Session 1: June 1-3, Eight Course Options | Session 2: June 4-6, Eight Course Options
Short Course Sessions and Groupings
We offer two sessions which allows course participants the opportunity to take two back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.
Complete Course Listing
Session 1 (Choose One) Session 2 (Choose One)
Mon. June 1 (all day), Tue. June 2 (all day), and Wed. June 3 (half day) Thr. June 4 (all day), Fri. June 5 (all day), and Sat. June 6 (half day)
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
Session 1: June 1-3, Eight Course Options (Choose One)
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.
Data mining refers to the process of uncovering patterns in data. Text mining refers to the same process, but applied to textual data instead of numeric data. These techniques support both prediction and explanation by discovering connections among variables that would be challenging to find with conventional techniques such as multiple regression. In this CARMA short course, we will use R and R-Studio to learn several techniques for analyzing numeric data sets, data sets with text data, and data sets containing both kinds of data.
On the first morning we will discuss the conceptual steps involved in data mining; after that we will use R to analyze several example data sets that I will provide. Students are also welcome to bring their own data, but this is not required or expected. We will learn data reduction, feature extraction, feature elimination, classifiers, and association rules mining. In the area of text mining, we will use R to extract text from websites, we will construct term-document matrices, and we will do analysis on those data structures using topic modeling, structural topic models, word embedding, penalized regression, and other text mining techniques.
If you have never used R, I recommend that you take CARMA’s introduction to R, but the data/text mining short course does not require extensive math or programming skills. You can expect to learn enough about data and text mining to begin working with these tools in research and/or teaching. The ideal participant will have an interest in improving their skills with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring alternative analytical strategies for working with complex data sets.
The Introduction to Structural Equation Methods Short Course provides (a) introductory coverage of latent variable techniques, including confirmatory factor analysis and structural equation methods with latent variables, (b) discussion of special issues related to the application of these techniques in organizational research, and (c) a comparison of these techniques with traditional analytical approaches. This Short Course will contain a balance of lecture and hands-on data analysis with examples and assignments, and emphasis will be placed on the application of SEM techniques to organizational research problems. Participants will:
develop skills required to conduct confirmatory latent variable data analysis, based on currently accepted practices, involving topics and research issues common to organizational research.
learn the conceptual and statistical assumptions underlying confirmatory latent variable analysis.
learn how to implement data analysis techniques using software programs for confirmatory modeling. Special emphasis will also be placed on the generation and interpretation of results using LAVAAN and LISREL.
learn how latent variable techniques can be applied to contemporary research issues in organizational research.
learn how the application of current latent variable techniques in organizational research differs from traditional techniques used in this literature
complete in-class exercises using LAVAAN and LISREL.
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.
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
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.
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.
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 natural language processing as an analytic approach for analyzing scraped data. Finally, we will walk through the various ethical and legal issues to be navigated whenever launching a web scraping project.
Session 2: June 4-6, Eight Course Options (Choose One)
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.
Traditional statistical models, such as linear regression and ANOVA, attempt to make useful predictions about people (e.g., employees’ standing on job performance) and groups (e.g., how teams differed in their mean performance). These models are relatively simple (i.e., any complex predictive relationships get overlooked), yet they might also capitalize on chance (i.e., not predict in data independent of those data used to develop the model).
To overcome these potential limitations, a large class of statistical learning models have been developed, some of which you may have heard of: e.g., random forests, LASSO regression, and support vector machines. These models determine whether complex relationships in the data can be reliably detected and then used to make predictions superior to those from traditional models.
This CARMA short course is a hands-on experience, where you will use R and RStudio to analyze and interpret those 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, interpret a variety of statistical learning models together. Time permitting, we will explore methods for comparing the performance of these statistical learning models one another.
This course will equip students with the skills to perform their own predictive modeling using statistical learning models. They can then apply these skills in their research, practice, and teaching.
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
The CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct 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, discontinuous growth models, and multilevel models for dichotomous outcomes). Practical exercises, with real-world research data, are conducted in R. 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: 2-Level Mixed Models Review
Module 2: Basic growth models for examining longitudinal data
Exercises using lme in R
Module 3: Discontinuous growth models for more complex longitudinal data
Exercises using lme in R
Module 4: Bayes Estimates
Exercises using lme in R
Module 5: Three-level models, emergence models, multilevel mediation, Multiple Imputation, and other extensions
Examples with MICE
Examples with mediation package
Module 6: Dichotomous Outcomes; Generalized Linear Mixed-Effects Models
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.
This workshop will help you develop and execute your data collection. Topics include designing your project (selecting variables and scales, sampling requirements) with a special emphasis on revising/creating new scales (scale development procedures, validation techniques). We will also review procedures for assessing construct validity (EFA/CFA) and focus on how to design your questionnaire to obtain high quality data. Finally, procedures for managing your data collection and for cleaning your data (missing data, outliers, identifying careless responders). There will be opportunities to advance your own project within the workshop.
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.
The open science revolution continues to gain momentum across the social and natural sciences, and in particular, the organizational sciences. This movement is driven in part by a crisis in confidence of scientific research. However, open science offers so much more to scholars and stakeholders of scientific work. Open science can serve to accelerate science, facilitate large scale collaboration, and aid individual research teams in conducting more rigorous and relevant work. This short course is intended to introduce open science concepts across the life cycle of research. After taking this course you will be able to engage in open science practices during the full research process and successfully leverage such practices in future journal submissions to demonstrate exceptional methodological rigor. We will cover (a) questionable research practices and publication bias, (b) study preregistration, registered reports, results-blind reviews, preprints, and how to use badges, (c) open data, proper annotation of analytic R code, reproducibility of analyses and transparency checklists, (d) Do’s and Dont’s for replication studies, (e) how to navigate open science platforms, such as the open science framework, large scale project collaboration in management, and finally (f) authorship and contributorship agreements. The course is introductory. Familiarity with some basic statistical concepts, such as null hypothesis significance testing is sufficient.
Prof. Assosication Member
(AOM,SIOP,SMA,AAOM, IACMR,EURAM,EAWOP,AIB, ANZAM,INDAM,MAM and IAOM) ****
2 Courses ***
2 Courses ***
2 Courses ***
02/21/2020 – 04/10/20
02/21/20 – 04/10/20
04/11/20 – 05/08/20
04/11/20 – 05/08/20
05/09/20 – 05/31/20
05/09/20 – 05/31/20
* – To receive these prices, you must complete your registration during the dates specified.
** – These prices reflect a 50% discount that you receive if you are student/faculty at an organization that is a member of the CARMA Institutional Premium Membership OR the CARMA Institutional Basic Membership Program.
*** – These prices reflect a discount in which you register for 2 courses and receive $100 off. Quantitative and qualitative courses can be combined to receive this discount.
****–These prices reflect a 20% discount for members of following associations; Academy of Management (AOM), Southern Management Association (SMA), Society for Industrial and Organizational Psychology (SIOP), Asia AOM (AAOM), International Association for Chinese Management Research (IACMR), European Academy of Management (EURAM), European Association of Work and Organizational Psychology (EAWOP), Academy of International of Business (AIB), Australia and New Zealand Academy of Management (ANZAM), Indian Academy of Management (INDAM), Midwest Academy of Management (MAM), and Iberoamerican Academy of Management (IAOM). This discount can not be applied if you are also using CARMA membership discount.
If you are a member of AOM, SIOP, SMA, AAOM, IACMR, EURAM, EAWOP, AIB, ANZAM, INDAM, MAM, and IAOM you can use one of the following discount codes when registering for these short courses:
Faculty Code: 4902-136b
Student Code: 3080-56a8
Note that we will be verifying association membership for all those who use these discount codes. Anyone who uses one of these discount codes and is not a member of those associations will be required to pay the non member rate.
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.