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Fail videos showing mishaps/accidents are very popular on YouTube. But is this genre affected by sexism, that is, are women portrayed more often than men in an objectifying, sexualized manner in the video clips (H1), and are women more likely than men to be the target of gendered online hate speech in the video comments (H2)? Quantitative content analyses of 500 video clips (derived from 50 videos) and of 1,000 video comments (derived from 5 “male” and 5 “female” videos) from YouTube’s most popular fail video channel FailArmy were conducted. Women in fail videos were portrayed in an objectifying, sexualized manner twice as often (H1), and were the target of gendered hate comments nearly five times more often (H2) compared to men. Future research could analyze videos and comments from additional fail channels and investigate the reasons for the sexualized portrayals as well as for the audience’s hateful reactions.

Have You Heard? Testing the Warranting Value of Third-Party Employer Reviews

This project highlights the differences between measurements, namely between Likert-type scale measures and two conjoint analysis techniques––that is traditional conjoint analysis (TCA) and choice-based conjoint analysis (CBC). When referencing this project, please cite its corresponding publication: Pelzer, E. (2019). The Potential of Conjoint Analysis for Communication Research. Communication Research Reports, 36(2), 136-147. doi: 10.1080/08824096.2018.1559138 The project includes: 1. Raw data file (in .csv) 2. Cleaned data file with student sample (in .csv) 3. Cleaned data file (#2) was restructured to long-form to analyze CBC (in .csv) 4. Bland-Altman-Scatterplots to compare mean ranking positions of Likert-type scale, TCA and CBC 5. Tables with detailed analyses of Likert-type scale (OLS regression, HC-SE 3, HC-SE 4), TCA (OLS regression, MONANOVA), and CBC (Cox-regression)

Data file for Audio-Only Character Narration's Elicitation of Identification Overcoming Resistance to Narrative Persuasion