CARMA Live Online Short Courses

June 5-8, 2023

Session 1

June 5-8, 2023 – Ten 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.

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: June 5-8, Nine Course Options (Choose One)

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

Introduction Video by Dr. Tonidandel (Most Recent Version)

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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: “Introduction to Multilevel Analysis with R” – Dr. James LeBreton, Pennsylvania State University 

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

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The CARMA Introduction to Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct basic multilevel analyses. Emphasis will be placed on techniques for traditional, hierarchically nested data (e.g., children in classrooms; employees in teams). The first part of the course introduces issues related to multilevel theory (e.g., multilevel constructs; principles of multilevel theory building; cross-level inferences and cross-level biases). The second part of the course discusses issues related to multilevel measurement (e.g., aggregation; aggregation bias; composition and compilation models of emergence; estimating within-group agreement). The last part of the course focuses on the specification of basic 2-level models (e.g., children nested in classrooms; soldiers nested in platoons; employees nested within work teams) analyzed via multilevel regression (i.e., random coefficient regression; hierarchical linear model; mixed effects model). The R software package will be introduced, explained, and emphasized during this short course in preparation for the advanced short course offered in Session II. Participants who prefer HLM, SAS, SPSS, or MPlus (and have expertise with these programs) have the option of completing some assignments with these programs. Participants are encouraged to also 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 are familiar with traditional (i.e., single-level) multiple regression analysis, but have little (if any) expertise related to conducting multilevel analyses.

• Module 1: Multilevel Theory: Constructs, Inferences, and Composition Models
• Module 2: Multilevel Measurement: Aggregation, Aggregation Bias, & Cross-Level Inference
• Module 3: Multilevel Measurement: Estimating Interrater Agreement & Reliability
  • Examples using R
  • Examples using SPSS Software (time permitting)
• Module 4: Multilevel Measurement and Multilevel Modeling: A Simple Illustration of Analyzing Composite Variables in Hierarchical Linear Models
  • Examples using R
  • Examples using SPSS Software (time permitting)
• Module 5: Review of the 2-Level Model and Final Q & A

• Other topics (only if time permits) might include:
  • Extension of the 2-level model to the study of growth and change (i.e., growth model)
  • Different centering/scaling stragies (e.g., group-mean centering vs. grand-mean centering)

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

Option #3: “Introduction to SEM with LAVAAN” – Dr. Betty Zhou, University of Minnesota

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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

Option #4: “Systematic Reviews and Meta Analysis in R” – Dr. Ernest O’Boyle, Indiana University

Introduction Video by Dr. O’Boyle (Most Recent Version, June 2021)

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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)

Option #5: “Introduction to Python for Research” – Dr. Jason T. Kiley, Clemson University

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

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Python is a general purpose programming language that includes a robust ecosystem of data science tools. These tools allow for fast, flexible, reusable, and reproducible data processes that make researchers more efficient and rigorous with existing study designs, while transparently scaling up to big data designs. This short course focuses on the foundational skills of identifying, collecting, and preparing data using Python. We will begin with an overview, emphasizing the specific skills that have a high return on investment for researchers. Then, we will walk through foundational Python skills for working with data. Using those skills, we will cover collecting data at scale using several techniques, including programmatic interfaces for obtaining data from WRDS, application
programming interfaces (APIs) for a wide range of academic and popular data (e.g., The New York Times), web scraping for quantitative and text data, and computer-assisted manual data collections. From there, we will assemble and transform data to produce a ready-for-analysis dataset that is authoritatively documented in both code and comments, and which maintains those qualities through the variable additions, alternative measure construction, and robustness checks common to real projects. By the end of the course, you will have the skills—and many hands–on code examples—to conduct a rigorous and efficient pilot study, and to understand the work needed to scale it up. The course design does not assume any prior training, though reasonable spreadsheet skills and some familiarity with one of the commonly–used commercial statistical systems is helpful. In particular, no prior knowledge of Python is required, and we will cover a general introduction to Python in the beginning of the course content.

Option #6: “Video Methods” – Dr. Curtis LeBaron, Brigham Young University

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

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This seminar is an intensive “hands-on” experience with video methods in organizational studies. Participants will learn how to collect and analyze video data that provide empirical support for scholarly evidence and arguments. People may bring their own video, already captured and ready for examination, or use video data provided by the instructor. On the one hand, participants will look closely at human interaction within organizational settings: we will examine how people orchestrate their talk and bodily movement, moment to moment, within social and material environments, all in the service of social action and sense-making. On the other hand, we will keep an eye on “big” social and organizational issues, such as:

• What do power and status (or weakness and inequity) look and sound like?
• How do new ideas emerge and evolve, necessarily taking a social and material form?
• How is expertise enacted and acquired?
• What are patterns of healthy (and deficient) collaboration within an organization?

Seminar activities and assignments have two purposes. First, we will become better acquainted with research methods that may include video (e.g., conversation analysis, context analysis, and ethnography). We will talk about the underlying assumptions, distinctive features, and strengths and weaknesses of various approaches. Second, we will talk about the practical issues of this kind of research, such as research design, site selection and entrée, recording equipment and data collection, transcribing, data management and analysis, paper writing and publication.

Option #7: “Macro Research Methods I: Introductory Quantitative Techniques for Data Management and Analysis”– Dr. Tim Quigley, University of Georgia

Introduction Video by Dr. Quigley (Most Recent Version, June 2023)

While macro-oriented management scholars often receive formal econometrics training during their doctoral studies, these courses are often heavily focused on Greek letters, equations, proofs, and theoretical application. In turn, these courses often miss the mark when it comes to the practical application of the techniques and tools most commonly used in macro research. This course will flip things around by focusing on the practical tasks and challenging decisions related to building datasets, completing empirical analysis, and writing up methods and results for successful submission to top journals. Much of the content comes from lessons learned during the review process as an author, reviewer, and associate editor and, collectively, the content can be best described as the things you need to know to get published but that are typically missing from formal classes. A key goal of this course is to help participants understand how to build a convincing body of evidence and preemptively answering reviewer questions in ways that builds confidence in underlying results. Sessions will include a mix of lecture, hands on exercises, and discussions.

Topics will include:

1. Merging and assembling datasets
2. Inspecting and evaluating data
3. Generating and using descriptive statistics
4. Dealing with missing data and outliers
5. Building reliable and repeatable empirical results through use of code
6. Understanding and interpreting p-values
7. How does regression (really) work?
8. Fixed versus random effects models – what they do and their limitations
9. How to write up methods and results to build a body of evidence that can win over reviewers and editors

Option #10: “Within Person Research” – Dr. Nikos Dimotakis, Oklahoma State University

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The CARMA Within Person Research short course provides an overview of the conceptual and operational knowledge and skills needed to conduct research that aims to understand individuals’ attitudes, thoughts, emotions, and behaviors over time. We will focus on techniques aimed at examining dynamic and fluctuating states that individuals are experiencing and analyzing data with a temporally nested structure (observations within individuals, observations within days within individuals, and so forth). We will begin by an overview of what within-person conceptualizations look like in terms of their underlying theory and their statistical modeling.  We will then introduce concepts and principles that are important in designing and conducting within-person research. The third module introduces the specification of basic within-person models analyzed via multilevel approaches (i.e., random coefficient regression or hierarchical linear models). We will then discuss some more advanced models and revisit some of the assumptions of multilevel work with a critical eye, and finish with a synthesis module with time set aside for a final Q&A. Participants are encouraged to bring their own data to the course, to facilitate their progress with existing research. This course is aimed at faculty and graduate students who have some familiarity with regular regression but have little experience or knowledge about multilevel analyses.

Module 1: Introduction to within-person conceptualization: Models and theories

Module 2: Within-person studies: Design, analytical needs, and measurement

Module 3: Within person models: An illustration of within-person and cross-level models

Module 4: Advanced person models and reconsideration of assumption

Module 5: Synthesis and review; final Q & A