In this article, we highlight three points. First, we counter Grant and Lebo's claim that the error correction model (ECM) cannot be applied to stationary data. We maintain that when data are properly stationary, the ECM is an entirely appropriate model. We clarify that for a model to be properly stationary, it must be balanced. Second, we contend that while fractional integration techniques can be useful, they also have important weaknesses, especially when applied to many time series typical in political science. We also highlight two related but often ignored complications in time series: low power and overfitting. We argue that the statistical tests used in time-series analyses have little power to detect differences in many of the sample sizes typical in political science. Moreover, given the small sample sizes, many analysts overfit their time-series models. Overfitting occurs when a statical model describes random error or noise instead of the underlying relationship. We argue that the results in the Grant and Lebo replications could easily be a function of overfitting.

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all too easy to computationally classify texts without attending to the methodological choices therein.

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