Welcome to CARMA’s Upcoming Events for SMA Members

November 18,2022

Upcoming CARMA Live Online Research Methods Short Courses, 50% Discount (See Below)

Course Descriptions, Preview Videos, and More

Pricing for Affiliate Program Members

Advanced Registration Faculty $425
10/07/2022 – 12/01/2022
Advanced Registration Student $325
10/07/2021 – 12/01/2022
Normal Registration Faculty $475
12/02/2022 – 12/16/2022
Normal Registration Student $375
12/02/2022 – 12/16/2022
Late Registration Faculty $525
12/17/2022 – 12/31/2022
Late Registration Student $425
12/17/2022 – 12/31/2022

SMA-CARMA Affiliate Program Benefits

  • 50% Discount on CARMA Live Online Short Courses

A 50% discount on CARMA Short Course registration fees for over 50 CARMA Live Online Short Courses will be offered in November, January, April, May, and June of 2022-2023. For the upcoming Live Online Short Courses click here.

Each CARMA short course is typically 20 hours long based on a research method or data analysis topic. CARMA Short Courses place an emphasis on hands-on experience and on the application of the methodology aimed at skills development through equal amount of lecture and lab-time. Instructors are leading methodological scholars recognized within the organizational studies and management areas as experts on their topics. Several are current or past editors of leading organizational journals. A list of CARMA Short Courses includes introductory and advanced training on quantitative and qualitative topics, including content that might not be readily available at your institution. In addition, our short courses provide students and faculty with the opportunity to network with leading scholars and other students/faculty in their areas of interest.

  • CARMA Video Library Access

Archived versions of all Live Webcast Lectures and other key events since 2004 are available within the CARMA Video Library. The Video Library currently contains over 200 recorded lectures from internationally recognized scholars on a variety of research methods (including qualitative and quantitative). We have improved the access and search process for the recordings and our Video Library which will result in a better viewing experience. All faculty and students from member universities will have on-demand individual access to all of these recorded lectures at any time from any location. These lectures and related materials can serve as a resource for authors, reviewers, and editors seeking guidance on basic and advanced research methods design and analysis questions. They also can be used as a teaching resource, adding variety to the in-class experience with group viewing and/or as assigned for individual access outside of class, thus enriching the learning experience. Instructors find our videos valuable as they provide insight into current issues in research methods and give students an opportunity to hear from experts in the field of research methods. The recordings enhance knowledge and application of research methods and by providing a basis of discussion and debate.

  • CARMA Live Events Access

    • Webcast Lecture Series

The CARMA Webcast Lecture Series was established in 2004 to provide university faculty, graduate students, and other researchers with advanced training in research methods and data analysis. This annual series consists of ten live webcast lectures presented by nationally recognized methodologists. These lectures are developed at an introductory and advanced doctoral student level and will typically include an introduction to the topic as well as a consideration of current technical issues. Emphasis is placed on the application of the research method technique. Also, Zoom Webinar platform allows individual live viewing for Affiliate Program Members (all faculty and students).  Prior to each lecture, background readings, references, and PowerPoint slides will be available on the CARMA website.

Schedule:

      1. Aug.26, 2022 – Multiple Linear Regression: Strengthening Conceptual Knowledge and Practical Skills, Dr. Fred Oswald
      2. Sep. 16, 2022 – Qualitative Data Analysis Beyond Templates – How to make your Analytic Approaches Adaptable and Relevant, Dr. Tine Koehler
      3. Oct. 7, 2022, Mixing and Matching Methods for Purpose, Dr. John Mathieu
      4. Oct. 28, 2022, Insufficient Effort Responding: Impact in Survey Data and Approaches for Detection, Dr. Jason Huang
      5. Nov. 18, 2022, Multiverse Analysis: A tool to Estimate, Illustrate, and Investigate the Impact of Researcher Degrees of Freedom, Dr. Aaron Hill
      6. Jan. 20, 2023, Epistemology, Abduction, Inference to the Best Explanation, Frequentist and Bayesian Methods, Dr. Brent Goldfarb
      7. Feb. 10, 2023, Interpreting Results with Practical Significance in Mind: An Overview of the Common Language Effect Size Indices, Dr. Dina Krasikova
      8. Mar.3, 2023, Methods for Collecting and Analyzing Data from Zoom Meetings, Dr. Andrew Knight
      9. Mar. 24, 2023, Tools for Qualitative Analysis, Dr. Jane Le
      10. Apr. 14, 2023, Special Event
    • Topic Interest Groups

Topic Interest Groups (TIG) are established to meet student and faculty researchers’ needs not currently met by CARMA’s Webcast Lecture Series, CARMA Global Scholar Series and CARMA Live Online Short Courses. CARMA TIG events will be hosted using the Zoom Meeting platform, with individual live access provided.

CARMA TIGs supplement other CARMA programs by providing:

1) instruction on specialized research methods topics via Tutorials,
2) access to guidance from noted methodologists through Ask the Experts Panels,
3) discussion of current research methods articles via ORM (Organizational Research Methods) Review sessions,
4) discussion of CARMA Video Library recordings with Webcast Reviews, and
5) opportunity to meet and interact with others sharing similar interests with our CARMA Networking Sessions.

Schedule:

      • Oct. 28, 2022
        • Session I – 3:00-4:00 pm ET 
          • Topic: Novel Approaches to Addressing Bias from Endogeneity
          • Session Chair: Dr. John Busenbark – University of Notre Dame
          • Panelists: Dr. Mike Withers – Texas A&M University, Dr. Evan Starr – University of Maryland, Dr. Lindsey Greco – Oklahoma State University, Dr. Arthurs Kalnins – The University of Iowa
        • Session II – 3:00-4:00 pm ET 
          • Topic: Moderation and Mediation Analysis
          • Session Chair: Dr. Jeremy Dawson – Sheffield University
          • Panelists: Dr. Jeff Edwards – University of North Carolina at Chapel Hill, Dr. Amanda Kay Montoya – UCLA, Dr. Kris Preacher – Vanderbilt University
      • Jan. 20, 2023
        • Session I – 9:00 – 10:00 am ET 
          • Topic: How to Publish Research More Transparently: Registered Reports, Pre-Registration & Open Science?
          • Session Chair: Dr. John Antonakis – University of Lausanne
          • Panelists: Dr. George Banks – UNCC, Dr. Fabiola Gerpott – Otta Beisheim School of Management
        • Session II – 11:00 am – 12:00 pm ET 
          • Topic: Process Research
          • Session Chair: Dr. Anne Langley – HEC Montreal
          • Panelists: Dr. Jane Howard-Kendal, Dr. Davide Revasi – UCL School of Management
        • Session III – 3:00 – 4:00 pm ET
          • Topic: Scale Development and Validation
          • Session Chair: Dr. Stefanie Castro – Florida Atlantic University
          • Panelists: Dr. Claudia Cogliser – Texas Tech University, Dr. Lisa Lambert – Oklahoma State University
      • Mar. 3, 2023 (Tentative)
        • Session I – 11:00 am – 12:00 pm ET
          • Topic: How to Write your Qualitative Research for Publication in Top Journals?
          • Session Chair: Dr. Jennifer Howard-Grenville – University of Cambridge
          • Panelists: Dr. Heather Vough – George Mason University, Dr. John Amis – University of Edinburgh
        • Session II – 3:00 – 4:00 pm ET
          • Topic: Network Analysis/Dynamic Networks
          • Session Chair: Dr. David Krackhardt – Carnegie Mellon University
          • Panelists: Dr. Mike Howard – Texas A&M University, Dr. Ralph Heidl – University of Oregon
        • Session III – 3:00 – 4:00 pm ET
          • Topic: Big Data
          • Session Chair: Dr. Fred Oswald – Rice University
          • Panelists: Dr. Scott Tonidandel – UNCC, Dr. Mike Pfarrer – University of Georgia
      • Apr. 14, 2023 (Tentative)
        • Session I – 3:00 – 4:00 pm ET
          • Topic: Qualitative/Comparative Case Study Designs
          • Session Chair: Dr. Melissa Graebner – University of Illinois Urbana-Champaign
          • Panelists: TBA
        • Session II – 3:00 – 4:00 pm ET
          • Topic: Bayesian Statistics
          • Session Chair: Dr. Andreas Schwab – Iowa State University
          • Panelists: Dr. Anup Nandialath – University of Wisconsin La Crosse, Dr. Jeff Dotson – Brigham Young University, Dr. Fred Oswald – Rice University
        • Session III – 3:00 – 4:00 pm ET
          • Topic: SEM/Multilevel
          • Session Chair: Dr. Mikko Ronkko – University of Jyvaskyla
          • Panelists: TBA
    • PhD Prep Groups

The PhD Prep Series consists of live online events that focus on developing research methods knowledge and skills needed for success as a doctoral student and faculty member. Different sessions will focus on topics related to the specific needs of early, middle, and late-stage doctoral students. Examples may include learning the basics of different research methods and data analysis techniques, preparing for comprehensive exams, and developing the research methods section of a dissertation proposal. Others may relate to submitting conference papers and articles to academic journals, editorial review of articles, and job search and career management.

Schedule:

      • Oct. 6, 2022 – 8:00 – 9:30 pm ET
        • Topic: Conducting Impactful Literature Reviews: Getting a Head Start on your Research and Dissertation
        • Host: Dr. Kang Yang Trevor Yu – Nanyang Technological University
        • Panelists: Dr. Zeki Simsek – Clemson University, Dr. In-Sue Oh – Temple University, Dr. Piers Steel – University of Calgary
      • Nov. 18, 2022 – 3:00 – 4:30 pm ET
        • Topic: Transforming Me-Search Into Research: Designing, Communicating, and Avoiding Pitfalls of Research Inspired by Personal Experiences
        • Host: Dr. Melanie Prengler – University of Virginia
        • Panelists: Dr. Oscar Holmes IV – Rutgers University, Dr. Lindsey Cameron – University of Pennsylvania (Wharton), Dr. Katina Sawyer – University of Arizona
      • Feb. 10, 2023 – TBA
      • Mar. 24, 2023 – 3:00 – 4:30 pm ET
        • Topic: Implications of the Open Science Movement for Emerging Scholars: Why Is It Important and How to Prepare
        • Host: Dr. Yifan Song – Texas A&M University
        • Panelists: Dr. Lillian Eby – University of Georgia, Dr. Zhen Zhang – Southern Methodist University, Dr. Tim Quiley – University of Georgia

January 3-6, 2023 / 10:00 am – 3:00 pm ET

(sponsored by University of South Carolina)

All courses in are taught concurrently, so a participant can take only one course.

Complete Course Listing

  1. “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte
  2. “Introduction to Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina
  3.  “Introduction to SEM with LAVAAN” – Dr. Robert Vandenberg, University of Georgia
  4. “Statistical Analysis of Text with R” – Dr. Jeff Stanton, Syracuse University
  5. “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama
  6. “Systematic Reviews and Meta-Analysis in R” – Dr. Ernest O’Boyle, Indiana University
  7. “Introduction to Python for Research”– Dr. Jason T. Kiley, Clemson University
  8. “Doing Grounded Theory Research” – Dr. Elaine Hollensbe, University of Cincinnati

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 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

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

Introduction Video by Dr. Tonidandel (Most Recent Version)

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. Paul Bliese, University of South Carolina

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. Robert Vandenberg, University of Georgia

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

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 (download here), RStudio (download here)

Option 4: “Statistical Analysis of Text with R” – Dr. Jeff Stanton, Syracuse University

The use of natural language text in research has proliferated in recent years. Many statistical tools are available to aid in the discovery of patterns in natural language text – particularly in large data sets of short text segments, such as one might find in social media, web pages, or news articles. Text mining techniques can support theory development by uncovering patterns that would be challenging to find with traditional analysis techniques. Text mining can also be used alongside standard confirmatory statistics such as regression and classification. In this CARMA short course, we will use R-Studio and Jupyter Notebooks to learn text mining and various related methods for statistical analysis of text.

No prior experience with R is required, but I recommend that you take CARMA’s basic R introductory R course if you have no previous exposure. We will discuss the conceptual steps involved in text mining, and then use R to put those concepts to work on a variety of open data sets I will provide. Students are welcome to bring their own data sets as well, but this is not required. We will examine the document feature matrices, dictionary based sentiment analysis, latent semantic analysis, exploratory topic modeling, structural topic modeling, and word embedding. We will test some predictive techniques, using features of text documents as predictors. Time permitting we will briefly examine a few aspects of natural language processing including deep learning techniques such as BERT.

Students who participate successfully in this short course can expect to learn enough about text mining to begin experimenting with these tools in research. The ideal participant will have an interest in improving their skills with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring empirically driven strategies for analysis of data sets containing text. No prior knowledge of text mining or natural language processing is needed.

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

Option 5: “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama

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

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

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

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

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

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

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

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.

Pricing

Faculty Student
One Course $425 $325
Two Courses $750 $550

Refund Policy: Full refund will be provided up to 2 weeks before the first day of the session. After that date, partial refund (50%) will be provided.

Registration

To register for 2022-23 CARMA Live Online Short Courses;