New publication: Advancing Research Methods with New Technologies, edited by Natalie Sappleton, published in 2013 with IGI Global
Advancing Research Methods with New Technologies examines the applicability and usefulness of new technologies, as well as the pitfalls of these methods in academic research practices. This book serves as a practical guide for designing and conduction research projects for scientists all of disciplines ranging from graduate students to professors and practitioners. Advancing digital technologies continue to shape all aspects of our society, with particular impact on the professional research community. These new and exciting developments offer considerable advantages in terms of speed, access connectivity, and economy.
One chapter is Measuring Wages Worldwide: Exploring the Potentials and Constraints of Volunteer Web Surveys, written by Stephanie Steinmetz (University of Amsterdam, The Netherlands), Damian Raess (University of Geneva, Switzerland), Kea Tijdens (University of Amsterdam, The Netherlands) and Pablo de Pedraza (University of Salamanca, Spain). This chapter discusses the potentials and constraints of using a volunteer Web survey as a worldwide data collection tool for wages. It provides a detailed description of the bias related to individual-level wages and core socio-demographic and employment-related variables across selected developed and developing countries and evaluates the efficiency of post-stratification weights in adjusting these biases. The results confirm that Web samples are particularly attractive to younger persons, part-timers, and persons working in non-manual occupations. This can be observed across countries, although the strength of the bias differs between them. With respect to the efficiency of post-stratification weights, the results are inconclusive. Whereas it is advisable to implement weights for descriptive purposes of socio-demographic variables, the contrary holds in case of wages. Additionally, weights can have the opposite effect by (moderately) increasing the difference in the estimated parameters between the reference and the Web sample.