Statistical Analysis For The Social Sciences
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Theory Based Data Analysis For The Social Sciences
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Author : Carol S. Aneshensel
language : en
Publisher: SAGE
Release Date : 2013
Theory Based Data Analysis For The Social Sciences written by Carol S. Aneshensel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Reference categories.
This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.
Statistical Analysis For The Social Sciences
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Author : Norman R. Kurtz
language : en
Publisher: Prentice Hall
Release Date : 1999
Statistical Analysis For The Social Sciences written by Norman R. Kurtz and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Business & Economics categories.
People are bombarded with statistical data every day, but not many have had training in how to interpret or analyze this information. Kurtz's accessible writing style provides a basic yet sophisticated introduction to understanding and analyzing statistical applications. The book gives careful attention to the flow of ideas and concepts so there is a stream of logic which flows throughout, adding to the book's readability. The book begins with a discussion of methods for describing the distribution of a variable. The introduction of probability avoids the traditional discussion of the basic laws of probability, providing instead an explanation which can be directly applied in the everyday use of statistical probability. The discussion of the book is focused primarily on the relationship of probability to outcomes. Sociologists, psychologists, social workers, political scientists, educators, as well as anyone who wants to analyze data.
Statistical Methods In Social Science Research
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Author : S P Mukherjee
language : en
Publisher: Springer
Release Date : 2018-10-05
Statistical Methods In Social Science Research written by S P Mukherjee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-05 with Social Science categories.
This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method – as distinct from a ‘science’ related to any one type of phenomena – is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.
Statistical Methods For The Social And Behavioural Sciences
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Author : David B. Flora
language : en
Publisher: SAGE
Release Date : 2017-12-11
Statistical Methods For The Social And Behavioural Sciences written by David B. Flora and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-11 with Social Science categories.
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.
Using R For Data Analysis In Social Sciences
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Author : Quan Li
language : en
Publisher: Oxford University Press
Release Date : 2018
Using R For Data Analysis In Social Sciences written by Quan Li and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
Dependent Data In Social Sciences Research
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Author : Mark Stemmler
language : en
Publisher: Springer Nature
Release Date : 2024-10-21
Dependent Data In Social Sciences Research written by Mark Stemmler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-21 with Social Science categories.
This book covers the following subjects: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models). It presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.
Statistics For The Social Sciences
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Author : Russell T. Warne
language : en
Publisher: Cambridge University Press
Release Date : 2017-12-14
Statistics For The Social Sciences written by Russell T. Warne and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-14 with Psychology categories.
Written by a quantitative psychologist, this textbook explains complex statistics in accessible language to undergraduates in all branches of the social sciences. Built around the central framework of the General Linear Model (GLM), Statistics for the Social Sciences teaches students how different statistical methods are interrelated to one another. With the GLM as a basis, students with varying levels of background are better equipped to interpret statistics and learn more advanced methods in their later courses. Russell Warne makes statistics relevant to students' varying majors by using fascinating real-life examples from the social sciences. Students who use this book will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice and reflection questions.
Statistical Power Analysis For The Social And Behavioral Sciences
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Author : Xiaofeng Steven Liu
language : en
Publisher:
Release Date : 2014
Statistical Power Analysis For The Social And Behavioral Sciences written by Xiaofeng Steven Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with BUSINESS & ECONOMICS categories.
"This will be the first book to demonstrate the application of power analysis to the newer more advanced techniques such as hierarchical linear modeling, meta-analysis, and structural equation modelling that are increasingly popular in behavioral and social science research."--Provided by publisher.
Statistical Methods For The Social Sciences
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Author : Alan Agresti
language : en
Publisher: Prentice Hall
Release Date : 2009
Statistical Methods For The Social Sciences written by Alan Agresti and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Social sciences categories.
When I undertook the first edition of this book my goal was to introduce statistical methods in a style that emphasized their concepts and their application to the social sciences rather than the mathematics and computational details behind them. This fourth edition has an even stronger emphasis on concepts and applications, with greater attention to "real data" both in the examples and exercises.