The art of writing literature review: What do we know and what do we need to know?
Author links open overlay panelJustinPaulabAlex RialpCriadocd
“The art of writing literature review: What do we know and what do we need to know?”
In explaining the purpose, methodology, and structure of a systematic review, the article provides guidelines for developing most insightful and useful review articles. By outlining steps and thumb rules to keep in mind, the article presents an overview of different types of review articles and explain how future researchers could potentially find them useful.
I strongly recommend this article to the younger researchers to get more insighful details how to write an effective literature review of the scholarly paper.
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Bilimsel araştırmaların kalitesi, büyük oranda kullanılan yöntemin ve verilerin amaca uygunluğuna ve tutarlılığına bağlıdır. Gerek nicel, gerekse nitel verilerle yapılan sosyal araştırmalarda anket yöntemi ağırlıklı olarak kullanıldığı için, bu yöntemin bilimselliği ve uygunluğu daha sıklıkla irdelenir hale gelmiştir. Anket sorularında uygun ölçeklerin kullanılması, geneli temsil edebilen örnekleme ulaşılması, doğru yanıtların alınması ve dönüt oranının yeterli olması anket yönteminin başlıca sorun alanlarıdır. Araştırmacıdan veya anketörden kaynaklanan sorunlar yanında, yanıtlayıcıdan veya çevresel ortamdan kaynaklanan hatalar da olabilmekte..
Soru Şu: Sosyal Bilimlerde “Anket Yöntemi” sonuçları ne kadar gerçekçi, ve bu sonuçların Sosyal Bilime katkısı ne?
Umarım bu çalışma, genç araştırmacılar tarafından bir rehber olarak kullanılır.
Handbook of Survey Methodology for the Social Sciences
Introduction
Lior Gideon
1.1 Introduction Surveys have become a major part of our lives. In an era in which a wealth of information is highly accessible and rapidly changing, many researchers use surveys to inform knowledge, challenge existing assumptions, and shape policies. Surveys are used by so many people for so many different purposes that it gives the impression that conducting a survey is as easy as a walk in the park. Many beginning researchers think surveys are simply a way of collecting information by asking questions—nothing sophisticated or difficult, just ‘‘ask and you will know.’’
Unfortunately, such an attitude pervades the foundations of social research, leading some people in the field to contribute knowledge that may be unreliable at best, and outright damaging at the worst. The dangers become even clearer when researchers design and execute a full survey-based study under the name of a respectable academic institution, while knowing very little about method. In the end, they deliver only low quality results that, due to the institution’s prestige, are nonetheless used to inform public policy.
This is all mainly due to the fact that in the course of their studies, not many social scientists have received adequate training in survey methodology. I have seen this time and again when graduate students have approached me to advise on their doctoral work, and just as often when looking at research papers presented in professional conferences by those who have already completed their dissertations and are now conducting independent research. While their topic of research is interesting, often their data collection tool is badly designed, so their results show low reliability and validity. All of them nonetheless proudly declare that their results are valid and can be generalized to the population, as they have used probability as the sampling technique. In fact, it seems that more emphasis is typically given to sampling techniques than to data collection methods and proper data collection protocols.
It is within this context that the current handbook has been written to provide social scientists with a simple point of reference and to educate on the nuts and bolts of this important method. The aim of this book is to examine the various issues surrounding survey methodology, and there is no better way to jump in than to begin with the concept of total survey error (TSE), the theoretical heart of survey methodology, as well as the chapters that follow. While there are many available books and guides on this topic, many of them are either too difficult for students or appear to be somewhat unfriendly to non-statisticians.
1.2 Total Survey Error
Many who use surveys as their primary data collection method fail to think of the process as a scientific method. ‘‘What’s the big deal about asking questions?’’ people may say with a shrug. Instead, the focus of research is usually on sampling and the number of questions to be asked. Much less attention is paid to the lurking sources of bias that are not sample-related. Weisberg (2005) warns that this single-minded focus on sampling error is only the tip of the iceberg in surveys: The total survey error is much greater than that. Unfortunately, the emphasis has been—and for many young researchers, continues to be—on sampling errors simply because they are easy to deal with mathematically and can be relatively estimated and resolved by increasing the sample size. On the other hand, errors not related to sampling— what we will call non-sampling errors —have typically been seen as too difficult to estimate, and it has been assumed that their effect on the results would be minimized if samples were big enough and properly representative. In Chap. 4, Bautista discusses the silent bias in survey research while focusing on the concept of total survey error. But for the purpose of paving the way to the other chapters in this book, we will make a brief introduction of this important theoretical framework here.
TSE, as the combined total of both sampling and non-sampling errors, should be the dominant paradigm for developing, analyzing, and understanding surveys and their results. Among researchers using surveys as the main method for data collection, many have assumed people will respond honestly to questions presented to them. There is also a basic assumption that people are generally willing to share their views, opinions, experiences, and attitudes with researchers and thus, all researchers have to do is ask the questions. Such assumptions, however, have been revealed to be untrue. As a result, survey researchers have recently shown an increased interest in what other factors that cause bias in surveys. Returning to the iceberg metaphor, survey researchers have since been able to identify and focus on the submerged part of the iceberg: the core of error not related to sampling, which was previously hidden from view. This effort, along with an accumulated wealth of survey experience in recent decades, has resulted in a better understanding of the multiple sources of survey bias. Figure 1.1 illustrates the concept of TSE using the wellknown Pythagorean Theorem as a metaphor: The sum of the squares of both the sampling error and the non-sampling error is equal to that of the squared total survey error—in short, the TSE becomes much bigger than each of its components. Differently put, when both sampling and non-sampling errors occur in a survey, the TSE is exponentially higher. Of course you cannot actually place actual error values and calculate this theorem for the TSE, but it should give readers a good idea of what the actual problem is.
Sampling errors stem from the sampling method used. So researchers must initially identify their population of interest and then clearly define their unit of analysis and what elements will best serve the aim of their study. Once these issues are addressed, researchers progress to the sampling method—either probability or non-probability. It is understood that by using non-probability sampling, bias will naturally be introduced into the research, and no generalization will be possible. This is not to say that one should never use such sampling techniques, but merely to indicate their salient weakness and the corresponding criticism of them. On the other hand, using probability sampling that relies on the principle of randomness will provide a more representative sample, one that better reflects the target population and thus enables generalizations from the sample to the larger population. However, depending on the type of probability sampling used (e.g., simple random, systematic random, stratified proportional, stratified non-proportional, cluster, etc.), the level of sampling error in the model may increase or decrease. Using such methods, a researcher can estimate the sampling error and warn the potential audience of the source and magnitude of the error. (In Chap. 5, Hibberts, Johnson, and Hudson discuss sampling in more detail, while focusing on the advantages and disadvantages of each sampling method in relation to the researcher’s ability to generalize.)
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Bilimsel bir makale nasıl yazılır: Yeni başlayanlar için adım adım rehber,
Yayıncı ELSEVIER (2015)
Tam Metin’i burada bulabilirsiniz:
European Geriatric Medicine
Volume 6, Issue 6, December 2015, Pages 573-579
Research paper
Writing a scientific article: A step-by-step guide for beginners
Author links open overlay panelF.EcarnotM.-F.SerondeR.ChopardF.SchieleN.Meneveau
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https://doi.org/10.1016/j.eurger.2015.08.005Get rights and content
Abstract
Many young researchers find it extremely difficult to write scientific articles, and few receive specific training in the art of presenting their research work in written format. Yet, publication is often vital for career advancement, to obtain funding, to obtain academic qualifications, or for all these reasons. We describe here the basic steps to follow in writing a scientific article. We outline the main sections that an average article should contain; the elements that should appear in these sections, and some pointers for making the overall result attractive and acceptable for publication.
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How to write the discussion section of a scientific article
Rogério Faria Vieira1* , Renan Cardoso de Lima2 and Eduardo Seiti Gomide Mizubuti3
1Empresa de Pesquisa Agropecuária de Minas Gerais , Vila Gianetti, 47, 36570-900, Viçosa, Minas Gerais, Brazil. 2Instituto Federal do Mato Grosso, Sorriso, Mato Grosso, Brazil. 3 Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. *Author for correspondence. E-mail: rfvieira@epamig.br
ABSTRACT. The Discussion is the hardest section of a scientific article to write, as cognitive skills must be used to properly contextualize the findings of a study. In this article, we guide scientific writers, particularly unexperienced ones, on how to structure a Discussion section based on an article by Docherty and Smith (1999). According to these authors, a discussion should be prepared by organizing information in the following order: (a) statement of principal findings; (b) strengths and weaknesses of the study; (c) strengths and weaknesses in relation to other studies, discussing particularly any differences in results;
(d) meaning of the study: possible mechanisms and implications; and (e) unanswered questions and future research. Each component of this sequence is discussed in detail with examples drawn from the literature.
Keywords: writing the discussion; discussion section; conclusion; scientific writing.
Received on October 26, 2017.
Accepted on March 16, 2018.
