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From the perspective of study management, research is surprisingly lacking on two of the major challenges for planning and performing content analysis: determining the sample size (i.e. number of objects to code) and the required number of coders to hire. The optimization of both of these numbers will ultimately determine how efficiently available resources are used. This study contributes to the methodological discussion on coding by identifying and conceptualizing the role of learning effects with respect to the coding task and by highlighting the importance of considering the coding process when managing a content analysis. We present empirical evidence for the existence and impact of learning effects on coders’ coding speed. Accordingly, study management should take account of learning effects when determining the sample size and number of coders. We also provide an illustrative example of how learning effects can impact the results of pretests.

Propensity score weighting often is used to correct for attrition biases in panel surveys. While methodological literature exists on the logic of propensity score weighting and its practical applic...

In this article, we present the results of three question design experiments on inter-item correlations, which tested a grid design against a single-item design. The first and second experiments examined the inter-item correlations of a set with five and seven items, respectively, and the third experiment examined the impact of the question design on inter-item correlations of follow-up questions. We found that the inter-item correlations in grid designs were higher for both the set of five and seven items than for questions in a single-item design. Additionally, the inter-item correlations of the set of follow-up questions were higher if the preceding set of questions was presented in a grid. According to the findings of the present research, respondents seem to apply the interpretative near means related heuristic when answering grid questions, and also when answering follow-up questions preceeded by grid questions.