R

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
Alex Ramiller
by Alex Ramiller. When working with observational (i.e. non-experimental) data, it is often challenging to establish the existence of causal relationships between interventions and outcomes. Propensity Score Matching (PSM) provides a powerful tool for causal inference with observational data, enabling the creation of comparable groups that allow us to directly measure the impact of an intervention. This blog post introduces MatchIt – a software package that provides all of the necessary tools for conducting Propensity Score Matching in R – and provides step-by-step instructions on how to conduct and evaluate matches.

Design Your Observational Study with the Joint Variable Importance Plot

March 12, 2024
Lauren Liao
by Lauren Liao. When evaluating causal inference in observational studies, there often is a natural imbalance in the data. Luckily, variables are often measured alongside that can be helpful for adjustment. However, deciding which variables should be prioritized for adjustment is not trivial – since not all variables are equally important to the intervention or the outcome. I recommend using the joint variable importance plot during the observational study design phase to visualize which variables should be prioritized. This post provides a gentle guide on how to do so and why it is important.

A Basic Introduction to Hierarchical Linear Modeling

March 4, 2024
Mingfeng Xue
by Mingfeng Xue. Hierarchical Linear Modeling (HLM) is an extension of linear models, which offers an approach to analyzing data structures with nested levels. This blog elucidates HLM's significance over traditional linear regression models, particularly in handling clustered data and multilevel predictors. Illustrated with an example from educational research, the blog demonstrates model implementation and interpretation steps. It showcases how HLM accommodates both independent variables from different levels and hierarchical structure data, providing insights into their impacts on the outcome variable. Recommended resources further aid readers in mastering HLM techniques.

What Are Vowels Made Of? Graphing a Classic Dataset with R

February 13, 2024
Anna Björklund
by Anna Björklund. Vowels are all around us. Mainstream US English has around twelve unique vowels. How can our brains tell these sounds apart? This blog post will help you answer this question by plotting vowel data from a classic American English dataset by Peterson and Barney (1952).

Chirag Manghani

Consulting Drop-In Hours: Wed 1pm-3pm

Consulting Areas: Python, R, SQL, Stata, SAS, LaTeX, HTML / CSS, Javascript, C++, APIs, Cloud & HPC Computing, Cybersecurity & Data Security, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Sources, Data Visualization, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, R Programming, Software Tools, Text Analysis, Web Scraping, Regression Analysis, Software Output Interpretation, Bash or Command Line, Excel, Git or Github, Qualtrics, RStudio, RStudio...

Nimita Gaggar

Consulting Drop-In Hours: By appointment only

Consulting Areas: Python, R, Qualitative methods, R Programming, Other, RStudio

Quick-tip: the fastest way to speak to a consultant is to first submit a request and then ...

Nicolas Nunez-Sahr

Consulting Drop-In Hours: By appointment only

Consulting Areas: Python, R, SQL, C++, APIs, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Visualization, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, R Programming, Text Analysis, Regression Analysis, Software Output Interpretation, Bash or Command Line, Git or Github, RStudio, Google Cloud, PostgreSQL, Python Django

Quick-tip: the fastest way to speak to a consultant is to first ...

Lauren Liao

Consulting Drop-In Hours: Fri 9am-11am

Consulting Areas: Python, R, LaTeX, Data Manipulation and Cleaning, Data Science, Data Visualization, Geospatial Data, Maps & Spatial Analysis, Machine Learning, Python Programming, R Programming, Regression Analysis, Means Tests, Other, Excel, Git or Github, RStudio, RStudio Cloud

Quick-tip: the fastest way to speak to a consultant is to first ...

Jane Angar

Consulting Drop-In Hours: Wed 9am-11am

Consulting Areas: R, Stata, LaTeX, Data Manipulation and Cleaning, Data Visualization, Qualitative methods, R Programming, Regression Analysis, Means Tests, Excel, Git or Github, Qualtrics, RStudio, Stata, Jupiter Notebook

Quick-tip: the fastest way to speak to a consultant is to first ...

Lauren Chambers

Consulting Drop-In Hours: Wed 11am-1pm

Consulting Areas: Python, R, HTML / CSS, APIs, Data Manipulation and Cleaning, Data Science, Data Visualization, Python Programming, R Programming, Software Tools, Web Scraping, Regression Analysis, Software Output Interpretation, Bash or Command Line, Git or Github, OCR, RStudio

Quick-tip: the fastest way to speak to a consultant is to first ...