eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2008-09-01 4 2 46 50 article Howard H. Lee hhl@ncsu 1 California State University, Northridge The Chow Test (Chow, 1960) is a method well known in econometrics. It was originally designed to analyze the same variables obtained in two different data sets to determine if they were similar enough to be pooled together. Regression discontinuity design is a variation of the two-group pre-test-post-test design. The usual method of data analysis for data collected using this design is multiple regression with one dummy coded variable representing the cut-off value. This article discusses the use of the Chow Test on data obtained in a regression discontinuity study. http://www.tqmp.org/Content/vol04-2/p046/p046.pdf Statistics Regression Regression discontinuity spa TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2008-09-01 4 2 51 60 article Rubén Ledesma rdledesma@gmail.com 1 Universidad Nacional de Mar del Plata, Argentina El bootstrap es un tipo de técnica de remuestreo de datos que permite resolver problemas relacionados con la estimación de intervalos de confianza o la prueba de significación estadística. Este enfoque puede resultar de interés para los investigadores en Psicología, no solo porque es menos restrictivo que el enfoque estadístico clásico, sino también porque es más general en su formulación y más simple de comprender en lo referente al procedimiento básico que subyace al método. En lugar de fórmulas o modelos matemáticos abstractos, el bootstrap simplemente requiere un ordenador capaz de simular un proceso de muestreo aleatorio de los datos. Sin embargo, y debido quizás a la escasa difusión de la técnica, los investigadores aún no han incorporado el bootstrap al repertorio habitual de herramientas de análisis de datos. En este trabajo realizamos una presentación conceptual del bootstrap, ilustramos la técnica mediante un ejemplo y revisamos algunas opciones disponibles en materia de software estadístico. El trabajo incluye además un programa para correr el ejemplo dentro de ViSta “The Visual Statistics System”, un sistema estadístico gratuito y abierto. http://www.tqmp.org/Content/vol04-2/p051/p051.pdf Stastitics Boostrap eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2008-09-01 4 2 61 64 article Richard D. Morey moreyr@missouri.edu 1 UUniversity of Missouri-Columbia Presenting confidence intervals around means is a common method of expressing uncertainty in data. Loftus and Masson (1994) describe confidence intervals for means in within-subjects designs. These confidence intervals are based on the ANOVA mean squared error. Cousineau (2005) presents an alternative to the Loftus and Masson method, but his method produces confidence intervals that are smaller than those of Loftus and Masson. I show why this is the case and offer a simple correction that makes the expected size of Cousineau confidence intervals the same as that of Loftus and Masson confidence intervals. http://www.tqmp.org/Content/vol04-2/p061/p061.pdf Stastitics Confidence intervals eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2008-09-01 4 2 65 78 article Sylvain Chartier sylvain.chartier@uottawa.ca 1 Andrew Faulkner af@uottawa.ca 1 University of Ottawa Generally, in psychology, the various statistical analyses are taught independently from each other. As a consequence, students struggle to learn new statistical analyses, in contexts that differ from their textbooks. This paper gives a short introduction to the general linear model (GLM), in which it is showed that ANOVA (one-way, factorial, repeated measure and analysis of covariance) is simply a multiple correlation/regression analysis (MCRA). Generalizations to other cases, such as multivariate and nonlinear analysis, are also discussed. It can easily be shown that every popular linear analysis can be derived from understanding MCRA. http://www.tqmp.org/Content/vol04-2/p065/p065.pdf Stastitics General linear model