Abstract
Plain language summaries (PLS) aim to communicate research findings to laypersons in an easily understandable manner. Despite the societal relevance of making psychological research findings available to the public, our empirical knowledge on how to write PLS of psychology studies is still scarce. In this article, we present two experimental studies investigating six characteristics of PLS for psychological meta-analyses. We specifically focused on approaches for (1) handling technical terms, (2) communicating the quality of evidence by explaining the methodological approach of meta-analyses, (3) explaining how synthesized studies operationalized their research questions, (4) handling statistical terms, (5) structuring PLS, and (6) explaining complex meta-analytic designs. To develop empirically validated guidelines on writing PLS, two randomized controlled studies including large samples stratified for education status, age, and gender (NStudy1=2,288 and NStudy2=2,211) were conducted. Eight PLS of meta-analyses from different areas of psychology were investigated as study materials. Main outcome variables were user experience (i.e., perceived accessibility, perceived understanding, and perceived empowerment) and knowledge acquisition, as well as understanding and knowledge of the quality of evidence. Overall, our hypotheses were partially confirmed, with our results underlining, among other things, the importance of explaining or replacing content-related technical terms (i.e., theoretical concepts) and indicating the detrimental effects of providing too many details on statistical concepts on user experience. Drawing on these and further findings, we derive five empirically well-founded rules on the lay-friendly communication of meta-analytic research findings in psychology. Implications for PLS authors and future research on PLS are discussed.
Highlights
Insightful and well-written scientific papers are sometimes difficult to understand
We found that the aims of Plain language summaries (PLS) mentioned, discussed or investigated in the literature could be classified into six categories: accessibility, understanding, knowledge, empowerment, communication of research, and improvement of research
In hypotheses 5 and 6, based on user feedback of Study 1, we propose that providing qualitative statements in the text of a PLS and a glossary on statistical terms promotes user experience and knowledge acquisition: Hypothesis 51: Statistical terms: incremental effect glossary: Accessibility (H5a), understanding (H5b), content-related knowledge (H5c), and empowerment (H5d) are higher when an effect size is reported and a glossary that explains statistical terms is provided after the PLS and a qualitative statement on the interpretation of the effect size is provided within the text of the PLS compared to reporting the effect size and providing a qualitative statement without a glossary
Summary
Insightful and well-written scientific papers are sometimes difficult to understand. Even researchers like ourselves at times encounter papers, perhaps focusing on new techniques or concepts that we are unfamiliar with, that we find hard to understand. Grasping the main ideas and results of this kind of paper might constitute an arduous task and could potentially hinder the interdisciplinary exchange of knowledge If this is the case for scientists, imagine the difficulties that non-scientists (i.e., laypersons) face when they are interested in (the findings of) scientific publications with high societal or personal relevance. To address the needs and interests of their scientific audience (and relevant gatekeepers such as editors), researchers may pay more attention to the scientific relevance of their findings and less attention to their practical relevance to the broader public when discussing their results in their publications (see Radford, 2011; Salita, 2015) This strong focus on a scientific audience may, make it burdensome for non-scientists to access the current scientific literature to fulfill their need for valid and understandable information (see Nunn and Pinfield, 2014). The current COVID-19 crisis highlighted that the public yearns – at least to some degree – for reliable scientific information and guidance (e.g., Post et al, 2021)
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