Gender on the box: Self-reflexivity as a starting point

This self-reflexive activity acts as an introduction to how we talk about and express gender identity, as well as the assumptions we may have about gender identity norms and expression. The activity illuminates student’s subconscious behaviors and understandings of gender, pushing them to sit self-reflexively with their own understandings of gender as an identity, expression, binary, and potential locus of shame/freedom.

Courses: Introduction to Women and Gender Studies, Intercultural Communication, Media Studies, Gender and Communication, Performance Studies

Objectives: Designed to introduce students to their own understandings and embodiment of gender, this activity asks students to be honest about their preconceived notions regarding gender that they bring with them into the classroom. The activity utilizes predesigned components that test students’ subconscious knowledge of the gender binary. This is a one-time activity that can be conducted in one 50- or 75-minute class period.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

A method for transcribing signed and spoken language

Transcribing sign communication used simultaneously with spoken English presents investigators with a unique problem: the singular quality of the bimodal communicative interactions cannot be accurately depicted using accepted conventions for recording either spoken or signed language samples. This article proposes guidelines for transcribing such data. The need for guidelines arose during an earlier study of the language development of a hearing child of deaf parents. To meet the immediate needs of that study, rules and conventions from previous studies were combined with newly generated ones, resulting in the guidelines proposed in this article. The guidelines can be applied to data in which intermodal linguistic influence is suspected.     

Examining the Impact of Opportunity Bursaries on the Financial Circumstances and Attitudes of Undergraduate Students in England

Notwithstanding the expansion of higher education across the OECD, there continues to be concern about the levels of participation amongst those from disadvantaged backgrounds. In response to this, a new form of financial support for students from low-income families, the 'opportunity bursary', was introduced for a limited period in England from 2001/02. Surveys of two cohorts of opportunity-bursary applicants were carried out and these suggested possible psychological, behavioural and economic impacts. Fewer opportunity-bursary recipients than non-recipients reported that part-time work had interfered with their studies and more reported that the bursary had made them less worried about meeting the costs of going to university. There was some evidence that the scheme led to increased retention in the first year of university study; it also appeared to lead to lower levels of debt, in particular bank overdrafts or credit card debt.