Schreibe dich in meinen neusten Kurs, Statistik mit R, hier ein.
Innerhalb der nächsten 5 Tage kannst du dich hier zum Bestpreis einschreiben.
Submit for the EARLI SIG17 conference 2020 in Vienna here: www.sig17.net
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.
Of course, I will report on the outcomes.
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:
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
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.
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.
After winning the teaching award of the University of Vienna earlier this year, the FHWien of the WKW awarded their prize to me, too. Both awards are about innovative teaching using digital media / flipped classroom.
At the FHWien of the WKW, I was awarded for a statistics course in a lab setting. In my SPSS online course you can find similar videos.