Short Courses

CARMA Classroom

Short Courses Overview

Each CARMA short course is typically a two or two and a half day session on a research method or data analysis topic. CARMA Short Courses place an emphasis on hands-on experience and on the application of the methodology aimed at skills development through equal amount of lecture and lab-time. Instructors are leading methodological scholars recognized within the organizational studies and management areas as experts on their topics. Several are current or past editors of leading organizational journals. Our list of short courses include introductory and advanced training on topics that might not be readily available at your institution. In addition, our short courses provide students and faculty with the opportunity to network with leading scholars and other students/faculty in their areas of interest.

More than 1,200 faculty and students from universities throughout the world have attended CARMA Short Courses since the summer of 2004. Past locations of these courses include Virginia and Michigan in the United States, as well as Brazil, Australia, India, The Netherlands, Israel, and China.

CARMA Short Course Evaluations

Past Participants had this to say about the CARMA Short Course program:

  • "Expert instructors, lively instruction, good balance between presentation and application."
  • "The instructors did a great job explaining (1) conceptual issues (2) statistical content; (3) actually working with the program package."
  • "Instructors were really awesome, the program as a whole was very organized and on schedule"
  • "The instructor was terrific! The venue was excellent."
  • "I really thought the course was outstanding in all regards! Very helpful to my own research/publication efforts."
  • "Excellent instructor! Extremely knowledgeable. We were able to actually practice/apply the theory to an article. Great experience!"
  • "Superb program – great contribution to academic community around the country."
  • "Everything was wonderful – thank you so much for creating a format through which the knowledge could be so easily and cleanly passed on."
  • "Absolutely awesome! Well worth the time and money!"
  • "This is an excellent program that I will recommend to my colleagues. I really appreciated the hospitality, the quality of lectures/materials, and the friendliness and accessibility of the instructors. Thank you very much – I learned a lot."

Short Courses in Adelaide, Australia, Nov 12-16, 2018 - Two Sessions, Two Courses

Hosted by University of South Australia

Session 1: November 12-14 | Session 2: November 14-16

University of South Australia logo

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday November 12 (all day), Tuesday November 13 (all day), and Wed. November 14 (AM half day)

Session 2

Wed. November 14 (PM half day), Thursday November 15 (all day), and Friday November 15 (all day)

Session I: November 12-14

Jean Bartunek

Option #1: "Qualitative Analysis of Organizational Change" - Professor Jean Bartunek, Boston College

Course Description
This CARMA Short Course concerns exploration and critique of several qualitative approaches that may be used to study various types of change within organizations from a somewhat mezzo perspective. Course topics will include several types of change that may occur within organizations, including action research/planned change, organizational learning, and dialectical/paradoxical change. It will also address experiences of recipients of organizational change, and affective and temporal processes within change. From a research perspective, it will also address roles of the researcher with regard to change. Researchers may play several roles, including change participant, external researcher, or collaborator with one or more members of the organization in studying the change. In the course we will review recent scholarship that addresses approaches to change and critique qualitative methods this scholarship uses to study them. Finally, using available materials, we will explore how some of the methods would be used in students’ own research.

Required Software: none

Session II: November 14-16

Lisa Lambert

Option #2: "Advanced Regression Analysis: Alternatives to Difference Scores, Polynomial and Response Surface Methods" - Associate Professor Lisa Lambert, Georgia State University

Course Description
For decades, difference scores have been used in studies of fit, similarity, and agreement in organizational research. Despite their widespread use, difference scores have numerous methodological problems. These problems can be overcome by using polynomial regression and response surface methodology to test hypotheses that motivate the use of difference scores. These methods avoid problems with difference scores, capture the effects difference scores are intended to represent, and can examine relationships that are more complex than those implied by difference scores.

This short course will review problems with difference scores, introduce polynomial regression and response surface methodology, and illustrate the application of these methods using empirical examples. Specific topics to be addressed include: (a) types of difference scores; (b) questions that difference scores are intended to address; (c) problems with difference scores; (d) polynomial regression as an alternative to difference scores; (e) testing constraints imposed by difference scores; (f) analyzing quadratic regression equations using response surface methodology; (g) difference scores as dependent variables; and (h) answers to frequently asked questions.

Required Software: none

Registration, Pricing, Advanced Registration Deadline

To register for 2018 CARMA Short Courses in Adelaide, Australia, 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.

Non-member prices per course: *All prices are in US Dollars (USD)
• Faculty/Professional: $900.00
• Students: $700.00
CARMA Member prices per course
• Faculty/Professional: $450.00
• Students: $350.00

If your organization is not yet a member but would like to become one, please contact us directly at

All participants are eligible for the following discount:
Register for both sessions, receive $75 off the total price.

Advanced Registration Deadline is October 12, 2018. After this date, a $75.00 fee will be added to all registrations.

Short Courses in Columbia, South Carolina, January 10-12, 2019 – One Session, Seven Course Options

Hosted by University of South Carolina

University of South Carolina logo

Short Course Sessions and Groupings

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

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

Scott Tonidandel

Course Description
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.

Option 2: “Regression with R” – Dr. Ron Landis, Illinois Institute of Technology

Ron Landis

Course Description
This short course will begin with an introduction to linear regression analysis with R. Models for single and multiple predictors will be covered, as will 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. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.

Option 3: “Introduction to Multi-level Analysis with R” – Dr. Paul Bliese, University of South Carolina

Paul Bliese

Course Description
The CARMA Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses. The course covers within-group agreement, nested 2-level multilevel modeling and growth modeling. All practical exercises are conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.

Option 4: “Introduction to SEM with R and LAVAAN” – Dr. Robert Vandenberg, University of Georgia

Robert Vandenberg

Course Description
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.

Option 5: “Web Scraping: Data Collection and Analysis” – Dr. Richard Landers, University of Minnesota

Richard Landers

Course Description
In this course, you will learn how to create novel datasets from information found for free on the internet using only R and your own computer. After a brief introduction to web architecture and web design, we will explore the collection of unstructured data by scraping webpages directly through several small hands-on projects. Next, we will explore the collection of structured data by learning how to send queries directly to service providers like Google, Facebook and Twitter via their APIs. Finally, we will conduct a complete scraping project from start to finish including some novel analytic approaches (e.g., automatic identification of gender for social media contributors, language processing to extract themes, and interactive visualization with a simple web app).

Option 6: “Introduction to Bayesian Analysis with R” – Dr. Steve Culpepper, University of Illinois

Steve Culpepper

Course Description
Many inferential statistical procedures include an examination of p-values, a strategy that is sometimes labeled as the frequentist approach. An alternative has emerged over recent decades, known as Bayesian inference, that uses different strategies for making statistical decisions. In this short course, we will compare and contrast traditional frequentist inference with Bayesian inference. We will use the R open source statistical platform to conduct Bayesian inference, starting simply with the t-test and working towards more complex multivariate techniques. We will also examine some research publications to see Bayesian inference in action. By the end of this short course, you will be able to substitute Bayesian inferential procedures in place of some of the frequentist analysis techniques you may currently use.

Option 7: “Analysis of Big Data” – Dr. Jeff Stanton, Syracuse University

Jeff Stanton

Course Description
Big data has been a buzzword for several years both in academia and industry. Although the term is vague and is certainly overused, it does encompass some interesting new ideas and unfamiliar analytical techniques. Notable among these is “data mining,” a family of analytical methods for clustering, classifying, and predicting that go a step beyond the statistical methods used by many social science researchers. In this short course, we will discuss the dimensions of big data and the conceptual steps involved in data mining. We will build hands-on skills for developing and running predictive models relevant to big data. We will discuss feature selection and dimension reduction. A range of predictive models will be covered: e.g., penalized regression models, random forest, stochastic gradient boosted trees, and support vector machines. We will touch briefly on text mining. We will use R and R-Studio for this course. Students are welcome to bring their own data sets for experimentation, but many data sets will be provided, so this is not required.