Graduate training in statistics, methodology, and measurement in psychology: A survey of PhD programs in North America.

Aiken, Leona S., West, Stephen G., Sechrest, Lee, Reno, Raymond R.·1990·American Psychologist

Abstract

A survey of all PhD programs in psychology in the United States and Canada assessed the extent to which advances in statistics, measurement, and method- ology have been incorporated into doctoral training. In all, 84% of the 222 departments responded. The statistical and methodological curriculum has advanced little in 20 years; measurement has experienced a substantial decline. Typical first-year courses serve well only those students who undertake traditional laboratory research. Training in top-ranked schools differs little from that in other schools. New PhDs are judged to be competent to handle traditional techniques, but not newer and often more useful procedures, in their own research. Proposed remedies for these deficiencies include revamping the basic required quantitative and methodological curriculum, culling available training opportunities across campus, and training students in more informal settings, along with providing retraining opportunities for faculty. These strat- egies also require psychology to attend carefully to the human capital needs that support high-quality quantitative and methodological training and practice.

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This page
CoChat. (2026). Citation check: Graduate training in statistics, methodology, and measurement in psychology: A survey of PhD programs in North America. https://app.cochat.ai/papers/10.1037//0003-066x.45.6.721
The paper
Aiken, L. S., West, S. G., Sechrest, L., & Reno, R. R. (1990). Graduate training in statistics, methodology, and measurement in psychology: A survey of PhD programs in North America. American Psychologist. https://doi.org/10.1037//0003-066x.45.6.721

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.