneself via the results of a blood test, whereby the traditional markers of personality and character are set aside and it is made clear that one is at base, where it really counts, a creatinine level of 3.2, a lactate dehydrogenase of 927, a leukocyte (per field) of 2 and a C-reactive protein of 2.42.

Evolutionary biologist Richard Dawkins, for example, might be a bit less certain in his gloomy assessment of human nature: “Be warned that if you wish, as I do, to build a society in which individuals cooperate generously and unselfishly towards a common good, you can expect little help from biological nature. Let us try to teach generosity and altruism, because we are born selfish.”10 Maybe, but cooperation runs deep in our species too. Recent findings in comparative primate intelligence have led researchers Vanessa Woods and Brian Hare to wonder whether an impulse toward cooperation might actually be the key to our species-defining intelligence. They write, “Instead of getting a jump start with the most intelligent hominids surviving to produce the next generation, as is often suggested, it may have been the more sociable hominids—because they were better at solving problems together—who achieved a higher level of fitness and allowed selection to favor more sophisticated problem-solving over time.”11 Humans got smart, they hypothesize, because our ancestors learned to cooperate. Innately selfish or not, the effects of food provisioning and habitat depletion on both wild chimpanzees and human foragers suggest that Dawkins and others who argue that humans are innately aggressive, selfish beasts should be careful about citing these chimp data in support of their case. Human groups tend to respond to food surplus and storage with behavior like that observed in chimps: heightened hierarchical social organization, intergroup violence, territorial perimeter defense, and Machiavellian alliances. In other words, humans—like chimps—tend to fight when there’s something worth fighting over. But for most of prehistory, there was no food surplus to win or lose and no home base to defend.

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Firstly, it meant that the issue between competing paradigms could not be resolved by simply appealing to ‘the data’ or ‘the facts’, for what a scientist counts as data, or facts, will depend on which paradigm she accepts. Perfectly objective choice between two paradigms is therefore impossible: there is no neutral vantage-point from which to assess the claims of each. Secondly, the very idea of objective truth is called into question.

In School of One, students have daily "playlists" of their learning tasks that are attuned to each student's learning needs, based on that student's readiness and learning style. For example, Julia is way ahead of grade level in math and learns best in small groups, so her playlist might include three or four videos matched to her aptitude level, a thirty-minute one-on-one tutoring session with her teacher, and a small group activity in which she works on a math puzzle with three peers at similar aptitude levels. There are assessments built into each activity so that data can be fed back to the teacher to choose appropriate tasks for the next playlist.

Phase Activities Action Establish relationships and common agenda between all stakeholders Collaboratively scope issues and information Agree on time-frame Reflection On research design, ethics, power relations, knowledge construction process, representation and accountability Action Build relationships Identify roles, responsibilities and ethics procedures Establish a Memorandum of Understanding Collaboratively design research process and tools Discuss and identify desired action outcomes Reflection On research questions, design, working relationships and information requirements Action Work together to implement research process and undertake data collection Enable participation of others Collaboratively analyse information generated Begin planning action together Reflection On research process Evaluate participation and representation of others Assess need for further research and/or various action options Action Plan research-informed action which may include feedback to participants and influential other Reflection Evaluate action and process as a whole Action Identify options for further participatory research and action with or without academic researchers Figure 2.1 Key stages in a typical PAR process

Pearson and Gallagher are the researchers who developed the gradual release of responsibility model of instruction (1983). The model gives us several opportunities for collecting assessment data during instruction.

Too often, features get added to a product without any quantifiable validation — which is a direct path toward scope creep and feature bloat. If you’re unable to quantify the impact of a new feature, you can’t assess its value, and you won’t really know what to do with the feature over time. If this is the case, leave it as is, iterate on it, or kill it.

Another vital component of the UDL is the constant flow of data from student work. Daily tracking for each lesson, as well as mid- and end-of-module assessment tasks, are essential for determining students’ understandings at benchmark points. Such data flow keeps teaching practice firmly grounded in students learning and makes incremental progress possible. When feedback is provided, students understand that making mistakes is part of the learning process.

by region and service provider. c) Cable/wireless Data Service Quality Assessment Since 2007, the KCC has been assessing the

service) and wireless data services (3G and WiBro). In 2010, the quality assessments performed in 2009 were referenced to conduct assessment

Be careful about caching in too many places! The more caches between you and the source of fresh data, the more stale the data can be, and the harder it can be to determine the freshness of the data that a client eventually sees. This can be especially problematic with a microservice architecture where you have multiple services involved in a call chain. Again, the more caching you have, the harder it will be to assess the freshness of any piece of data. So if you think a cache is a good idea, keep it simple, stick to one, and think carefully before adding more!

FLATOW: So you would - how would you treat a patient like Sybil if she showed up in your office BRAND: Well, first I would start with a very thorough assessment, using the current standardized measures that we have available to us that assess for the range of dissociative disorders but the whole range of other psychological disorders, too. I would need to know what I'm working with, and I'd be very careful and make my decisions slowly, based on data about what she has. And furthermore, with therapists who are well-trained in dissociative disorders, we do keep an eye open for suggestibility. But that research, too, is not anywhere near as strong as what the other two people in the interview are suggesting.It shows - for example, there's eight studies that have a total of 11 samples. In the three clinical samples that have looked at the correlation between dissociation and suggestibility, all three clinical samples found non-significant correlations. So it's just not as strong as what people think. That's a myth that's not backed up by science." Exploring Multiple Personalities In 'Sybil Exposed' October 21, 2011 by Ira Flatow

In his recent guest editorial, Richard McNally voices skepticism about the National Vietnam Veteran’s Readjustment Study (NVVRS) data reporting that over one-half of those who served in the Vietnam War have posttraumatic stress disorder (PTSD) or subclinical PTSD. Dr McNally is particularly skeptical because only 15% of soldiers served in combat units (1). He writes, “the mystery behind the discrepancy in numbers of those with the disease and of those in combat remains unsolved today” (4, p 815). He talks about bizarre facts and implies many, if not most, cases of PTSD are malingered or iatrogenic. Dr McNally ignores the obvious reality that when people are deployed to a war zone, exposure to trauma is not limited to members of combat units (2,3). At the Operational Trauma and Stress Support Centre of the Canadian Forces in Ottawa, we have assessed over 100 Canadian soldiers, many of whom have never been in combat units, who have experienced a range of horrific traumas and threats in places like Rwanda, Somalia, Bosnia, and Afghanistan. We must inform Dr McNally that, in real world practice, even cooks and clerks are affected when faced with death, genocide, ethnic cleansing, bombs, landmines, snipers, and suicide bombers ... One theory suggests that there is a conscious decision on the part of some individuals to deny trauma and its impact. Another suggests that some individuals may use dissociation or repression to block from consciousness what is quite obvious to those who listen to real-life patients." Cameron, C., & Heber, A. (2006). Re: Troubles in Traumatology, and Debunking Myths about Trauma and Memory/Reply: Troubles in Traumatology and Debunking Myths about Trauma and Memory. Canadian journal of psychiatry, 51(6), 402.

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why has almost everyone done the calendar thing, but almost no one has moved everything else in their life into a similar zone, by capturing it all and creating the habit of assessing it all appropriately? Three reasons: First, the data that is entered onto a calendar has already been thought through and determined; it’s been translated down to the physical action level. You agreed to call Jim at noon on Monday: there is no more thinking required about what the appropriate action is, or where and when you’re going to do it. Second, you know where those kinds of actions need to be parked (calendar), and it’s a familiar and available tool.

why has almost everyone done the calendar thing, but almost no one has moved everything else in their life into a similar zone, by capturing it all and creating the habit of assessing it all appropriately? Three reasons: First, the data that is entered onto a calendar has already been thought through and determined; it’s been translated down to the physical action level. You agreed to call Jim at noon on Monday: there is no more thinking required about what the appropriate action is, or where and when you’re going to do it. Second, you know where those kinds of actions need to be parked (calendar), and it’s a familiar and available tool. And third, if you lose track of calendar actions and commitments, you will encounter obvious and rapid negative feedback from people you consider important.

Beyond objective assessment data, there is subjective information that best comes from the school professionals who work with the students every day. These observational data are vital to identifying students for additional help and determining why each student is struggling. For this reason, the third way a school should identify students for additional support is to create a systematic and timely process for staff to recommend and discuss students who need help.

The government’s Intelligence Assessment Department is a very small federal agency with very large computers, located in Sterling, Virginia. The IAD’s purpose is to maintain files of names, faces, physical attributes and personal preferences of national security threats and to analyze data about all of the above. If anybody’s ever wondered why the CIA or the military can be so certain that one bearded thirty-year-old on the streets of Kabul is an innocent businessman and, to our Western eyes, an identical one a block away is an al Qaeda operative, IAD is the reason. However,

Chevron, for example, has a decision-analysis group whose members facilitate decision-framing workshops; coordinate data gathering for analysis; build and refine economic and analytical models; help project managers and decision makers interpret analyses; point out when additional information and analysis would improve a decision; conduct an assessment of decision quality; and coach decision makers.