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This Element will overview research using models to understand scientific practice. Models are useful for reasoning about groups and processes that are complicated and distributed across time and space, i.e., those that are difficult to study using empirical methods alone. Science fits this picture. For this reason, it is no surprise that researchers have turned to models over the last few decades to study various features of science. The different sections of the element are mostly organized around different modeling approaches. The models described in this element sometimes yield take-aways that are straightforward, and at other times more nuanced. The Element ultimately argues that while these models are epistemically useful, the best way to employ most of them to understand and improve science is in combination with empirical methods and other sorts of theorizing.
Part of what distinguishes science from other ways of knowing is that scientists show their work. Yet when probed, it turns out that much of the process of research is hidden away: in personal files, in undocumented conversations, in point-and-click menus, and so on. In recent years, a movement toward more open science has arisen in psychology. Open science practices capture a broad swath of activities designed to take parts of the research process that were previously known only to a research team and make them more broadly accessible (e.g., open data, open analysis code, pre-registration, open research materials). Such practices increase the value of research by increasing transparency, which may in turn facilitate higher research quality. Plus, open science practices are now required at many journals. This chapter will introduce open science practices and provide plentiful resources for researchers seeking to integrate these practices into their workflow.
Publication bias and p-hacking are threats to the scientific credibility of experiments. If positive results are more likely to be published than null results conditional on the quality of the study design, then effect sizes in meta-analyses will be inflated and false positives will be more likely. Publication bias also has other corrosive effects as it creates incentives to engage in questionable research practices such as p-hacking. How can these issues be addressed such that the credibility of experiments is improved in political science? This chapter discusses seven specific solutions, which can be enforced by both formal institutions and informal norms.
Recent developments in psychology and related disciplines have highlighted common research practices that fail to lead to reliable and reproducible effects. In clinical psychology, the use of such practices has had negative consequences for public health. This chapter reviews the foundations of the scientific method, describes the origins and impacts of the reproducibility problem in that context, and makes recommendations for generating reproducible findings in clinical psychology.
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