Minimizing Social Desirability in Questionnaires of Non-Cognitive Measurements

Abstract

Data obtained through questionnaires sometimes respond to the items presented by social norms, so sometimes they do not suit themselves. High social desirability (SD) in non-cognitive measurements will cause item bias. Several ways are used to reduce item bias, including freeing respondents from not writing their names or being anonymous, explaining to the participants to respond to each statement honestly, as they are or according to themselves, and responding to the questionnaire online or offline. This research aims to prove that several methods can minimize the possibility of item bias SD and academic dishonesty (AD). The research was carried out with an experimental study using a factorial design. There were 309 respondents who were willing to be involved in this research. Data analysis was carried out using multivariate ANOVA. The research results show differences for all variables, Self-Deceptive Enhancement (SDE), Impression Management (IM), and AD in the anonymous group. There are differences in AD in the groups that provide a complete explanation and do not explain, and there is an interaction between the average AD based on the anonymous and explanation group.