May 17-20, 24-28, 2021 – Two Sessions, Eight Course Options
Session 1: May 17-20, Four Course Options | Session 2: May 24-28, Four Course Options
Short Course Sessions and Groupings
We offer two sessions which allows course participants the opportunity to take two back-to-back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.
Complete Course Listing
Session 1 (Choose One) Session 2 (Choose One)
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 1: May 17-20, Four Course Options (Choose One)
This short course will begin with an introduction to linear regression analysis with R, including models for single/multiple predictors and methods to compare models. Particular attention will be paid to using regression to test models involving mediation and moderation, including nonlinear and higher order interactions. Further advanced topics will include the general linear model, generalized linear models including logistic regression and Poisson regression, and polynomial regression. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.
Multilevel analysis allows researchers to estimate relationships between variables that span across different levels of analysis (e.g., individual, group, organization). For instance, is a team’s climate related to employee satisfaction? Does the relationship between employee job stress and wellbeing depend on team leadership? In this course, participants will learn: a. the logic underlying multilevel analysis, b. how to build basic multilevel models, c. how to estimate these models by using SPSS, and d. how to interpret the involved parameters. After acquiring a base knowledge, participants will practice multilevel analysis with real data.
Required Software: SPSS
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.
This applied module seeks to engage researchers in the practice of doing qualitative research. While it introduces a variety of different approaches to qualitative research methods, the predominant focus is on interpretive designs. In working through how to conduct interpretive research, the module reviews the entire research process, giving particular emphasis to data coding, analysis and presentation. Using a combination of learning techniques, including taught sessions, individual work and group work, the module seeks to demystify the research process. In order to maximize the relevance of this session to your own research, some of the exercises will be based on your current research project and data. Hence, if you have already collected data or conducted analyses, please bring these research materials with you (e.g. full transcripts, field notes, documents; CAQDAS file). Alternatively, if you are in the early stages of your research project, you might like to work on a fictional project for the purposes of the course, e.g. interview peers about doing a PhD, Faculty about being an academic, friends about working from home during lockdown, etc.
This CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct basic and 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, multilevel moderated-mediation models, and multiple-unit multilevel models). Practical exercises, with real-world research data, are conducted in R and Mplus. 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: Basic mixed effects (2-level) models, testing in R and Mplus
Module 2: Longitudinal studied in R and Mplus: within-person experiential methods models and growth models
Module 3: Complex multilevel models part 1: 3-level models in R and moderated-mediation models in Mplus
Module 4: Complex multilevel models part 2 (plus open discussion and consultations): Multiple unit memberships in R (using lme4 package)
The purpose of this workshop is to introduce researchers to the underlying tenets of the grounded theory approach and to aid them in designing and conducting a grounded theory study. In the course, we will cover the following three major topics: (a) Understanding the approach and different methodological traditions, (b) designing a grounded theory study, (i.e., data collection methods, purposive sampling, gaining access, sources, triangulation), and (c) analyzing data following the grounded theory method (i.e., different approaches to coding, constant comparison, memoing, triangulation, theoretical saturation, etc.). Furthermore, the workshop will provide specific examples of practical challenges and strategies to manage them. No software is necessary for this course. Participants are invited to bring samples of their own data to the course.
When there are more and more publications and research findings, it is challenging to comprehend these results scientifically. The systematic review provides well-accepted procedures to identify and extract information relevant to the problems. On the other hand, meta-analysis is the de facto statistical technique to synthesize research findings in educational, social, behavioral, and medical sciences.
This workshop provides a practical introduction of systematic review and meta-analysis using the open-source R statistical platform. We will also cover advanced techniques such as multivariate meta-analysis and meta-analytic structural equation modeling (MASEM).
This course attempts to achieve the following objectives: (1) learn essential ideas of systematic review; (2) learn basic and advanced techniques in the meta-analysis; (3) know how to conduct and interpret meta-analysis in R. Participants are expected to have basic knowledge in regression analysis. Proficiency in R is not required.