MethLab: Qualitative analysis of multimodal data with Crina Damsa


REGISTER HERE.

This webinar will offer insights into what it means to analyze rich sets of qualitative data. This data, for example, (video)recordings of verbal or non-verbal interaction, interviews, observations, require in-depth analyses and sophisticated interpretations. The aim is to support participants to understand qualitative content analysis in connection with other types of analyses applied to rich datasets. For example, interaction analysis supports interpretations of dialogues in educational contexts; while, other data can be used to generate complementary interpretations. The aim will be addressed by:

a) Presenting and discussing foundations for thinking and working qualitatively;
b) Exploring and coding sampled interaction data; engaging in-depth with a data excerpt generated through coding;
c) Relating the sampled data to other data types and combining interpretations.

Agraphie: major update

I have implemented a major updated to the Agraphie podcast. Agraphie was used to be hosted on this very server. I now broke this link in order for the podcast to grow more freely. Agraphie is now available on all major plattforms (e.g., now also on Spotify).

The episode list here will not be updated and will eventually be taken offline.

Subscribe to the new RSS feed (by searching for Agraphie in your favorite podcast player!).

Hear you there!

[German] Neuer Kurs: Statistik mit R

Schreibe dich in meinen neusten Kurs, Statistik mit R, hier ein.

Innerhalb der nächsten 5 Tage kannst du dich hier zum Bestpreis einschreiben.

EARLI SIG17 conference 2020 Vienna

Submit for the EARLI SIG17 conference 2020 in Vienna here: www.sig17.net

Coming Up: TeachingClinic

I’m excited to finally launch the TeachingClinic in the upcoming semester. The TeachingClinic connects teachers with aspiring teachers and newly-qualified teachers (students) for mutual benefit: Using design-based research, the students explore current challenges of the teachers and provide evidence-based solutions.

Are you a teacher? Register here!

Of course, I will report on the outcomes.

New Book: Mixed Methods Social Network Analysis

Mixed Methods Social Network Analysis brings together diverse perspectives from 42 international experts on how to design, implement, and evaluate mixed methods social network analysis (MMSNA). There is an increased recognition that social networks can be important catalysts for change and transformation.

This edited book from leading experts in mixed methods and social network analysis describes how researchers can conceptualize, develop, mix, and intersect diverse approaches, concepts, and tools. In doing so, they can improve their understanding and insights into the complex change processes in social networks. Section 1 includes eight chapters that reflect on “Why should we do MMSNA?”, providing a clear map of MMSNA research to date and why to consider MMSNA. In Section 2 the remaining 11 chapters are dedicated to the question “How do I do MMSNA?”, illustrating how concentric circles, learning analytics, qualitative structured approaches, relational event modeling, and other approaches can empower researchers.

This book shows that mixing qualitative and quantitative approaches to social network analysis can empower people to understand the complexities of change in networks and relations between people. It shows how mixed analysis can be applied to a wide range of data generated by diverse global communities: American school children, Belgian teachers, Dutch medical professionals, Finnish consultants, French school children, and Swedish right-wing social media users, amongst others. It will be of great interest to researchers and postgraduate students in education and social sciences and mixed methods scholars.

(Text from the book cover.)

Oh, and here is a voucher:

New Paper available

The paper “On the Relation between Task-Variety, Social Informal Learning, and Employability” is now available as “Online First” here: http://link.springer.com/article/10.1007/s12186-018-9212-4

Abstract

Fluctuating demands and fast-changing job-requirements require organizations to invest in employees so that they are able to take up new tasks. In this respect, fostering employees’ employability is high on the agenda of many organizations. As a prerequisite for creating employability, many scholars have focused on the role of social informal learning. In this study, we extend this perspective and examine the relationships between task variety, social informal learning, and employability. We hypothesized that task variety is a catalyst for social informal learning, which in turn enhances employees’ employability. We contribute empirical evidence for this mechanism. However, while task variety leads to social informal learning and, subsequently, the competences needed for employability, task variety also may have negative direct effects on employability. We discuss the implications of these findings for future research and practice.

Full text here.

Cite As

Froehlich, D. E., Segers, M. S. R., Beausaert, S. A. J., & Kremer, M. (2018). On the relation between task-variety, social informal learning, and employability. Vocations and Learning, 1–15. https://doi.org/10.1007/s12186-018-9212-4

 

Froehlich, D. E. (2019). Exploring social relationships in “a mixed way”: Mixed Structural Analysis. Paper presented at the AERA Annual Meeting 2019, Toronto.

My paper “Exploring social relationships in “a mixed way”: Mixed Structural Analysis” was accepted for presentation at the next AERA Annual Meeting in Toronto in April 2019. If you prefer to read about this, wait for the soon-to-be-published  edited volume on Mixed Methods Social Network Analysis: Theories and Methodologies in Learning and Education.

Reference

Froehlich, D. E. (2019). Exploring social relationships in “a mixed way”: Mixed Structural Analysis. Paper presented at the AERA Annual Meeting 2019, Toronto.
Froehlich, D. E., Rehm, M., & Rienties, B. C. (2019). Mixed Methods Social Network Analysis: Theories and Methodologies in Learning and Education. London: Routledge.
By the way: You may also check out my course on social network analysis on udemy. This one is in English. You can get a discount here.

New Publication: Chapter in “Schlüsselwerke der Netzwerkforschung”

If you are interested in social network analysis and happen to speak German, you should get yourself the newly published “Schlüsselwerke der Netzwerkforschung”. It’s a massive book that contains many short summaries and evaluations of important social network related texts. Find more information at the book’s homepage. I contributed a chapter focusing on Burt (2005): Brokerage and Closure.

By the way: You may also check out my course on social network analysis on udemy. This one is in English. You can get a discount here.

Citation

Froehlich, D. E. (2018). Burt (2005): Brokerage & Closure. In B. Holzer & C. Stegbauer (Eds.), Schlüsselwerke der Netzwerkforschung. VS Verlag für Sozialwissenschaften.

Preprint: Explanatory sequential research designs on autopilot: Using R Markdown to increase research and evaluation efficiency

Abstract

In this paper, we show how automation on the side of the quantitative strand of research may help to alleviate this issue. For that purpose, we focus on explanatory sequential designs, where a quantitative strand of research is followed by a qualitative strand of research (Creswell, 2009). This is a common research design found in MMMR where quantitative results are further explained using qualitative methods (Schoonenboom, Johnson, & Froehlich, 2018). For instance, a survey may be followed by in-depth interviews with individuals from the survey population to help with contextualizing and interpreting the results. We report how R Markdown, a tool for report automation based on R (Froehlich, 2018b; Xie, 2013), may be used to increase research efficiency when applying such designs. We strongly believe that the quantitative strands of explanatory sequential designs lend themselves to such automation in order to free up resources for the (often labor intensive) qualitative strand. Next to increasing research efficiency, this measure is also helpful in aiding practitioners that do want to apply scientific methods, but do not possess the necessary in-depth knowledge about (quantitative) research methods.

Access

https://doi.org/10.17605/OSF.IO/JD8PF

Cite as

Froehlich, D. E. (2018). Explanatory sequential research designs on autopilot: Using R Markdown to increase research and evaluation efficiency. https://doi.org/10.17605/OSF.IO/JD8PF
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