CARMA Live Online Short Courses

June 12-15, 2023

Session 2

June 12-15, 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.

1. “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama
2. “Advanced Multilevel Analysis I: Growth Models, Mediation, Moderation, Multi-Unit Membership” – Dr. Gilad Chen, University of Maryland
3. “Advanced SEM I: Measurement Invariance, LGM, and Non-recursive Models” – Dr. Robert Vandenberg, University of Georgia
4. “Web Scraping: Data Collection and Analysis” – Dr. Richard Landers, University of Minnesota
5. “Theory, Methods, and Analysis for Research with Dyads” – Dr. Janaki Gooty, University of North Carolina Charlotte
6. “Questionnaire Design” – Dr. Lisa Schurer Lambert,  Oklahoma State University
7. “Macro Research Methods II: Endogeneity – Concepts, Techniques, and Tools for Addressing Unexplained Heterogeneity” – Dr. John Busenbark, University of Notre Dame (**) This course will be offered from Monday through Thursday from 12 PM to 5 PM ET (5 PM to 10 PM London Time).
8. “Doing Grounded Theory Research” – Dr. Elaine Hollensbe, University of Cincinnati 
9. “Advanced Qualitative Methods for Macro Management Research” – Dr. Rhonda Reger, University of North Texas
10. “Predictive Modeling and Machine Learning in R” – Dr. Louis Hickman, Virginia Tech

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 2: June 12-15, Nine Course Options (Choose One)

Option #1: “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama

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

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This short course will begin with an introduction to linear regression analysis with R, including models for single/multiple predictors and 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. Exploratory factor analysis and MANOVA will also be covered. 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 (download here), R Studio (download here)

Option #2: “Advanced Multilevel Analysis I: Growth Models, Mediation, Moderation, Multi-Unit Membership” – Dr. Gilad Chen, University of Maryland

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

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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 sampling methods 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)

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

Option #3: “Advanced SEM I: Measurement Invariance, LGM, and Non-recursive Models” – Dr. Robert Vandenberg, University of Georgia

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

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The short course covers three advanced structural equation modeling (SEM) topics: (a) testing measurement invariance; (b) latent growth modeling; and (c) evaluating reciprocal relationships in SEM. 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 measurement invariance testing section focuses on the procedures as outlined in the Vandenberg and Lance (2000) Organizational Research Methods article. Namely, we will cover the 9 invariance tests starting with the tests of equal variance-covariance matrices and ending with tests of latent mean differences. We will use a multi-sample approach in undertaking the invariance tests, and you will be shown how to test latent mean differences using the latent means of the latent variables within each group. The workshop then advances to operationalizing latent growth models within the SEM framework. Essentially, this is how to use one’s longitudinal data to actually capture the dynamic processes in one’s theory by creating vectors of change across time. The participant will also be exposed to modeling how the change in one variable impacts change in another. We will also use mixed modeling. And at the end of it, I introduce the participants to latent profile modeling with latent growth curves. The final piece is the testing of models with feedback loops via an SEM-Journal article by Edward Rigdon (1995). We will go through his 4 different models and what they mean. In doing so, we will extensively cover model identification as it is particularly important to testing reciprocal effects.

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

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

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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. First, after a brief introduction to data source theory, web architecture, and web design, we will explore the collection of unstructured data by scraping web pages directly through several small hands-on projects. Second, 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. Third, we will briefly explore analytic techniques for analyzing scraped data. Finally, we will walk through the various ethical and legal issues to be navigated whenever launching a web scraping project.

Option #5: “Theory, Methods, and Analysis for Research with Dyads” – Dr. Janaki Gooty, University of North Carolina Charlotte

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

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Multi-level research in the organizational sciences (e.g., OB, strategy, entrepreneurship) has become fairly mainstream in the last few decades. Despite this attention to levels issues in general, dyads and the dyadic level of analyses remains a “forgotten level” ( Kenny, Kashy & Cook, 2006) relative to individuals, teams and organizations. This trend is unfortunate as relationships (one-one associations in organizations such as supervisor-subordinate, coworkers, firm-firm) are the building block of all phenomena that pervades organizational life. This course introduces 1) the importance of dyadic research, 2) the pitfalls of ignoring the dyadic level (both conceptually and statistically) and 3) a six step model building exercise for dyads as a unique level of analysis conceptually and empirically. This last component includes a focus on how to build dyad level theories, conceptualizing constructs and their emergence at this level, research design choices with a focus on nesting vs. cross-classification and data analyses. Students who participate successfully in this short course can expect to leave with a toolbox of conceptual and empirical knowledge and hands on skills to develop and test dyadic models in their research. The presenter will demonstrate cross-classified modeling via HCM (available in the HLM software) but the same principles could be applied via R as well.

Option #6: “Questionnaire Design, Scale Development, Construct Validity, and Data Management and Preparation” – Dr. Lisa Schurer Lambert, Oklahoma State University

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

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This introductory course will help you develop your model, develop and select measures, design survey instruments and execute your data collection. Topics include designing your project (developing a model, selecting variables, sampling requirements). Because it is necessary to establish adequate construct validity before testing hypotheses, we cover a wide variety of procedures for assessing construct validity (including EFA/CFA). Then we will apply this understanding of up-to-date construct validity practices to scale development techniques by creating new measures or revising existing measures that can pass the hurdles posed by tests of construct validity. We draw from research on how respondents interpret surveys to reveal principles for how to design your questionnaire to obtain high quality data. Finally, we will cover procedures for managing the data collection and for cleaning your data (missing data, outliers, identifying careless responders). If you wish, bring your research ideas because there will be opportunities to advance your own project within the workshop.

Option #8: “Doing Grounded Theory Research” – Dr. Elaine Hollensbe, University of Cincinnati

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

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This course will explore the process of conducting a grounded theory study. Through readings, discussion (exemplar and how-to articles) and hands-on exercises, the session begins with generating research questions and interview protocols; collecting data (e.g., participatory, interview, secondary); the coding process; other data analytic processes beyond coding; generating a grounded model; and navigating the review process. This seminar will examine how to ensure trustworthiness and rigor in grounded theory research, and consider challenges of conducting such research when you’ve been trained primarily in quantitative research.

Option #9: “Advanced Qualitative Methods for Macro-Management Research” – Dr. Rhonda Reger, University of North Texas

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

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In this course, students will be exposed to research methods currently used in macro-level management fields, specifically in strategic management, organization theory and entrepreneurship. This course assumes limited prior knowledge of qualitative methods, but it will still provide a deep grounding in several advanced qualitative methods and text analysis as applied in management research. Methods covered include comparative case study research, content analysis, discourse analysis, rhetorical analysis, sentiment analysis (also called tenor or tone analysis), and the construction of dictionaries. The course will be interactive with discussion of exemplar papers that showcase each of these methods. Students will also be given the opportunity to “pilot test” the methods by interviewing each other and content analyzing a small sample of text. A focus of this workshop will be on matching methods to research questions and the interests and strengths of the research team.

Required Software: LIWC2015 (30 day rental available for $9.95; purchase for $89.95 from Linguistic Inquiry and Word Count)

Option #10: “Predictive Modeling and Machine Learning in R” – Dr. Louis Hickman, Virginia Tech

Organizational research and practice has frequently approached research questions using traditional statistical methods, whether it is simple predictive modeling (e.g., linear regression and ANOVA), factor analysis (e.g., EFA and CFA), or more extensive modeling procedures (e.g., SEM). However, under certain circumstances there are limits to these traditional methods. For example, in some instances these models may not fully capture the complexity of the data; important relationships might be overlooked, or the developed algorithms might fail to optimally predict dependent variables. Alternatively, traditional modeling methods can also be too complex for some data, such that even though the developed model may fit well in the sample it was developed upon, the model may have capitalized on chance to achieve good fit and therefore fail to generalize when applied to new, independent data.

A variety of machine learning algorithms exist that help overcome both these limitations. These machine learning methods can be used to avoid underfitting the data (e.g., mining for complexity where it exists) yet not overfit the data as well (e.g., cross-validating models on new data). Many forms of predictive modeling are used for these purposes, including random forests, LASSO regression, and neural networks. Such models can usefully supplement traditional approaches in organizational research — or in some cases they may supplant them, even out of necessity (e.g., when the number of variables exceeds the number of cases).

This CARMA short course is a hands-on experience, where you will use R and RStudio to analyze and interpret these predictive models. [If you are not familiar with the basics of how to navigate and use R, then you are strongly recommended to take CARMA’s introductory R course.] We will use openly available data sets, R code that has already been developed, and we will discuss, run, and interpret a wide variety of important machine learning models together. Time permitting, we will explore methods for comparing the performance of these statistical learning models against one another.

This course will equip workshop attendees with the skills to perform their own predictive modeling using machine learning, where they can then apply these skills in their research, practice, and teaching.

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