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In order to effectively interpret your observations you need to relate it to existing theoretical frameworks, take rival explanations into account and explain how the data supports your interpretation. Interpretations are articulated in the form of hypotheses or theories.
Mouton, J. 2001. How to succeed in your master's & doctoral studies: a South African guide and resource book. Pretoria: Van Schaik.
The type of analysis you do is generally dependent on whether your data is quantitative or qualitative. Methods of analysis may also differ by scientific discipline. The optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought.
Qualitative data analysis: From start to finish by Jamie HardingThis is the ideal book to get you up and running with the basics of qualitative data analysis. It breaks everything down into a series of simple steps and introduces the practical tools and techniques you need to turn your transcripts into meaningful research.
Using multidisciplinary data from interviews and focus groups Jamie Harding provides clear guidance on how to apply key research skills such as making summaries, identifying similarities, drawing comparisons and using codes.
The book sets out real world applicable advice, provides easy to follow best practice and helps you to:
· Manage and sort your data
· Find your argument and define your conclusions
· Answer your research question
· Write up your research for assessment and dissemination
Clear, pragmatic and honest this book will give you the perfect framework to start understanding your qualitative data and to finish your research project.
Call Number: 001.42 HAR
Publication Date: 2019
Qualitative data analysis: Practical strategies by Patricia BazeleyThis book takes the reader on a step by step journey through the different stages of analysis, from first reading transcripts to presenting findings in a report or dissertation. For each stage of the process there are demonstrations using real data and exercises for the reader to perform. While acknowledging that there are many different forms that qualitative data analysis can take, the book provides a series of ideas and examples that readers will find helpful when analysing their own data.
There are a variety of statistical techniques used to analyse quantitative data that masters students, advanced undergraduates and researchers in the social sciences are expected to be able to understand and undertake. This book explains these techniques, when it is appropriate to use them, how to carry them out and how to write up the results. Most books which describe these techniques do so at too advanced or technical a level to be readily understood by many students who need to use them.
Multivariate data analysis: A global perspective by Joseph F. HairFor graduate and upper-level undergraduate marketing research courses. For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques
Adventures in social research: Data analysis using IBM SPSS statistics by William E. Wagner; Earl Robert Babbie (Editor); Fred S. Halley; Jeanne S. ZainoWritten by esteemed social science research authors,Adventures in Social Research: Data Analysis Using IBM® SPSS® Statistics, encourages students to practice SPSS as they read about it and provides a practical, hands-on introduction to conceptualization, measurement, and association through active learning. This fully revised workbook will guide students through step-by-step instruction on data analysis using the latest version of SPSS and the most up to date General Social Survey data. Arranged to parallel most introductory research methods texts, this text starts with an introduction to computerized data analysis and the social research process, then walks readers step-by-step through univariate, bivariate, and multivariate analysis using SPSS Statistics. In this revised edition, active and collaborative learning will be emphasized as students engage in a series of practical investigative exercises.
Call Number: JS Gericke Library Upper Level (519.50285 HAY )
Publication Date: 2011-10-19
Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.
Examples of how to write programs using the R programming language are provided. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.
Call Number: JS Gericke Library Lower Level (300.727 CUM )
Publication Date: 2011-07-14
This book introduces the new statistics - effect sizes, confidence intervals, and meta-analysis. Practical examples and tips are provided on how to analyze and report research results using these techniques. Of interest in terms of meeting the new APA Publication Manual guidelines by adopting the new statistics.
This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion.
A comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.