To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model and interpret results. The mission of this book is to expose the reader to advanced quantitative methods as it pertains to individual level analysis, multilevel analysis, item-level analysis, and covariance structure analysis. Each chapter is self-contained and follows a common format so that readers can run the analysis and correctly interpret the output for reporting.
The mission of this book is to address advances in quantitative methods that are not covered in other texts, such as item response theory, the family of multilevel models (including cross-classified and growth), and mixture modeling.
This original textbook provides a comprehensive and integrated approach to using quantitative methods in the social sciences. Thomas R Black guides the student and researcher through the minefield of potential problems that may be confronted, and it is this emphasis on the practical that distinguishes his book from others which focus exclusively on either research design and measurement or statistical methods. Focusing on the design and execution of research, key topics such as planning, sampling, the design of measuring instruments, choice of statistical text and interpretation of results are examined within the context of the research process. In a lively and accessible style, the student is introduced to researc design issues alongside statistical procedures and encouraged to develop analytical and decision-making skills.
"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--
This accessible and authoritative introduction is essential for education students and researchers needing to use quantitative methods for the first time. Using datasets from real-life educational research and avoiding the use of mathematical formulae, the author guides students through the essential techniques that they will need to know, explaining each procedure using the latest version of SPSS. The datasets can also be downloaded from the book′s website, enabling students to practice the techniques for themselves. This revised and updated second edition now also includes more advanced methods such as log linear analysis, logistic regression, and canonical correlation. Written specifically for those with no prior experience of quantitative research, this book is ideal for education students and researchers in this field.
Designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond, this title includes chapters that address traditional and emerging quantitative methods of data analysis.
"The book provides a reference point for beginning educational researchers to grasp the most pertinent elements of designing and conducting research..." —Megan Tschannen-Moran, The College of William & Mary Quantitative Research in Education: A Primer, Second Edition is a brief and practical text designed to allay anxiety about quantitative research. Award-winning authors Wayne K. Hoy and Curt M. Adams first introduce readers to the nature of research and science, and then present the meaning of concepts and research problems as they dispel notions that quantitative research is too difficult, too theoretical, and not practical. Rich with concrete examples and illustrations, the Primer emphasizes conceptual understanding and the practical utility of quantitative methods while teaching strategies and techniques for developing original research hypotheses. The Second Edition includes suggestions for empirical investigation and features a new section on self-determination theory, examples from the latest research, a concluding chapter illustrating the practical applications of quantitative research, and much more. This accessible Primer is perfect for students and researchers who want a quick understanding of the process of scientific inquiry and who want to learn how to effectively create and test ideas.
Compassionate Statistics: Applied Quantitative Analysis for Social Services (With Instructions for SPSS 14.0) is an attempt to "de-mythologize" a content area that is both essential for professional social service practitioners, yet dreaded by some of the most experienced among them. Using friendly, straightforward language as well as concrete illustrations and exercises from social service practice, author Vincent E. Faherty catapults students and experienced professionals to a pragmatic level where they can handle quantitative analysis for all their research and evaluation needs. Key Features Provides comprehensive coverage of the most important aspects of quantitative analysis: This is a complete, yet pragmatic, resource for social service professionals to use standard descriptive and inferential statistical techniques. Offers an accessible format: Using unpretentious and plain language, this book introduces essential statistical procedures, one-at-a-time, in relatively short chapters in order to assist recall and facilitate new learning. Applies statistical content to social service practice situations: Concrete applications are drawn from counseling, criminal justice, human services, social work, therapeutic recreation and vocational rehabilitation. Presents case illustrations of how statistical material is reported in professional literature: Since social service professionals need to write up the results of their quantitative analysis, this book provides actual illustrations of how the various statistical procedures and tests are presented in published articles. Addresses the use of SPSS on each covered statistical procedure and test: Specific directions are given so students can use the latest version of SPSS to complete each assigned exercise. Includes in-chapter exercises: A series of realistic data sets that students can use to perform a number of planned exercises are offered in each chapter. Intended Audience This is an excellent core or supplemental text for a variety of advanced undergraduate and graduate courses such as Statistics for Social Services, Applied Statistics, Quantitative Analysis for Social Services, Statistics for Social Work, Social Science Research, Research Methods, Program Evaluation, and Grant Writing in the departments of counseling, human services, social services, social work, therapeutic recreation, and vocational rehabilitation.
Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and "Practical Matters" sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs. ÿ
Quantitative methodology is a highly specialized field, and as with any highly specialized field, working through idiosyncratic language can be very difficult made even more so when concepts are conveyed in the language of mathematics and statistics. The Sage Handbook of Quantitative Methodology for the Social Sciences was conceived as a way of introducing applied statisticians, empirical researchers, and graduate students to the broad array of state-of-the-art quantitative methodologies in the social sciences. The contributing authors of the Handbook were asked to write about their areas of expertise in a way that would convey to the reader the utility of their respective methodologies. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter. The Handbook consists of six sections comprising twenty-five chapters, from topics in scaling and measurement, to advances in statistical modelling methodologies, and finally to broad philosophical themes that transcend many of the quantitative methodologies covered in this handbook.
