Anticipating, avoiding, and alleviating measurement error: A synthesis of the literature with practical recommendations




Measurement, construct, indicator, model, operationalisation


Researchers’ ability to draw inferences from their empirical work hinges on the degree of measurement error. The literature in Information Systems and other behavioural disciplines describes a plethora of sources of error. While it helps researchers deal with them when taking specific steps in the measurement process, like modelling constructs, developing instruments, collecting data, and analysing data, it does not provide an overall guide to help them prevent and deal with measurement error. This paper presents a synthesis of the insights in the literature through a decomposition of the logic of measurement. It shows how researchers can classify sources of error, evaluate their impact, and refine their measurement plans, in terms of specific steps or overall measurement approaches. We hope this will aid researchers in anticipating, avoiding, and alleviating error in measurement, and in drawing valid research conclusions.

Author Biographies

Sander Paul Zwanenburg, University of Otago

Department of Information Science, Lecturer

Israr Qureshi, Professor of Social Entrepreneurship and ICTD Australian National University Research School of Management

Israr Qureshi is a Professor at Research School of Management, Australian National University. His research investigates various aspects of social value creation through electronic commerce, social entrepreneurship, and information and communication technology and has been published in Academy of Management Learning and Education, Journal of Business Ethics, Journal of Management, Journal of Management Studies, Journal of Organization Behavior, MIS Quarterly, Organizational Research Methods, Organization Studies, among others.




How to Cite

Zwanenburg, S. P., & Qureshi, I. (2019). Anticipating, avoiding, and alleviating measurement error: A synthesis of the literature with practical recommendations. Australasian Journal of Information Systems, 23.



Selected Papers from the Australasian Conference on Information Systems (ACIS)