Written in a friendly, accessible style by an expert team of authors with years of experience in both conducting and supervising systematic reviews, this is the perfect guide to using systematic review methodology in a research project. It provides clear answers to all review-related questions, including: How do I formulate an appropriate review question? What's the best way to manage my review? How do I develop my search strategy? How do I get started with data extraction? How do I assess the quality of a study? How can I analyse and synthesize my data? How should I write up the discussion and conclusion sections of my dissertation or thesis?

Focused on actively using systematic review as method, this book provides clear, step-by-step advice on the logic and processes of systematic reviewing. Stressing the importance of precision and accuracy, this new edition carefully balances a need for insightful theory with real-world pragmatism; it introduces a wide range of cutting-edge approaches to research synthesis including text mining, living reviews and new ideas in mixed methods reviews such as qualitative comparative analysis. The book also includes: A new chapter on statistical synthesis Coverage of computer-assisted methods and relevant software Expanded sections on data extraction and management A guide to working with many different types of data including longitudinal and panel. Packed with examples from across the social sciences, this book helps students and researchers alike in turning systematic reviews into recommendations for policy and practice.

The literature review is a compulsory part of research and, increasingly, may form the whole of a student research project. This highly accessible book guides students through the production of either a traditional or a systematic literature review, clearly explaining the difference between the two types of review, the advantages and disadvantages of both, and the skills needed. It gives practical advice on reading and organising relevant literature and critically assessing the reviewed field. Contents include:  using libraries and the internet  note making  presentation  critical analysis  referencing, plagiarism and copyright. This book will be relevant to students from any discipline. It includes contributions from two lecturers who have many years experience of teaching research methods and the supervision of postgraduate research dissertations and a librarian, each offering expert advice on either the creation and assessment of literature reviews or the process of searching for information. The book also highlights the increasing importance for many disciplines of the systematic review methodology and discusses some of the specific challenges which it brings.   Jill K. Jesson has worked with multi-disciplinary research teams within the Aston School of Pharmacy, Aston Business School and with M-E-L Research, an independent public services research consultancy. She has now left Aston University and is working as a Consultant.   Lydia Matheson is an Information Specialist working for Library & Information Services at Aston University.   Fiona M. Lacey is an academic pharmacist, a member of the pharmacy practice teaching group in the School of Pharmacy, and Associate Dean in the School of Life and Health Sciences at Aston.

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts - The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors' thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.