This lesson is designed for librarians and library professionals with little or no prior experience with R to be more acquainted with the programming language. Having a level of familiarity with R is beneficial in assisting users with requests regarding the cleaning, formatting, and visualization with data along for librarians and library professionals themselves when it comes to data they intend to use and analyze for their internal workflows.
Learners will become familiar with both R, R Studio software environment, and the Tidyverse. The R Studio environment allows one to run their code and see the immediate results of one’s code separate panels. While R originally started as a being a statistical programming language, R is used for various applications such as data visualization, deploying of web applications, and creating reproducible documentation. Given the extensive applications of R, we will solely be focusing on importing, cleaning, and visualizing data.
En lugar de presentar todos los pormenores de R de manera lineal, se irán presentando distintos aspectos de R conforme se vayan necesitando; es decir, no vamos a presentar R como un lenguaje de programación sino como una herramienta para hacer análisis estadísticos.
This Shiny App is to provide a user-friendly interface for users to conduct item analysis based on Classical Test Theory (CTT). Item analysis is used for examining responses to individual test items in order to assess the quality of items and of the test as a whole. It is valuable in improving items or eliminating poorly written or ambiguous item. This Shiny App provides several strategies used for item analysis, including reliability estimates, item difficulty, item discrimination (i.e.,item total correlation).
This is one page of a series of tutorials for using R in psychological research. Much of material has also covered been covered in number of short courses or in a set of tutorials for specific problems.
Psychometrics is concerned with theory and techniques of psychological measurement. Psychometricians have also worked collaboratively with those in the field of statistics and quantitative methods to develop improved ways to organize, analyze, and scale corresponding data. Since much functionality is already contained in base R and there is considerable overlap between tools for psychometry and tools described in other views, particularly in SocialSciences, we only give a brief overview of packages that are closely related to psychometric methodology.
Lista de paquetes relacionados con psicometría
This page is devoted to teaching others about psychometric theory as well as R. It consists of chapters of an in progress text as well as various short courses on R.
This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. The introductory methods course has two primary aims. First, students will be introduced to quantitative methodology that researchers and policymakers use in answering social, political and economic questions. Second, the course will equip students to use one or more of the discussed techniques in their MSc dissertation.
By the end of the course, you should be able to understand basic research methods, apply them to real world problems and evaluate their use in published research. Students will also acquire competency in performing statistical analyses using a popular statistical program (R).
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
A wealth of tutorials, articles, and examples exist to help you learn R and its extensions. Scroll down or click a link below for a curated guide to learning R and its extensions.
This book aims to teach the following:
Getting started with your own R Markdown document
Improve workflow:
With rstudio projects
Using keyboard shortcuts
Export your R Markdown document to PDF, HTML, and Microsoft Word
Better manage figures and tables
Reference figures and tables in text so that they dynamically update
Create captions for figures and tables
Change the size and type of figures
Save the figures to disk when creating an rmarkdown document
Work with equations
inline and display
caption equations
reference equations
Manage bibliographies
Cite articles in text
generate bibliographies
Change bibliography styles
Debug and handle common errors with rmarkdown
Next steps in working with rmarkdown - how to extend yourself to other rmarkdown formats
Este texto ha sido editado en respusta a la aparente falta de un libro de texto introductorio al análisis cuantitativo y estadísticas acesible y moderno en castellano. Si bien fue concebido como material de cátedra para Métodos cuantitativos materia que dicta el autor en la Escuela de Humanidades de la Universidad Nacional San Martín, se adaptará fácilmente a cursos introductorios de estadísticas en general.