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Author: Dr. Ranjit K. Singh

Is a postdoc at the Survey Design and Methodology Department of GESIS. There he practices, researches, and consults on the ex-post harmonization of survey instruments and survey data. Further research interests are survey instrument quality and self-control.

Trust, but verify: Harmonization with dedicated control variables

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 …

16/06/2021 Dr. Ranjit K. Singh

Swiss cheese and MICE: Harmonizing instruments with multiple imputation

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 …

26/04/2021 Dr. Ranjit K. Singh

Cats are liquids: Equipercentile equating of different instruments

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. …

26/03/2021 Dr. Ranjit K. Singh

The new normal: Linear equating of different instruments

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 …

28/02/2021 Dr. Ranjit K. Singh

(Not) by any stretch of the imagination: A cautionary tale about linear stretching

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 …

19/01/2021 Dr. Ranjit K. Singh

Ceci n’est pas une pipe: Disentangling measurement and reality in ex-post harmonization

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 …

21/12/2020 Dr. Ranjit K. Singh

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