Short Courses 2022-2023

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

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.

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.