| | | | We are a non-profit academic unit, and the world's leading provider of research methods education for organizational scholars. We are excited to announce our Short Course offerings for 2019 and would like to emphasize the following: - Short Courses will be held at the University of South Carolina January 10-12, 2019.
- Short Courses are Co-Sponsored by SIOP and SMA (SIOP and SMA members register at CARMA member rate.)
- Short Courses feature use of R software, with free "Basics of R Workshop" offered in-person January 9 that will also be available online on-demand.
- Our Short Course instructors are former editors and current editorial board members from leading organizational journals who are recognized experts on their topics and who understand how their methods are applied in organizational settings.
- Our Short Courses include a mix of lecture and hands-on experiential activities, and participants have the opportunity to socialize and network with our instructors and faculty/doctoral students from leading universities.
More information on CARMA and our Short Courses, including course descriptions, instructor biographies, preview videos from our instructors, and registration information can be found here. | | | Offerings at The University of South Carolina Columbia, South Carolina | | | | | | | Web Scraping: Data Collection and Anaysis | | | | | | *Please note all courses in a session are taught concurrently, so a participant can take only one course per session. | | | To register for 2019 CARMA Short Courses at the University of South Carolina, you must first log in to your CARMA account. (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select "Purchase Short Course" on the right side of the page. Prices Per Course: | | Non-Member | CARMA Member* | SMA and SIOP Members** | | | $800.00 | $400.00 | $400.00 | | | $600.00 | $300.00 | $300.00 | | | Non-Member | CARMA Member* | SMA and SIOP Members** | | Faculty/Professional | $ 900.00 | $ 450.00 | $ 450.00 | | Student | $ 700.00 | $ 350.00 | $ 350.00 | **All Southern Management Association Members and SIOP members receive discounted prices on Short Course registration fees for all South Carolina Short Courses. | | CARMA Workshop: Basics of R 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 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. During this Basics of R Workshop, attendees will learn: 1. Using R through the R Studio interface 3. R data sets (a.k.a data frames & tibbles) 5. Subsetting columns of data and selecting cases 6. Recoding data and dealing with missing data 7. Merging data (columns and rows) 9. User defined functions Introduction to R Course Description: 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. Regression with R Course Description: This short course will begin with an introduction to linear regression analysis with R. Models for single and multiple predictors will be covered, as will 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. 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 Multi-level Analysis with R Course Description: The CARMA Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses. The course covers within-group agreement, nested 2-level multilevel modeling and growth modeling. All practical exercises are conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research. Introduction to SEM with R and LAVAAN Course Description: 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. Web Scraping: Data Collection and Analysis Course Description: 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. After a brief introduction to web architecture and web design, we will explore the collection of unstructured data by scraping webpages directly through several small hands-on projects. Next, 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. Finally, we will conduct a complete scraping project from start to finish including some novel analytic approaches (e.g., automatic identification of gender for social media contributors, language processing to extract themes, and interactive visualization with a simple web app). Introduction to Bayesian Analysis with R Course Description: Many inferential statistical procedures include an examination of p-values, a strategy that is sometimes labeled as the frequentist approach. An alternative has emerged over recent decades, known as Bayesian inference, that uses different strategies for making statistical decisions. In this short course, we will compare and contrast traditional frequentist inference with Bayesian inference. We will use the R open source statistical platform to conduct Bayesian inference, starting simply with the t-test and working towards more complex multivariate techniques. We will also examine some research publications to see Bayesian inference in action. By the end of this short course, you will be able to substitute Bayesian inferential procedures in place of some of the frequentist analysis techniques you may currently use. Analysis of Big Data Course Description: Big data has been a buzzword for several years both in academia and industry. Although the term is vague and is certainly overused, it does encompass some interesting new ideas and unfamiliar analytical techniques. Notable among these is "data mining," a family of analytical methods for clustering, classifying, and predicting that go a step beyond the statistical methods used by many social science researchers. In this short course, we will discuss the dimensions of big data and the conceptual steps involved in data mining. We will build hands-on skills for developing and running predictive models relevant to big data. We will discuss feature selection and dimension reduction. A range of predictive models will be covered: e.g., penalized regression models, random forest, stochastic gradient boosted trees, and support vector machines. We will touch briefly on text mining. We will use R and R-Studio for this course. Students are welcome to bring their own data sets for experimentation, but many data sets will be provided, so this is not required. | | If you have any questions or concerns, please feel free to contact us. We hope to hear from you soon, and thanks in advance for sharing this email with colleagues at your school! Good luck in your research!
Sincerely,
Dr. Larry J. Williams, CARMA Founding Director
Jessi Jensen, CARMA Assistant Director
Consortium for the Advancement of Research Methods & Analysis College of Business Administration University of Nebraska-Lincoln | | | |