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Saturday, November 28, 2020 | History

4 edition of Statistical Modelling for Social Researchers found in the catalog.

Statistical Modelling for Social Researchers

Roger Tarling

Statistical Modelling for Social Researchers

Principles and Practice

by Roger Tarling

  • 4 Want to read
  • 6 Currently reading

Published by Routledge .
Written in English

    Subjects:
  • Psychology,
  • Social Science / General,
  • Statistics,
  • Social Science,
  • Sociology

  • The Physical Object
    FormatHardcover
    Number of Pages424
    ID Numbers
    Open LibraryOL10207082M
    ISBN 100415448379
    ISBN 109780415448376


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Statistical Modelling for Social Researchers by Roger Tarling Download PDF EPUB FB2

Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed.

To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical Cited by: The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling.

Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research 5/5(1).

This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social by: 8.

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling.

The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research Cited by: The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling.

Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher. This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D.

students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists/5(3).

Models in Statistical Social Research provides a comprehensive insight of models used in statistical social research based on statistical data and methods. While traditionally understood statistical models relate to data generating processes which presuppose facts, this book focuses on analytical models which relate to substantial processes generating social by: 6.

"Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation. This revised edition organizes the chapters differently, making reading much easier.

Moreover, it includes many new examples and by: Book Description. Models and likelihood are the backbone of modern statistics and data analysis. Anthony Davison here blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practitioners.

The coverage is unrivaled, with sections on survival analysis, missing data, Markov chains, /5(3). STATISTICAL METHODS FOR THE SOCIAL SCIENCES. The purpose of this book is to acquaint the reader with the increasing number of applications of statistics in engineering and the social sciences.

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences) W.

Holmes Finch. out of 5 stars 1. Kindle Edition. $ has never studied or engaged in this type of data analysis or research design before. You may need to proceed to thicker treatises to make a thorough analysis and find out how to /5(17).

SPSS, standing for Statistical Package for the Social Sciences, is a powerful, user-friendly software package for the manipulation and statistical analysis of data. The package is particularly useful for students and researchers in psychology, sociology, psychiatry, and other behavioral sciences, contain.

If you have not reset your password sinceplease use the 'forgot password' link below to reset your password and access your SAGE online account. Making sense of statistical methods in social research, Yang () is an easy-to-follow book will be extremely useful to students taking PhD, statistics and/or quantitative research methods course because of the detailed formulas that are provided.

I also recommend it to instructors in statistics and quantitative methods. Abstract. Recent developments in statistical models for social networks reflect an increasing theoretical focus in the social and behavioral sciences on the interdependence of social actors in dynamic, network-based social settings (e.g., Abbott, ; White,).Cited by: 2.

STATISTICAL METHODS 1 STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA, La Jolla, USA. Email: [email protected] Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics represents that body of methods by which characteristics.

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.

This book is of value to sociologists, applied and statistical mathematicians, and researchers. Show less Quantitative Sociology: International Perspective on Mathematical and Statistical Modeling presents diverse mathematical modeling procedures involving different strategies for.

Researchers who use quantitative research convert their data into numbers for the purpose of analysis, and the numbers are then analyzed by statistical methods. Recommend this book Email your librarian or administrator to recommend adding this book to your organisation's collection. Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit.

Statistical Modeling and Inference for Social Science This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. students. Focusing on the connection between statistical proce-dures and social science theory, Sean Gailmard develops core statistical.

Social Science Research: Principles, Methods, and Practices, 2nd edition By Anol Bhattacherjee First published ISBN ISBN Creative Commons Attribution-NonCommercial-ShareAlike Unported License: Users are free to use, copy, share, distribute, display, and reference this book under the following conditions.

Department of Statistics Carnegie Mellon University Pittsburgh, PA USA Ingram Olkin Department of Statistics Stanford University Stanford, CA USA Library of Congress Cataloging-in-Publication Data Rawlings, John O., – Applied regression analysis: a research tool. — 2nd ed. / John O.

Rawlings, Sastry G. Pentula, David A. This book is intended as required reading material for my course, Experimen-tal Design for the Behavioral and Social Sciences, a second level statistics course for undergraduate students in the College of Humanities and Social Sciences at Carnegie Mellon University.

This course is also cross-listed as a graduate level. Political representation Trench warfare Rational voting Candidate positioning Recap Themes I Mathematical models in social science are cool I But they tend to give qualitative rather than quantitative predictions I Statistical modeling as an alternative I Collaborations with Hayward Alker, Aaron Edlin, Noah Kaplan, Gary King, and Jonathan Katz Andrew Gelman Mathematical vs.

statistical. Statistical models are mathematical simplifications of the real world. The goal of statistical modeling is to explain complex social facts as simply as possible. A statistical model might be as simple as a graph showing that richer parents have richer children, as depicted in Figure statistics and modern parameter estimation via MCMC simulation methods in great depth.

Throughout the book, I painstakingly show the modeling process from model development, through development of an MCMC algorithm to es-timate its parameters, through model evaluation, and.

The Institute for Digital Research and Education (IDRE) has a collection of books on statistics and statistical computing available for UCLA researchers to borrow on a short term basis to help with research.

Many of the books have web pages associated with them that have the data files for the book and web pages showing how to perform the. This book is designed to introduce doctoral and graduate students to the process of scientific research in the social sciences, business, education, public health, and related disciplines.

This book is based on my lecture materials developed over a decade of teaching the doctoral-level class on Research Methods at the University of South Florida.

The target audience for this book includes Ph.D /5(34). Apply statistics to your everyday life. Statistics and Data Analysis for Social Science helps students to build a strong foundational understanding of statistics by providing clarity around when and why statistics useful.

Rather than focusing on the “how to” of statistics, author Eric J. Krieg simplifies the complexity of statistical calculations by introducing only what is necessary to. Nowadays, statistical software can be used to fit complicated statistical models, which has allowed less statistically-literate researchers to access very powerful modelling tools.

But as is widely acknowledged, easy access does not bring about understanding, or lead to the close integration of research question and data analysis that marks out. terminology of data analysis, and be prepared to learn about using JMP for data analysis.

Introduction: A Common Language for Researchers Research in the social sciences is a diverse topic. In part, this is because the social sciences represent a wide variety of disciplines, including (but File Size: KB. Search the world's most comprehensive index of full-text books.

My libraryMissing: Statistical modelling. Instead of following the standard approach used in social statistics textbooks, this book focuses on statistical concepts and techniques as they are actually used in the social sciences.

Thus for example the very commonly used linear regression model is introduced early, while mathematically simpler but less commonly used means and standard.

researchers’ personal experiences, and the audiences for the study. Thus, in this book, research approaches, research designs, and research methods are three key terms that represent a perspective about research that presents information in a successive way from broad con - structions of research to the narrow procedures of Size: KB.

This Specialization covers research methods, design and statistical analysis for social science research questions. In the final Capstone Project, you’ll apply the skills you learned by developing your own research question, gathering data, and analyzing and reporting on the results using statistical Price: $ Representing some of the best research efforts currently found among social workers, Quantitative Methods in Social Work serves as both a guide and a challenge to social work researchers interested in the application of quantitative methods to social work problem solving.

This application of research methods has not been described or discussed adequately in any formal way until now. Using Statistics in Social Research: A Concise Approach, DOI / Economic and philosophic manuscripts of (M.

Milligan, Trans.) Amherst Jan S M Lynch. Social research is a research conducted by social scientists following a systematic plan. Social research methodologies can be classified as quantitative and qualitative. Quantitative designs approach social phenomena through quantifiable evidence, and often rely on statistical analysis of many cases (or across intentionally designed treatments in an experiment) to create valid and reliable.The Applied Social Research Methods book series by multiple authors includes books Survey Research Methods, Research Synthesis and Meta-Analysis: A Step-by-Step Approach, Methods for Policy Research, and several more.

See the complete Applied Social Research Methods series book list in order, box sets or omnibus editions, and companion titles.Scope. Statistical theory provides an underlying rationale and provides a consistent basis for the choice of methodology used in applied statistics.

Modelling. Statistical models describe the sources of data and can have different types of formulation corresponding to these sources and to the problem being studied.

Such problems can be of various kinds.