Item non-response in open-ended questions: Who does not answer on the meaning of left and right?

The left-right ideological dimension plays a central role in political science research. The meaning of left and right is often based on analyses of responses to open-ended survey questions. In the 2008 German General Social Survey, item non-response to two particular open-ended questions on the meaning of left and right was considerable and calls for analyses of the value of the data have been received. In our paper we investigate item non-response to these two open-ended questions in terms of cognitive abilities, motivational aspects, and finally, in relation to the left-right self-placement scale itself. Item non-response in the open-ended questions is not based on randomness alone, and analyses show some significant systematic results. Thus, the open-ended questions seem to be biased and the validity of this data is questionable in some respect. In this context, left-right self-placement of non-respondents and “cognitive don’t knowers” need to be given careful consideration.     

From ‘What the F#@% is a Facebook?’ to ‘Who Doesn’t Use Facebook?’: The role of criminal lifestyles in the adoption and use of the Internet

Concerns about a digital divide persist and recent calls have been made for understanding how lifestyles influence Internet adoption and use. Online criminal behaviors have drawn attention from law enforcement, but diffusion of innovation theory suggests higher propensities for crime, particularly street crime, reduces the likelihood of Internet use. Drawing from this theory and research on the role of street criminal lifestyles on technology adoption, this study examined patterns of Internet use among a sample of 585 individuals at-risk for and involved in street crime. Results from our logistic and negative binomial regression analyses lead to two general conclusions: (1) compared to research on the general population, similar predictors and lower rates of Internet participation and usage are observed, and (2) mixed evidence suggests participation in criminal lifestyles contributes to digital inequality. The results support a theory of technological diffusion to marginalized populations. We conclude by discussing the expansion of technology, digital inequality, and crime.