Sometimes in the methodological literature, models for continuous outcomes are presumed to use the Gaussian likelihood. In the sixth post of this series, we saw the gamma likelihood is a great alternative when your continuous data are restricted to positive values, such as in reaction times and bodyweight. In this ninth post, we practice making causal inferences with the beta likelihood for continuous data restricted within the range of \((0, 1)\).| A. Solomon Kurz
So far in this series, we have used the posttreatment scores as the dependent variables in our analyses. However, it’s not uncommon for researchers to frame their questions in terms of change from baseline with a change-score (aka gain score) analysis. The goal of this post is to investigate whether and when we can use change scores or change from baseline to make causal inferences. Spoiler: Yes, sometimes we can (with caveats).| A. Solomon Kurz
We social scientists love collecting ordinal data, such as those from questionnaires using Likert-type items.1 Sometimes we’re lazy and analyze these data as if they were continuous, but we all know they’re not, and the evidence suggests things can go terribly horribly wrong when you do ( Liddell & Kruschke, 2018). Happily, our friends the statisticians and quantitative methodologists have built up a rich analytic framework for ordinal data (see Bürkner & Vuorre, 2019).| A. Solomon Kurz
| xcelab.net