Does one size fit all? The suitability of answer formats for different constructs measured

Survey research is used to investigate a variety of different constructs, such as beliefs, behavioural intentions, perceptions, preferences and so on. Despite the wide range of constructs studied by social scientists, the ordinal answer format tends to be used across the majority of survey research studies. We challenge this standard approach in survey research by hypothesizing that the ordinal answer format is not optimal under all circumstances. Instead, we propose that the suitability of answer formats depends on the construct measured. We conduct a repeat measurement study using binary, ordinal and metric answer formats measuring two different constructs: beliefs and behavioural intentions. A clear interaction effect between answer formats and constructs is revealed. This supports the notion that no single answer format is optimal for all research problems, but that some constructs are naturally more suitable for certain answer formats than others. These findings call for increased use of pre-studies to determine the optimal answer format before fieldwork is conducted rather than relying on standard answer formats

On the optimal number of scale points in graded paired comparisons

In market research, it is common practice to measure individuals’ brand or product preference through graded paired comparisons (GPCs). One important decision concerns the (odd) number of scale points (e.g., five, seven, nine, or eleven) that has to be assigned to either brands or products in each pair. Using data from an experiment with 122 students, we assessed the extent to which GPCs with a higher number of scale points (requiring more cognitive effort) really outperform GPCs with a smaller number of scale points (requiring less cognitive effort). Our data analyses have shown that one may reduce the (odd) number of scale points from eleven to nine or seven, depending on what minor compromises one is willing to make. The detailed psychometric results presented in this paper are useful to applied researchers as they help them in making well-informed decisions on the number of scale points in a GPC task