Challenges that surveys are facing are increasing data collection costs and declining budgets. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done into the effect on quality and costs of reducing the use of interviewers in mixed-mode surveys starting with internet observation, followed by telephone or face-to-face observation of internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. Currently, adaptive survey design is an option in redesigns of social surveys at Statistics Netherlands. In 2018 it has been implemented in the Health Survey and the Public Opinion Survey, in 2019 in the Life Style Monitor and the Leisure Omnibus, in 2021 in the Labour Force Survey, and in 2022 it is planned for the Social Coherence Survey. This paper elaborates on the development of the adaptive survey design for the Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and the six-month parallel design with corresponding response results.
In December of 2004 the Directorate General for Research and Technological Development (DG RTD) of the European Commission (EC) set up a High-Level Expert Group to propose a series of measures to stimulate the reporting of Intellectual Capital in research intensive Small and Medium-Sized Enterprises (SMEs). The Expert Group has focused on enterprises that either perform Research and Development (R&D), or use the results of R&D to innovate and has also considered the implications for the specialist R&D units of larger enterprises, dedicated Research & Technology Organizations and Universities. In this report the Expert Group presents its findings, leading to six recommendations to stimulate the reporting of Intellectual Capital in SMEs by raising awareness, improving reporting competencies, promoting the use of IC Reporting and facilitating standardization.
This study examines completion rate for a self-assessment survey designed to assess employees' digital skills levels in the workplace. The aim is to improve data quality by investigating completion of the survey. The study reviews the theoretical background related to self-assessment surveys and completion rate, and explores the influence of survey length and format in survey design on completion rate. The research design and data analysis are described in detail, with a focus on identifying factors that may influence completion rate. Results suggest that survey designers should consider using Likert scales to optimize completion rate and completion time. However, this study did not find a significant increase in completion rate as a result of motivation, which was claimed from the literature. The study concludes with implications for the design and implementation of self-assessment surveys in the workplace, including the importance of reducing length and complexity of survey items and questions.
