CARMA Live Online Short Courses

North America Region – January 2021


Live Online Short Courses – January 6-8, 2021

Sponsored by University of South Carolina

Short Course Sessions and Groupings

All courses in are taught concurrently, so a participant can take only one course.

Complete Course Listing

  1. “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte
  2. “Advanced Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina
  3.  “Introduction to SEM with LAVAAN” – Dr. Robert Vandenberg, University of Georgia
  4. “Statistical Analysis of Big Data with R” – Dr. Jeff Stanton, Syracuse University
  5. “Intermediate SEM, Model Evaluation” – Dr. Larry Williams, Texas Tech University
  6. “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina-Charlotte 
  7. “Advanced Data Analysis with R” – Dr. Ron Landis, Illinois Institute of Technology
  8. “Systematic Reviews and Meta-Analysis” – Dr. Ernest O’Boyle, Indiana University

CARMA Workshop: Basics of R

This four-hour Workshop provides information on the package R to prepare attendees for follow-up training in CARMA Short Courses that use R. By attending this online workshop, participants will learn basic skills for using the R Studio interface to: load and activate R packages, import and manage data, and create and execute syntax. Having these basic skills will allow Short Course participants to more easily learn about use of R for data analysis and will enable Short Course instructors to better plan and deliver their content. This Workshop is only available to those who will be attending one of the CARMA Short Courses. It will be available on-line.

During this Basics of R Workshop, attendees will learn:
1. Using R through the R Studio interface
2. Importing data into R
3. R data sets (a.k.a data frames and tibbles)
4. Data types
5. Subsetting columns of data and selecting cases
6. Recoding data and dealing with missing data
7. Merging data (columns and rows)
8. Output objects
9. User defined functions
10. Getting help

Option 1: “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte

This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (descriptive, correlation, regression, T-test, ANOVA) and graph those results. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in other CARMA short courses.

Required Software: R (download here), R Studio (download here)

Option 2: “Advanced Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina

The CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct advanced multilevel analyses.  The course covers both basic models (e.g., 2-level mixed and growth models), and more advanced topics (e.g., 3-level models, discontinuous growth models, and multilevel models for dichotomous outcomes).  Practical exercises, with real-world research data, are conducted in R.  Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.  The course is best suited for faculty and graduate students who have at least some foundational understanding of conducting multilevel analyses.

  • Module 1: 2-Level Mixed Models Review
  • Module 2: Basic growth models for examining longitudinal data
    • Exercises using lme in R
  • Module 3: Discontinuous growth models for more complex longitudinal data
    • Exercises using lme in R
  • Module 4: Bayes Estimates
    • Exercises using lme in R
  • Module 5:  Three-level models, emergence models, multilevel mediation, Multiple Imputation, and other extensions
    • Examples with MICE
    • Examples with mediation package
  • Module 6: Dichotomous Outcomes; Generalized Linear Mixed-Effects Models
    • Exercises using glmer (lme4) in R

Required Software: R (download here), RStudio (download here)

Option 3: “Introduction to SEM with LAVAAN” – Dr. Robert Vandenberg, University of Georgia

This introductory course requires no previous knowledge of structural equation modeling (SEM), but participants should possess a strong understanding of regression AND have understanding about the basic data handling functions using R. All illustrations and in-class exercises will make use of the R LAVAAN package, and participants will be expected to have LAVAAN installed on their laptop computers prior to beginning of the course. No course time will be spent going over basic R data handling and installing the LAVAAN package. The course will start with an overview of the principals underlying SEM. Subsequently, we move into measurement model evaluation including confirmatory factor analysis (CFA). Time will be spent on interpreting the parameter estimates and comparing competing measurement models for correlated constructs. We will then move onto path model evaluation where paths representing “causal” relations are placed between the latent variables. Again, time will be spent on interpreting the various parameter estimates and determining whether the path models add anything above their underlying measurement models. If time permits, longitudinal models will be introduced.

Required Software: R installed with LAVAAN package (download here), RStudio (download here)

Option 4: “Statistical Analysis of Big Data with R” – Dr. Jeff Stanton, Syracuse University

Data mining refers to the process of uncovering patterns in data. Text mining refers to the same process, but applied to textual data instead of numeric data. These techniques support both prediction and explanation by discovering connections among variables that would be challenging to find with conventional techniques such as multiple regression. In this CARMA short course, we will use R and R-Studio to learn several techniques for analyzing numeric data sets, data sets with text data, and data sets containing both kinds of data.

On the first morning we will discuss the conceptual steps involved in data mining; after that we will use R to analyze several example data sets that I will provide. Students are also welcome to bring their own data, but this is not required or expected. We will learn data reduction, feature extraction, feature elimination, classifiers, and association rules mining. In the area of text mining, we will use R to extract text from websites, we will construct term-document matrices, and we will do analysis on those data structures using topic modeling, structural topic models, word embedding, penalized regression, and other text mining techniques.

If you have never used R, I recommend that you take CARMA’s introduction to R, but the data/text mining short course does not require extensive math or programming skills. You can expect to learn enough about data and text mining to begin working with these tools in research and/or teaching. The ideal participant will have an interest in improving their skills with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring alternative analytical strategies for working with complex data sets.

Required Software: R (download here), R Studio (download here)

Option 5: “Intermediate SEM, Model Evaluation” – Dr. Larry Williams, Texas Tech University

This course is aimed at faculty and students with an introductory understanding of structural equation methods who seek a better understanding of the challenging process of making judgments about the adequacy of their models. Those who attend should have experience in fitting structural equation models with software such as LISREL, MPlus, EQS, AMOS, or LAVAAN. This experience requirement can be met by completion of graduate coursework or Introduction to SEM Short Course. Attendees will be expected to use their own laptop computers installed with their SEM software, and they should also know how to import data from an SPSS/Excel/CSV save file into their SEM software program. Attendees will learn out to interpret and report results from SEM analyses, and how to conduct model comparisons to obtain information relevant to inferences about their models, as well as advantages and disadvantages of different approaches to model evaluation. Attendees are encouraged to bring their own data for use during parts of the short course.

The course will consist of five sections, with each section having a lecture and lab component using exercises and data provided by the instructor:
• Review of model specification and parameter estimation
• Overview of model evaluation
• Logic and computations for goodness-of-fit measures
• Analysis of residuals and latent variables
• Model comparison strategies

Required Software: Your preferred SEM software package

Option 6: “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

The open science revolution continues to gain momentum across the social and natural sciences, and in particular, the organizational sciences. This movement is driven in part by a crisis in confidence of scientific research. However, open science offers so much more to scholars and stakeholders of scientific work.  Open science  can serve to accelerate science, facilitate large scale collaboration, and aid individual research teams in conducting more rigorous and relevant work. This short course is intended to introduce open science concepts across the life cycle of research. After taking this course you will be able to engage in open science practices during the full research process and successfully leverage such practices in future journal submissions to demonstrate exceptional methodological rigor. We will cover (a) questionable research practices and publication bias, (b) study preregistration, registered reports, results-blind reviews, preprints, and how to use badges, (c) open data, proper annotation of analytic R code, reproducibility of analyses and transparency checklists, (d) Do’s and Dont’s for replication studies, (e) how to navigate open science platforms, such as the open science framework, large scale project collaboration in management, and finally (f) authorship and contributorship agreements. The course is introductory. Familiarity with some basic statistical concepts, such as null hypothesis significance testing is sufficient.

Option 7: “Advanced Data Analysis with R” – Dr. Ron Landis, Illinois Institute of Technology

This short course will begin with an introduction to linear regression analysis with R, including models for single/multiple predictors and model comparison techniques.  Particular attention will be paid to using regression to test models involving mediation and moderation, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. Exploratory factor analysis and MANOVA will also be covered. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.

Required Software: R (download here), R Studio (download here)

Option 8: “Systematic Reviews and Meta-Analysis” – Dr. Ernest O’Boyle, Indiana University

Meta-analyses have now become a staple of research in the organizational sciences. Their purpose is to summarize and clarify the extant literature through systematic and transparent means. Meta-analyses help answer long-standing questions, address existing debates, and highlight opportunities for future research. Despite their prominence, knowledge and expertise in meta-analysis is still restricted to a relatively small group of scholars. This short course is intended to expand that group by familiarizing individuals with the key concepts and procedures of meta-analysis with a practical focus. Specifically, the goal is to provide the necessary tools to conduct and publish a meta-analysis/systematic review using best practices. We will cover how to; (a) develop research questions that can be addressed with meta-analysis, (b) conduct a thorough search of the literature, (c) provide accurate and reliable coding, (d) correct for various statistical artifacts, and (e) analyze bivariate relationships (e.g., correlations, mean differences) as well as multivariate ones using meta-regression and meta-SEM. The course is introductory, so no formal training in meta-analysis is needed. Familiarity with some basic statistical concepts such as sampling error, correlation, and variation is sufficient.

Required Software: R (download here), RStudio (download here)

Time Schedule/Registration/Pricing/Deadlines

January 6-8, 2021
Time Zone Wed Thur Fri
U.S. ET 10 AM – 4 PM 10 AM – 4 PM 10 AM – 4 PM

For the time zone converter click here.

To register for 2021 CARMA Live Online Short Courses, you must first log in to your CARMA account (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select “Purchase Short Course” on the right side of the page.

Pricing Dates CARMA Member (*) Non-CARMA Member Prof. Association Member (***)
1 Course 2 Courses (**) 1 Course 2 Courses (**) 1 Course 2 Courses (**)
Advanced Registration Faculty $425 $750 $850 $1,600 $680 $1,260
09/30/2020 – 12/18/2020
Advanced Registration Student $325 $550 $650 $1,200 $520 $940
09/30/2020 – 12/18/2020
Normal Registration Faculty $475 $850 $950 $1,800 $760 $1,420
12/19/2020 – 01/04/2021
Normal Registration Student $375 $650 $750 $1,400 $600 $1,100
12/19/2020 – 01/04/2021

* – These prices reflect a 50% discount that you receive if you are student/faculty at an organization that is a member of the CARMA Institutional Premium Membership OR the CARMA Institutional Basic Membership Program.

** – These prices reflect a discount in which you register for 2 courses and receive $100 off.

***–These prices reflect a 20% discount for members of following associations; Academy of Management (AOM), Southern Management Association (SMA), Society for Industrial and Organizational Psychology (SIOP), Asia AOM (AAOM), International Association for Chinese Management Research (IACMR), European Academy of Management (EURAM), European Association of Work and Organizational Psychology (EAWOP), Academy of International of Business (AIB), Australia and New Zealand Academy of Management (ANZAM), Indian Academy of Management (INDAM), Midwest Academy of Management (MAM), and Iberoamerican Academy of Management (IAOM). This discount can not be applied if you are also using CARMA membership discount.

If you are a member of AOM, SIOP, SMA, AAOM, IACMR, EURAM, EAWOP, AIB, ANZAM, INDAM, MAM, and IAOM you can use one of the following discount codes when registering for these short courses:

Faculty Code: 1ea8-b38e
Student Code: 1577-ae4d

Note that we will be verifying association membership for all those who use these discount codes. Anyone who uses one of these discount codes and is not a member of those associations will be required to pay the non member rate.

Refund Policy: Full refund will be provided up to 2 weeks before the first day of the session. After that date, partial refund (50%) will be provided.

Find out if your organization is a CARMA Member. (US and Canada institutions only).

If your organization is not yet a member but would like to become one, you can find purchasing and renewal information here.