Even modestly large (< 1 GB) datasets can quickly overwhelm modern personal computers. Working with such datasets in R can be still more frustrating because of how R uses memory. Here are a few tips on how to work with modestly large datasets in R. Setting Memory Limits On Windows,

Deliberative Poll proceeds as follows: Respondents are surveyed, provided ‘balanced’ briefing materials, randomly assigned to moderated small group discussions, allowed the opportunity to quiz experts or politicians in plenary sessions, and re-interviewed at the end. The “effect” is conceptualized as the average Post–Pre across all participants. The effect of

Say there are two datasets—one that carries attitudinal variables, and demographic variables (dataset 1), and another that carries just demographic variables (dataset 2). Also assume that Dataset 2 is the more accurate and larger dataset for demographics (e.g. CPS). Our goal is to weight a dataset (dataset 3)

Achieving statistical significance is entirely a matter of sample size. In the frequentist world, we can always distinguish between two samples if we have enough data (except of course if the samples are exactly the same). On the other hand, we may fail to reject even large differences when sample

Consider the following scenario: control group is 50% female while the participant sample is 60% female. Also, assume that this discrepancy is solely a matter of chance and that the effect of the experiment varies by gender. To estimate the effect of the experiment, one needs to adjust for the