Data collection is expensive, and we often only get one bite at the apple. In response, we often conduct an inexpensive (and small) pilot test to help better design the study. Pilot studies have many virtues, including practicing the logistics of data collection and improving measurement tools. But using pilots to get noisy estimates in order to determine sample sizes for scale up comes with risks.
We’re in an observational study setting in which treatment assignment was not controlled by the researcher. We have pre-treatment data on baseline outcomes and we’d like to incorporate them, mainly to decrease bias due to confounding and but also, ideally, to increase precision. One approach is to use the difference between pre and post outcomes as the outcome variable; another is to use the baseline data as a control. Which is better?
Mostly we use design diagnostics to assess issues that arise because of design decisions. But you can also use these tools to examine issues that arise after implementation. Here we look at risks from publication bias and illustrate two distinct types of upwards bias that arise from a “significance filter.” A journal for publishing null results might help, but the results in there are also likely to be biased, downwards.
We’ll be back on January 7 – Happy New Year!
In designs in which a treatment is assigned in clusters (e.g. classrooms), it’s usual practice to account for cluster-level correlations when you generate estimates of uncertainty about estimated effects. But units often share commonalities at higher levels, such as at a block level (e.g. schools). Sometimes you need to take account of this and sometimes you don’t. We show an instance of the usual procedure of clustering by assignment cluster (classrooms) working well and show how badly you can do with a more conservative approach (clustering by schools). We then show an example of a design in which clustering at the level of treatment assignment (classroom) is not good enough; in the troublesome example, schools are thought of as being sampled from a larger population of schools and treatment effects are different in different schools. In this case, if you want estimates of uncertainty for population level effects you have to cluster at the school level even though treatment is assigned within schools.