The Standards Committee of the Experimental Research Section of the American Political Science Association has produced reporting guidelines that aim to increase the clarity of experimental research reports. This paper describes the Committee's rationale for the guidelines it developed and includes our Recommended Reporting Standards for Experiments (Laboratory, Field, Survey). It begins with a content analysis of current reporting practices in published experimental research. Although researchers report most important aspects of their experimental designs and data, we find substantial omissions that could undermine the clarity of research practices and the ability of researchers to assess the validity of study conclusions. With the need for reporting guidelines established, the report describes the process the Committee used to develop the guidelines, the feedback received during the comment period, and the rationale for the final version of the guidelines.

Legislator preferences are typically represented as measures of general ideology estimated from roll call votes on legislation, potentially masking important nuances in legislators' political attitudes. In this paper we introduce a method of measuring more specific legislator attitudes using an alternative expression of preferences: tweeting. Specifically, we present an embedding-based model for predicting the frequency and sentiment of legislator tweets. To illustrate our method, we model legislators' attitudes towards President Donald Trump as vector embeddings that interact with embeddings for Trump himself constructed using a neural network from the text of his daily tweets. We demonstrate the predictive performance of our model on tweets authored by members of the U.S. House and Senate related to the president from November 2016 to February 2018. We further assess the quality of our learned representations for legislators by comparing to traditional measures of legislator preferences.

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