Error, Error in My Model, Who’s the Fairest Error of Them All?

Abstract

We examine some key errors that influence the validity of scientific inquiry in the social and behavioral sciences, emphasizing the need for researchers to be attuned to the varied sources of error. We discuss error beyond simple inferential processes inherent in null hypotheses testing, for example. and broaden the scope of influences in the modeling process, from model specification, to model evaluation, modification, and comparison as well as the dissemination process. From this broadened perspective on the myriad possible sources of error, error management becomes a multipronged endeavor that involves all persons contributing to the peer-review bedrock of scientific inquiry.

Keywords:

• Structural equation modeling
• error management
• philosophy of science

FUNDING

This work was supported in part by the Institute for Measurement, Methodology, Analysis, and Policy (Todd D. Little, Director) at Texas Tech University.

Additional information

Funding

This work was supported in part by the Institute for Measurement, Methodology, Analysis, and Policy (Todd D. Little, Director) at Texas Tech University.