International Institute for Qualitative Methodology

Mixed Methods Webinar Series

 

The Mixed Methods Webinar Series is an online colloquium for those interested in Mixed Methods research. Connect to learn from and engage with internationally known researchers around a variety of topics linked to Mixed Methods. This new series kicked off in February 2016 and is co-presented by IIQM and the Mixed Methods International Research Association (MMIRA). In one-hour session, researchers will present as well as engage with registered participants to facilitate the exchange of ideas and experiences. You are invited to participate in these free webinars irrespective your discipline or career stage.


Upcoming Webinars:

Peggy Shannon-Baker, Ph.D., Co-Chair for the MMIRA MOOC

IIQM-MMIRA Joint Webinar Series

Webinar Topic/Title:

Launching the MOOC, An Online Series of Modules

Date/Time:

November 12, 2019 at 1:00pm MST (3:00pm EST)

Webinar Summary:

The purpose of this webinar is to introduce the audience to the Mixed Methods International Research Association Massive Open Online Course (or MMIRA MOOC). To address the lack of access to mixed methods training opportunities around the world, MMIRA launched the MOOC on October 1, 2019 as a seasonal online course on mixed methods research available to MMIRA members. Each contains a set of five “core” modules based on foundational information about the mixed methods field and implementing mixed methods approaches, as well as a set of five “specialized” modules offering unique perspectives on the implementation of mixed methods in specific fields, advanced techniques, etc. Paid MMIRA members have free access to the flexible online course to be completed at their own pace. Season 1 is currently available until March 1, 2020, and includes modules on defining mixed methods, purposes for and sampling in mixed methods, theory development, social network analysis, and more. The webinar will briefly cover the history and development of the MOOC, introduce modules in Seasons 1 and 2, discuss some frequently asked questions about registration, review current use of the MOOC, and address any questions from webinar attendees.


Dr. Peggy Shannon-Baker, Ph.D. is Assistant Professor, Department of Curriculum, Foundations, and Reading And Affiliate Faculty, Women's, Gender, and Sexuality Studies Program at Georgia Southern University. She is a member of the MMIRA Executive Board.


Register for November Webinar


Hisako Kakai & Dr. Tomoki Kamei - How to Evaluate Mixed Methods – Focusing on the Quality of Data Integration
混合型研究の評価:データ統合の質に焦点を当てて

December 18, 2019 at 6:00pm MST   - 8:00pm EST  -  Dec 19, 10 am JST

Please note that this webinar will be presented in Japanese. 

This Webinar will discuss the evaluation criteria for mixed methods with a particular focus on the quality of data integration. In the first half of the session, Kakai will introduce the recent development of mixed methods evaluation criteria put forth by the American Psychological Association (Levitt, et al., 2018). She will then address issues involving MM evaluation and explore alternative evaluation criteria in the MM literature. In the second half of the session, Kamei will discuss mixed methods evaluation criteria with a particular focus on data integration. She will introduce “m-STAR21,” a supporting tool for planning a mixed methods study developed by her research team (Kamei et al., 2017).

The Organization of the Webinar
Introduction (by KAKAI, H., Ph.D.) (25 min.)
- Introducing the recent development of mixed methods evaluation criteria put forth by the American Psychological Association (Levitt.et.al, 2018)
- Addressing issues involving MM evaluation and exploring alternative evaluation criteria in MM literature
The Quality of Data Integration (by KAMEI, T., Ph.D.) (25 min.)
- Reviewing types of joint display and discussing challenges of data integration
- Introducing “m-STAR21,” a supporting tool for planning a mixed methods study developed by Kamei et al. (2017)
Q & A session (10 min.)

Register for December Webinar