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Provides a user-friendly, practical discussion of (a) what the procedure is and why it is used, (b) the assumptions underlying the procedure, (c) what to look for when interpreting output, and (d) how to write the results of the analysis in correct APA style.

Numerical data are everywhere. Charts and statistics appear not just in geography journals but also in the media, in public policy, and in business and commerce too. To engage with quantitative geography, we must engage with the quantitative methods used to collect, analyse, present and interpret these data. Quantitative Geography: The Basics is the perfect introduction for undergraduates beginning any quantitative methods course. Written in short, user-friendly chapters with full-colour diagrams, the book guides the reader through a wide range of topics from the basic to the more advanced, including: • Statistics • Maths • Graphics • Models • Mapping and GIS • R Closely aligned with the Q-Step quantitative social science programme, Quantitative Geography: The Basics is the ideal starting point for understanding and exploring this fundamental area of Geography.

This workbook is a practical introduction to the skill of social research. It is intended for use in introductory sociology courses and may be combined with most of the standard textbooks in the field.

This textbook offers an accessible and comprehensive introduction to statistics for all undergraduate psychology students, but particularly those in their second and third years who have already covered an initial introductory course. It covers all of the key areas in quantitative methods including sampling, significance tests, regression, and multivariate techniques and incorporates a range of exercises and problems at the end of each chapter for the student to follow.The free CD-ROM with tutorial modules complements and enhances the exercises in the text, offers scope for distance learning, and makes both the traditional and non-traditional approaches much more accessible.Key points of the book are: an emphasis on measurement, data summaries and graphs; a clear explanation of statistical inference using sampling distributions and confidence intervals, making significance tests much easier to understand; and help for students to understand and judge the use of particular tests in the research context beyond simple recipe following.

In fields as varying as education, politics and health care, assessment and the use of measurement and statistics have become an integral part of almost every activity undertaken. These activities require the organization of ideas, the generation of hypotheses, the collection of data and the interpretation, illustration and analysis of data. The Encyclopedia of Measurement and Statistics, in two-volumes, presents state-of-the-art information and ready-to-use facts from the fields of measurement and statistics in a non-intimidating and accessible style. The Encyclopedia is specifically written to appeal to undergraduate students as well as practitioners, researchers and consumers of information. Whilst there are reference works covering statistics and assessment in depth, none provide as comprehensive a resource in as focused and approachable a manner as this encyclopedia. Key features include: - coverage of every major facet of these two different, but highly integrated disciplines with reference to mean, mode and median; reliability, validity, significance and correlation without overwhelming the informed reader; - cross-disciplinary coverage, with contributions from and applications to the fields of: psychology; education; sociology; human development; political science; business and management; and public health; - cross-referenced terms, a list of further readings and Website URLs after each entry, as well as an extensive set of appendices and an annotated list of organizations relevant to measurement and statistics. Appendices: - Appendix A is a guide to basic statistics for those readers who require an instructional step-by-step presentation of basic concepts in statistics and measurement; - Appendix B is a table of critical values used in hypothesis testing and an important part of any reference in this area; - Appendix C represents a collection of some most important and useful measurement and statistics Websites.

You may stop looking now. Quantitative Nursing Research is the answer to the prayers of graduate students and practitioners who have sought the key to this often intimidating subject. In this highly readable (dare we say enjoyable?) work, Thomas R. Knapp guides the reader through the basic definitions, fundamentals of design, and techniques of quantitative research.

The given paper proposes a new statistical framework based on the combination of the Fav-Jerry distribution (FJD) and a joint type-II censoring scheme (JT-II-CS) to examine heterogeneous and censored data. The FJD offers tractability in analysis by using its closed form of the quantile function, whereas with missing or incomplete data, the JT-II-CS offers multi-sample comparisons. Bayesian estimation is based on Markov chain Monte Carlo procedures, while the maximum likelihood estimation is obtained via a Newton–Raphson method. Both estimation strategies provide estimates of the parameters along with corresponding measures of uncertainty. The proposed methodology is also used on coded survey data on the knowledge of autism in both Hong Kong and Canada, which illustrates its potential in the measurement of cultural variance. In addition to this use, the framework highlights the potential for integrating more complex distributional modeling with censoring methods for general applications in engineering, natural sciences, and social sciences.

How doctoral programs train future researchers in quantitative methods has important implications for the quality of scientifically based research in education. The purpose of this article, therefore, is to examine how quantitative methods are used in the literature and taught in doctoral programs. Evidence points to deficiencies in quantitative training and application in several areas: (a) methodological reporting problems, (b) researcher misconceptions and inaccuracies, (c) overreliance on traditional methods, and (d) a lack of coverage of modern advances. An argument is made that a culture supportive of quantitative methods is not consistently available to many applied education researchers. Collective quantitative proficiency is defined as a vision for a culture representative of broader support for quantitative methodology (statistics, measurement, and research design).

This piece responds to the Benchmarking Rewevv of UK Sociology's assertion that the discipline has a deficit in quantitative methods and that the solution involves a recognition that: '... statistical methods form the core of social science.' It argues that whilst a quantitative programme is essential and we can agree that there are problems in relation to the quantitative competencies of sociologists at all levels in the UK, a turn to conventional statistical methods is not the way to go. The argument is developed first in relation to epistemic critiques of those methods by Pawson and Goldthorpe and then by the outlining of an alternative founded in a synthesis of complexity and systematic comparison. The key issue is that we need a quantitative programme which actually corresponds to social reality and that is not to be found in statistical methods which reify variables and consider causality in linear terms.

This article argues that concept misformation and conceptual stretching undermine efforts to combine qualitative and quantitative methods in multimethod research (MMR). Two related problems result from the mismatch of qualitatively and quantitatively construed concepts. Mechanism muddling occurs when differences in the connotation of qualitatively and quantitatively construed concepts embed different causal properties into conceptual definitions. Conceptual slippage occurs when qualitatively and quantitatively construed concepts use incompatible nominal, ordinal, or radial scales. Instead of gaining leverage from the synthesis of large- and small-N analysis, these problems can push MMR in two diametrically opposed directions, emphasizing one methodological facet at the cost of the other.