Relationship Between the Level of Non-Responses in Personal Interviews and the Type of Housing of the Respondent (on the Example of Analysis of Paradata ESS)




paradata, ESS, metadata


Collection and analysis of respondents in mass surveys can improve the quality of information obtained, in particular by reducing the level of non-response. The study of paradata allows us to find out how those who refuse to participate in the study differ from the participants that agreed. This allows researchers to improve the interviewer's guide and develop an individual approach to each respondent. The importance of measuring the respondent's life circumstances according to the results of the ESS study was revealed, including the influence of the respondent's real estate characteristics on the probability of participating in the interview. On the example of ESS, the methodological documents of the research and the array of counselors were analyzed in order to investigate how characteristics of apartment affect the level of non-response in the ESS study.

The analysis was based on paradata and a guide for the ESS 2010 (ESS-5) and 2018 (ESS-9) interviewers in Germany. It was found that the methodological and accompanying documents of the German ESS have undergone changes that should solve the research problems and should help the interviewer to determine correctly the characteristics of the respondent's home and the respondent's commitment to the interviewer. The interview plan and the interviewer's instructions include all the respondent's answers and follow-up, they still do not have written instructions for the respondents depending on the type of house in which he lives. The importance of parada has been emphasized in previous ESS studies, but despite this, the influence of paradata on the level of non-response is not raised and is not taken into account in the ESS study of almost all countries.

To analyze the impact of the respondent's real estate characteristics on the probability of non-response to the questionnaire, a binary logistic regression was constructed. It was found that the variables "house type" and "house condition". only partially explain the possibility of establishing contact with a potential respondent.


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