A Review of Best Practice Recommendations for Text Analysis in R (and a User-Friendly App)

In recent decades, the amount of text available for organizational science research has grown tremendously. Despite the availability of text and advances in text analysis methods, many of these techniques remain largely segmented by discipline. Moreover, there is an increasing number of open-source tools (R, Python) for text analysis, yet these tools are not easily taken advantage of by social science researchers who likely have limited programming knowledge and exposure to computational methods. In this article, we compare quantitative and qualitative text analysis methods used across social sciences. We describe basic terminology and the overlooked, but critically important, steps in pre-processing raw text (e.g., selection of stop words; stemming). Next, we provide an exploratory analysis of open-ended responses from a prototypical survey dataset using topic modeling with R. We provide a list of best practice recommendations for text analysis focused on (1) hypothesis and question formation, (2) design and data collection, (3) data pre-processing, and (4) topic modeling. We also discuss the creation of scale scores for more traditional correlation and regression analyses. All the data are available in an online repository for the interested reader to practice with, along with a reference list for additional reading, an R markdown file, and an open source interactive topic model tool (topicApp; see https://github.com/wesslen/topicApp, https://github.com/wesslen/text-analysis-org-science, https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/R4W7ZS).     

Effects of exposure to alcohol‐related cues on racial discrimination

Prior research has shown that exposure to alcohol‐related images exacerbates expression of implicit racial biases, and that brief exposure to alcohol‐related words increases aggressive responses. However, the potential for alcohol cue exposure to elicit differential aggression against a Black (outgroup) relative to a White (ingroup) target—that is, racial discrimination—has never been investigated. Here, we found that White participants (N = 92) exposed to alcohol‐related words made harsher judgments of a Black experimenter who had frustrated them than participants who were exposed to nonalcohol words. These findings suggest that exposure to alcohol cues increases discriminatory behaviors toward Blacks.     

Letting Go: Conceptualizing Intervention De‐implementation in Public Health and Social Service Settings

The discontinuation of interventions that should be stopped, or de‐implementation, has emerged as a novel line of inquiry within dissemination and implementation science. As this area grows in human services research, like public health and social work, theory is needed to help guide scientific endeavors. Given the infancy of de‐implementation, this conceptual narrative provides a definition and criteria for determining if an intervention should be de‐implemented. We identify three criteria for identifying interventions appropriate for de‐implementation: (a) interventions that are not effective or harmful, (b) interventions that are not the most effective or efficient to provide, and (c) interventions that are no longer necessary. Detailed, well‐documented examples illustrate each of the criteria. We describe de‐implementation frameworks, but also demonstrate how other existing implementation frameworks might be applied to de‐implementation research as a supplement. Finally, we conclude with a discussion of de‐implementation in the context of other stages of implementation, like sustainability and adoption; next steps for de‐implementation research, especially identifying interventions appropriate for de‐implementation in a systematic manner; and highlight special ethical considerations to advance the field of de‐implementation research.