In the finale of season 1 of the blog series, I explore the idea to craft control variables that help us mitigate quality and comparability issues in our source surveys. Embedding such control variables into our ex-post harmonized datasets creates a living documentation of our source material and our harmonization …
This time, we apply multiple imputation to harmonize data for the same construct measured different instruments. We will treat data as swiss cheese and then unleash mice, sorry MICE, on it. The approach will pose some hurdles regarding the required data and the analysis complexity. However, if those hurdles are …
Equipercentile equating is an alternative version of observed score equating that can accommodate non-normal response distributions. It corrects for differences in mean and standard deviation but also higher distribution moments, such as skewness and kurtosis. This helps harmonize instruments, where, for example, respondents mostly choose high (or low) response options. …
As an alternative to linear stretching, we look into the observed score equating approach in this post and the next. The easiest form of observed score equating is linear equating: a powerful approach that corrects for biases in the mean and standard deviation while harmonizing different instruments. Als Alternative zu …
Linear stretching is a frequently used approach to combine data from response scales with different numbers of response options. In linear stretching, the scales’ minimum and maximum scores are set as equal, respectively, and all values in between are spread with equal distances within this range. However, while temptingly easy …
The scores in our dataset are not reality itself; they are glimpses at reality through the lens of the respective measurement instrument. In research practice, that distinction sometimes takes a backseat. However, if we want to combine numerical scores of different instruments, then the relationship between measurement and reality becomes …