CARMA Classroom

Upcoming Short Courses

Short Courses Overview

Each CARMA short course is typically a two or two and a half day session on a research method or data analysis topic. CARMA Short Courses place an emphasis on hands-on experience and on the application of the methodology aimed at skills development through equal amount of lecture and lab-time. Instructors are leading methodological scholars recognized within the organizational studies and management areas as experts on their topics. Several are current or past editors of leading organizational journals. Our list of short courses include introductory and advanced training on topics that might not be readily available at your institution. In addition, our short courses provide students and faculty with the opportunity to network with leading scholars and other students/faculty in their areas of interest.

More than 1,200 faculty and students from universities throughout the world have attended CARMA Short Courses since the summer of 2004. Past locations of these courses include Virginia and Michigan in the United States, as well as Brazil, Australia, India, The Netherlands, Israel, and China.

CARMA Short Course Evaluations

Past Participants had this to say about the CARMA Short Course program:

  • "Expert instructors, lively instruction, good balance between presentation and application."
  • "The instructors did a great job explaining (1) conceptual issues (2) statistical content; (3) actually working with the program package."
  • "Instructors were really awesome, the program as a whole was very organized and on schedule"
  • "The instructor was terrific! The venue was excellent."
  • "I really thought the course was outstanding in all regards! Very helpful to my own research/publication efforts."
  • "Excellent instructor! Extremely knowledgeable. We were able to actually practice/apply the theory to an article. Great experience!"
  • "Superb program – great contribution to academic community around the country."
  • "Everything was wonderful – thank you so much for creating a format through which the knowledge could be so easily and cleanly passed on."
  • "Absolutely awesome! Well worth the time and money!"
  • "This is an excellent program that I will recommend to my colleagues. I really appreciated the hospitality, the quality of lectures/materials, and the friendliness and accessibility of the instructors. Thank you very much – I learned a lot."

Short Courses in Detroit, Michigan, June 3-8, 2019 - Two Sessions, Twelve Course Options

Hosted by Wayne State University

Session 1: June 3-5, Six Course Options | Session 2: June 6-8, Six 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

Monday June 3 (all day), Tuesday June 4 (all day), and Wed. June 5 (half day)

Session 2

Thursday June 6 (all day), Friday June 7 (all day), and Sat. June 8 (half day)

  1. "Introduction to Meta-Analysis" - Dr. Ernest O’Boyle
  2. "Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling" - Dr. Robert Vandenberg
  3. "Introduction to Multilevel Analysis" - Dr. James LeBreton
  4. "Introduction to R" - Dr. Steven Culpepper
  5. "Intro to Big Data and Data Mining with R" - Dr. Jeff Stanton
  6. "Intermediate Regression: Multivariate/Logistic, Mediation/Moderation" - Dr. Ron Landis
  1. "Introduction to Social Network Analysis" - Dr. Richard DeJordy
  2. "Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions" - Dr. Robert Vandenberg
  3. "Advanced Multilevel Analysis" - Dr. Paul Bliese
  4. "Multivariate Statistics with R" - Dr. Steve Culpepper
  5. "Analysis of Big Data" - Dr. Fred Oswald
  6. "Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods" - Dr. Jeff Edwards

Session 1: June 3-5, Six Course Options

Option #1: “Introduction to Meta-Analysis" - Dr. Ernest O’Boyle, Indiana University

Course Description
This course provides the participant with knowledge concerning the major meta-analysis models used in research in organizational science and other sciences. The course also details all steps in conducting a systematic review. Thus, this course is not solely a statistics/methods course but provides the participant with knowledge needed to conduct a meta-analysis and systematic review consistent with the Meta­Analysis Reporting Standards (MARS). Free software is made available to the participants and hands-on practice in the software is incorporated into the course. The course also addresses emerging topics in meta-analysis and systematic reviews including meta-regression, meta-structural equation modeling, and publication bias.

Required Software: R and Microsoft Excel


Option #2: "Advanced SEM I: Measurement Invariance, Latent Growth Modeling & Nonrecursive Modeling" - Dr. Robert Vandenberg, University of Georgia

Course Description

Note the following change in software for this course:
The primary lecture tool will be the R package, lavaan, using the R-studio interface. The instructor will make the Mplus modules (e.g., data, code, output) available for those want to use MPlus for in-class exercises. The topics will be the same as for the past few years this course has been offered – only the primary software for lecture has changed. Students wanting to work with either lavaan or MPlus are welcomed to the course.

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.

Required Software: R installed with LAVAAN package / (order the full version, try the free demo version)

Option #3: "Introduction to Multilevel Analysis" - 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 )

Option #4: "Introduction to R" - Dr. Steven Culpepper, University of Illinois Urbana-Champaign

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 later CARMA short courses.

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


Option #5: “Intro to Big Data and Data Mining with R” - Dr. Jeff Stanton, Syracuse University

Course Description

Big data has been a buzzword for several years both in academia and industry. Although the term is vague and is certainly overused, it does encompass some interesting new ideas and unfamiliar analytical techniques. Notable among these is “data mining,” a family of analytical methods for clustering, classifying, and predicting that go a step beyond the statistical methods used by many social science researchers. In this short course, we will discuss the dimensions of big data and the conceptual steps involved in data mining. Students are welcome to bring their own data sets for experimentation on their own, but this is not required.

We will use the open source statistical processing language, R, for most of the work we do in the course. Extensibility is the hallmark of R; its system of add-on packages provides access to an unequaled range of analytical tools and techniques. You do not have to be an expert in R to take this course, although you will find the course easier if you also take the introduction to R offered by CARMA earlier in the week. Prior to the course, I will ask students to install R on their personal computers and review the first few chapters of my free eTextbook, An Introduction to Data Science. Depending on the interests and preferences of the students, we also use the Rattle or R-Studio graphical user interfaces.

The ideal student will have an interest in using R, knowledge of some basic descriptive and inferential statistics, and some curiosity about exploring alternative, empirically driven strategies for analysis of large data sets. No prior experience with data mining is required and students who participate successfully in this short course can expect to learn enough about data mining to begin experimenting with these tools in research or teaching.

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

Option #6: "Intermediate Regression: Multivariate/Logistic, Mediation/Moderation" - Ron Landis, Illinois Institute of Technology

Course Description
This short course will begin with a brief review of linear regression, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. We will pay particular attention to using regression to test models involving mediation and moderation. 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) or SPSS (free trial version)


Session 2: June 6-8, Six Courses

Option #1: “Introduction to Social Network Analysis" - Dr. Richard DeJordy, California State University, Fresno

Dr. Richard DeJordy, California State University, Fresno

Course Description
The CARMA Introduction to Social Network Analysis short course provides both (1) the theoretical foundation, and (2) hands-on experience to prepare attendees to embark on social network research. Emphasis will be placed on fundamental aspects of social network research (e.g., data consideration, commonly used measures). After a brief introduction to fundamental concepts and an in-class tutorial of the software used in class (UCINET for Windows), each module will first cover a specific topic relating to social network theory, then walk through a hands-on exercise using the software. Hands-on exercises are designed to use sample datasets that come with the software, but participants are encouraged to bring their own data and use it to explore the concepts and software functions covered in each session.

• Module 1: Introduction and Fundamentals of Social Network Analysis and a tutorial of UCINET and NetDraw.
• Module 2: Data considerations in Social Network research
• Module 3: Centrality – basic measures of network position
• Module 4: Cohesion – basic measures describing networks
• Module 5: Social Capital – Structural holes and network composition

If time permits, we will also cover: Testing network hypotheses – Nodal and dyadic hypotheses.

Required Software: UCINET download AFTER May 15. (There is a free trial for 30 days from download, so do not download too early.)

NOTE for MAC users: UCINET is Windows software. It does not run natively on Macs. If you use a Mac, your best option is to run it in a Windows emulator (such as Parallels or VMWare Fusion). These are often available for free to university faculty and students through campus arrangements (check with your campus technology group). Other options are listed on the FAQ on the download page. You need to have the software installed in advance to participate in the hands-on portion of the course.

Option #2: "Advanced SEM II: Missing Data Issue in SEM, Multi-Level SEM and Latent Interactions" - Dr. Robert Vandenberg, University of Georgia

Course Description

Note the following change in software for this course:
The primary lecture tool will be the R package, lavaan, using the R-studio interface. The instructor will make the Mplus modules (e.g., data, code, output) available for those want to use MPlus for in-class exercises. The topics will be the same as for the past few years this course has been offered – only the primary software for lecture has changed. Students wanting to work with either lavaan or MPlus are welcomed to the course.

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.

Required Software: R installed with LAVAAN package / MPlus (order the full version, try the free demo version)

Option #3: "Advanced Multilevel Analysis" - 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 )

    Option #4: “Multivariate Statistics with R” - Dr. Steven Culpepper, University of Illinois Urbana-Champaign

    Course Description
    This course continues the introduction to R from the first session by covering advanced topics related to multivariate statistics. We will cover topics related to data management for multivariate data and will provide an overview of plotting and visualizing multivariate data in R. Specific learning outcomes include learning how to conduct analyses involving:

    • Multiple regression and diagnostics
    • Exploratory factor analysis and principal components
    • Multivariate regression, canonical correlation, and MANOVA
    • Topics in statistical computation (e.g., bootstrapping, Monte Carlo simulation)
    • Structural equation modeling with the lavaan package
    • Reproducible research for quantitative reports

    The session will provide participants with some discussion of necessary background knowledge and practical exercises.

    Required Software: R (download here), R Studio (download here), and tex (for Windows: http://miktex.org/ , for OS X http://www.tug.org/mactex/, for Ubuntu/Debian (Linux): apt-get install texlive or http://www.tug.org/texlive/ )

    Option #5: "Analysis of Big Data" - Dr. Fred Oswald, Rice University

    Oswald

    Course Description

    This short course provides students with hands-on skills for developing and running predictive models for relevant to 'big data' in organizations. A range of predictive models will be covered: e.g., lasso and elastic net regression, random forest, stochastic gradient boosted trees, and support vector machines. R and all required R packages need to be set up on your laptop beforehand; the instructor will provide set-up instructions and guidance in advance; other data, materials, and assignments will be provided by the instructor (code, files).

    Required Software: TBA


    Option #6: “Advanced Regression: Alternatives to Difference Scores, Polynomial and Response Surface Methods” - Dr. Jeff Edwards, University of North Carolina

    Course Description
    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: SPSS (free trial version) or STATA

    Registration/Pricing/Deadlines

    To register for 2019 CARMA Short Courses at Wayne State University in Detroit, 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.

    Pricing Dates * Non-Member
    1 Course
    Non-Member
    2 Courses ***
    Member
    1 Course **
    Member
    2 Courses ** | ***
    Advanced Registration
    02/22/19 - 03/15/19
    Faculty $800 $1,525 $400 $725
    Advanced Registration
    02/22/19 - 03/15/19
    Student $600 $1,125 $300 $525
    Normal Registration
    03/16/19 - 05/06/19
    Faculty $900 $1,725 $450 $825
    Normal Registration
    03/16/19 - 05/06/19
    Student $700 $1,325 $350 $625
    Late Registration
    05/07/19 - 06/04/19
    Faculty $1,000 $1,925 $550 $1,025
    Late Registration
    05/07/19 - 06/04/19
    Student $800 $1,525 $450 $825

    * - 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 Consortium Webcast Program OR the International Video Library Program.

    *** - These prices reflect a discount in which you register for 2 courses and receive $75 off.

    Find out if your organization is a CARMA Consortium Webcast 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.

    Lodging: On-Campus Housing

    View Housing Details for Wayne State Short Courses

Qualitative Courses in Boston, Massachusetts, June 2019 - Two Sessions, 8 Course Options

Hosted by Boston College

Session 1: June 10-12, Three Course Options | Session 2: June 13-15, Three Course Options

Complete Course Listing

Session 1

Monday June 10 (all day), Tuesday June 11 (all day), and Wed. June 12 (half day)

Session 2

Thursday June 13 (all day), Friday June 14 (all day), and Sat. June 15 (half day)

  1. "Introduction to Qualitative Methods/Ethnography" - Dr. Michael Pratt
  2. "Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)" - Dr. Anne Smith
  3. "Interviewing for Qualitative Research" - Dr. Ashley Mears
  4. "Mixed Methods" - Dr. Thomas Greckhamer
  1. "Advanced Qualitative Analysis" - Dr. Rhonda Reger
  2. "The Craft of Inductive Qualitative Research" - Dr. Michel Anteby
  3. "Grounded Theory Method and Analysis" - Dr. Tine Kӧhler
  4. "Qualitative Analysis for Organizational Change" - Dr. Jean Bartunek

Session 1: June 10-12, Four Course Options

Option #1: "Introduction to Qualitative Methods/Ethnography" - Dr. Michael Pratt, Boston College

Micheal Pratt

Course Description

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.

Required Software: None


Option #2: "Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)" - Dr. Anne Smith, University of Tennessee, Knoxville

Course Description

Analyzing textual data can be approached inductively or deductively, depending on the selected methodological approach of the research project. In this workshop, we will discuss and undertake hands-on text analysis exercises. Top down or a more confirmatory approach to text analysis will cover topics such as: dictionary application (e.g., Zavyalova, et al., 2016 & LIWC dictionary; Short et al., 2009); dictionary creation (Franco, Alexander, & Smith, working paper; Short et al., 2010); template utilization (Crabtree & Miller, 1999; King, 2004); and collocation analysis (Gephart, 1997). Students will be working with textual data to explore these techniques. Bottom up or more exploratory approach will include an in vivo, manual coding exercise and a demonstration of coding techniques using computer aided qualitative data analysis software (CAQDAS). No prior knowledge of software or text analysis is required.

Required Software: CAQDAS


Option #3: "Interviewing for Qualitative Research" - Dr. Ashley Mears, Boston University

Course Description

Interviewing is a common method in sociology, and it is gaining popularity in business management, marketing, and health services research for the access it grants into people’s subjective experiences, meaning-making and accounting processes, and unspoken assumptions about social life. This workshop aims to provide an introductory “how to” of interview research, and in the process we examine the epistemology, conduct, and politics of qualitative methods. We will discuss interview projects from beginning to end, starting with the formation of the research question, orientation to theory, the nuts and bolts of sampling and conducting interviews, data analysis, and writing up results, in addition to ethical and practical considerations. We consider the benefits and limitations of interview methods across disciplines, and within sociology in particular, both long-standing and current debates on interview methodology. The course emphasizes hands-on learning through engagement with students’ own interests in interview projects, with an eye towards successful strategies for publishing interview research in top scholarly journals.

Required Software: None



Option #4: "Mixed Methods" - Dr. Thomas Greckhamer, Louisiana State University

Course Description

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 13-15, Four Course Options

Option #1: "Advanced Qualitative Analysis" - Dr. Rhonda Reger, University of Missouri

Rhonda Reger

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 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: "The Craft of Inductive Qualitative Research" - Dr. Michel Anteby, Boston University

Course Description

Qualitative inductive research projects are like gems that need polishing and the craft of polishing them to uncover a “theoretical contribution” can partly be learned. This course is designed to help participants polish their “gems” in the making and sharpen their emerging contributions. The common denominator for participants is that they be engaged in research projects reliant primarely on qualitative data (e.g., archives, interviews, and/or field observations). Each participant must be prepared to share a draft analytical field memo, paper or chapter from their ongoing research. Like in an art studio, the goal is to provide participants with constructive feedback on their works in progress.

Required Software: None


Dr. Tine Kӧhler

Option #3: "Grounded Theory Method and Analysis" - Dr Tine Kӧhler, The University of Melbourne

Course Description
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.


Jean Bartunek

Option #4: "Qualitative Analysis of Organizational Change" - Professor Jean Bartunek, Boston College

Course Description
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.


Required Software: None

Session Structure and Course Sequences

  • Courses within a Session are offered concurrently, so it is only possible to attend one course in Session I and one course in Session II.
  • You may attend up to two courses, as long as they are being offered in separate Sessions (see above).

Lodging: On-Campus Housing

View Housing Details for Boston College Short Courses

Short Courses in Padova, Italy, July 1-6, 2019 - Two Sessions, Four Course Options

Hosted by Department of Economics and Management at the University of Padova

Session 1: July 1-3 | Session 2: July 4-6

Padova logo

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.


Session 1

Monday July 1 (all day), Tuesday July 2 (all day), and Wed. July 3 (half day)

Session 2

Thursday July 4 (all day), Friday July 5 (all day), and Sat. July 6 (half day)

Complementary 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 Padova (July 1-6, 2019), it will be offered in person for Session II, July 3 from 1-5 pm, and it will also be available on-line for Session I and II. 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

Session I: July 1-3, Two Course Options

Choose one of the following two course options to attend during Session I.
Ron Landis

Option #1: “Intermediate Regression: Multivariate/Logistic, Mediation/Moderation” - Dr. Ron Landis, Illinois Institute of Technology

Course Description
This short course will begin with a brief review of linear regression, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. We will pay particular attention to using regression to test models involving mediation and moderation. 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 or SPSS


Lisa Lambert

Option #2: “Questionnaire Design” – Dr. Lisa Lambert, Oklahoma State University

Course Description
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 our own project within the workshop.


Session II: July 4-6, Two Course Options

Choose one of the following two course options to attend during Session II.
Robert Vandenberg

Option #1: “Introduction to Structural Equation Methods” – 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. I will use R LAVAAN package in my examples. You need to have R installed with LAVAAN package on your computer, and you also need to have a very strong understanding about the data handling functions using R. There is not enough workshop time to go through R basics, and to assist you in installing LAVAAN. 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 and other advanced topics will be introduced.

Required Software: R installed with LAVAAN package


Paul Bliese

Option #2: “Introduction to Multilevel/Longitudinal Analysis” – Dr. Paul Bliese, University of South Carolina

Course Description
The CARMA Multilevel/Longitudinal Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses, including those associated with longitudinal data. 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.

Required Software: SPSS


Registration, Pricing, Advanced Registration Deadline

To register for 2019 CARMA Short Courses in Padova, Italy, 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: $800.00
• Students: $600.00
CARMA Member prices per course
• Faculty/Professional: $400.00
• Students: $300.00

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

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

Advanced Registration Deadline is June 21, 2019. After this date, a $75.00 fee will be added to all registrations.

Accomodations

Hotel Information
Hotel Internet Address Address Rating
Hotel Galileo www.hotelgalileopadova.it via Venezia, 30 (Fair district) three stars
Hotel Donatello www.hoteldonatello.net via del Santo, 102/104 (near Basilica del Santo) four stars
Hotel Al Santo www.alsanto.it via del Santo, 147 (near Basilica del Santo) three stars
Hotel Giotto www.hotelgiotto.com Piazzale Pontecorvo, 33 (near Basilica del Santo) three stars
Hotel NH www.nh-hotels.com Via G.B. Pergolesi, 24 (Fair district) four stars
Hotel Europa www.hoteleuropapd.it Largo Europa, 9 (city centre) three stars

University Residence Options