CARMA Live Online Short Courses

Europe Region

Sponsor

Summer Short Courses, June 15-20 2020 – Two Sessions, 4 Course Options

Sponsored by University of Padova

Session 1: June 15-17 2020, Two Course Options | Session 2: June 18-20 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.

Registration, Pricing, Advanced Registration Deadline

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 – 05/01/20
Faculty $425 $750 $850 $1,600 $680 $1,260
Advanced Registration
03/19/2020 – 05/01/20
Student $325 $550 $650 $1,200 $520 $940
Normal Registration
05/02/20 – 06/10/20
Faculty $475 $850 $950 $1,800 $760 $1,420
Normal Registration
05/02/20 – 06/10/20
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: e62c-57a4
Student Code: 8c39-bc57

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.