Amazon Mechanical Turk in Organizational Psychology: An Evaluation and Practical Recommendations

  • Janelle H. Cheung
  • Deanna K. Burns
  • Robert R. Sinclair
  • Michael Sliter
Original Paper

DOI: 10.1007/s10869-016-9458-5

Cite this article as:
Cheung, J.H., Burns, D.K., Sinclair, R.R. et al. J Bus Psychol (2016). doi:10.1007/s10869-016-9458-5

Abstract

Purpose

Amazon Mechanical Turk is an increasingly popular data source in the organizational psychology research community. This paper presents an evaluation of MTurk and provides a set of practical recommendations for researchers using MTurk.

Design/Methodology/Approach

We present an evaluation of methodological concerns related to the use of MTurk and potential threats to validity inferences. Based on our evaluation, we also provide a set of recommendations to strengthen validity inferences using MTurk samples.

Findings

Although MTurk samples can overcome some important validity concerns, there are other limitations researchers must consider in light of their research objectives. Researchers should carefully evaluate the appropriateness and quality of MTurk samples based on the different issues we discuss in our evaluation.

Implications

There is not a one-size-fits-all answer to whether MTurk is appropriate for a research study. The answer depends on the research questions and the data collection and analytic procedures adopted. The quality of the data is not defined by the data source per se, but rather the decisions researchers make during the stages of study design, data collection, and data analysis.

Originality/Value

The current paper extends the literature by evaluating MTurk in a more comprehensive manner than in prior reviews. Past review papers focused primarily on internal and external validity, with less attention paid to statistical conclusion and construct validity—which are equally important in making accurate inferences about research findings. This paper also provides a set of practical recommendations in addressing validity concerns when using MTurk.

Keywords

Amazon Mechanical Turk MTurk Validity Best practices Recommendations 

Supplementary material

10869_2016_9458_MOESM1_ESM.docx (64 kb)
Supplementary material 1 (DOCX 48 kb)

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Janelle H. Cheung
    • 1
  • Deanna K. Burns
    • 1
  • Robert R. Sinclair
    • 1
  • Michael Sliter
    • 2
  1. 1.Department of PsychologyClemson UniversityClemsonUSA
  2. 2.FurstPersonChicagoUSA

Personalised recommendations