Introduction to Modern Modelling Methods PDF Download
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Author: D. Betsy McCoach
Publisher: Sage Quantitative Research
ISBN: 9781526424037
Category : Social Science
Languages : en
Pages : 312
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Book Description
Using concise and direct language, Betsy McCoach's book imparts a wide range of modeling techniques for use with quantitative data, including: From 2-level multilevel models to longitudinal modeling using multilevel and stuctural equation modeling (SEM) techniques. Part of The SAGE Quantitative Research Kit, this book offers the know-how and confidence needed to succeed on your quantitative research journey.
Author: D. Betsy McCoach
Publisher: Sage Quantitative Research
ISBN: 9781526424037
Category : Social Science
Languages : en
Pages : 312
Get Book Here
Book Description
Using concise and direct language, Betsy McCoach's book imparts a wide range of modeling techniques for use with quantitative data, including: From 2-level multilevel models to longitudinal modeling using multilevel and stuctural equation modeling (SEM) techniques. Part of The SAGE Quantitative Research Kit, this book offers the know-how and confidence needed to succeed on your quantitative research journey.
Author: D. Betsy McCoach
Publisher: SAGE
ISBN: 1529711088
Category : Social Science
Languages : en
Pages : 198
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Book Description
Using simple and direct language, this concise text provides practical guidance on a wide range of modeling methods and techniques for use with quantitative data. It covers: · 2-level Multilevel Models · Structural Equation Modeling (SEM) · Longitudinal Modeling using multilevel and SEM techniques · Combining organizational and longitudinal models Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1506302750
Category : Psychology
Languages : en
Pages : 489
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Book Description
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
Author: Britt Anderson
Publisher: SAGE
ISBN: 1446297373
Category : Psychology
Languages : en
Pages : 241
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Book Description
"For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed." - Anjan Chatterjee, Professor of Neurology, University of Pennsylvania "Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites...I recommend it with enthusiasm." - Asohan Amarasingham, The City University of New York This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python. Key Features include: Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spread sheet methods. Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for contex. Numerous worked examples to demonstrate the themes and procedures of cognitive modelling. An excellent text for postgraduate students taking courses in research methods, computational neuroscience, computational modelling, cognitive science and neuroscience. It will be especially valuable to psychology students.
Author: Rex B. Kline
Publisher: Guilford Publications
ISBN: 1462523005
Category : Social Science
Languages : en
Pages : 554
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Book Description
This book has been replaced by Principles and Practice of Structural Equation Modeling, Fifth Edition, ISBN 978-1-4625-5191-0.
Author: Jenine K. Harris
Publisher: SAGE Publications
ISBN: 148332205X
Category : Social Science
Languages : en
Pages : 138
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Book Description
This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social network structure that goes beyond the descriptive methods conventionally used in social network analysis. Although it was developed to handle the inherent non-independence of network data, the results of ERGM are interpreted in similar ways to logistic regression, making this a very useful method for examining social systems. Recent advances in statistical software have helped make ERGM accessible to social scientists, but a concise guide to using ERGM has been lacking. This book fills that gap, by using examples from public health, and walking the reader through the process of ERGM model-building using R statistical software and the statnet package. An Introduction to Exponential Random Graph Modeling is a part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which has helped countless students, instructors, and researchers learn cutting-edge quantitative techniques.
Author: Niels Blunch
Publisher: SAGE
ISBN: 1446204790
Category : Social Science
Languages : en
Pages : 281
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Book Description
Introduction to Structural Equation Modelling using SPSS and AMOS is a complete guide to carrying out your own structural equation modelling project. Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to structural equation modelling (SEM). Each chapter begins with learning objectives, and ends with a list of the new concepts introduced and questions to open up further discussion. Exercises for each chapter, incuding the necessary data, can be downloaded from the book′s website. Helpful real life examples are included throughout, drawing from a wide range of disciplines including psychology, political science, marketing and health. Introduction to Structural Equation Modelling using SPSS and AMOS provides engaging and accessible coverage of all the basics necessary for using SEM, making it an invaluable companion for students taking introductory SEM courses in any discipline.
Author: Ann A. O'Connell
Publisher: IAP
ISBN: 164802873X
Category : Education
Languages : en
Pages : 645
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Book Description
Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation. In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs. Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.
Author: Claudius Ptolemaeus
Publisher: Lee & Seshia
ISBN: 1304421066
Category : Computers
Languages : en
Pages : 687
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Book Description
This book is a definitive introduction to models of computation for the design of complex, heterogeneous systems. It has a particular focus on cyber-physical systems, which integrate computing, networking, and physical dynamics. The book captures more than twenty years of experience in the Ptolemy Project at UC Berkeley, which pioneered many design, modeling, and simulation techniques that are now in widespread use. All of the methods covered in the book are realized in the open source Ptolemy II modeling framework and are available for experimentation through links provided in the book. The book is suitable for engineers, scientists, researchers, and managers who wish to understand the rich possibilities offered by modern modeling techniques. The goal of the book is to equip the reader with a breadth of experience that will help in understanding the role that such techniques can play in design.
Author: Måns Thulin
Publisher:
ISBN: 9781032497457
Category : Mathematics
Languages : en
Pages : 0
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Book Description
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
