Using Nonnaive Participants Can Reduce Effect Sizes.

Although researchers often assume their participants are naive to experimental materials, this is not always the case. We investigated how prior exposure to a task affects subsequent experimental results. Participants in this study completed the same set of 12 experimental tasks at two points in time, first as a part of the Many Labs replication project and again a few days, a week, or a month later. Effect sizes were markedly lower in the second wave than in the first. The reduction was most pronounced when participants were assigned to a different condition in the second wave. We discuss the methodological implications of these findings.

The pen is mightier than the keyboard: advantages of longhand over laptop note taking.

Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students' capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers' tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning.

Nonnaïveté among Amazon Mechanical Turk workers: consequences and solutions for behavioral researchers.

Crowdsourcing services--particularly Amazon Mechanical Turk--have made it easy for behavioral scientists to recruit research participants. However, researchers have overlooked crucial differences between crowdsourcing and traditional recruitment methods that provide unique opportunities and challenges. We show that crowdsourced workers are likely to participate across multiple related experiments and that researchers are overzealous in the exclusion of research participants. We describe how both of these problems can be avoided using advanced interface features that also allow prescreening and longitudinal data collection. Using these techniques can minimize the effects of previously ignored drawbacks and expand the scope of crowdsourcing as a tool for psychological research.