January 2017, Open Science: Open Resources & Communication

Time: 11am-12:30pm, January 19, 2018 (Friday)
Location: Scarfe Room 2415
Moderator: Ed Kroc (Postdoctoral Fellow, ECPS & Department of Statistics)
Penal Discussion Speakers:
Ben Hives (M.Sc. student, School of Kinesiology)
Geri Ruissen (PhD student, School of Kinesiology)
Scott Lapinski (Scholarly Communication & Research Librarian, Countway Library of Medicine, Harvard University)

Highlights: In this event, we will discuss the most updated practices of open science, including open resources, open access and open communication across research fields. Scott Lapinski will also talk about the current open science initiatives at Harvard University.

Ben Hives will briefly discuss various mediums of social media and their role in communicating science. He will also discuss some of the benefits and perils of using social media as a scientist.

Geri Ruissen will talk address both the why and how of preregistering research with practical examples. Pre-registration, the practice of making all aspects of your research process publicaly available before data collection commences, is gaining considerable momentum as it is a simple and effective way of protecting against some of the pitfalls of NHST.

Since 2006, Scott Lapinski Scott has lead campus wide Open Science initiatives. Dominating this activity is to ensure Harvard researchers are aware of the choices and tools available to help them navigate any granting agency obligations to share scholarly publications and any relevant supplemental data within Open Access Repositories. Scott will share some of the challenges, opportunities and trends that have been observed over the course of this initiative, and recommend some direction that may help academic communities to foster a research culture that better integrates the “reproducibility of science” with the published scientific record.

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133. http://doi.org/10.1080/00031305.2016.1154108


February 2017, Research Talks

Time: 11am-12pm, February 15, 2018 (Thursday)
Location: Scarfe Room 2415
Moderator: Nathan Roberson (Ph. D. candidate, MERM program, UBC)
Ashley Pullman (Postdoctoral Fellow, Education Policy Research Initiative at the University of Ottawa)
Michelle Chen (Psychometrician, Paragon Testing Enterprises)
Joanna Zeng (PhD Student, Department of Language and Literacy Education, UBC)

A latent profile analysis approach to understanding science and technology attitudes in China, Japan, South Korea and the United States 
By Ashley Pullman and Michell Chen
Abstract: The following presentation will provide an overview of a latent profile/class analysis approach to studying science and technology attitudes across four top countries on the Bloomberg’s 2015 list of high-tech centralization, including the United States, China, South Korea and Japan. Alongside describing the methodology and World Values Survey data, the main research findings will be presented.

The first language phonological interference and second language phonological mediation in bilingual visual word recognition
By Joanna Zeng
Abstract: This study explored the effects of the first language phonology on second language visual word recognition via a mixed design ANOVA approach. The study found that first language phonology phonological interference and second language phonological mediation were involved in bilingual visual word recognition.

March 2017, Workshop on Power Analysis

Time: TBA
Location: Education Library Block Room 278
Ryan Ji (Ph. D. student, MERM program, UBC)
Ben Hives (M.Sc. student, School of Kinesiology)

Outline of the workshop:
Power Analysis is used to determine an adequate sample size needed to detect a given effect size for a planned study. It can also be used to determine the power, given an effect size and a sample size.

In this workshop, we will approach power analysis conceptually other than mathematically, then we will illustrate power analysis via GPower for t-test (independent samples and paired samples), ANOVA, Repeated-Measures ANOVA, Mixed-Design ANOVA, and Multiple Linear Regression. Finally, we will discuss the difference between prospective power analysis and retrospective power analysis, and why the retrospective power analysis should be avoided.