Repetitive Model Refinement for Questionnaire Design Improvement in the Evaluation of Working Characteristics in Construction Enterprises

This paper presents an iterative confidence interval based parametric refinement approach for questionnaire design improvement in the evaluation of working characteristics in construction enterprises. This refinement approach utilizes the 95% confidence interval of the estimated parameters of the model to determine their statistical significance in a least-squares regression setting. If this confidence interval of particular parameters covers the zero value, it is statistically valid to remove such parameters from the model and their corresponding questions from the designed questionnaire. The remaining parameters repetitively undergo this sifting process until their statistical significance cannot be improved. This repetitive model refinement approach is implemented in efficient questionnaire design by using both linear series and Taylor series models to remove non-contributing questions while keeping significant questions that are contributive to the issues studied, i.e., employees’ work performance being explained by their work values and cadres’ organizational commitment being explained by their organizational management. Reducing the number of questions alleviates the respondent burden and reduces costs. The results show that the statistical significance of the sifted contributing questions is decreased with a total mean relative change of 49%, while the Taylor series model increases the R-squared value by 17% compared with the linear series model.