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Introduction to R for Social Scientists

Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science;

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Introduction to R for Social Scientists

Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science;

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Network Psychometrics with R

A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social;

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Network Psychometrics with R

A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social;

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An Introduction to Molecular Biology

This book explains molecular biology concepts clearly and in practical terms. It represents an invaluable introduction to molecular biology;

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Python for R Users

The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to;

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Network Psychometrics with R

A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social;

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An Introduction to Exponential Random Graph Modeling

Explaining the techniques and applications of exponential random graph modeling (ERGM) for social scientists, this is a uniquely;

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Applied Univariate, Bivariate, & Multivariate Stat istics

and R contains an accessible introduction to statistical modeling techniques commonly used in the social and natural sciences. The text offers;

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Advances in Social Science Research Using R

Quantitative social science research has been expanding due to the ava- ability of computers and data over the past few decades. Yet the;

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The Basics of Social Research

This thorough revision of Babbie's standard-setting text presents a succinct, straightforward introduction to the field of research methods;

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Introduction to Probability and Statistics for Engineers and Scientists

Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs;

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Statistics for Data Scientists

This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science;

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Introduction to Data Analysis with R for Forensic Scientists

Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their;

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Doing Discourse Research: An Introduction for Social Scientists

This new translation of a popular German text gives a clear and accessible introduction and overview of the most important approaches to;

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An Introduction to the Bootstrap

, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and;

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Computing For Scientists

for Scientists R. J. Barlow and A. R. Barnett Computing for Scientists focuses on the principles involved in scientific programming. Topics;

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Interviewing for Social Scientists

frequently used research method. Interviewing for Social Scientists provides a comprehensive and authoritative introduction to interviewing. It;

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Biostatistics with R: An Introduction to Statistics Through Biological Data

applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation;

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Knowledge Sharing among Scientists

findings illuminate attributes of reputation conducive to the voluntary transfer of timely, relevant, technological knowledge among individual R&D;

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Knowledge Sharing Among Scientists

findings illuminate attributes of reputation conducive to the voluntary transfer of timely, relevant, technological knowledge among individual R&D;

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Using R for Numerical Analysis in Science and Engineering

Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and;

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Applied Multiple Imputation: Advantages, Pitfalls, New Developments and Applications in R

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the;

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Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists

introduction to mixed-effects modeling in S-PlusA(r) and R for analyzing natural resource data sets with varying error structures and dependencies Each;

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Qualitative and Mixed Methods Data Analysis Using Dedoose

Qualitative and Mixed Methods Data Analysis Using Dedoose (R): A Practical Approach for Research Across the Social Sciences provides both;

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MATLAB® Essentials

All disciplines of science and engineering use numerical methods for complex problem analysis, due to the highly mathematical nature of the;

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MATLAB® Essentials

All disciplines of science and engineering use numerical methods for complex problem analysis, due to the highly mathematical nature of the;

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