Social scientists are now in an era of data abundance, and machine learning tools are
increasingly used to extract meaning from data sets both massive and small. We explain …

Making causal inferences regarding human behaviour is difficult given the complex interplay
between countless contributors to behaviour, including factors in the external world and our …

Machine-learning (ML) methods have gained prominence in the quantitative sciences.
However, there are many known methodological pitfalls, including data leakage, in ML …

Path models to test claims about mediation and moderation are a staple of psychology. But
applied researchers may sometimes not understand the underlying causal inference …

It is common practice in correlational or quasiexperimental studies to use statistical control to
remove confounding effects from a regression coefficient. Controlling for relevant …

Quantitative studies of racial health disparities often use static measures of self-reported
race and conventional regression estimators, which critics argue is inconsistent with social …

The random intercept cross-lagged panel model (RI-CLPM) is an extension of the traditional
cross-lagged panel model (CLPM) that allows controlling for stable trait factors when …

Behavioral researchers increasingly recognize the need for more diverse samples that
capture the breadth of human experience. Current attempts to establish generalizability …

The rapid rise of digital media use for political participation has coincided with an increase in
concerns about citizens' sense of their capacity to impact political processes. These dual …

An individual's level of education is increasingly significant in explaining their political
attitudes and behaviour, with higher education proposed as a new political cleavage …