Eric Loken, Ph.D.

Associate Professor of Educational Psychology University of Connecticut

  • Storrs CT

Professor Loken studies advanced statistical modeling with applications to large-scale educational testing.

Contact

University of Connecticut

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Biography

There are many exciting opportunities for applying statistical advances to research in education and health. I have ongoing work examining web-based assessment and interventions in education, as well as work looking at weight status and obesity.

Areas of Expertise

Online Testing
Educational Psychology
Educational Testing
Bayesian Inference

Education

Harvard University

Ph.D.

Developmental Psychology

2001

Harvard University

A.M.

Statistics

1997

University of Michigan

M.A.

Developmental Psychology

1994

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Social

Media

Media Appearances

The replication crisis is good for science

The Conversation  online

2019-04-08

Science is in the midst of a crisis: A surprising fraction of published studies fail to replicate when the procedures are repeated.

The Reproducibility Project, a collaboration of 270 psychologists, has attempted to replicate 100 psychology studies, while a 2018 report examined studies published in the prestigious scholarly journals Nature and Science between 2010 and 2015. These efforts find that about two-thirds of studies do replicate to some degree, but that the strength of the findings is often weaker than originally claimed.

Is this bad for science? It’s certainly uncomfortable for many scientists whose work gets undercut, and the rate of failures may currently be unacceptably high. But, as a psychologist and a statistician, I believe confronting the replication crisis is good for science as a whole.

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Scientists, fishing for significance, get a meager catch

STAT  online

2017-02-17

A pair of researchers argue in a recent issue of Science, the p-value may be doing more harm than good. Statistician Andrew Gelman, of Columbia University, and Eric Loken, a psychologist at the University of Connecticut, say scientists have bought into a “fallacy” — that if a statistically significant result emerges from a “noisy” experiment, a.k.a. one with many variables that are difficult to account for, that result is by definition a sound one.

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Articles

Measurement error and the replication crisis

Science

2017

Measurement error adds noise to predictions, increases uncertainty in parameter estimates, and makes it more difficult to discover new phenomena or to distinguish among competing theories. A common view is that any study finding an effect under noisy conditions provides evidence that the underlying effect is particularly strong and robust. Yet, statistical significance conveys very little information when measurements are noisy.

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The statistical crisis in science

The Best Writing on Mathematics

2016

There is a growing realization that reported “statistically significant” claims in scientific publications are routinely mistaken. Researchers typically express the confidence in their hypothesis using the p-value: the probability that a result at least as extreme as what was observed would occur just from random variation, if there were really nothing going on. The value of p (for “probability”) is a way of measuring the extent to which a data set provides evidence against a so-called null hypothesis.

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Effects of restriction on children’s intake differ by child temperament, food reinforcement, and parent’s chronic use of restriction Authors

Appetite

2014

Parents’ use of restrictive feeding practices is counterproductive, increasing children’s intake of restricted foods and risk for excessive weight gain. The aims of this research were to replicate Fisher and Birch’s (1999b) original findings that short-term restriction increases preschool children’s (3–5 y) selection, intake, and behavioral response to restricted foods, and to identify characteristics of children who were more susceptible to the negative effects of restriction.

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