Meta-Analysis from A to Z

This workshop focuses on the practical “how to” of quantitative research synthesis. Preventive health research, like many disciplines, has a body of empirical research that is growing rapidly. Over the last four decades, data science aggregation and statistical methods have been developed to systematically and reproducibly synthesize the literature for a particular research question. These methods are commonly incorporated under the terms research synthesis or meta-analysis. Meta-analysis is used to derive an estimate of an overall effect size, combined with detailed moderator analyses with respect to the diversity and sensitivity of results.
 Learning objectives
In this workshop participants will learn how to:
  1. - Formulate a research question
  2. - Search and evaluate the literature
  3. - Extract and code data
  4. - Analyze and interpret meta-analytic models
Schedule
This is a full day workshop beginning at 9.30 am and ending at 4.30 pm with a one hour break for lunch and will take place on Friday May 1st at the Vanier Library (Loyola Campus) in room VL 122.
This workshop is led by Jennifer J McGrath, PhD MPH, PERFORM Chair in Childhood Preventive Health & Data Science and founder of Data Scientifique, an initiative that fosters data science ​opportunities among researchers. She has pioneered rigorous pediatric ambulatory measurement standards and advocates for reproducibility of science through open-source data science methods. Dr. McGrath has facilitated several meta-analyses through to completion, spanning diverse topics such as heart rate variability, executive functioning, socioeconomic status, racial discrimination, and goal adjustment.

 

Introduction to R

R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs. This multi-part introduction to R programming requires little previous experience and is aimed at helping workshop participants to master the basics of R.
 
In this workshop, the following topics will be covered:
  • - Mastering R basics: will include an introduction to the R environment, packages and data types
  • - Describing data: will demonstrate how to generate descriptive statistics, table outputs and simple statistical tests (i.e. t tests)
  • - Visualizing data: will show participants how to generate multiple types of plots and charts
Learning Objectives
In this workshop participants will:
  1. - Set the working directory from within an R session
  2. - Create and manipulate data structures including vectors, lists, matrices, and data frames
  3. - Generate frequency tables, means & standard deviations
  4. - Run a simple t test
  5. - Generate a bar plot, a pie chart, a scattet plot and a histogram
Schedule
 There will be three 2-hour sessions on the following Mondays: January 13, 20 and 27 from 5-7 pm at the Vanier Library (Loyola Campus) in room VL122
This workshop is led by Yara Abu Awad, ScD, a Harvard University trained epidemiologist with expertise analyzing big data by applying causal inference methods and machine learning techniques using R. She is currently at the Department of Psychology investigating the impact of stress on obesity among children, the impact of parental expectations on children’s sleep quality and the impact of social inequalities on children’s health.

Data Management in R

More and more users are migrating to R for data analysis. Before they can get to the analysis however they need to know how to import data, recode variables, handle missing observations and reshapre their dataset as needed. This workshop teaches many of the skills you will need for data management. Some prior R knowledge is required.
 
In this workshop, the following topics will be covered:
  • Session I: Importing data and recoding variables
  • Session II: Merging and reshaping data
Learning Objectives
In this workshop participants will:
  1. - Import data from different formats including the SPSS .sav, SAS .sas7bdat and .csv
  2. - Change variable format
  3. - Recode variables into new categories
  4. - Estimate the number of missing observations in data
  5. - Merge datasets
  6. - Concatenate datasets
  7. - Save datasets
Schedule
There will be two 2-hour sessions on Monday February 17th and Tuesday March 3rd from 5-7 pm at Vanier Library (Loyola Campus) in room VL122
Leader
This workshop is led by Yara Abu Awad

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Advanced Functions in R


R is becoming an increasingly popular coding language used by researchers for the most complex data analysis tasks. In order to tap into the true power of R, some more advanced knowledge of R functions is required. These functions allow users to optimize code for repetitive tasks, reduce coding mistakes, improve reproducibility and save time. For example, it is possible to import a thousand files or run a thousand regressions using one line of code!

In this workshop, the following topics will be covered:
  • Session I: The apply family of functions
  • Session II: Writing loops
Learning Objectives
In this workshop participants will:
  1. - Use the tapply function to perform several functions by factor levels
  2. - Use the lapply function to import multiple files
  3. - Use the lapply function to run multiple regressions and generate multiple plots
  4. - Convert a list type of object to a data frame while retaining original information
  5. - Write a loop
Schedule
There will be two 2-hour sessions on Tuesday March 24th and Tuesday March 31st from 5-7 pm at the Webster Library in room LB207.
Leader
This workshop is led by Yara Abu Awad

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