Moral Foundations of U.S. Political News Organizations

The media ecosystem has grown, and political opinions have diverged such that there are competing conceptions of objective truth. Commentators often point to political biases in news coverage as a catalyst for this political divide. The Moral Foundations Dictionary (MFD) facilitates identification of ideological leanings in text through frequency of the occurrence of certain words. Through web scraping, the researchers extracted articles from popular news sources' websites, calculated MFD word frequencies, and identified words' respective valences. This process attempts to uncover news outlets' positive or negative endorsements of certain moral dimensions concomitant with a particular ideology. In Experiment 1, the researchers gathered political articles from four sources. We were unable to reveal significant differences in moral or political endorsements, but we solidified the method to be employed in further research. In Experiment 2, the researchers expanded their number of sources to 10 and analyzed articles that pertain to two specific topics: the 2018 confirmation hearings of U.S. Supreme Court Justice Brett Kavanaugh and the partial U.S. Government Shutdown of 2018-2019. Once again, no significant differences in moral or political endorsements were found

Thesis Version

The media ecosystem has grown, and political opinions have diverged such that there are competing conceptions of objective truth. Commentators often point to political biases in news coverage as a catalyst for this political divide. The Moral Foundations Dictionary (MFD) facilitates identification of ideological leanings in text through frequency of the occurrence of certain words. Through web scraping, the researchers extracted articles from popular news sources' websites, calculated MFD word frequencies, and identified words' respective valences. This process attempts to uncover news outlets' positive or negative endorsements of certain moral dimensions concomitant with a particular ideology. In Experiment 1, the researchers gathered political articles from four sources. We were unable to reveal significant differences in moral or political endorsements, but we solidified the method to be employed in further research. In Experiment 2, the researchers expanded their number of sources to 10 and analyzed articles that pertain to two specific topics: the 2018 confirmation hearings of U.S. Supreme Court Justice Brett Kavanaugh and the partial U.S. Government Shutdown of 2018-2019. Once again, no significant differences in moral or political endorsements were found

Moral Foundations of U.S. Political News Organizations

Partisan differences and diviseness have become an increasing hot topic in psychological research. Many theories have been proposed to explain these differences and divisions including Moral Foundations Theory. The current research seeks to use a linguistic measure of Moral Foundations, the Moral Foundations Dictionary (MFD), to test the theory in terms of predicted partisan differences. Through web scraping, we extracted articles from popular partisan news sources' websites, calculated MFD word frequencies, and identified words' respective valences. This process attempts to uncover news outlets' positive or negative endorsements of certain moral dimensions concomitant with a particular ideology. In Experiment 1, we gathered political articles from four sources. We were unable to reveal significant differences in moral endorsements, but we solidified the method to be employed in further research. In Experiment 2, we expanded their number of sources to 10 and analyzed articles that pertain to two specific topics: the 2018 confirmation hearings of U.S. Supreme Court Justice Brett Kavanaugh and the partial U.S. Government Shutdown of 2018-2019. Once again, no significant differences in moral endorsements were found. Together with past work, the results shed doubt on the validity of the MFD as a reliable measurement tool

MOTE: The Shiny App to Calculate Effect Sizes and Their Confidence Intervals

Recent developments in the psychological sciences have shown the de-emphasis of p-values with a renewed focus on effect sizes as a measure of the importance of research findings (Cumming, 2014). Even with the shift in focus, report rates for effect sizes are very low (Fidler et al., 2005; Fritz, Scherndl, & Kühberger, 2013). Given what we’ve been told about reporting effect sizes, why are researchers omitting these values in their journal articles? Several effect size calculators currently exist, including Soper’s webpage (2013) as well as macros available for SPSS/SAS (Smithson, 2003; Wilson, 2010). However, the flexibility of these calculators, as well as the extent to which they explain the calculations, varies greatly. One way to encourage a change in report rates of effect sizes is to train the next generation of researchers to include these values as part of or in lieu of the traditional hypothesis test. However, as statistics teachers know, it can be difficult to get students to understand which test to select, much less which effect size then corresponds to that statistical test. In this presentation, we will demonstrate a new application that could be used as a teaching tool in statistics and research method courses. This application is designed to allow the user to select the research design and corresponding effect size through drop down menus. For each effect, users type in relevant numbers to calculate those effects, and the effect size and related statistics are presented in APA style. For teaching purposes, helpful description text and YouTube how-to videos are coupled with each effect size page. A previous version of this application was implemented in statistics classrooms wherein students indicated that the application was easy to use and helpful for their homework. Faculty feedback from presentations of the new application during beta testing have been overwhelmingly positive. We believe this application will aid in teaching and learning in statistics and research methods courses for students at the undergraduate and graduate level