CARMA Live Online Short Courses

June 20-23, 2023

Session 3

June 20-23, 2023 – Six Course Options

Course Listing

Note: ECTS credits are available for CARMA Short Courses! For course participants from institutions using the European Credit Transfer and Accumulation System (ECTS), CARMA recommends that successful course participation be rewarded with 2 (two) ECTS credits per short course.

  1. “Alternatives to Difference Scores: Polynomial Regression, and Response Surface Methodology” – Dr. Jeff Edwards, University of North Carolina
  2. “Advanced Multilevel Analysis II: Longitudinal, Consensus Emergence, Bayes and Dichotomous Outcomes” – Dr. Paul Bliese, University of South Carolina
  3. “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg, University of Georgia
  4. “Introduction to Bayesian Analysis” – Dr. Steve Culpepper, University of Illinois
  5. “Qualitative Comparative Analysis” – Dr. Thomas Greckhamer, Louisiana State University
  6. “Case Study Methods” – Dr. Rebecca Piekkari, Aalto University (***) This course will be offered from June 19 to June 22 (Monday through Thursday).

CARMA Workshop: Basics of R (included free with the short course registration)

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 free of charge and 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

Session 3: June 20-23, Eight Course Options (Choose One)

Option #1: “Alternatives to Difference Scores: Polynomial Regression, and Response Surface Methodology” – Dr. Jeff Edwards, University of North Carolina

Introduction Video by Dr. Edwards (Most Recent Version, June 2021)

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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: SPSS, Stata, R (download here) with RStudio (download here)

Option #2: “Advanced Multilevel Analysis II: Longitudinal, Consensus Emergence, Bayes and Dichotomous Outcomes” – Dr. Paul Bliese, University of South Carolina

Introduction Video by Dr. Bliese (Most Recent Version, June 2021)

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The CARMA “Advanced Multilevel Analysis II: Longitudinal, Consensus Emergence, Bayes and Dichotomous Outcomes” short course provides the (1) theoretical foundation, and (2) resources and skills necessary to conduct a variety of advanced multilevel and longitudinal analyses using the R mixed-effect modeling packages nlme and lme4. The course briefly reviews basic models (e.g., 2-level mixed and growth models) before addressing more advanced topics (econometric fixed-effect models for panel data, discontinuous growth models, consensus emergent models, and multilevel models for dichotomous outcomes). Practical exercises, with real-world research data are provided. 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 a foundational understanding of mixed-effects models.

Module 1: Two-Level Mixed-Effect and Growth Model Review
Exercises and examples using lme in R
Module 2: Econometric Fixed-Effects Models vs Mixed-Effects Models
Exercises and examples using lme in R
Module 3: Discontinuous growth models for more complex longitudinal data
Exercises and examples using lme in R
Module 4: Bayes Estimates
Exercises and examples using lme in R
Module 5: Three-level models and consensus emergence models
Exercises and examples using lme and lmer (lme4) in R
Module 6: Generalized Linear Mixed-Effects Models for Dichotomous Outcomes
Exercises and examples using glmer (lme4) in R

Option #3: “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg, University of Georgia

The workshop covers three advanced structural equation modeling (SEM) topics: (a) multilevel modeling; (b) latent interactions; and (c) dealing with missing data in SEM applications. The instructor lectures about half of the time with the remaining time devoted to having participants run examples with actual data provided by the instructor. Participants go home with usable examples and syntax. The multilevel modeling section starts out using observed variables only, and no latent variables. Parallels are drawn in this approach and the other packages such as HLM. The main purpose here, though, is to teach participants the basics of multilevel modeling such as aggregation and cross-level interactions. The workshop advances to using latent variables in a multi-level environment. Particular focus will be on multilevel confirmatory factor analysis whereby separate measurement models are estimated at both the within and between levels. The topic then switches to multilevel path modeling with emphasis on between vs. within modeling, and the estimation of cross-level interaction effects among latent variables. The latent interaction section focuses on specifying interactions among latent variables in SEM models. This section starts out with a review of basic interaction testing within a regression environment. From this foundation, participants will move into specifying interactions among latent variables and how to test hypotheses with interactions. The final segment of the short course deals with missing data. A great deal of time at the beginning is spent on missing data patterns and why they occur. The workshop then moves into the old methods of dealing with missing data such as listwise and pairwise deletion, and mean or regression based imputation. The disadvantages of those methods are discussed. We then move into covering the newer methods for dealing with missing data such as and full information maximum likelihood.

While the instruction will be carried out using the R-package LAVAAN, participants are welcome to use another SEM package. If you do so, you should have strong familiarity with that package and its functionality as the instructor will not be able to provide assistance in its use

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

Option #4: “Introduction to Bayesian Analysis” – Dr. Steve Culpepper, University of Illinois

Introduction Video by Dr. Culpepper (Most Recent Version, June 2021)

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This short course introduces the concepts and methodology of Bayesian statistics. Topics include Bayes’ rule, likelihood functions, prior and posterior distributions, Bayesian point estimates and intervals, Bayesian hypothesis testing, and prior specification. Additional topics include Bayesian regression, model selection, prediction, diagnostics, Bayes factors, and exploratory factor analysis. We also review practical implementations of Markov chain Monte Carlo and hierarchical models using R and JAGS and discuss conceptual differences between the Bayesian and frequentist paradigms.

Option #5: “Qualitative Comparative Analysis” – Dr. Thomas Greckhamer, Louisiana State University

Introduction Video by Dr. Greckhamer (Most Recent Version)

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Qualitative Comparative Analysis (QCA) is a configurational approach that has become increasingly popular in management research. This short course is intended as an introduction to QCA and its application to an array of research questions in various domains of organizational research as well as for the analysis of both qualitative and quantitative data. In the course we will unpack and illustrate the theoretical and methodological foundations of both ‘crisp set’ and ‘fuzzy set’ QCA and we will apply an understanding of these foundations to hands-on applications of the steps and procedures in QCA research designs as well as to assessing illustrative examples from published research studies. The course will also cover and illustrate best practices for all elements of QCA research designs as well as how to avoid common errors. Overall, participants will gain an understanding for all steps involved in designing research as well as how to interpret the results of QCA studies.

Option #6: “Case Study Methods” – Dr. Rebecca Piekkari, Aalto University

(***) This course will be offered from June 19 to June 22 (Monday through Thursday).

This short course provides an overview of recent trends and debates on the case study in management and organization research. The case study is a popular methodological choice for management researchers, but what differentiates the case study from other approaches to qualitative research? What are the different options that researchers have when designing a case study? As researchers, how can we theorize from case studies? How can we evaluate the quality of a case study? What is the ‘disciplinary convention’ regarding the case study in your own field of research, and why does it matter? What are your options when writing up your case study for publication? What are the current trends in case research in top management journals? What can management researchers learn from case study developments in other fields, such as political science? Detailed course notes, examples and relevant literature will be provided to course participants.