Conceptual and Statistical Knowledge

Effect Sizes: Why, When, and How to Use Them

The effect size (ES) is the magnitude of a study outcome or research finding, such as the strength of the relationship obtained between an independent variable and a dependent variable. Two types of ES indicators are sampled here: the difference-type …

Emerging Scientific Research Practices

This course aims to introduce students to current controversies and new developments in recommended scientific practices. The course is meant to help students think critically about how to conduct better empirical research and how to draw …

Erroneous analyses of interactions in neuroscience: a problem of significance.

In theory, a comparison of two experimental effects requires a statistical test on their difference. In practice, this comparison is often based on an incorrect procedure involving two separate tests in which researchers conclude that effects differ …

Evading Open Science: The Black Box of Student Data Collection

While Open Science has arguably initiated positive changes at some stages of the research process (e.g., increasing transparency through preregistration), problematic behaviors during data collection are still almost impossible to detect and pose a …

Evaluating Content-Related Validity Evidence Using a Text-Based Machine Learning Procedure

Validity evidence based on test content is critical to meaningful interpretation of test scores. Within high-stakes testing and accountability frameworks, content-related validity evidence is typically gathered via alignment studies, with panels of …

Evaluating the R-Index and the P-Curve

This blog evaluates the R-Index and the P-Curve

Evolution of Reporting P Values in the Biomedical Literature, 1990-2015

Importance: The use and misuse of P values has generated extensive debates. Objective: To evaluate in large scale the P values reported in the abstracts and full text of biomedical research articles over the past 25 years and determine how frequently …

Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power.

Traditional methods of analyzing data from psychological experiments are based on the assumption that there is a single random factor (normally participants) to which generalization is sought. However, many studies involve at least two random factors …

Exploratory Factor Analysis

This book provides a non-mathematical introduction to the underlying theory of Efa and reviews the key decisions that must be made in its implementation. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the …

Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

Preregistration has been proposed as a useful method for making a publicly verifiable distinction between confirmatory hypothesis tests, which involve planned tests of ante hoc hypotheses, and exploratory hypothesis tests, which involve unplanned …