A Lightweight Environment for Learning Experimental IR Research Practices

Author(s):  
Zeynep Akkalyoncu Yilmaz ◽  
Charles L. A. Clarke ◽  
Jimmy Lin
ASHA Leader ◽  
2005 ◽  
Vol 10 (15) ◽  
pp. 8-27 ◽  
Author(s):  
Jennifer Horner ◽  
Michael Wheeler
Keyword(s):  

2013 ◽  
Vol 2 (4) ◽  
pp. 356-380
Author(s):  
Daniel Makagon

This article uses a course that meets from 10 p.m. to 1 a.m. as a context to critically examine collective collaborative fieldwork as an experiential pedagogy that helps students better understand and practice qualitative fieldwork interviews. A collective interviewing experience can provide each student with practice and establish a situation for relatively sustained learning-focused dialogue and debate about interviewing ethics. With this context in mind, I critically examine how interviewing participants in a group scenario can help students understand spurned interview requests, the effects on researcher-participant relationships, and the alteration of temporal and spatial scenes in which interviews take shape as well as teach students about the important nuances of translation during interviews. Taken together, these four issues offer important ways to think about team-based fieldwork projects as an alternative to lone-ethnographer models of research practices that are foregrounded in qualitative research literature and in fieldwork-based courses.


2018 ◽  
Author(s):  
Dick Bierman ◽  
Jacob Jolij

We have tested the feasibility of a method to prevent the occurrence of so-called Questionable Research Practices (QRP). A part from embedded pre-registration the major aspect of the system is real-time uploading of data on a secure server. We outline the method, discuss the drop-out treatment and compare it to the Born-open data method, and report on our preliminary experiences. We also discuss the extension of the data-integrity system from secure server to use of blockchain technology.


2019 ◽  
Author(s):  
Rens van de Schoot ◽  
Elian Griffioen ◽  
Sonja Désirée Winter

The trial-and-roulette method is a popular method to extract experts’ beliefs about a statistical parameter. However, most studies examining the validity of this method only use ‘perfect’ elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among PhD candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an over-view of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results, and how we resolved these sup-ported by annotated R-syntax.


Sign in / Sign up

Export Citation Format

Share Document