Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
9,678
result(s) for
"Research Evaluation Statistical methods."
Sort by:
Meta-Regression Analysis in Economics and Business
The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising \"Tower of Babel\" that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis-meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods.
eBook
Compassionate Statistics
This book provides a comprehensive, yet pragmatic, resource for social service professionals to use standard descriptive and inferential statistical techniques in ways that are meaningful to them, to their social agencies, to their funding sources, and, ultimately, to their clients.
eBook
Compassionate statistics : applied quantitative analysis for social services : with exercises and instructions in SPSS
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 FeaturesProvides 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 AudienceThis 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.
eBook
Quantitative methods for health research
A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community
This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods.
A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include:
* Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods
* Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application
* Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice
* Integration of practical data analysis exercises to develop skills and confidence
* Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text
Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference.
eBook
Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data
Background
A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied.
Results
We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate
p
-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called
jaccard
(
https://cran.r-project.org/package=jaccard
).
Conclusion
We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.
Journal Article
Small Telescopes: Detectability and the Evaluation of Replication Results
This article introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are consistent with an effect size big enough to have been detectable in the original study. The approach is demonstrated by examining replications of three well-known findings. Its benefits include the following: (a) differentiating \"unsuccessful\" replication attempts (i.e., studies yielding p > .05) that are too noisy from those that actively indicate the effect is undetectably different from zero, (b) \"protecting\" true findings from underpowered replications, and (c) arriving at intuitively compelling inferences in general and for the revisited replications in particular.
Journal Article
The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses
Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of \"model-free bootstrap\", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.
Journal Article