Introduction to Systematic Reviewing and Meta-Analysis
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
DATA COLLECTION METHODS Section III 5
AN OVERVIEW OF QUANTITATIVE AND QUALITATIVE DATA COLLECTION METHODS Section III 5. DATA COLLECTION METHODS: SOME TIPS AND COMPARISONS In the previous chapter, we identified two broad types of evaluation methodologies: quantitative and qualitative. In this section, we talk more about the debate over the relative virtues of these approaches and discuss some of the advantages and disadvantages of different types of instruments. In such a debate, two types of issues are considered: theoretical and practical. Theoretical Issues Most often these center on one of three topics: · The value of the types of data · The relative scientific rigor of the data · Basic, underlying philosophies of evaluation Value of the Data Quantitative and qualitative techniques provide a tradeoff between breadth and depth, and between generalizability and targeting to specific (sometimes very limited) populations. For example, a quantitative data collection methodology such as a sample survey of high school students who participated in a special science enrichment program can yield representative and broadly generalizable information about the proportion of participants who plan to major in science when they get to college and how this proportion differs by gender. But at best, the survey can elicit only a few, often superficial reasons for this gender difference. On the other hand, separate focus groups (a qualitative technique related to a group interview) conducted with small groups of men and women students will provide many more clues about gender differences in the choice of science majors, and the extent to which the special science program changed or reinforced attitudes. The focus group technique is, however, limited in the extent to which findings apply beyond the specific individuals included in the groups. -
On Becoming a Pragmatic Researcher: the Importance of Combining Quantitative and Qualitative Research Methodologies
DOCUMENT RESUME ED 482 462 TM 035 389 AUTHOR Onwuegbuzie, Anthony J.; Leech, Nancy L. TITLE On Becoming a Pragmatic Researcher: The Importance of Combining Quantitative and Qualitative Research Methodologies. PUB DATE 2003-11-00 NOTE 25p.; Paper presented at the Annual Meeting of the Mid-South Educational Research Association (Biloxi, MS, November 5-7, 2003). PUB TYPE Reports Descriptive (141) Speeches/Meeting Papers (150) EDRS PRICE EDRS Price MF01/PCO2 Plus Postage. DESCRIPTORS *Pragmatics; *Qualitative Research; *Research Methodology; *Researchers ABSTRACT The last 100 years have witnessed a fervent debate in the United States about quantitative and qualitative research paradigms. Unfortunately, this has led to a great divide between quantitative and qualitative researchers, who often view themselves in competition with each other. Clearly, this polarization has promoted purists, i.e., researchers who restrict themselves exclusively to either quantitative or qualitative research methods. Mono-method research is the biggest threat to the advancement of the social sciences. As long as researchers stay polarized in research they cannot expect stakeholders who rely on their research findings to take their work seriously. The purpose of this paper is to explore how the debate between quantitative and qualitative is divisive, and thus counterproductive for advancing the social and behavioral science field. This paper advocates that all graduate students learn to use and appreciate both quantitative and qualitative research. In so doing, students will develop into what is termed "pragmatic researchers." (Contains 41 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document. On Becoming a Pragmatic Researcher 1 Running head: ON BECOMING A PRAGMATIC RESEARCHER U.S. -
Data Extraction for Complex Meta-Analysis (Decimal) Guide
Pedder, H. , Sarri, G., Keeney, E., Nunes, V., & Dias, S. (2016). Data extraction for complex meta-analysis (DECiMAL) guide. Systematic Reviews, 5, [212]. https://doi.org/10.1186/s13643-016-0368-4 Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.1186/s13643-016-0368-4 Link to publication record in Explore Bristol Research PDF-document This is the final published version of the article (version of record). It first appeared online via BioMed Central at http://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-016-0368-4. Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ Pedder et al. Systematic Reviews (2016) 5:212 DOI 10.1186/s13643-016-0368-4 RESEARCH Open Access Data extraction for complex meta-analysis (DECiMAL) guide Hugo Pedder1*, Grammati Sarri2, Edna Keeney3, Vanessa Nunes1 and Sofia Dias3 Abstract As more complex meta-analytical techniques such as network and multivariate meta-analyses become increasingly common, further pressures are placed on reviewers to extract data in a systematic and consistent manner. Failing to do this appropriately wastes time, resources and jeopardises accuracy. This guide (data extraction for complex meta-analysis (DECiMAL)) suggests a number of points to consider when collecting data, primarily aimed at systematic reviewers preparing data for meta-analysis. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
Reasoning with Qualitative Data: Using Retroduction with Transcript Data
Reasoning With Qualitative Data: Using Retroduction With Transcript Data © 2019 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 Reasoning With Qualitative Data: Using Retroduction With Transcript Data Student Guide Introduction This example illustrates how different forms of reasoning can be used to analyse a given set of qualitative data. In this case, I look at transcripts from semi-structured interviews to illustrate how three common approaches to reasoning can be used. The first type (deductive approaches) applies pre-existing analytical concepts to data, the second type (inductive reasoning) draws analytical concepts from the data, and the third type (retroductive reasoning) uses the data to develop new concepts and understandings about the issues. The data source – a set of recorded interviews of teachers and students from the field of Higher Education – was collated by Dr. Christian Beighton as part of a research project designed to inform teacher educators about pedagogies of academic writing. Interview Transcripts Interviews, and their transcripts, are arguably the most common data collection tool used in qualitative research. This is because, while they have many drawbacks, they offer many advantages. Relatively easy to plan and prepare, interviews can be flexible: They are usually one-to one but do not have to be so; they can follow pre-arranged questions, but again this is not essential; and unlike more impersonal ways of collecting data (e.g., surveys or observation), they Page 2 of 13 Reasoning With Qualitative Data: Using Retroduction With Transcript Data SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. -
Repko Research Process (Study Guide)
Beginning the Interdisciplinary Research Process Repko (6‐7) StudyGuide Interdisciplinary Research is • A decision‐making process – a deliberate choice • A decision‐making process – a movement, a motion • Heuristic – tool for finding out – process of searching rather than an emphasis on finding • Iterative – procedurally repetitive – messy, not linear – fluid • Reflexive – self‐conscious or aware of disciplinary or personal bias – what influences your work (auto) Integrated Model (p.141) Problem – Insights – Integration – Understanding fine the problem The Steps include: A. Drawing on disciplinary insights 1. Define the problem 2. Justify using an id approach 3. Identify relevant disciplines 4. Conduct a literature search 5. Develop adequacy in each relevant discipline 6. Analyze the problem and evaluate each insight into it B. Integrate insights to produce id understanding 7. Identify conflicts between insights and their sources 8. Create or discover common ground 9. Integrate insights 10. Produce an id understanding of the problem (and test it) Cautions and concerns (1) Fluid steps (2) Feedback loops – not a ladder Beginning the Interdisciplinary Research Process Repko (6‐7) StudyGuide (3) Don’t skip steps – be patient (4) Integrate as you go STEP ONE: Define the Problem • Researchable in an ID sense? • What is the SCOPE (parameters; disclaimers; what to include, exclude; what’s your focus – the causes, prevention, treatment, effects, etc. • Is it open‐ended • Too complex for one discipline to solve? Writing CHECK – Craft a well‐composed -
Chapter 5 Statistical Inference
Chapter 5 Statistical Inference CHAPTER OUTLINE Section 1 Why Do We Need Statistics? Section 2 Inference Using a Probability Model Section 3 Statistical Models Section 4 Data Collection Section 5 Some Basic Inferences In this chapter, we begin our discussion of statistical inference. Probability theory is primarily concerned with calculating various quantities associated with a probability model. This requires that we know what the correct probability model is. In applica- tions, this is often not the case, and the best we can say is that the correct probability measure to use is in a set of possible probability measures. We refer to this collection as the statistical model. So, in a sense, our uncertainty has increased; not only do we have the uncertainty associated with an outcome or response as described by a probability measure, but now we are also uncertain about what the probability measure is. Statistical inference is concerned with making statements or inferences about char- acteristics of the true underlying probability measure. Of course, these inferences must be based on some kind of information; the statistical model makes up part of it. Another important part of the information will be given by an observed outcome or response, which we refer to as the data. Inferences then take the form of various statements about the true underlying probability measure from which the data were obtained. These take a variety of forms, which we refer to as types of inferences. The role of this chapter is to introduce the basic concepts and ideas of statistical inference. The most prominent approaches to inference are discussed in Chapters 6, 7, and 8. -
Research Questions in Design-Based Research Arthur Bakker
Research Questions in Design-Based Research Arthur Bakker Freudenthal Institute Utrecht University [email protected] August 25, 2014 Design-based research (DBR) is a relatively new method in the learning sciences (Anderson & Shattuck, 2012; Brown, 1992; also see special issues in Educational Researcher, 2003; Educational Psychologist, 2004; Journal of the Learning Sciences, 2004). For older and more established methods such as randomized controlled trials (RCTs), the argumentative logic has already been worked out more clearly (Cobb et al., in press). Several predominantly qualitative approaches such as ethnography, case studies and action research have been around so long that they are discussed in most research methods books (Creswell, 2007; Denscombe, 2007), but DBR has not yet reached this stage. Despite its potential and importance for the improvement of education and educational research (Plomp & Nieveen, 2007; Van den Akker et al., 2006), there is thus still some way to go. In this document I focus on the topic of research questions. Many design-based researchers struggle with formulating good research questions, and in many cases the debate on formulations continues until the end of a project. To some extent this inherent in qualitative research approaches, but in DBR the situation seems more poignant. I address the issue of what counts as a good research question, summarize some of the discussion points and propose an argumentative structure that seems to work for the type of DBR that our students in mathematics and science education do. I do not rehearse the main characteristics of DBR (see Bakker & van Eerde, 2014; Cobb et al., 2003). -
How to Write a Systematic Review Article and Meta-Analysis Lenka Čablová, Richard Pates, Michal Miovský and Jonathan Noel
CHAPTER 9 How to Write a Systematic Review Article and Meta-Analysis Lenka Čablová, Richard Pates, Michal Miovský and Jonathan Noel Introduction In science, a review article refers to work that provides a comprehensive and systematic summary of results available in a given field while making it pos- sible to see the topic under consideration from a new perspective. Drawing on recent studies by other researchers, the authors of a review article make a critical analysis and summarize, appraise, and classify available data to offer a synthesis of the latest research in a specific subject area, ultimately arriving at new cumulative conclusions. According to Baumeister and Leary (1997), the goal of such synthesis may include (a) theory development, (b) theory evalu- ation, (c) a survey of the state of knowledge on a particular topic, (d) problem identification, and (e) provision of a historical account of the development of theory and research on a particular topic. A review can also be useful in science and practical life for many other reasons, such as in policy making (Bero & Jadad, 1997). Review articles have become necessary to advance addiction sci- ence, but providing a systematic summary of existing evidence while coming up with new ideas and pointing out the unique contribution of the work may pose the greatest challenge for inexperienced authors. How to cite this book chapter: Čablová, L, Pates, R, Miovský, M and Noel, J. 2017. How to Write a Systematic Review Article and Meta-Analysis. In: Babor, T F, Stenius, K, Pates, R, Miovský, M, O’Reilly, J and Candon, P. -
Data Collection, Data Quality and the History of Cause-Of-Death Classifi Cation
Chapter 8 Data Collection, Data Quality and the History of Cause-of-Death Classifi cation Vladimir Shkolnikov , France Meslé , and Jacques Vallin Until 1996, when INED published its work on trends in causes of death in Russia (Meslé et al . 1996 ) , there had been no overall study of cause-specifi c mortality for the Soviet Union as a whole or for any of its constituent republics. Yet at least since the 1920s, all the republics had had a modern system for registering causes of death, and the information gathered had been subject to routine statistical use at least since the 1950s. The fi rst reason for the gap in the literature was of course that, before perestroika, these data were not published systematically and, from 1974, had even been kept secret. A second reason was probably that researchers were often ques- tioning the data quality; however, no serious study has ever proved this. On the contrary, it seems to us that all these data offer a very rich resource for anyone attempting to track and understand cause-specifi c mortality trends in the countries of the former USSR – in our case, in Ukraine. Even so, a great deal of effort was required to trace, collect and computerize the various archived data deposits. This chapter will start with a brief description of the registration system and a quick summary of the diffi culties we encountered and the data collection methods we used. We shall then review the results of some studies that enabled us to assess the quality of the data. -
INTRODUCTION, HISTORY SUBJECT and TASK of STATISTICS, CENTRAL STATISTICAL OFFICE ¢ SZTE Mezőgazdasági Kar, Hódmezővásárhely, Andrássy Út 15
STATISTISTATISTICSCS INTRODUCTION, HISTORY SUBJECT AND TASK OF STATISTICS, CENTRAL STATISTICAL OFFICE SZTE Mezőgazdasági Kar, Hódmezővásárhely, Andrássy út 15. GPS coordinates (according to GOOGLE map): 46.414908, 20.323209 AimAim ofof thethe subjectsubject Name of the subject: Statistics Curriculum codes: EMA15121 lect , EMA151211 pract Weekly hours (lecture/seminar): (2 x 45’ lectures + 2 x 45’ seminars) / week Semester closing requirements: Lecture: exam (2 written); seminar: 2 written Credit: Lecture: 2; seminar: 1 Suggested semester : 2nd semester Pre-study requirements: − Fields of training: For foreign students Objective: Students learn and utilize basic statistical techniques in their engineering work. The course is designed to acquaint students with the basic knowledge of the rules of probability theory and statistical calculations. The course helps students to be able to apply them in practice. The areas to be acquired: data collection, information compressing, comparison, time series analysis and correlation study, reviewing the overall statistical services, land use, crop production, production statistics, price statistics and the current system of structural business statistics. Suggested literature: Abonyiné Palotás, J., 1999: Általános statisztika alkalmazása a társadalmi- gazdasági földrajzban. Use of general statistics in socio-economic geography.) JATEPress, Szeged, 123 p. Szűcs, I., 2002: Alkalmazott Statisztika. (Applied statistics.) Agroinform Kiadó, Budapest, 551 p. Reiczigel J., Harnos, A., Solymosi, N., 2007: Biostatisztika nem statisztikusoknak. (Biostatistics for non-statisticians.) Pars Kft. Nagykovácsi Rappai, G., 2001: Üzleti statisztika Excellel. (Business statistics with excel.) KSH Hunyadi, L., Vita L., 2008: Statisztika I. (Statistics I.) Aula Kiadó, Budapest, 348 p. Hunyadi, L., Vita, L., 2008: Statisztika II. (Statistics II.) Aula Kiadó, Budapest, 300 p. Hunyadi, L., Vita, L., 2008: Statisztikai képletek és táblázatok (oktatási segédlet). -
International Journal of Quantitative and Qualitative Research Methods
International Journal of Quantitative and Qualitative Research Methods Vol.8, No.3, pp.1-23, September 2020 Published by ECRTD-UK ISSN 2056-3620(Print), ISSN 2056-3639(Online) CIRCULAR REASONING FOR THE EVOLUTION OF RESEARCH THROUGH A STRATEGIC CONSTRUCTION OF RESEARCH METHODOLOGIES Dongmyung Park 1 Dyson School of Design Engineering Imperial College London South Kensington London SW7 2DB United Kingdom e-mail: [email protected] Fadzli Irwan Bahrudin Department of Applied Art & Design Kulliyyah of Architecture & Environmental Design International Islamic University Malaysia Selangor 53100 Malaysia e-mail: [email protected] Ji Han Division of Industrial Design University of Liverpool Brownlow Hill Liverpool L69 3GH United Kingdom e-mail: [email protected] ABSTRACT: In a research process, the inductive and deductive reasoning approach has shortcomings in terms of validity and applicability, respectively. Also, the objective-oriented reasoning approach can output findings with some of the two limitations. That meaning, the reasoning approaches do have flaws as a means of methodically and reliably answering research questions. Hence, they are often coupled together and formed a strong basis for an expansion of knowledge. However, academic discourse on best practice in selecting multiple reasonings for a research project is limited. This paper highlights the concept of a circular reasoning process of which a reasoning approach is complemented with one another for robust research findings. Through a strategic sequence of research methodologies, the circular reasoning process enables the capitalisation of strengths and compensation of weaknesses of inductive, deductive, and objective-oriented reasoning. Notably, an extensive cycle of the circular reasoning process would provide a strong foundation for embarking into new research, as well as expanding current research.