Psychological measurement and theory are afflicted with an ongoing proliferation of new constructs and scales. Given the often redundant nature of new scales, psychological science is struggling with arbitrary measurement, construct dilution, and disconnection between research groups. To address these issues, we introduce an easy-to-use online application: the Semantic Scale Network. The purpose of this application is to automatically detect semantic overlap between scales through latent semantic analysis. Authors and reviewers can enter the items of a new scale into the application, and receive quantifications of semantic overlap with related scales in the application’s corpus. Contrary to traditional assessments of scale overlap, the application can support expert judgments on scale redundancy without access to empirical data or awareness of every potentially related scale. After a brief introduction to measures of semantic similarity in texts, we introduce the Semantic Scale Network and provide best practices for interpreting its outputs.

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Why do connected users in online social networks express similar emotions? Past approaches have suggested situational emotion transfers (i.e., contagion) and the phenomenon that emotionally similar users flock together (i.e., homophily). We analyze these mechanisms in unison by exploiting the hierarchical structure of YouTube through multilevel analyses, disaggregating the video- and channel-level effects of YouTuber emotions on audience comments. Dictionary analyses using the National Research Council emotion lexica were used to measure the emotions expressed in videos and user comments from 2,083 YouTube vlogs selected from 110 vloggers. We find that video- and channel-level emotions independently influence audience emotions, providing evidence for both contagion and homophily effects. Random slope models suggest that contagion strength varies between YouTube channels for some emotions. However, neither average channel-level emotions nor number of subscribers significantly moderate the strength of contagion effects. The present study highlights that multiple, independent mechanisms shape emotions in online social networks.

Machine learning methods for prediction and pattern detection are increasingly prevalent in psychological research. We provide an introductory overview of machine learning, its applications, and describe how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-ofsample evaluation, and summarize standard prediction algorithms including linear regressions, ridge regressions, decision trees, and random forests (plus additional algorithms in the supplementary materials). We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology's status as a predictive science. 1 | INTRODUCTION Psychologists are increasingly interested in adopting powerful computational techniques from the field of machine learning to accurately predict real-world phenomena (see Yarkoni & Westfall, 2017). The current work introduces machine learning as a collection of methods and tools that can be used in prediction. We review fundamental concepts of machine learning, discuss its relationship with standard psychological methods, and give This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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