The (Non) Religion of Mechanical Turk Workers

Social science researchers have increasingly come to utilize Amazon's Mechanical Turk (MTurk) to obtain adult, opt‐in samples for use with experiments. Based on the demographic characteristics of MTurk samples, studies have provided some support for the representativeness of MTurk. Others have warranted caution based on demographic characteristics and comparisons of reliability. Yet, what is missing is an examination of the most glaring demographic difference in MTurk—religion. We compare five MTurk samples with a student convenience sample and the 2012 General Social Survey, finding that MTurk samples have a consistent bias toward nonreligion. MTurk surveys significantly overrepresent seculars and underrepresent Catholics and evangelical Protestants. We then compare the religiosity of religious identifiers across samples as well as relationships between religiosity and partisanship, finding many similarities and a few important differences from the general population.     

Skill Acquisition and the LISP Tutor

An analysis of student learning with the LISP tutor indicates that while LISP is complex, learning it is simple. The key to factoring out the complexity of LISP is to monitor the learning of the 500 productions in the LISP tutor which describe the programming skill. The learning of these productions follows the power-law learning curve typical of skill acquisition. There is transfer from other programming experience to the extent that this programming experience involves the same productions. Subjects appear to differ only on the general dimensions of how well they acquire the productions and how well they retain the productions. Instructional manipulations such as remediation, content of feedback, and timing of feedback are effective to the extent they give students more practice programming, and explain to students why correct solutions work.     

The lexical bias effect in bilingual speech production: Evidence for feedback between lexical and sublexical levels across languages

The lexical bias effect (LBE) is the tendency for phonological substitution errors to result in existing words (rather than nonwords) at a rate higher than would be predicted by chance. This effect is often interpreted as revealing feedback between the phonological and lexical levels of representation during speech production. We report two experiments in which we tested for the LBE (1) in second-language production (Experiment 1) and (2) across the two languages of a bilingual (Experiment 2). There was an LBE in both situations. Thus, to the extent that the LBE reveals the presence of interactivity between the phonological and the lexical levels of representation, these effects suggest that there is feedback in second-language production and that it extends across the two languages of a bilingual.