CARMA's January Short Courses: Quantitative Methods/Design/Analysis
CARMA's January 2025 Live Online Short Courses are available for registration at a discounted rate for AOM members through our AOM-CARMA Affiliate Program. At CARMA, we believe that many who need/want to learn research methods prefer live instruction, where questions can be asked and answered in real time about issues and topics related to their research. And we recognize the importance of courses that feature a quantitative courses on:
· advanced data analysis with R
· meta-analysis
· multilevel analysis
· questionnaire design
· within person research
CARMA's instructors are "The Best In Our Business" and are recognized within the organizational studies and management areas as leading methodological scholars. Our instructors include experts who are current or former Editors/Associate Editors for ORM, AMJ, JAP, and OBHDP. They also include several AOM-RMD Distinguished Career and Early Career award winners. Our courses provide opportunities to advance knowledge and skills, while networking with instructors and fellow participants.
We have five great courses on the topics above scheduled for January 6-9, 2025 (10:00amET-3:00pmET, mon-thurs). Visit each course's web page (links below) and find the course description, instructor bio, and a short video preview by the instructor.
Through the AOM-CARMA Affiliate Program, faculty and students who register by December 16 enjoy the discounted pricing of $400 available to our AOM-CARMA Affiliate Members.
We appreciate your patience as we re-establish direct access to CARMA content following the AOM Member Portal update. While this process continues, we've created an alternative way for AOM members to register and receive their discount.
Review the course list below to find the one that best suits your needs, and once you've decided, click here to register.
· Quantitative Methods: Design/Methods/Analysis Focus
- Advanced Data Analysis with R, Dr. Justin DeSimone
- 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.
- Introduction to Meta-Analysis, Dr. Dana Joseph
- Meta-analysis 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.
- Introduction to Multilevel Analysis, Dr. Paul Bliese
- 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., soldiers nested in platoons; employees nested within work teams) analyzed via multilevel regression (i.e., random coefficient regression; hierarchical linear model; mixed effects model). All analyses will be undertaking using R and RStudio. 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.
- Questionnaire Design, Dr. Lisa Schurer Lambert
- This introductory course will help you develop your model, develop and select measures, design survey instruments and execute your data collection. Before testing hypotheses about relationships between constructs, i.e., your hypotheses, it is imperative to demonstrate that your measures have construct validity. There will be special focus on more stringent standards and techniques and evolving trends in evaluating construct validity. 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. Topics include designing your project (developing a model, selecting variables, sampling requirements), writing survey items, content and discriminant validity tests, and EFA/CFA procedures. 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 process including techniques for dealing with missing data, outliers, and careless responders. If you wish, bring your research ideas because there will be opportunities to advance your own project within the workshop.
- Within Person Research, Dr. Niko Dimotakis
- 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
CARMA is a non-profit academic center at Texas Tech University now in our 26th year of providing research methods education. For more information about CARMA, visit our website: carmattu.com. To ensure that you don't miss out on any upcoming CARMA events, add subscribe to our CARMA Calendar (subscribe button located at the bottom of the calendar).
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Larry Williams
Professor
Texas Tech University
Lubbock TX
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