Organizational Power and the Institutional Review Board

In this essay, we respond to the narratives and essays in this issue by examining organizational power issues related to researchers and institutional research boards (IRBs). Rather than taking a singular perspective on power, we use multiple perspectives and suggest that the cumulative analyses provide the best understanding of IRBs and organizational power.     

Lighting the Shroud of Turin for Exhibition in 2015

ABSTRACT

Lighting a cultural heritage artifact requires balancing visual perception with preventive conservation, by providing the best lighting (in terms of spectral distribution and quantity) to enable the viewer to appreciate details and color, while limiting photo-induced degradation. The paper outlines the methodology applied by a multi-disciplinary team while lighting the Shroud of Turin at its last public exhibition in 2015. The methodology considered the special requirements of the Shroud, including exposure to ultraviolet light, while providing appropriate display conditions that would meet audience expectations. The desired appearance (readability of the body image and color) was defined with the help of Shroud researchers and confirmed by subjective tests, while appropriate light levels for preservation were set in agreement with standard requirements and using knowledge of the degradation of linen in visible and UV light. The installation provided a controlled environment and a managed visitor route to the Shroud, assuring excellent perception of both details and color, with the lowest illuminance level about 15?lx.     

Historical case study: A research strategy for diachronic analysis

History and case study are two diachronic research strategies commonly used to study political discourse as it relates to public libraries. Though suitable for some studies, these strategies are inexhaustive. A new, blended research strategy is therefore needed to accomplish what history and case study alone cannot. This hybrid strategy, historical case study, must analyze cases from the distant past to the present, using eclectic data sources, in order to produce both idiographic and nomothetic knowledge. To develop historical case study, the universal components of diachronic analysis are first identified. A general research design for historical case study is then introduced. Finally, historical case study is tested in an actual research project. Findings from this project reveal that historical case study is a successful diachronic research strategy. Historical case study is a new and valuable research design suitable for addressing questions related to change, continuity, development, and evolution.     

Social Comparisons and Need Fulfillment: Interpreting Video Game Enjoyment in the Context of Leaderboards

This study examines how social comparison information provided by video game leaderboards may influence players’ retrospective judgments of autonomy, competence, and relatedness need fulfillment. Participants played a video game and were randomly assigned to receive no postgame feedback or were shown a leaderboard that placed them in the top or bottom quartile of players. Results indicate downward social comparisons increase enjoyment by increasing competence and relatedness perceptions. However, upward comparisons did not have an opposite effect, nor did either type of social comparison influence players’ autonomy perceptions. Implications for applying Self-Determination Theory to video game enjoyment in the context of social comparison feedback is discussed.     

Persistent Misperceptions: Americans’ Misplaced Confidence in Privacy Policies, 2003–2015

This paper examines the persistence of Americans’ misunderstanding of the function of privacy policies. We also identify groups that have misplaced confidence in the privacy policy label and address whether the groups’ patterns of misperception have changed over time. The findings add a new dimension to the argument that the usefulness of privacy policies needs to be reassessed. As a remedy, we call for media literacy programs to address structural features of media systems that lead to broadly held misperceptions such as the one examined here.     

Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology

ABSTRACT

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.     

Scaling up Content Analysis

ABSTRACT

Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.     

More than Bags of Words: Sentiment Analysis with Word Embeddings

ABSTRACT

Moving beyond the dominant bag-of-words approach to sentiment analysis we introduce an alternative procedure based on distributed word embeddings. The strength of word embeddings is the ability to capture similarities in word meaning. We use word embeddings as part of a supervised machine learning procedure which estimates levels of negativity in parliamentary speeches. The procedure’s accuracy is evaluated with crowdcoded training sentences; its external validity through a study of patterns of negativity in Austrian parliamentary speeches. The results show the potential of the word embeddings approach for sentiment analysis in the social sciences.