The following is in response to a question on the R-Help list. Consider two datasets: reported <- structure(list(Product = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 3L, 4L, 5L, 5L), .Label = c("Cocoa", "Coffee C", "GC", "Sugar No 11"

Differential measurement error across control and treatment groups or in a within-subjects experiment, pre- and post-treatment measurement waves, can vitiate estimates of treatment effect. One reason for differential measurement error in surveys is differential motivation. For instance, if participants in the control group (pre-treatment survey) are less motivated to respond

Lacking direct measures of the theoretical variable of interest, some rely on “proxy variables.” For instance, some have used years of education as a proxy for cognitive ability. However, using “proxy variables” can be problematic for the following reasons: (1) proxy variables may not track the theoretical variable of interest

Survey datasets typically require a fair bit of repetitive recoding of variables. Reducing errors in recoding can be done by writing functions carefully (see some tips here) and automating and systematizing naming, and application of the recode function (which can be customized) – fromlist <- c("var1", "

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