Suchergebnisse

Respondent driven sampling (RDS) is a network sampling technique typically employed for hard-to-reach populations (e.g. drug users, men who have sex with men, people with HIV). Similar to snowball sampling, initial seed respondents recruit additional respondents from their network of friends. The recruiting process repeats iteratively, thereby forming long referral chains. Unlike in snowball sampling, it is crucial to obtain estimates of respondents' personal network size (i.e., number of acquaintances in the target population) and information about who recruited whom. Markov chain theory makes it possible to derive population estimates and sampling weights. We introduce a new Stata program for RDS and illustrate its use.

Occupation coding, an important task in official statistics, refers to coding a respondent's text answer into one of many hundreds of occupation codes. To date, occupation coding is still at least partially conducted manually, at great expense. We propose three methods for automatic coding: combining separate models for the detailed occupation codes and for aggregate occupation codes, a hybrid method that combines a duplicate-based approach with a statistical learning algorithm, and a modified nearest neighbor approach. Using data from the German General Social Survey (ALLBUS), we show that the proposed methods improve on both the coding accuracy of the underlying statistical learning algorithm and the coding accuracy of duplicates where duplicates exist. Further, we find defining duplicates based on ngram variables (a concept from text mining) is preferable to one based on exact string matches.

"Collecting biomarkers as part of general purpose surveys offers scientists - and social scientists in particular - the ability to study biosocial phenomena, e.g. the relation between genes and human behavior. The authors explore the feasibility of collecting buccal cells for genetic analyses with normal interviewers as part of a pretest for the German Socio-economic Panel Study (SOEP) using a probability sample. They introduce a new non-invasive technique for collecting cell material for genetic analyses and test its quality. They found no technical difficulties in collecting the genetic material and almost all samples collected could be analyzed. However, one third of interviewers reported it was hard to convince panel members to participate. The 'biomarker wave' showed no reduction in response rate compared to the previous wave that included no biomarkers and the sample exhibited very little selectivity. The authors conclude that collecting cell material for genetic analyses with normal interviewers is feasible with no apparent same-wave attrition, though so far we cannot rule out attrition in subsequent waves." (author's abstract)