301 Academic Skills Workshop Programme: Dissertation Planning
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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. -
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. -
Discussion Notes for Aristotle's Politics
Sean Hannan Classics of Social & Political Thought I Autumn 2014 Discussion Notes for Aristotle’s Politics BOOK I 1. Introducing Aristotle a. Aristotle was born around 384 BCE (in Stagira, far north of Athens but still a ‘Greek’ city) and died around 322 BCE, so he lived into his early sixties. b. That means he was born about fifteen years after the trial and execution of Socrates. He would have been approximately 45 years younger than Plato, under whom he was eventually sent to study at the Academy in Athens. c. Aristotle stayed at the Academy for twenty years, eventually becoming a teacher there himself. When Plato died in 347 BCE, though, the leadership of the school passed on not to Aristotle, but to Plato’s nephew Speusippus. (As in the Republic, the stubborn reality of Plato’s family connections loomed large.) d. After living in Asia Minor from 347-343 BCE, Aristotle was invited by King Philip of Macedon to serve as the tutor for Philip’s son Alexander (yes, the Great). Aristotle taught Alexander for eight years, then returned to Athens in 335 BCE. There he founded his own school, the Lyceum. i. Aside: We should remember that these schools had substantial afterlives, not simply as ideas in texts, but as living sites of intellectual energy and exchange. The Academy lasted from 387 BCE until 83 BCE, then was re-founded as a ‘Neo-Platonic’ school in 410 CE. It was finally closed by Justinian in 529 CE. (Platonic philosophy was still being taught at Athens from 83 BCE through 410 CE, though it was not disseminated through a formalized Academy.) The Lyceum lasted from 334 BCE until 86 BCE, when it was abandoned as the Romans sacked Athens. -
A Pragmatic Stylistic Framework for Text Analysis
International Journal of Education ISSN 1948-5476 2015, Vol. 7, No. 1 A Pragmatic Stylistic Framework for Text Analysis Ibrahim Abushihab1,* 1English Department, Alzaytoonah University of Jordan, Jordan *Correspondence: English Department, Alzaytoonah University of Jordan, Jordan. E-mail: [email protected] Received: September 16, 2014 Accepted: January 16, 2015 Published: January 27, 2015 doi:10.5296/ije.v7i1.7015 URL: http://dx.doi.org/10.5296/ije.v7i1.7015 Abstract The paper focuses on the identification and analysis of a short story according to the principles of pragmatic stylistics and discourse analysis. The focus on text analysis and pragmatic stylistics is essential to text studies, comprehension of the message of a text and conveying the intention of the producer of the text. The paper also presents a set of standards of textuality and criteria from pragmatic stylistics to text analysis. Analyzing a text according to principles of pragmatic stylistics means approaching the text’s meaning and the intention of the producer. Keywords: Discourse analysis, Pragmatic stylistics Textuality, Fictional story and Stylistics 110 www.macrothink.org/ije International Journal of Education ISSN 1948-5476 2015, Vol. 7, No. 1 1. Introduction Discourse Analysis is concerned with the study of the relation between language and its use in context. Harris (1952) was interested in studying the text and its social situation. His paper “Discourse Analysis” was a far cry from the discourse analysis we are studying nowadays. The need for analyzing a text with more comprehensive understanding has given the focus on the emergence of pragmatics. Pragmatics focuses on the communicative use of language conceived as intentional human action. -
Mathematics: Analysis and Approaches First Assessments for SL and HL—2021
International Baccalaureate Diploma Programme Subject Brief Mathematics: analysis and approaches First assessments for SL and HL—2021 The Diploma Programme (DP) is a rigorous pre-university course of study designed for students in the 16 to 19 age range. It is a broad-based two-year course that aims to encourage students to be knowledgeable and inquiring, but also caring and compassionate. There is a strong emphasis on encouraging students to develop intercultural understanding, open-mindedness, and the attitudes necessary for them LOMA PROGRA IP MM to respect and evaluate a range of points of view. B D E I DIES IN LANGUA STU GE ND LITERATURE The course is presented as six academic areas enclosing a central core. Students study A A IN E E N D N DG two modern languages (or a modern language and a classical language), a humanities G E D IV A O L E I W X S ID U IT O T O G E U or social science subject, an experimental science, mathematics and one of the creative IS N N C N K ES TO T I A U CH E D E A A A L F C T L Q O H E S O R I I C P N D arts. Instead of an arts subject, students can choose two subjects from another area. P G E A Y S A E R S It is this comprehensive range of subjects that makes the Diploma Programme a O S E A Y H T demanding course of study designed to prepare students effectively for university entrance. -
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. -
Scientific Discovery in the Era of Big Data: More Than the Scientific Method
Scientific Discovery in the Era of Big Data: More than the Scientific Method A RENCI WHITE PAPER Vol. 3, No. 6, November 2015 Scientific Discovery in the Era of Big Data: More than the Scientific Method Authors Charles P. Schmitt, Director of Informatics and Chief Technical Officer Steven Cox, Cyberinfrastructure Engagement Lead Karamarie Fecho, Medical and Scientific Writer Ray Idaszak, Director of Collaborative Environments Howard Lander, Senior Research Software Developer Arcot Rajasekar, Chief Domain Scientist for Data Grid Technologies Sidharth Thakur, Senior Research Data Software Developer Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill, NC, USA 919-445-9640 RENCI White Paper Series, Vol. 3, No. 6 1 AT A GLANCE • Scientific discovery has long been guided by the scientific method, which is considered to be the “gold standard” in science. • The era of “big data” is increasingly driving the adoption of approaches to scientific discovery that either do not conform to or radically differ from the scientific method. Examples include the exploratory analysis of unstructured data sets, data mining, computer modeling, interactive simulation and virtual reality, scientific workflows, and widespread digital dissemination and adjudication of findings through means that are not restricted to traditional scientific publication and presentation. • While the scientific method remains an important approach to knowledge discovery in science, a holistic approach that encompasses new data-driven approaches is needed, and this will necessitate greater attention to the development of methods and infrastructure to integrate approaches. • New approaches to knowledge discovery will bring new challenges, however, including the risk of data deluge, loss of historical information, propagation of “false” knowledge, reliance on automation and analysis over inquiry and inference, and outdated scientific training models. -
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. -
Aristotle's Prime Matter: an Analysis of Hugh R. King's Revisionist
Abstract: Aristotle’s Prime Matter: An Analysis of Hugh R. King’s Revisionist Approach Aristotle is vague, at best, regarding the subject of prime matter. This has led to much discussion regarding its nature in his thought. In his article, “Aristotle without Prima Materia,” Hugh R. King refutes the traditional characterization of Aristotelian prime matter on the grounds that Aristotle’s first interpreters in the centuries after his death read into his doctrines through their own neo-Platonic leanings. They sought to reconcile Plato’s theory of Forms—those detached universal versions of form—with Aristotle’s notion of form. This pursuit led them to misinterpret Aristotle’s prime matter, making it merely capable of a kind of participation in its own actualization (i.e., its form). I agree with King here, but I find his redefinition of prime matter hasty. He falls into the same trap as the traditional interpreters in making any matter first. Aristotle is clear that actualization (form) is always prior to potentiality (matter). Hence, while King’s assertion that the four elements are Aristotle’s prime matter is compelling, it misses the mark. Aristotle’s prime matter is, in fact, found within the elemental forces. I argue for an approach to prime matter that eschews a dichotomous understanding of form’s place in the philosophies of Aristotle and Plato. In other words, it is not necessary to reject the Platonic aspects of Aristotle’s philosophy in order to rebut the traditional conception of Aristotelian prime matter. “Aristotle’s Prime Matter” - 1 Aristotle’s Prime Matter: An Analysis of Hugh R. -
LING 211 – Introduction to Linguistic Analysis
LING 211 – Introduction to Linguistic Analysis Section 01: TTh 1:40–3:00 PM, Eliot 103 Section 02: TTh 3:10–4:30 PM, Eliot 103 Course Syllabus Fall 2019 Sameer ud Dowla Khan Matt Pearson pronoun: he or they he office: Eliot 101C Vollum 313 email: [email protected] [email protected] phone: ext. 4018 (503-517-4018) ext. 7618 (503-517-7618) office hours: Wed, 11:00–1:00 Mon, 2:30–4:00 Fri, 1:00–2:00 Tue, 10:00–11:30 (or by appointment) (or by appointment) PREREQUISITES There are no prerequisites for this course, other than an interest in language. Some familiarity with tradi- tional grammar terms such as noun, verb, preposition, syllable, consonant, vowel, phrase, clause, sentence, etc., would be useful, but is by no means required. CONTENT AND FOCUS OF THE COURSE This course is an introduction to the scientific study of human language. Starting from basic questions such as “What is language?” and “What do we know when we know a language?”, we investigate the human language faculty through the hands-on analysis of naturalistic data from a variety of languages spoken around the world. We adopt a broadly cognitive viewpoint throughout, investigating language as a system of knowledge within the mind of the language user (a mental grammar), which can be studied empirically and represented using formal models. To make this task simpler, we will generally treat languages as though they were static systems. For example, we will assume that it is possible to describe a language structure synchronically (i.e., as it exists at a specific point in history), ignoring the fact that languages constantly change over time. -
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).