Short Courses 2019-2020

Summer Short Courses, June 14-21 2020

Sponsored by University of Padova

Session 1: June 14-17 2020, Two Course Options | Session 2: June 18-21 2020, Two 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                                                                                                                  Session 2

  1. “Introduction to Qualitative Methods/Ethnography”- Dr. Michael Pratt, Boston College
  2. “Systematic Reviews and Meta-Analysis with R”- Dr. Ernest O’Boyle, Indiana University
  1. “Introduction to Data Mining with R” – Dr. Jeff Stanton, Syracuse University
  2. “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina-Charlotte 

1.   “Introduction to Qualitative Methods/Ethnography” – Dr. Michael Pratt, Boston College

The purpose of this workshop is to provide an introduction to qualitative methods by examining ethnography. Ethnographic approaches involve both study design and analysis, which makes them ideal for a beginner’s class. However, where applicable we will also discuss parallels with case studies and grounded theory. The course will be comprised of three major sections: (a) designing a qualitative study; (b) skill building, including interviews, observation, and data analysis; and (c) writing and publishing your qualitative research. The course will combine readings, “tales from the field” / discussions regarding the unique tensions and challenges of doing qualitative/ ethnographic research, and hands-on learning. Participants are invited to bring samples of their own data to the session. However, no experience with qualitative methods is required prior to taking this course.

2. “Systematic Reviews and Meta-Analysis with R”– Dr. Ernest O’Boyle, Indiana University

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.

3. “Introduction to Data Mining with R” – Dr. Jeff Stanton, Syracuse University

Data mining refers to the discovery of novel patterns in data – particularly in large, semi-structured or unstructured data sets. Data mining techniques can support theory development by uncovering connections among phenomena that would be challenging to find with a typical survey or experimental method. In this CARMA short course, we will use R and R-Studio to get started with data mining.

We will begin by briefly reviewing the basics of R, add on packages, and data mining concepts. I recommend that you take CARMA’s basic R introductory R course if you have no prior familiarity with programming languages. We will discuss the conceptual steps involved in data mining, and then use R to put some of those concepts to work open data sets I will provide. Students are welcome to bring their own data sets for experimentation on their own, but this is not required. We will examine data reduction, feature extraction, feature elimination, several forms of clustering, association rules mining, and text mining (including topic modeling). Time permitting, we will explore various classifiers and compare their performance to one another.

Students who participate successfully in this short course can expect to learn enough about data mining to begin experimenting with these tools in research and/or teaching. The ideal participant will have an interest in improving their skill with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring alternative, empirically driven strategies for analysis of large data sets.

4. “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

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.

July 13-16 & 20-23, 2020 – Two Sessions, Two Course Options

Sponsored by Tel Aviv University

Session 1: July 13-16 | Session 2: July 20-23

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1: July 13-16

“Introduction to Multilevel Analysis” – Dr. Gilad Chen, University of Maryland

The purpose of this CARMA Short Course is to develop the theoretical/conceptual background and methodological/statistical skills required for conducting multilevel research in the areas of Organizational Behavior, Human Resource Management, and related fields (e.g., Strategy, Behavioral Marketing). Topic areas to be covered include: (1) multilevel construct and measurement development and aggregation, (2) basics of multilevel modeling, and (3) advanced topics in multilevel modeling (growth modeling, 3-level models, & multilevel moderated-mediation models). The Short Course assumes participants have basic background in scientific principles, psychometrics (e.g., classical test theory, reliability & validity), and general linear model methods (e.g., ANOVA, regression). Participants are expected to have access to MPlus (Base Program and Multilevel Add-On; see http://www.statmodel.com/) as well as the R program for SPlus (see http://www.r-project.org/). We will use both programs during the Short Course, for some overlapping as well as unique purposes and capabilities. If you cannot gain access to MPlus, you can still participate in this Short Course, but will not be able to perform some of the analyses we will conduct.

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

Session 2: July 20-23

“Introduction to Structural Equation Methods” – Dr. Larry Williams – Texas Tech University

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.

Required Software: R installed with LAVAAN package (R (download here), RStudio (download here)) or LISREL (free trial edition)

Time Schedule/Registration/Pricing/Deadlines

Session 1 (July 13-16) & Session 2 (July 20-23)
Location Mon Tues Wed Thur
Tel Aviv 1 PM- 6 PM 1 PM- 6 PM 1 PM- 6 PM 1 PM- 6 PM

*Times are Tel Aviv Local Time

For the time schedule of different countries/cities click here.

For the time zone converter click here.

To register for 2020 CARMA Live Online Short Courses, 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. You will then be brought to a page in which you can select your course(s) and continue on to pay for them.

* – 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 for the second course.

****–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, 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: 3fcb-81b0
Student Code: 24a7-18ea

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.
If your organization is not yet a member but would like to become one, please contact us directly at carma@ttu.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $100 off the total price.

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.

Asia Short Courses July 6-10, 2020 – One Session, Four Course Options

Complete Course Listing

  1. “Questionnaire Design” – Dr. Lisa Schurer Lambert,  Oklahoma State University
  2. “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama
  3. “Introduction to SEM” – Dr. Larry Williams, Texas Tech University
  4. “Grounded Theory Method & Analysis” – Dr. Tine Koehler, The University of Melbourne

“Questionnaire Design” – Dr. Lisa Schurer Lambert,  Oklahoma State University

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.

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

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)

“Introduction to SEM” – Dr. Larry Williams, Texas Tech University

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.

Required Software: R installed with LAVAAN package (R (download here), RStudio (download here)) or LISREL (free trial edition)

“Grounded Theory Method & Analysis” – Dr. Tine Koehler, The University of Melbourne

The purpose of this workshop is to introduce researchers to the underlying tenets of the grounded theory approach and to aid them in designing and conducting a grounded theory study. In the course, we will cover the following three major topics: (a) Understanding the approach and different methodological traditions, (b) designing a grounded theory study, (i.e., data collection methods, purposive sampling, gaining access, sources, triangulation), and (c) analyzing data following the grounded theory method (i.e., different approaches to coding, constant comparison, memoing, triangulation, theoretical saturation, etc.). Furthermore, the workshop will provide specific examples of practical challenges and strategies to manage them. No software is necessary for this course. Participants are invited to bring samples of their own data to the course.

Time Schedule/Registration/Pricing/Deadlines

July 6-10
Location Mon Tues Wed Thur Fri
India 4 PM- 8 PM 4 PM- 8 PM 4 PM- 8 PM 4 PM- 8 PM 4 PM- 8 PM

*These times are in India Local Time

For the time schedule of different countries/cities click here.

For the time zone converter click here.

To register for 2020 CARMA Live Online Short Courses, 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.

* – 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 for the second course.

****–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, 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: 3fcb-81b0
Student Code: 24a7-18ea

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.
If your organization is not yet a member but would like to become one, please contact us directly at carma@ttu.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $100 off the total price.

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.

June 1-6, 2020 – Two Sessions, Sixteen Course Options

Sponsored by Wayne State University

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)

  1. “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte
  2. “Introduction to Big Data and Text Mining with R” – Dr. Jeff Stanton, Syracuse University
  3. “Introduction to SEM” – Dr. Larry Williams, Texas Tech University and Dr. Betty Zhou, University of Minnesota
  4. “Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling” – Dr. Robert Vandenberg, University of Georgia
  5. “Introduction to Multilevel Analysis with R” – Dr. James LeBreton, Pennsylvania State University 
  6. “Systematic Reviews and Meta-Analysis” – Dr. Ernest O’Boyle, Indiana University
  7. “Theory, Methods, and Analysis for Research with Dyads” – Dr. Janaki Gooty, University of North Carolina Charlotte
  8. “Web Scraping: Data Collection and Analysis with R” –Dr. Richard Landers, University of Minnesota
  1. “Advanced Data Analysis with R” – Dr. Ron Landis, Illinois Institute of Technology
  2. “Statistical Analysis of Big Data with R” – Dr. Jeff Stanton, Syracuse University
  3. “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg, University of Georgia
  4. “Advanced Multilevel Analysis with R” – Dr. Paul Bliese,  University of South Carolina
  5. “Introduction to Bayesian Analysis” – Dr. Steve Culpepper, University of Illinois 
  6. “Questionnaire Design” – Dr. Lisa Schurer Lambert,  Oklahoma State University
  7. “Advanced Regression: Alternatives to Difference Scores, Polynomial Regression, and Response Surface Analysis” – Dr. Jeff Edwards, University of North Carolina
  8. “Open Science: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

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 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)

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

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 Big Data and Text Mining with R”” – Dr. Jeff Stanton, Syracuse University

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.

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

Option #3: “Introduction to SEM” – Dr. Larry Williams, Texas Tech University and Dr. Betty Zhou, University of Minnesota

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.

Required Software: R installed with LAVAAN package (R (download here), RStudio (download here)) or LISREL (free trial edition)

Option #4: “Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling”– Dr. Robert Vandenberg, University of Georgia

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.

Required Software: R installed with LAVAAN package (R (download here), RStudio (download here))

Option #5: “Introduction to Multilevel Analysis with R” – Dr. James LeBreton, Pennsylvania State University

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

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: “Theory, Methods, and Analysis for Research with Dyads” – Dr. Janaki Gooty, University of North Carolina Charlotte

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.

Option #8: “Web Scraping: Data Collection and Analysis with R” – Dr. Richard Landers, University of Minnesota

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)

Option #1: “Advanced Data Analysis with R” – Dr. Ron Landis, Illinois Institute of Technology

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.

Option #2: “Statistical Analysis of Big Data with R” – Dr. Jeff Stanton, Syracuse University

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.

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

Option #3: “Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions” – Dr. Robert Vandenberg, University of Georgia

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

Required Software: R installed with LAVAAN package (R (download here), RStudio (download here))

Option #4: “Advanced Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina

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
    • Exercises using glmer (lme4) in R

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

Option #5: “Introduction to Bayesian Analysis” – Dr. Steve Culpepper, University of Illinois 

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.

Option #6: “Questionnaire Design” – Dr. Lisa Schurer Lambert, Oklahoma State University

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.

Option #7: “Advanced Regression: Alternatives to Difference Scores, Polynomial Regression, and Response Surface Analysis” – Dr. Jeff Edwards, University of North Carolina

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.

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

Option #8: “Open Science: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

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.

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

Time Schedule/Registration/Pricing/Deadlines

Session 1 (June 1-3) Session 2 (June 4-6)
City Country Time Zone Mon Tues Wed Thurs Fri Sat
Detroit, MI US ET 10AM – 5PM 10AM – 5PM 10AM – 1 PM 10AM – 5PM 10AM – 5PM 10AM – 1 PM

To register for 2020 CARMA Short Courses, 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. You will then be brought to a page in which you can select your course(s) and continue on to reserve your spot(s).

Pricing Dates * CARMA
Member **
Non-CARMA
Member
Prof. Assosication Member
(AOM,SIOP,SMA,AAOM,   IACMR,EURAM,EAWOP,AIB, ANZAM,INDAM,MAM and IAOM) ****
1 Course 2 Courses *** 1 Course 2 Courses *** 1 Course 2 Courses ***
Advanced Registration
02/21/2020 – 04/10/20
Faculty $425 $750 $850 $1,600 $680 $1,260
Advanced Registration
02/21/20 – 04/10/20
Student $325 $550 $650 $1,200 $520 $940
Normal Registration
04/11/20 – 05/08/20
Faculty $475 $850 $950 $1,800 $760 $1,420
Normal Registration
04/11/20 – 05/08/20
Student $375 $650 $750 $1,400 $600 $1,100
Late Registration
05/09/20 – 05/31/20
Faculty $525 $950 $1,050 $2,000 $840 $1,580
Late Registration
05/09/20 – 05/31/20
Student $425 $750 $850 $1,600 $680 $1,260

* – 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.

Find out if your organization is a CARMA Member. (US and Canada institutions only).

If your organization is not yet a member but would like to become one, you can find purchasing and renewal information here.

June 1-6, 2020 – Two Sessions, Eight Course Options

Sponsored by Wayne State University

Session 1: June 1-3, Four Course Options | Session 2: June 4-6, Four 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)

  1. “Advanced Qualitative Methods for Micro-Management Research” – Dr. Elaine Hollensbe,  University of CincinnatiPOSTPONED
  2. “Crafting High Quality Qualitative Research via a Phronetic Iterative Approach” – Dr. Sarah J. Tracy, Arizona State University
  3. “Qualitative Analysis for Organizational Change” – Dr. Jean Bartunek, Boston College – POSTPONED
  4. “Mixed Methods and Qualitative Comparative Analysis”– Dr. Thomas Greckhamer, Louisiana State University
  1. “Advanced Qualitative Methods for Macro-Management Research” – Dr. Rhonda Reger, University of Missouri
  2. “Doing Grounded Theory Research” – Dr. Glen Kreiner, Pennsylvania State University
  3. “Video Methods”– Dr. Curtis LeBaron, Brigham Young University
  4. “Introduction to Python and Content Analysis of Text”– Dr. Jason T. Kiley, Oklahoma State University

Session 1: June 1-3, Four Course Options (Choose One)

Option #1: “Advanced Qualitative Methods for Micro-Management Research” – Dr. Elaine Hollensbe,  University of Cincinnati – POSTPONED

This course focuses on Advanced Qualitative Methods for Micro-Management Research, the first of a two-course sequence.  We will explore ethnography, interviewing techniques, and narrative analysis.  We will briefly review the epistemological foundations of grounded theory qualitative research, then move immediately into aspects of early-stage grounded theory, including protocol development and interviewing, taking an applied focus through the use of exercises and activities.  We will examine tactics for designing a grounded theory study, tips and techniques for coding, and pitfalls and strategies associated with the review process for qualitative papers. Students will gain hands-on experience with observation, interviewing, coding, and reviewing.  We will also discuss published exemplars, with a focus on deconstructing the methods used.

Option #2: “Crafting High Quality Qualitative Research via a Phronetic Iterative Approach” – Dr. Sarah J. Tracy, Arizona State University

This workshop offers strategies for achieving quality in qualitative research across disciplines and paradigmatic leanings. Based upon material in the instructor’s book Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact and article, Qualitative quality: Eight “big-tent” criteria for excellent qualitative research, participants will learn techniques so that their research evidences: 1) worthy topic, 2) rich rigor, 3) sincerity, 4) credibility, 5) resonance, 6) significant contribution, 7) ethics and 8) meaningful coherence.  Along the way, they will be presented with claim-making and theory building heuristics that help their research have resonance and significance beyond the case at hand. This workshop is ideal for researchers, grant-writers, and instructors of qualitative methods—both those new to these areas as well as experienced. This eight-point conceptualization offers a useful pedagogical model and provides a common language of qualitative best practices that can be recognized as integral by a variety of audiences.

As a result of the workshop, participants will learn to:

  • Craft a topic that is heard as relevant, timely, significant and interesting to core audiences
  • Create rich rigor through using sufficient, abundant, appropriate, and complex theories, data, constructs, and analysis processes
  • Communicate sincerity by being self-reflexive and transparent
  • Mark credibility through thick description, triangulation, crystallization, multivocality, and member reflections
  • Fashion resonant research that influences and moves audiences through aesthetic representation, naturalistic generalization, and transferable findings
  • Develop a significant contribution—theoretically, practically, morally, methodologically, and heuristically
  • Practice qualitative ethics–including procedural, situational, relational, and exiting considerations
  • Craft meaningful coherence by interconnecting literature, research questions, findings and interpretations so that they fit together, cohere with the study’s goals, and connect with the audience’s expectations.

Tracy, S. J. (2020). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact, 2nd Ed. Hoboken, NJ: Wiley-Blackwell.

Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16, 837-851.

Option #3: “Qualitative Analysis for Organizational Change” – Dr. Jean Bartunek, Boston College – POSTPONED

This CARMA Short Course concerns exploration and critique of several qualitative approaches that may be used to study various types of change within organizations from a somewhat mezzo perspective. Course topics will include several types of change that may occur within organizations, including action research/planned change, organizational learning, and dialectical/paradoxical change. It will also address experiences of recipients of organizational change, and affective and temporal processes within change. From a research perspective, it will also address roles of the researcher with regard to change. Researchers may play several roles, including change participant, external researcher, or collaborator with one or more members of the organization in studying the change. In the course we will review recent scholarship that addresses approaches to change and critique qualitative methods this scholarship uses to study them. Finally, using available materials, we will explore how some of the methods would be used in students’ own research.

Option #4: “Mixed Methods and Qualitative Comparative Analysis”– Dr. Thomas Greckhamer, Louisiana State University

This course begins with an overview of mixed methods research designs, including sequential explanatory, exploratory, and transformational versions, as well as concurrent triangulation, nested, and transformative alternatives. Next, Qualitative Comparative Analysis (QCA) is introduced as an increasingly popular approach in management research that is relevant for qualitative and quantitative researchers alike. The course includes hands-on application of QCA, Crisp- and Fuzzy-Set analyses, the interpretation of QCA results, and the potential of using QCA as part of mixed methods research designs.

Session 2: June 4-6, Four Course Options (Choose One)

Option #1: “Advanced Qualitative Methods for Macro-Management Research” – Dr. Rhonda Reger, University of Missouri

In this course, students will be exposed to research methods currently used in macro-level management fields, specifically in strategic management, organization theory and entrepreneurship. This course assumes limited prior knowledge of qualitative methods, but it will still provide a deep grounding in several advanced qualitative methods and text analysis as applied in management research. Methods covered include comparative case study research, content analysis, discourse analysis, rhetorical analysis, sentiment analysis (also called tenor or tone analysis), and the construction of dictionaries. The course will be interactive with discussion of exemplar papers that showcase each of these methods. Students will also be given the opportunity to “pilot test” the methods by interviewing each other and content analyzing a small sample of text. A focus of this workshop will be on matching methods to research questions and the interests and strengths of the research team.

Required Software: LIWC2015 (30 day rental available for $9.95; purchase for $89.95 from Linguistic Inquiry and Word Count)

Option #2: “Doing Grounded Theory Research” – Dr. Glen Kreiner, Pennsylvania State University

We will explore the process of conducing a grounded theory study. We will discuss 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. We 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. Our approach will be a mixture of readings discussion (exemplar and how-to articles) and hands-on exercises.

Option #3: “Video Methods”– Dr. Curtis LeBaron, Brigham Young University

This seminar is an intensive “hands-on” experience with video methods in organizational studies. Participants will learn how to collect and analyze video data that provide empirical support for scholarly evidence and arguments. People may bring their own video, already captured and ready for examination, or use video data provided by the instructor. On the one hand, participants will look closely at human interaction within organizational settings: we will examine how people orchestrate their talk and bodily movement, moment to moment, within social and material environments, all in the service of social action and sense-making. On the other hand, we will keep an eye on “big” social and organizational issues, such as:

  • What do power and status (or weakness and inequity) look and sound like?
  • How do new ideas emerge and evolve, necessarily taking a social and material form?
  • How is expertise enacted and acquired?
  • What are patterns of healthy (and deficient) collaboration within an organization?

Seminar activities and assignments have two purposes. First, we will become better acquainted with research methods that may include video (e.g., conversation analysis, context analysis, and ethnography). We will talk about the underlying assumptions, distinctive features, and strengths and weaknesses of various approaches. Second, we will talk about the practical issues of this kind of research, such as research design, site selection and entrée, recording equipment and data collection, transcribing, data management and analysis, paper writing and publication.

Day 1: Analyzing talk as action

  • Overview of methods for studying naturally occurring discourse
  • Data collection (consent and confidentiality; observing; recording; logging; transcribing)
  • Conversation analysis

Day 2: Multimodality: Embodied Interaction

  • Using multimedia technologies
  • Analyzing visible (nonverbal) behavior
  • Context analysis

Day 3: Organizational studies: Knowledge, Power and Identity

  • The emergence and evolution of new ideas
  • Organizational routines
  • Business strategy as practice
  • Ethnography

Option #4: “Introduction to Python and Content Analysis of Text”– Dr. Jason T. Kiley, Oklahoma State University

Content analysis is a structured way to extract meaning from artifacts (e.g., texts, images, videos), and its application ranges from qualitative to quantitative research designs. Using modern computational techniques, we can bridge the two designs to varying degrees, retaining more of the depth of qualitative research at the traditionally larger scale of quantitative research. This short course focuses on computational approaches to content analysis that enable large scale quantitative research using text data, with a particular emphasis on the foundational skills of identifying, collecting, and preparing text 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 text data at scale using several techniques, including web scraping and application
programming interfaces (APIs). From there, we will extract meaning from text, in the form of quantitative measures, using computer-augmented human coding, dictionary methods, supervised machine learning, and unsupervised machine learning. 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 an introduction to Python in the beginning of the course content.

Time Schedule/Registration/Pricing/Deadlines

Session 1 (June 1-3) Session 2 (June 4-6)
City Country Time Zone Mon Tues Wed Thurs Fri Sat
Detroit, MI US ET 10AM – 5PM 10AM – 5PM 10AM – 1 PM 10AM – 5PM 10 AM- 5PM 10AM – 1 PM

To register for 2020 CARMA Short Courses, 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. You will then be brought to a page in which you can select your course(s) and continue on to reserve your spot(s).

Pricing Dates * CARMA
Member **
Non-CARMA
Member
Prof. Assosication Member
(AOM,SIOP,SMA,AAOM,   IACMR,EURAM,EAWOP,AIB, ANZAM,INDAM,MAM, and IAOM) ****
1 Course 2 Courses *** 1 Course 2 Courses *** 1 Course 2 Courses ***
Advanced Registration
02/21/20 – 04/10/20
Faculty $425 $750 $850 $1,600 $680 $1,260
Advanced Registration
02/21/20 – 04/10/20
Student $325 $550 $650 $1,200 $520 $940
Normal Registration
04/11/20 – 05/08/20
Faculty $475 $850 $950 $1,800 $760 $1,420
Normal Registration
04/11/20 – 05/08/20
Student $375 $650 $750 $1,400 $600 $1,100
Late Registration
05/09/20 – 05/31/20
Faculty $525 $950 $1,050 $2,000 $840 $1,580
Late Registration
05/09/20 – 05/31/20
Student $425 $750 $850 $1,600 $680 $1,260

* – 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.

Find out if your organization is a CARMA Member. (US and Canada institutions only).

If your organization is not yet a member but would like to become one, you can find purchasing and renewal information here.

April 20-24, 2020 – Two Sessions, Two Courses

Sponsored by University of South Australia

Session 1: April 20-22 | Session 2: April 22-24

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday April 20 (all day), Tuesday April 21 (all day), and Wednesday April 22 (AM half day)

Session 2

Wednesday April 22 (PM half day), Thursday April 23 (all day), and Friday April 24 (all day)

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 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

“Introduction to Multilevel Analysis with R” – Dr. James LeBreton, Pennsylvania State University

Course Description

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)

“Advanced Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina

Course Description

The CARMA Advanced Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct more advanced multilevel analyses. Emphasis will be placed on techniques for longitudinal data. The R software package will be introduced, explained, and used throughout this short course. The topics covered in this course include specifying and analyzing basic, 2-level, models (e.g., individuals nested in teams; repeated observations nested in individuals), as well as, more advanced 3-level models (e.g., individuals nested in teams that are nested in organizations; repeated observations nested in individuals that are nested in teams). Other topics include: multilevel mediation and the analysis of dyadic data. Exercises using real-world data, are conducted in R. Participants who prefer HLM, SAS, SPSS, or MPlus (and have expertise with these programs) will 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 have at least some foundational understanding of issues related to multilevel data and how to analyze simple, 2-level, models.

    • Module 1: 2-Level Mixed Models: Cross-Level Main Effects & Interactions
      • Examples using R
    • Module 2: Analyzing Change and Growth: 2-Level Growth Model
      • Examples using R
    • Module 3: 3-level Models
      • Examples using R
    • Module 4: Multilevel Mediation
      • Examples using R
    • Module 5: Analyzing Dyadic Data
      • Examples using R
      • Other topics (only if time permits) might include:
        • Multilevel Models for Non-Normal Outcome Variables
        • Bayes Estimates in R
        • Discontinuous Growth Models

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

Registration, Pricing, Advanced Registration Deadline and Time Schedule

To register for 2020 CARMA Live Online Short Courses, 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.

Pricing Dates * CARMA
Member **
Non-CARMA
Member
Prof. Assosication Member
(AOM,SIOP,SMA,AAOM,   IACMR,EURAM,EAWOP,AIB, ANZAM,INDAM) ****
1 Course 2 Courses *** 1 Course 2 Courses *** 1 Course 2 Courses ***
Advanced Registration
03/19/2020 – 04/01/20
Faculty $425 $750 $850 $1,600 $680 $1,260
Advanced Registration
03/19/2020 – 04/01/20
Student $325 $550 $650 $1,200 $520 $940
Normal Registration
04/02/20 – 04/15/20
Faculty $475 $850 $950 $1,800 $760 $1,420
Normal Registration
04/02/20 – 04/15/20
Student $375 $650 $750 $1,400 $600 $1,100

* – 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.

****–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), and Indian Academy of Management (INDAM). 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, and INDAM, you can use one of the following discount codes when registering for these short courses:

Faculty Code: 4bb9-ec55
Student Code: 13bf-d354

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.
If your organization is not yet a member but would like to become one, please contact us directly at carma@ttu.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $100 off the total price.

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.

Short Courses in Columbia, South Carolina, January 9-11, 2020 – One Session, Nine Course Options

Hosted by University of South Carolina

Short Course Sessions and Groupings

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

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. This Workshop is only available to those who will be attending one of the CARMA Short Courses to be held at the University of South Carolina (January 9-11, 2020), it will be offered in person January 8 from 2-6 pm, and it will also be available on-line. There is no separate registration fee for this workshop.

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

CARMA Short Courses

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

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.

Option 2: “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama

Course Description

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.

Option 3: “Introduction to Data Mining with R” – Dr. Jeff Stanton, Syracuse University

Course Description

Data mining refers to the discovery of novel patterns in data – particularly in large, semi-structured or unstructured data sets. Data mining techniques can support theory development by uncovering connections among phenomena that would be challenging to find with a typical survey or experimental method. In this CARMA short course, we will use R and R-Studio to get started with data mining.

We will begin by briefly reviewing the basics of R, add on packages, and data mining concepts. I recommend that you take CARMA’s basic R introductory R course if you have no prior familiarity with programming languages. We will discuss the conceptual steps involved in data mining, and then use R to put some of those concepts to work open data sets I will provide. Students are welcome to bring their own data sets for experimentation on their own, but this is not required. We will examine data reduction, feature extraction, feature elimination, several forms of clustering, association rules mining, and text mining (including topic modeling). Time permitting, we will explore various classifiers and compare their performance to one another.

Students who participate successfully in this short course can expect to learn enough about data mining to begin experimenting with these tools in research and/or teaching. The ideal participant will have an interest in improving their skill with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring alternative, empirically driven strategies for analysis of large data sets.

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

Option 4: “Introduction to Statistical Learning with Big Data in R” – Dr. Fred Oswald, Rice University

Course Description

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.

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

Option 5: “Introduction to SEM with LAVAAN” – Dr. Robert Vandenberg, University of Georgia

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.

Required Software: R installed with LAVAAN package

Option 6: “Introduction to Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina

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.

Option 7: “Web Scraping: Data Collection and Analysis” – Dr. Richard Landers, University of Minnesota

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 web pages 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).

Option 8: “Systematic Reviews and Meta-Analysis with R” – Dr. Ernest O’Boyle, Indiana University

Course Description

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

Option 9: “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

Course Description

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.

Required software: R

Registration Details

To register for 2020 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.

The early registration date is December 6, 2019.

Price Per Course

Early Registration  Non-Member  CARMA Member*
Faculty/Professional $900.00 $450.00
Student $700.00 $350.00
Late Registration  Non-Member  CARMA Member* 
Faculty/Professional $1000.00 $500.00
Student $800.00 $400.00

*Not sure if your Institution is a CARMA Member? Universities in the US and Canada may check here.

Accommodations/Overnight Lodging Suggestions

Hotel Address Phone
Courtyard Columbia Downtown at USC 630 Assembly St (approximately 5 minute walk to Business School) (803) 779-7800
Hilton Columbia Center Hotel 924 Senate St (approximately 7 minute walk to Business School) (803) 744-7800
Inn at USC Wyndham Garden Columbia 1619 Pendelton St (approximately 15 minute walk to Business School
but they offer a complimentary shuttle service)
(803) 779-7779

Short Courses in Adelaide, Australia, November 18-22, 2019 – Two Sessions, Two Courses

Hosted by University of South Australia

Session 1: November 18-20 | Session 2: November 20-22

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday November 18 (all day), Tuesday November 19 (all day), and Wednesday November 20 (AM half day)

Session 2

Wednesday November 20 (PM half day), Thursday November 21 (all day), and Friday November 22 (all day)

“Advanced Qualitative Methods for Macro-Management Research” – Dr. Rhonda Reger, University of Missouri

Course Description

In this course, students will be exposed to research methods currently used in macro-level management fields, specifically in strategic management, organization theory and entrepreneurship. This course assumes limited prior knowledge of qualitative methods, but it will still provide a deep grounding in several advanced qualitative methods and text analysis as applied in management research. Methods covered include comparative case study research, content analysis, discourse analysis, rhetorical analysis, sentiment analysis (also called tenor or tone analysis), and the construction of dictionaries. The course will be interactive with discussion of exemplar papers that showcase each of these methods. Students will also be given the opportunity to “pilot test” the methods by interviewing each other and content analyzing a small sample of text. A focus of this workshop will be on matching methods to research questions and the interests and strengths of the research team.

Required Software: LIWC2015 (30 day rental available for $9.95; purchase for $89.95 from Linguistic Inquiry and Word Count)

“Advanced Qualitative Methods for Micro-Management Research” – Dr. Elaine Hollensbe, University of Cincinnati

Course Description

One of the most challenging aspects of qualitative research is telling a convincing and compelling story that clears the hurdle for contribution in a top journal.  This course focuses on qualitative techniques well represented in micro-level management fields.  Students will be introduced to ethnography, participant observation, interviewing techniques, action research, and narrative analysis.  We will then briefly review the epistemological foundations of grounded theory qualitative research, then move immediately into research design and data collection, taking an applied focus for most of the course.  We will examine tactics for designing a grounded theory study, managing the coding and analysis process, writing up the paper, ensuring rigor and trustworthiness, and responding to reviewers.  Students will gain hands-on experience with coding, thematic analysis, and emergent model development.  We will also read and discuss published exemplars to deconstruct the methods used.

Registration, Pricing, Advanced Registration Deadline

To register for 2019 CARMA Short Courses in Adelaide, Australia, 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.

Non-member prices per course: *All prices are in US Dollars (USD)
• Faculty/Professional: $900.00
• Students: $700.00
CARMA Member prices per course
• Faculty/Professional: $450.00
• Students: $350.00

If your organization is not yet a member but would like to become one, please contact us directly at carma@ttu.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $75 off the total price.