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Online Program

Specific mixed-mode methodology to reach sensory disabled people in quantitative surveys

*Sébastien Fontaine, University Of Liège Belgium 

Keywords: Mixed-mode, Quantitative Surveys, Disabled, Deaf, Blind

The deaf and dumb are a little known “hard-to-reach” population. In Belgium, the number of deaf and hard of hearings is estimated around 400.000 people (within 40.000 complete deaf). In the European Social Survey (one of the largest social surveys designed to chart and explain the interaction between Europe's changing institutions as well as the attitudes, beliefs and behavior patterns of its diverse populations), most of the sensory disabled people will not be included in the final databases of respondents. Following the strict contact procedure, some of them will be classified as “ineligibles”, if they live in an institution, or they will be classified as “nonresponses” with a specific code, because they cannot participate to the opinion survey following the usual way (face to face). This has different consequences on the response rate calculation, but the general consequence for sensory disabled people is that they are not represented in the European Social Survey data collection. It is highly probable that “ineligible” and “non-response because of a sensory handicap” are not “at random”, and that a strong link exists between non-participation to surveys and the responses given to opinion surveys. Psychological and sociological studies have often proved that there are differences between disabled and non-disabled people on subjects like participation, citizenship, social inclusion, but also happiness and quality of life, and this is the core of the ESS questionnaire. We will present some mixed-mode techniques: web-based questionnaires and face-to-face interviews with specially trained interviewers (sign language) that could be integrated in fieldwork procedure to organize general opinion surveys among this kind of population. We will also show some results, some group comparisons and also examples of bias reduction.

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