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FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017) <doi:10.1201/b10345-2>.

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psych: Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research.

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nFactors: Parallel Analysis and Non Graphical Solutions to the Cattell Scree Test

Indices, heuristics and strategies to help determine the number of factors/components to retain: 1. Acceleration factor (af with or without Parallel Analysis); 2. Optimal Coordinates (noc with or without Parallel Analysis); 3. Parallel analysis (components, factors and bootstrap); 4. lambda > mean(lambda) (Kaiser, CFA and related); 5. Cattell-Nelson-Gorsuch (CNG); 6. Zoski and Jurs multiple regression (b, t and p); 7. Zoski and Jurs standard error of the regression coeffcient (sescree); 8. Nelson R2; 9. Bartlett khi-2; 10. Anderson khi-2; 11. Lawley khi-2 and 12. Bentler-Yuan khi-2.

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R Markdown Help Articles

A collection of articles to help in the use of R Markdown.

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R Markdown Reference Guide

Quick guide to syntax and functions in R Markdown.

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R Markdown Cheat Sheet

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Science & Society

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Communication & Collaboration

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Interdisciplinary Nature of Science

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Modeling & Simulation

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Quantitative Reasoning

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Process of Science

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Access NEON Data Using the Data Portal – A Visual Guide

This guide walks viewers through how to access data through the NEON data portal and a few related resources for working with NEON data.  The NEON data portal remains in dynamic development. This photographic guide will be regularly updated. If the data portal you are viewing does not match this guide, please check for an updated version on the NEONScience.org website and let Megan know so she can update it here.

The guide is laid out to be followed sequentially from starting on the NEONScience.org website to downloading data to your computer. After this sequence additional slides are provided that details on other pages or topics of interest to data users.

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tEST POST

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NWBC Introductory Biology Learning Outcomes

Learning outcomes for the first year of biology compiled and consented to at the NWBC 2018 Winter Workshop.

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Core competency reference sheet

Reference sheet from the workshop. Adapted from 2011 Vision and Change report by Stasinos Stavrianeas.

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NW PULSE resources

Scroll down to find links to webinars on Dynamic Governance, Curriculum Mapping, and more.

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BioSkills Learning Outcomes

Coming soon! Full set will be posted after validation.

The BioSkills Guide is a validated set of essential learning outcomes for the six core competencies for graduating general biology majors.

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Slides from workshop

Powerpoint slides used during the SABER West 2019 "Scaffolding Core Competency Learning Outcomes across the Undergraduate Biology Curriculum" workshop.

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Northwest BioSciences Consortium

QUBES site for the NWBC group. To learn more about NWBC.

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Vision and Change in Undergraduate Biology: A Call to Action

The 2011 report. See pages 14-15 for core competencies.

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Test

Blah

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R for Data Science: Chapter 4: Communicate

So far, you’ve learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.

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R for Data Science: Chapter 4: Model

Now that you are equipped with powerful programming tools we can finally return to modelling. You’ll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you’ll learn a few basic tools that help you understand the variation within your models.

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R for Data Science: Chapter 3: Program

In this part of the book, you’ll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper.

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