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While the following guidelines are intended specifically for writing personal statements for medical residency and medical fellowship in the U. S., these same concepts apply to personal statements for all other programs as well. A personal statement is a person's narrative of how he or she came to apply for the position being sought. It should be concise and efficient, is generally best organized in chronological order, and should generally range in length from 650 to 850 words. The only clients who have used our personal statement services and were not accepted into a program either ignored our suggestions or did not meet the program's minimum requirements. Client Acceptance Rates Generally speaking, a fully developed personal statement will be approximately 750. dentistry), though, may require shorter word counts. With few exceptions, if your personal statement is over 850 words, it is too long. The Electronic Residency Application Service (ERAS®) allows up to 28,000 characters with spaces, which is approximately 5,200 words. If everyone else writes it in their personal statements, then you should not, unless it is particularly relevant to you. A second example is a candidate who writes that he wants to pursue residency in a program that will give him the knowledge and training he will need to succeed in his chosen field. However, no program director will read a personal statement that long. An example of this is an IMG who writes, without any obvious reason for doing so, that she wants to pursue residency in the United States because the U. These are both vague statements that should be included only if they relate specifically to your personal career path. Second is is a rephrasing of the first: to write only of your particular experience. This is your greatest strength and what will set you apart. If you write that you want to pursue a career in medicine in order to serve the community, we will ask what kind of community and what way do you see yourself serving. We will ask where this desire has come from and how you have pursued it. If you write that you want to be a leader, we will ask where you want to be a leader, why you want to be a leader, what kind of leader you want to be, and in what way specifically you plan to lead others. To use a quote successfully, it must be both personally and particularly relevant to the candidate. It must be the driving theme through every aspect of the essay. We have seen this done successfully—meaning that there was no way for the personal statement to be better without it—in just a handful of the personal statements we have read. An example of this is a personal statement that compares the pursuit of medicine to building a robot or any other activity. As with a quote, to use a simile or metaphor successfully, it must be both personally and particularly relevant to the candidate and the driving theme through every aspect of the essay, and it has been likewise rare to see this done successfully. To define the specialty in the personal statement, or otherwise to make statements that the program director what he/she will already know. An example of this is to start a personal statement with: "Internal medicine requires an understanding of how the different systems of the body affect each other." Start with a simple, straightforward statement with how you started on the path that you are on. An example of this is: "The first time I saw how medicine can help people was when I was five years old and visited my mother in the hospital." Second is to write of your particular experience. This is your greatest strength and what will set you apart. 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Preparing for a Masters dissertation takes a lot of hard work. Beginning from choosing a topic, that in itself, requires knowledge of what you would like to add to the chosen field you have decided to earn your Masters degree in, and you have to make sure you do not choose something that is beyond your knowledge, regardless if you go through numerous research and a set of references. Once you have started your Masters degree, the expectations are from a different level. You are likely to have a higher comprehension of your chosen course, and at the same time, when writing a dissertation, you are expected to have a better probability in creating a dissertation compared to when you were in your undergraduate studies. One’s expertise is of a higher level, and that is, but normal. Remember, choosing a topic for your Masters dissertation and writing one is not the end of a very tedious, and complex task. It is tough, demanding, and for most, something that could not be paired with another undertaking. Once you have written and have it published, you will now be tasked to prove your line of reasoning of your Masters dissertation, or rationalize and validate your research questions. Take into account that for this Masters dissertation, mastery in writing skills will not be adequate, not even your chosen topic. Even, to an extent, having to have really good communication skills when you validate your research to a panel once the dissertation is done. Knowing how to choose the best topic for you, write about it, create the best dissertation that is worth the diploma you will be receiving, and justifying what your dissertation is about are, what the essential attributes you need to have as a Masters graduate. When creating and writing your master dissertation, one misleading notion that one needs to be aware of is to learn how to balance one’s writing. 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Any suggested advise will not only be additional ideas to you, as a writer, but because of these plausible thoughts and opinion, would you be able to come up with a better outcome. The Quantitative Dissertations part of Lærd Dissertation helps guide you through the process of doing a quantitative dissertation. When we use the word quantitative to describe quantitative dissertations, we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques. Quantitative research takes a particular approach to theory, answering research questions and/or hypotheses, setting up a research strategy, making conclusions from results, and so forth. It is also a type of dissertation that is commonly used by undergraduates, master's and doctoral students across degrees, whether traditional science-based subjects, or in the social sciences, psychology, education and business studies, amongst others. This introduction to the Quantitative Dissertations part of Lærd Dissertation has two goals: (a) to provide you with a sense of the broad characteristics of quantitative research, if you do not know about these characteristics already; and (b) to introduce you to the three main types (routes) of quantitative dissertation that we help you with in Lærd Dissertation: replication-based dissertations; data-driven dissertations; and theory-driven dissertations. When you have chosen which route you want to follow, we send you off to the relevant parts of Lærd Dissertation where you can find out more. If you have already read our article that briefly compares qualitative, quantitative and mixed methods dissertations [here], you may want to skip this section now. If not, we can say that quantitative dissertations have a number of core characteristics: If you choose to take on a quantitative dissertation, you will learn more about these characteristics, not only in the Fundamentals section of Lærd Dissertation, but throughout the articles we have written to help guide you through the choices you need to make when doing a quantitative dissertation. For now, we recommend that you read the next section, Types of quantitative dissertation, which will help you choose the type of dissertation you may want to follow. When taking on a quantitative dissertation, there are many different routes that you can follow. We focus on three major routes that cover a good proportion of the types of quantitative dissertation that are carried out. We call them Route #1: Replication-based dissertations, Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations. Each of these three routes reflects a very different type of quantitative dissertation that you can take on. In the sections that follow, we describe the main characteristics of these three routes. Rather than being exhaustive, the main goal is to highlight what these types of quantitative research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you. Most quantitative dissertations at the undergraduate, master's or doctoral level involve some form of replication, whether they are duplicating existing research, making generalisations from it, or extending the research in some way. In most cases, replication is associated with duplication. In other words, you take a piece of published research and repeat it, typically in an identical way to see if the results that you obtain are the same as the original authors. In some cases, you don't even redo the previous study, but simply request the original data that was collected, and reanalyse it to check that the original authors were accurate in their analysis techniques. However, duplication is a very narrow view of replication, and is partly what has led some journal editors to shy away from accepting replication studies into their journals. The reality is that most research, whether completed by academics or dissertation students at the undergraduate, master's or doctoral level involves either generalisation or extension. This may simply be replicating a piece of research to determine whether the findings are generalizable within a different population or setting/context, or across treatment conditions; terms we explain in more detail later in our main article on replication-based dissertations [here]. Alternately, replication can involve extending existing research to take into account new research designs, methods and measurement procedures, and analysis techniques. As a result, we call these different types of replication study: Route A: Duplication, Route B: Generalisation and Route C: Extension. We simply give them these names because (a) they reflect three different routes that you can follow when doing a replication-based dissertation (i.e., Route A: Duplication, Route B: Generalisation and Route C: Extension), and (b) the things you need to think about when doing your dissertation differ somewhat depending on which of these routes you choose to follow. At this point, the Lærd Dissertation site focuses on helping guide you through Route #1: Replication-based dissertations. When taking on a Route #1: Replication-based dissertation, we guide you through these three possible routes: Route A: Duplication; Route B: Generalisation; and Route C: Extension. Each of these routes has different goals, requires different steps to be taken, and will be written up in its own way. To learn whether a Route #1: Replication-based dissertation is right for you, and if so, which of these routes you want to follow, start with our introductory guide: Route #1: Getting started. Sometimes the goal of quantitative research is not to build on or test theory, but to uncover the antecedents (i.e., the drivers or causes) of what are known as stylized facts (also known referred to as empirical regularities or empirical patterns). Whilst you may not have heard the term before, a stylized fact is simply a fact that is surprising, undocumented, forms a pattern rather than being one-off, and has an important outcome variable, amongst other characteristics. A classic stylized fact was the discovery of the many maladies (i.e., diseases or aliments) that resulted from smoking (e.g., cancers, cardiovascular diseases, etc.). Such a discovery, made during the 1930s, was surprising when you consider that smoking was being promoted by some doctors as having positive health benefits, as well as the fact that smoking was viewed as being stylish at the time (Hambrick, 2007). The challenge of discovering a potential stylized fact, as well as collecting suitable data to test that such a stylized fact exists, makes data-driven dissertations a worthy type of quantitative dissertation to pursue. Sometimes, the focus of data-driven dissertations is entirely on discovering whether the stylized fact exists (e.g., Do domestic firms receive smaller fines for wrongdoings compared with foreign firms? ), and if so, uncovering the antecedents of the stylized fact (e.g., if it was found that domestic firms did receive smaller fines compared with foreign firms for wrongdoings, what was the relationship between the fines received and other factors you measured; e.g., factors such as industry type, firm size, financial performance, etc.? These data-driven dissertations tend to be empirically-focused, and are often in fields where there is little theory to help ground or justify the research, but also where uncovering the stylized fact and its antecedents makes a significant contribution all by itself. On other occasions, the focus starts with discovering the stylized fact, as well as uncovering its antecedents (e.g., the reasons why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). However, the goal is to go one step further and theoretically justify your findings. This can often be achieved when the field you are interested in is more theoretically developed (e.g., theories of decision-making, consumer behaviour, brand exposure, and so on, which may help to explain why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). We call these different types of data-driven dissertation: Route A: Empirically-focused and Route B: Theoretically-justified. In the part of Lærd Dissertation that deals exclusively with Route #2: Data-driven dissertations, which we will be launching shortly, we introduce you to these two routes (i.e., Route A: Empirically-focused and Route B: Theoretically-justified), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback. We have all come across theories during our studies. Well-known theories include social capital theory (Social Sciences), motivation theory (Psychology), agency theory (Business Studies), evolutionary theory (Biology), quantum theory (Physics), adaptation theory (Sports Science), and so forth. Irrespective of what we call these theories, and from which subjects they come, all dissertations involves theory to some extent. However, what makes theory-driven dissertations different from other types of quantitative dissertation (i.e., Route #1: Replication-based dissertations and Route #2: Data-driven dissertations) is that they place most importance on the theoretical contribution that you make. By theoretical contribution, we mean that theory-driven dissertations aim to add to the literature through their originality and focus on testing, combining or building theory. We emphasize the words testing, combining and building because these reflect three routes that you can adopt when carrying out a theory-driven dissertation: Route A: Testing, Route B: Combining or Route C: Building. In reality, it doesn't matter what we call these three different routes. They are just there to help guide you through the dissertation process. The important point is that we can do different things with theory, which is reflected in the different routes that you can follow. Sometimes we test theories (i.e., Route A: Testing). For example, a researcher may have proposed a new theory in a journal article, but not yet tested it in the field by collecting and analysing data to see if the theory makes sense. Sometimes we want to combine two or more well-established theories (i.e., Route B: Combining). This can provide a new insight into a problem or issue that we think it is important, but remains unexplained by existing theory. In such cases, the use of well-established theories helps when testing these theoretical combinations. On other occasions, we want to go a step further and build new theory from the ground up (i.e., Route C: Building). Whilst there are many similarities between Route B: Combining and Route C: Building, the building of new theory goes further because even if the theories you are building on are well-established, you are likely to have to create new constructs and measurement procedures in order to test these theories. In the part of Lærd Dissertation that deals exclusively with Route #3: Theory-driven dissertations, which we will be launching shortly, we introduce you to these three routes (i.e., Route A: Testing, Route B: Combining and Route C: Building), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback. A majority of students at the undergraduate, master's, and even doctoral level will take on a Route #1: Replication-based dissertation. At this point, it is also the only route that we cover in depth [NOTE: We will be launching Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations at a later date]. The field of management's devotion to theory: Too much of a good thing? To learn whether a Route #1: Replication-based dissertation is right for you, and if so, how to proceed, start with our introductory guide: Route #1: Getting started. If there is anything you find unclear about what you have just read, please leave feedback.
This article was originally published on the Work-Learning Research website ( in 2002. It was moved to my Will At Work Learning Blog in 2006, and has now been moved here in late 2017. Even after more than a decade, this blog post still provides valuable information explaining the issues — and the ramifications for learning. However, further research has uncovered additional information and has been published in a scientific journal in 2014. People do NOT remember 10% of what they read, 20% of what they see, 30% of what they hear, etc. That information, and similar pronouncements are fraudulent. Moreover, general statements on the effectiveness of learning methods are not credible—learning results depend on too many variables to enable such precision. Unfortunately, this bogus information has been floating around our field for decades, crafted by many different authors and presented in many different configurations, including bastardizations of Dale’s Cone. After reading the cited article several times and not seeing the graph—nor the numbers on the graph—I got suspicious and got in touch with the first author of the cited study, Dr. Michelene Chi of the University of Pittsburgh (who is, by the way, one of the world’s leading authorities on expertise). She said this about the graph: I often begin my workshops on instructional design and e-learning and my conference presentations with this graph as a warning and wake up call. Typically, over 90% of the audience raises their hands when I ask whether anyone has seen the numbers depicted in the graph. Treichler didn’t cite any research, but our field has unfortunately accepted his/her percentages ever since. Later I often hear audible gasps and nervous giggles as the information is debunked. NTL Institute still claims that they did the research that derived the numbers. Michael Molenda, a professor at Indiana University, is currently working to track down the origination of the bogus numbers. Clearly, lots of experienced professionals in our field know this graph and have used it to guide their decision making. The numbers presented on the graph have been circulating in our industry since the late 1960’s, and they have no research backing whatsoever. JC Kinnamon (2002) of Midi, Inc., searched the web and found dozens of references to those dubious numbers in college courses, research reports, and in vendor and consultant promotional materials. His efforts have uncovered some evidence that the numbers may have been developed as early as the 1940’s by Paul John Phillips who worked at University of Texas at Austin and who developed training classes for the petroleum industry. During World War Two Phillips taught Visual Aids at the U. Army’s Ordnance School at the Aberdeen (Maryland) Proving Grounds, where the numbers have also appeared and where they may have been developed. Strange coincidence: I was born on these very same Aberdeen Proving Grounds. Ernie Rothkopf, professor emeritus of Columbia University, one of the world’s leading applied research psychologists on learning, reported to me that the bogus percentages have been widely discredited, yet they keep rearing their ugly head in one form or another every few years. Many people now associate the bogus percentages with Dale’s “Cone of Experience,” developed in 1946 by Edgar Dale. It provided an intuitive model of the concreteness of various audio-visual media. Dale included no numbers in his model and there was no research used to generate it. In fact, Dale warned his readers not to take the model too literally. Dale’s Cone, copied without changes from the 3rd and final edition of his book, is presented below: Dale’s Cone of Experience (Dale, 1969, p. 107) You can see that Dale used no numbers with his cone. Somewhere along the way, someone unnaturally fused Dale’s Cone and Treichler’s dubious percentages. The source cited in the diagram above by Wiman and Meierhenry (1969) is a book of edited chapters. Though two of the chapters (Harrison, 1969; Stewart, 1969) mention Dale’s Cone of Experience, neither of them includes the percentages. In other words, the diagram above is citing a book that does not include the diagram and does not include the percentages indicated in the diagram. Here are some more examples: The percentages, and the graph in particular, have been passed around in our field from reputable person to reputable person. For example, one version of these numbers says that people remember 95% of the information they teach to others. The people who originally created the fabrications are to blame for getting this started, but there are clearly many people willing to bend the information to their own devices. People have not only cited Treichler, Chi, Wiman and Meierhenry for the percentages, but have also incorrectly cited William Glasser, and correctly cited a number of other people who have utilized Treichler’s numbers. Kinnamon’s (2002) investigation found that Treichler’s percentages have been modified in many ways, depending on the message the shyster wants to send. It seems clear from some of the fraudulent citations that deception was intended. On the graph that prompted our investigation, the title of the article had been modified from the original to get rid of the word “students.” The creator of the graph must have known that the term “students” would make people in the training / development / performance field suspicious that the research was done on children. Film and Audio-Visual Communication, 1, 14-16, 28-30, 48. The creator of Wiman and Meierhenry diagram did four things that make it difficult to track down the original source: (1) the book they cited is fairly obscure, (2) one of the authors names is spelled wrong, (3) the year of publication is incorrect, (4) and the name Charles Merrill, which was actually a publishing house, was ambiguously presented so that it might have referred to an author or editor. Were two people talking about the information they were learning? Were they “doing” it correctly, or did they get feedback? The numbers are not credible, and even if they made sense, they’d still be dangerous. If so, weren’t they “hearing” what the other person had to say? If they were getting feedback, how do we know the learning didn’t come from the feedback—not the “doing? If we look at the numbers a little more closely, they are highly unconvincing. ” Do we really believe that people learn more “hearing” a lecture, than “reading” the same material? Don’t people who “read” have an advantage in being able to pace themselves and revisit material they don’t understand? Meierhenry (Eds.) Educational media: Theory into practice. And how did the research produce numbers that are all factors of ten? Doesn’t this suggest some sort of review of the literature? If so, shouldn’t we know how the research review was conducted? A learning-systems concept as applied to courses in education and training. Shouldn’t we get a clear and traceable citation for such a review? Even the idea that you can compare these types of learning methods is ridiculous. As any good research psychologist knows, the measurement situation affects the learning outcome. If we have a person learn foreign-language vocabulary by listening to an audiotape and vocalizing their responses, it doesn’t make sense to test them by having them write down their answers. We’d have a poor measure of their ability to verbalize vocabulary. People who learn vocabulary by seeing it on the written page cannot be fairly evaluated by asking them to say the words aloud. It’s not fair to compare these different methods by using the same test, because the choice of test will bias the outcome toward the learning situation that is most like the test situation. But why not compare one type of test to another—for example, if we want to compare vocabulary learning through hearing and seeing, why don’t we use an oral test and written one? It’s really impossible to compare two things on different indices. Can you imagine comparing the best boxer with the best golfer by having the boxer punch a heavy bag and having the golfer hit for distance? Would Muhammad Ali punching with 600 pounds of pressure beat Tiger Woods hitting his drives 320 yards off the tee? Even if the numbers presented on the graph had been published in a refereed journal—research we were reasonably sure we could trust—it would still be dangerous not to know where they came from. Research conclusions have a way of morphing over time. Newer research has revealed that monounsaturated oils like olive oil might actually be good for us. If a person doesn’t cite their sources, we might not realize that their conclusions are outdated or simply based on poor research. Conversely, we may also lose access to good sources of information. Suppose Teichler had really discovered a valid source of information? Because he/she did not use citations, that research would remain forever hidden in obscurity. The context of research makes a great deal of difference. If we don’t know a source, we don’t really know whether the research is relevant to our situation. For example, an article by Kulik and Kulik (1988) concluded that immediate feedback was better than delayed feedback. Most people in the field now accept their conclusions. Efforts by Work-Learning Research to examine Kulik and Kulik’s sources indicated that most of the articles they reviewed tested the learners within a few minutes after the learning event, a very unrealistic analog for most training situations. Their sources enabled us to examine their evidence and find it faulty. Meierhenry (Eds.) Educational media: Theory into practice. The original shysters are not the only ones to blame. The fact that many people who have disseminated the graph used the same incorrect citation makes it clear that they never accessed the original study. Everyone who uses a citation to make a point (or draw a conclusion) ought to check the citation. That, of course, includes all of us who are consumers of this information. It tells us that we may not be able to trust the information that floats around our industry. It tells us that even our most reputable people and organizations may require the Wizard-of-Oz treatment—we may need to look behind the curtain to verify their claims. At Work-Learning Research, our goal is to provide research-based information that practitioners can trust. We began our research efforts several years ago when we noticed that the field jumps from one fad to another while at the same time holding religiously to ideas that would be better cast aside. The fact that our field is so easily swayed by the mildest whiffs of evidence suggests that we don’t have sufficient mechanisms in place to improve what we do. Because we’re not able or willing to provide due diligence on evidence-based claims, we’re unable to create feedback loops to push the field more forcefully toward continuing improvement. We’re supposed to be the learning experts, but because we too easily take things for granted, we find ourselves skipping down all manner of yellow-brick roads. It will seem obvious, but each and every one of us must take responsibility for the information we transmit to ensure its integrity. Self-explanations: How students study and use examples in learning to solve problems. More importantly, we must be actively skeptical of the information we receive. We ought to check the facts, investigate the evidence, and evaluate the research. Finally, we must continue our personal search for knowledge—for it is only with knowledge that we can validly evaluate the claims that we encounter. Even after more than a decade, this blog post still provides valuable information explaining the issues — and the ramifications for learning. However, further research has uncovered additional information and has been published in a scientific journal in 2014. All of the university campuses and the surrounding nehborhoods where we hold Wave Week are extremely safe. Wave Week is considered to be a “closed campus” – delegates may not leave desnated areas and are always supervised by trained staff.