Fall 2019

ILR 525:001 (crn: 87832)

• Syllabus

  • ILR 525:001 Syllabus

• Meta-Sim - Group Project

  This semester we will try out a new group project exercise, one that will rely on a simulated data set that I developed. In a nutshell, the data set is intended to represent a large-scale data collection effort from employees in one or more organizations (imagine collecting a lot of data using Qualtrics). The data set was generated from a 50×49 meta-analytic correlation matrix that was populated from a series of metaBUS (see metaBUS.org) queries. This means that all descriptive statistics, correlations, etc. are supported by meta-analytic evidence and, thus, reflect phenomena in the real world. Project teams will use the simulated data set to complete a semester-long project, which will require them to use an established theoretical model to examine at least five research questions. A project timetable is provided in the course syllabus.

  • Data file for the group project (n = 10,000)
  • Group Project Pre-Registration Form

• Module 1 - Everything Data

  • PowerPoint presentation for Module 1 lectures
  • Suggested readings to be completed for Module 1
  • My notes from (Monday) August 26
  • Module 1 Review

  • Some TedTalks for Module 1

    • Battling bad science
    • The upside of data
    • The human insights missing from big data

• Module 2 - Descriptive Statistics

  • PowerPoint presentation for Module 2 lectures [updated on 10/2/2019]
  • R code that visualizes a group difference and conducts a t-test to examine the significance of the observed mean difference (updated on 9/30/2019)
    • Sample data set #1 for Module 2 R script
    • Sample data set #2 for Module 2 R script
    • Sample data set #3 for Module 2 R script
  • Module 2 Review

• Module 3 - General Linear Model

  • PowerPoint presentation for Module 3 lectures [updated on 10/16/2019]

• Software Tutorial 1: How to install R and navigate RStudio

  • Link to tutorial

• Software Tutorial 2: How to import data in R

  • Link to tutorial
  • Code for this tutorial
  • Place this .csv file in the working directory folder so that the corresponding R script will work
  • Place this .xlsx file in the working directory folder so that the corresponding R script will work

• Software Tutorial 3: Estimating descriptive statistics and performing a t-test in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial

• Software Tutorial 4: Estimating correlations and corresponding p-values in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial
  • APA formatted table

• Software Tutorial 5: Simple Linear Regression in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial

• Software Tutorial 6: Multiple Regression (and hierarchical regression) in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial

ILR 525:002 (crn: 87833)

• Syllabus

  • ILR 525:002 Syllabus

• Meta-Sim - Group Project

  This semester we will try out a new group project exercise, one that will rely on a simulated data set that I developed. In a nutshell, the data set is intended to represent a large-scale data collection effort from employees in one or more organizations (imagine collecting a lot of data using Qualtrics). The data set was generated from a 50×49 meta-analytic correlation matrix that was populated from a series of metaBUS (see metaBUS.org) queries. This means that all descriptive statistics, correlations, etc. are supported by meta-analytic evidence and, thus, reflect phenomena in the real world. Project teams will use the simulated data set to complete a semester-long project, which will require them to use an established theoretical model to examine at least five research questions. A project timetable is provided in the course syllabus.

  • Data file for the group project (n = 10,000)
  • Group Project Pre-Registration Form

• Module 1 - Everything Data

  • PowerPoint presentation for Module 1 lectures
  • Suggested readings to be completed for Module 1
  • My notes from (Monday) August 26
  • Module 1 Review

  • Some TedTalks for Module 1

    • Battling bad science
    • The upside of data
    • The human insights missing from big data

• Module 2 - Descriptive Statistics

  • PowerPoint presentation for Module 2 lectures [updated on 10/2/2019]
  • R code that visualizes a group difference and conducts a t-test to examine the significance of the observed mean difference (updated on 9/30/2019)
    • Sample data set #1 for Module 2 R script
    • Sample data set #2 for Module 2 R script
    • Sample data set #3 for Module 2 R script
  • Module 2 Review

• Module 3 - General Linear Model

  • PowerPoint presentation for Module 3 lectures [updated on 10/16/2019]

• Software Tutorial 1: How to install R and navigate RStudio

  • Link to tutorial

• Software Tutorial 2: How to import data in R

  • Link to tutorial
  • Code for this tutorial
  • Place this .csv file in the working directory folder so that the corresponding R script will work
  • Place this .xlsx file in the working directory folder so that the corresponding R script will work

• Software Tutorial 3: Estimating descriptive statistics and performing a t-test in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial

• Software Tutorial 4: Estimating correlations and corresponding p-values in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial
  • APA formatted table

• Software Tutorial 5: Simple Linear Regression in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial

• Software Tutorial 6: Multiple Regression (and hierarchical regression) in R

  • Link to tutorial
  • Code for this tutorial
  • This is the .csv file for this tutorial