Amazon’s Mechanical Turk (MTurk) is an increasingly popular tool for the recruitment of research subjects. While there has been much focus on the demographic differences between MTurk samples and the national public, we know little about whether liberals and conservatives recruited from MTurk share the same psychological dispositions as their counterparts in the mass public. In the absence of such evidence, some have argued that the selection process involved in joining MTurk invalidates the subject pool for studying questions central to political science. In this paper, we evaluate this claim by comparing a large MTurk sample to two benchmark national samples – one conducted online and one conducted face-to-face. We examine the personality and value-based motivations of political ideology across the three samples. All three samples produce substantively identical results with only minor variation in effect sizes. In short, liberals and conservatives in our MTurk sample closely mirror the psychological divisions of liberals and conservatives in the mass public, though MTurk liberals hold more characteristically liberal values and attitudes than liberals from representative samples. Overall, our results suggest that MTurk is a valid recruitment tool for psychological research on political ideology.

When fitting any statistical model, there are many useful pieces of information that are simultaneously calculated and stored beyond coefficient estimates and general model fit statistics. Although there exist some generic functions to obtain model information and data, many package-specific modeling functions do not provide such methods to allow users to access such valuable information.

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations.