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Jeremy N.V. Miles

Bio: Jeremy N.V. Miles is an academic researcher. The author has contributed to research in topics: Research question & Psychological research. The author has an hindex of 3, co-authored 4 publications receiving 24861 citations.

Papers
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Book
01 Jan 2000
TL;DR: Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R).
Abstract: Hot on the heels of the 3rd edition of Andy Field's award-winning Discovering Statistics Using SPSS comes this brand new version for students using SAS(R). Andy has teamed up with a co-author, Jeremy Miles, to adapt the book with all the most up-to-date commands and programming language from SAS(R) 9.2. If you're using SAS(R), this is the only book on statistics that you will need! The book provides a comprehensive collection of statistical methods, tests and procedures, covering everything you're likely to need to know for your course, all presented in Andy's accessible and humourous writing style. Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R). A 'cast of characters' supports the learning process throughout the book, from providing tips on how to enter data in SAS(R) properly to testing knowledge covered in chapters interactively, and 'real world' and invented examples illustrate the concepts and make the techniques come alive. The book's companion website (see link above) provides students with a wide range of invented and real published research datasets. Lecturers can find multiple choice questions and PowerPoint slides for each chapter to support their teaching.

25,020 citations

Journal Article
TL;DR: Most reports of RCTs in MEDLINE can now be identified easily using "Randomized Controlled Trial" (Publication Type), and more sensitive searches can be achieved by a brief strategy, the Centre for Reviews and Dissemination/Cochrane Highly Sensitive Search Strategy (2005 revision).
Abstract: PMCID: PMC1435857 This article has been corrected. See J Med Libr Assoc. 2006 July; 94(3): 354. http://europepmc.org/articles/PMC1525311/

525 citations

01 Jan 2007
TL;DR: In this article, the authors summarize some of the problems with significance testing and introduce some modern methods in a non-technical way that will help psychology researchers and students to think about the most appropriate ways to approach their research question.
Abstract: Null hypothesis significance testing is the staple of psychological research. It is a technique so ingrained in the teaching of psychological research methods that we rarely have cause to question it. This article overviews some of the problems with significance testing by summarizing some of the many objections that have been raised: it is logically flawed, it rarely, if ever, tells us what we want to know, it is reliant on sample size, uses arbitrary values to determine importance and leads to misunderstandings about replication. The article concludes with a brief overview of four popular adjunct and alternatives to significance testing: power analysis, confidence intervals, meta-analysis and Bayesian methods. It is hoped that by overviewing these issues and introducing some modern methods in a non-technical way that this article will help psychology researchers and students to think about the most appropriate ways to approach their research question.

3 citations

Journal ArticleDOI
TL;DR: In this article, the authors summarize some of the problems with significance testing, including the fact that it is logically flawed, it rarely tells us what we want to know, it is dependent on sample size, uses arbitrary values to determine importance, and leads to misunderstandings about replication.
Abstract: Null hypothesis significance testing is the staple of psychological research. It is a technique so ingrained in the teaching of psychological research methods that we rarely have cause to question it. This article overviews some of the problems with significance testing by summarising some of the many objections that have been raised: it is logically flawed, it rarely, if ever, tells us what we want to know, it is reliant on sample size, uses arbitrary values to determine importance, and leads to misunderstandings about replication. The article concludes with a brief overview of four popular adjunct and alternatives to significance testing: power analysis, confidence intervals, meta-analysis, and Bayesian methods. It is hoped that by overviewing these issues, and introducing some modern methods in a non-technical way, that this article will help psychology researchers and students to think about the most appropriate ways to approach their research question.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
Abstract: Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.

2,782 citations

Journal ArticleDOI
01 Oct 2013
TL;DR: An overview of the statistical technique and how it is used in various research designs and applications is given, to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.
Abstract: The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.

2,214 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined the relative impact of different types of leadership on students' academic and non-academic outcomes and concluded that the average effect of instructional leadership on student outcomes was three to four times that of transformational leadership.
Abstract: Purpose: The purpose of this study was to examine the relative impact of different types of leadership on students' academic and nonacademic outcomes.Research Design:The methodology involved an analysis of findings from 27 published studies of the relationship between leadership and student outcomes. The first meta-analysis, including 22 of the 27 studies, involved a comparison of the effects of transformational and instructional leadership on student outcomes. The second meta-analysis involved a comparison of the effects of five inductively derived sets of leadership practices on student outcomes. Twelve of the studies contributed to this second analysis.Findings: The first meta-analysis indicated that the average effect of instructional leadership on student outcomes was three to four times that of transformational leadership. Inspection of the survey items used to measure school leadership revealed five sets of leadership practices or dimensions: establishing goals and expectations; resourcing strategi...

2,112 citations

Journal ArticleDOI
15 Aug 2009-Spine
TL;DR: Instead of recommending Levels of Evidence, this update adopts the GRADE approach to determine the overall quality of the evidence for important patient-centered outcomes across studies and includes a new section on updating reviews.
Abstract: STUDY DESIGN. Method guidelines for systematic reviews of trials of treatments for neck and back pain. OBJECTIVE. To help review authors design, conduct and report systematic reviews of trials in this field. SUMMARY OF BACKGROUND DATA. In 1997, the Cochrane Back Review Group published Method Guidelines for Systematic Reviews, which was updated in 2003. Since then, new methodologic evidence has emerged and standards have changed. Coupled with the upcoming revisions to the software and methods required by The Cochrane Collaboration, it was clear that revisions were needed to the existing guidelines. METHODS. The Cochrane Back Review Group editorial and advisory boards met in June 2006 to review the relevant new methodologic evidence and determine how it should be incorporated. Based on the discussion, the guidelines were revised and circulated for comment. As sections of the new Cochrane Handbook for Systematic Reviews of Interventions were made available, the guidelines were checked for consistency. A working draft was made available to review authors in The Cochrane Library 2008, issue 3. RESULTS. The final recommendations are divided into 7 categories: objectives, literature search, inclusion criteria, risk of bias assessment, data extraction, data analysis, and updating your review. Each recommendation is classified into minimum criteria (mandatory) and further guidance (optional). Instead of recommending Levels of Evidence, this update adopts the GRADE approach to determine the overall quality of the evidence for important patient-centered outcomes across studies and includes a new section on updating reviews. CONCLUSION. Citations of previous versions of the method guidelines in published scientific articles (1997: 254 citations; 2003: 209 citations, searched February 10, 2009) suggest that others may find these guidelines useful to plan, conduct, or evaluate systematic reviews in the field of spinal disorders. © 2009 Lippincott Williams & Wilkins, Inc.

1,434 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explored the role of four key factors that influence perceptions of trust and consumer choice within a hotel context, and found that consumers tend to rely on easy-to-process information, when evaluating a hotel based upon reviews.

1,250 citations