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Author: David Buchanan William Reid Publisher: ISBN: Category : Psychology, Experimental Languages : en Pages : 67
Book Description
"Biological response is characterized by variation: different organisms do not react in exactly the same way to identical situations; nor do individual organisms, on different occasions. Such lack of uniformity of response causes many difficulties in the interpretation of biological data. This has resulted in the development, in recent years, of special techniques for obtaining and analyzing experimental results. In particular, there has been great improvement in experimental designs, to which modern statistical methods may be applied in a satisfactory manner. While some statistical techniques have long been recognized as valuable in the reduction of large masses of data to comprehensible form, it has been only recently that exact methods of treating small numbers of observations have been worked out. Most biological data is of the latter sort. The methods have been found of great value in agricultural investigations, while notable improvements have been made in the field of biological assay. The difficulties which are encountered in biological assay are multiplied many times when the responses used are "subjective" rather than "physical." Such is the case when panels of judges are used in assessing the quality of foodstuffs. It is the intention of this thesis to illustrate how modern statistical procedures may be applied to such psychometric investigations in a manner similar to that in which they have been applied in other fields. [...]" --
Author: David Buchanan William Reid Publisher: ISBN: Category : Psychology, Experimental Languages : en Pages : 67
Book Description
"Biological response is characterized by variation: different organisms do not react in exactly the same way to identical situations; nor do individual organisms, on different occasions. Such lack of uniformity of response causes many difficulties in the interpretation of biological data. This has resulted in the development, in recent years, of special techniques for obtaining and analyzing experimental results. In particular, there has been great improvement in experimental designs, to which modern statistical methods may be applied in a satisfactory manner. While some statistical techniques have long been recognized as valuable in the reduction of large masses of data to comprehensible form, it has been only recently that exact methods of treating small numbers of observations have been worked out. Most biological data is of the latter sort. The methods have been found of great value in agricultural investigations, while notable improvements have been made in the field of biological assay. The difficulties which are encountered in biological assay are multiplied many times when the responses used are "subjective" rather than "physical." Such is the case when panels of judges are used in assessing the quality of foodstuffs. It is the intention of this thesis to illustrate how modern statistical procedures may be applied to such psychometric investigations in a manner similar to that in which they have been applied in other fields. [...]" --
Author: Patrick Mair Publisher: Springer ISBN: 3319931776 Category : Social Science Languages : en Pages : 458
Book Description
This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.
Author: Rand Wilcox Publisher: CRC Press ISBN: 1439834563 Category : Mathematics Languages : en Pages : 862
Book Description
In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.
Author: Publisher: Elsevier ISBN: 0080466702 Category : Psychology Languages : en Pages : 1191
Book Description
The area of Psychometrics, a field encompassing the statistical methods used in Psychological and educational testing, has become a very important and active area of research, evident from the large body of literature that has been developed in the form of books, volumes and research papers. Mainstream statisticians also have found profound interest in the field because of its unique nature. This book presents a state of the art exposition of theoretical, methodological and applied issues in Psychometrics. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with recent challenging psychometric problems. Key Features/ - Emphasis on the most recent developments in the field - Plenty of real, often complicated, data examples to demonstrate the applications of the statistical techniques - Information on available software Authors from the leading testing companies Emphasis on the most recent developments in the field Plenty of real, often complicated, data examples to demonstrate the applications of the statistical techniques Information on available software
Author: Kenneth Knoblauch Publisher: Springer Science & Business Media ISBN: 1461444756 Category : Computers Languages : en Pages : 376
Book Description
Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
Author: Nicholas T. Longford Publisher: Springer Science & Business Media ISBN: 1846281954 Category : Mathematics Languages : en Pages : 357
Book Description
This book evolved from lectures, courses and workshops on missing data and small-area estimation that I presented during my tenure as the ?rst C- pion Fellow (2000–2002). For the Fellowship I proposed these two topics as areas in which the academic statistics could contribute to the development of government statistics, in exchange for access to the operational details and background that would inform the direction and sharpen the focus of a- demic research. After a few years of involvement, I have come to realise that the separation of ‘academic’ and ‘industrial’ statistics is not well suited to either party, and their integration is the key to progress in both branches. Most of the work on this monograph was done while I was a visiting l- turer at Massey University, Palmerston North, New Zealand. The hospitality and stimulating academic environment of their Institute of Information S- ence and Technology is gratefully acknowledged. I could not name all those who commented on my lecture notes and on the presentations themselves; apart from them, I want to thank the organisers and silent attendees of all the events, and, with a modicum of reluctance, the ‘grey ?gures’ who kept inquiring whether I was any nearer the completion of whatever stage I had been foolish enough to attach a date.
Author: David B. Flora Publisher: SAGE ISBN: 1526421925 Category : Social Science Languages : en Pages : 786
Book Description
Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
Author: Helge Toutenburg Publisher: Springer Science & Business Media ISBN: 1441911480 Category : Mathematics Languages : en Pages : 625
Book Description
This book is the third revised and updated English edition of the German textbook \Versuchsplanung und Modellwahl" by Helge Toutenburg which was based on more than 15 years experience of lectures on the course \- sign of Experiments" at the University of Munich and interactions with the statisticians from industries and other areas of applied sciences and en- neering. This is a type of resource/ reference book which contains statistical methods used by researchers in applied areas. Because of the diverse ex- ples combined with software demonstrations it is also useful as a textbook in more advanced courses, The applications of design of experiments have seen a signi?cant growth in the last few decades in di?erent areas like industries, pharmaceutical sciences, medical sciences, engineering sciences etc. The second edition of this book received appreciation from academicians, teachers, students and applied statisticians. As a consequence, Springer-Verlag invited Helge Toutenburg to revise it and he invited Shalabh for the third edition of the book. In our experience with students, statisticians from industries and - searchers from other ?elds of experimental sciences, we realized the importance of several topics in the design of experiments which will - crease the utility of this book. Moreover we experienced that these topics are mostly explained only theoretically in most of the available books.
Author: Judith Greene Publisher: McGraw-Hill Education (UK) ISBN: 0335225608 Category : Psychology Languages : en Pages : 232
Book Description
Praise for the first edition: "An excellent textbook which is well planned, well written, and pitched at the correct level for psychology students. I would not hesitate to recommend Greene and d'Oliveira to all psychology students looking for an introductory text on statistical methodology." Bulletin of the British Psychological Society Learning to Use Statistical Tests in Psychology third edition has been updated throughout. It continues to be a key text in helping students to understand and conduct statistical tests in psychology without panic! It takes students from the most basic elements of statistics teaching them: How psychologists plan experiments and statistical tests Which considerations must be made when planning experiments How to analyze and comprehend test results Like the previous editions, this book provides students with a step-by-step guide to the simplest non-parametric tests through to more complex analysis of variance designs. There are clear summaries in progress boxes and questions for the student to answer in order to be sure that they have understood what they have read. The new edition is divided into four discrete sections and within this structure each test covered is illustrated through a chapter of its own. The sections cover: The principles of psychological research and psychological statistics Statistical tests for experiments with two or three conditions Statistical tests based on ANOVA (Analysis of Variance) conditions as well as tests for multiple comparisons between individual conditions Statistical tests to analyze relationships between variables Presented in a student-friendly textbook format, Learning to Use Psychological Tests in Psychology enables readers to select and use the most appropriate statistical tests to evaluate the significance of data obtained from psychological experiments. An errata sheet detailing the Decision Chart which is referred to can be downloaded by clicking here
Author: Alina A. von Davier Publisher: Springer Nature ISBN: 3030743942 Category : Education Languages : en Pages : 265
Book Description
This book defines and describes a new discipline, named “computational psychometrics,” from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners’ performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks. Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term “computational” has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, “computational” has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community. In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.
Author: David Buchanan William Reid
Author: David Buchanan William Reid
Author: Patrick Mair
Author: Rand Wilcox
Author: 
Author: Kenneth Knoblauch
Author: Nicholas T. Longford
Author: David B. Flora
Author: Helge Toutenburg
Author: Judith Greene
Author: Alina A. von Davier