AOM-CARMA June 2026 Live Online Short Courses:
Advanced Methods & Analysis
CARMA (Consortium for the Advancement of Research Methods & Analysis) is a non-profit academic center at Texas Tech University, proudly celebrating 26 years of providing top-tier research methods education. We are excited to continue our partnership with AOM through our Affiliate Program, which offers access to some of CARMA's many education resources. We want to let AOM members know that we have seven great courses focusing on advanced methods and analysis: Adv. Multilevel I, Adv. Regression I, Adv. ESM, Adv. Multilevel II, Adv. Regression II, Adv. Regression III, and Adv. SEM. Details on these courses and 16 others are provided below.
Unbeatable Pricing-Register Now!
To support students, educators, and researchers, we are offering our lowest prices ever on CARMA's Live Online June Short Courses. AOM-CARMA Affiliate Program Members register now for just $300 through April 26; registration pricing will be $350 April 27 – May 10. Take advantage of our lowest pricing model ever -review the course list below, find the best courses for you, and register now!.
Choose from 23 Live Online Short Courses in June 2026 Offered Across Two Sessions!
CARMA's June Live Online Short Courses are built to strengthen your research skills alongside leading management scholars. You can choose from 23 courses offered across two sessions, spanning four focus areas: Data Technology, Advanced Methods & Analysis, Introductory Methods & Analysis, and Qualitative Methods. Session I runs June 1–4, followed by Session II on June 8–11.
View the Short Course Preview Playlist
View the full June 2026 Live Online Short Course Preview playlist on CARMA's YouTube Channel.
Advanced Methods & Analysis
Session I
- Advanced Multilevel Analysis I: Growth Models, Mediation, and Moderation (Dr. Gilad Chen)
- 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.
- Advanced Regression I: Polynomial and Response Surface Methods. (Dr. Jeff Edwards)
o 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.
Session II
- Advanced Experience Sampling Methods: Analysis and Interpretation (Dr. Nikos Dimotakis)
- Building on the foundations laid in our introductory workshop, we're excited to present the follow-up class focused on "Data Analysis and Interpretation in Experience Sampling." This advanced course is designed to seamlessly connect with its predecessor, yet stands independently, catering to both new and returning participants. Dive into the complexities of analyzing data collected through experience sampling methods and related methodologies. We'll guide you through various analytical techniques, from basic descriptive statistics to more sophisticated multilevel modeling, ensuring you can uncover the rich narratives hidden within your data. Emphasis will be placed on interpreting results in a meaningful way, linking back to your research questions and theoretical frameworks. This course is perfect for those looking to enhance their skills in making informed, data-driven decisions and insights. Whether you've just begun exploring experience sampling or are building on knowledge from our previous class, this workshop promises to enrich your understanding and application of this powerful research tool.
- Advanced Multilevel Analysis II: Panel Data, Consensus/Emergent Models, and Dichotomous Outcomes (Dr. Paul Bliese)
- The CARMA "Advanced Multilevel Analysis II: Panel Data, Consensus/Emergent Models, 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.
- Advanced Regression II: Binary Outcomes: Interactions, Endogeneity, and Panel Data (Dr. Jesper Wulff)
- This short course provides a deep dive into advanced binary regression models for management research, with a focus on average partial effects (APEs). The course develops a unified framework for interpreting how covariates affect outcome probabilities, rather than relying on model coefficients. We begin with the linear probability model (LPM) and probit, covering estimation, interpretation, and practical significance. We then address interaction analysis using predicted probabilities, marginal effects, and visualization, combined with formal testing and sensitivity analysis. A key feature of the course is that these core tools-APEs, interaction analysis, practical significance, and sensitivity analysis-are developed in foundational models and then extended to advanced settings, including instrumental variables (2SLS and control function approaches) and panel data models (correlated random effects and pooled probit). Throughout, a running example from management research is used, with hands-on implementation in both Stata and R.
- Advanced Regression III: Mediation and Moderation (Dr. Justin DeSimone)
- This short course features a deep dive into regression analysis with a particular focus on mediation and moderation analyses. The course will balance conceptual explanations, follow-along demonstrations, and discussions about best practices for conducting and interpreting various regression models. Participants should finish the short course with a better understanding of the conceptual and practical considerations involved in regression analysis, especially as related to mediation and moderation modeling. Topics covered include brief review of various forms of correlation, single and multiple regression, and model comparison techniques. This course will then focus on mediated regression, moderated regression, and moderated mediation. Additional advanced topics including response surface analysis and relative importance assessment will also be introduced. For all topics, examples will be discussed and follow-along assignments completed using data and syntax provided by the instructor. This short course uses both Excel and R for demonstrations of these techniques.
- Advanced SEM Longitudinal Models (Dr. Todd Little)
- Embark on an advanced journey of expertise with an intensive seminar focused on the nuanced analysis of longitudinal data through Structural Equation Modeling (SEM). Join a dynamic program featuring expert-led lectures and hands-on computer workshops, meticulously designed to provide participants with unparalleled training in utilizing SEM for the comprehensive analysis of longitudinal data. Elevate your skills, refine your approach, and gain mastery in the craft of Longitudinal Structural Equation Modeling. Seize this opportunity to dive deep into advanced methodologies and enhance your proficiency in handling longitudinal data sets. Enroll now for a transformative learning experience at the forefront of statistical analysis.
Session I: (Displayed as divided by content areas)
- Introductory Methods and Analysis
- Qualitative Methods
Session II: (Displayed as divided by content areas)
- Introductory Methods and Analysis
o Introduction to Meta-Analysis (Dr. Dana Joseph)
o Measurement Development, Evaluation, and Adaptation (Dr. Lisa Schurer Lambert)
Dr. Larry Williams, CARMA Director
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Larry Williams
Professor
Texas Tech University
Lubbock TX
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