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
Seven Course Options (Choose One)
Introduction Video by Dr. Dawson (Most Recent Version, May 2021)
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.
Required Software: R (R (download here) and RStudio (download here))
Introduction Video by Dr. Lang (Most Recent Version, May 2021)
The Introduction to Structural Equation Methods Short Course provides (a) introductory coverage of latent variable techniques, including confirmatory factor analysis and structural equation methods with latent variables, (b) discussion of special issues related to the application of these techniques in organizational research, and (c) a comparison of these techniques with traditional analytical approaches. This Short Course will contain a balance of lecture and hands-on data analysis with examples and assignments, and emphasis will be placed on the application of SEM techniques to organizational research problems. Participants will:
- develop skills required to conduct confirmatory latent variable data analysis, based on currently accepted practices, involving topics and research issues common to organizational research.
- learn the conceptual and statistical assumptions underlying confirmatory latent variable analysis.
- learn how to implement data analysis techniques using software programs for confirmatory modeling. Special emphasis will also be placed on the generation and interpretation of results using LAVAAN and Mplus.
- learn how latent variable techniques can be applied to contemporary research issues in organizational research.
- learn how the application of current latent variable techniques in organizational research differs from traditional techniques used in this literature
- complete in-class exercises using LAVAAN and Mplus.
Required Software: R installed with LAVAAN package (R (download here), RStudio (download here)) or Mplus
Introduction Video by Dr. Koehler (Most Recent Version, May 2021)
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.
Introduction Video by Dr. Silver (Most Recent Version, May 2022)
This workshop introduces methods and tools for qualitative text analysis, to aid researchers in planning and undertaking analysis using digital tools designed for the purpose. We begin with an overview of traditions in text analysis, spanning the methodological spectrum, and the range of digital tools designed to facilitate these approaches. Using one of the leading Computer Assisted Qualitative Data AnalysiS (CAQDAS) programs as an example – MAXQDA (www.maxqda.com) – we bring methods to life by implementing an analysis, focusing on common analytic activities and how they can be accomplished using software tools. This involves preparing texts for analysis, importing and organising texts, exploring content and building dictionaries, coding texts using both inductive and deductive approaches, managing interpretations through analytic note-taking, summarising and mapping, interrogating patterns and relationships and visualising and reporting. Participants will be provided with a training version of the software to follow this course, and are invited to use their own data throughout the course, as well as sample texts provided.
Introduction Video by Dr. Gonzalez-Roma (Most Recent Version, May 2021)
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
Introduction Video by Dr. Cheung (Most Recent Version, May 2021)
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. 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 to systematic review and meta-analysis using the open-source R statistical platform. We will also cover advanced techniques, such as handling non-independent effect sizes 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 of regression analysis. Proficiency in R is not required.
Introduction Video by Dr. Welch (Most Recent Version, May 2021)
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.