eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2009-03-01 5 1 1 10 article Natalia Jaworska njawo040@uottawa.ca 1 Angelina Chupetlovska-Anastasova achupet@yahoo.com 1 University of Ottawa This paper aims to provide a non-technical overview of multidimensional scaling (MDS) so that a broader population of psychologists, in particular, will consider using this statistical procedure. A brief description regarding the type of data used in MDS, its acquisition and analyses via MDS is provided. Also included is a commentary on the unique challenges associated with assessing the output of MDS. Our second aim, by way of discussing representative studies, is to highlight and evaluate the utility of this method in various domains in psychology. http://www.tqmp.org/Content/vol05-1/p001/p001.pdf Stastitics Multidimensional scaling eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2009-03-01 5 1 11 24 article Heather Andruff heather.andruff@uottawa.ca 1 Natasha Carraro natasha_carraro@yahoo.com 1 Amanda Thompson amanda_t_11@hotmail.com 1 Patrick Gaudreau patrick.gaudreau@uottawa.ca 1 Benoît Louvet benoit.louvet@univ-rouen.fr 2 University of Ottawa Université de Rouen The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi-parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases. The analysis identifies distinct subgroups of individuals following a distinct pattern of change over age or time on a variable of interest. The aim of the present tutorial is to introduce readers to LCGM and provide a concrete example of how the analysis can be performed using a real-world data set and the SAS software package with accompanying PROC TRAJ application. The advantages and limitations of this technique are also discussed. http://www.tqmp.org/Content/vol05-1/p011/p011.pdf Stastitics Latent class eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2009-03-01 5 1 25 34 article Rubén Daniel Ledesma rdledesma@gmail.com 1 Guillermo Macbeth guillermo.macbeth@mail.salvador.edu.ar 2 Nuria Cortada de Kohan ncortada@psi.uba.ar 3 Universidad Nacional de Mar del Plata Universidad del Salvador, Argentina Universidad de Buenos Aires, Argentina Effect size measures are recognized as a necessary complement to statistical hypothesis testing because they provide important information that such tests alone cannot offer. In this paper we: a) briefly review the importance of effect size measures, b) describe some calculation algorithms for the case of the difference between two means, and c) provide a new and easy-to-use computer program to perform these calculations within ViSta “The Visual Statistics System”. A worked example is also provided to illustrate some practical issues concerning the interpretation and limits of effect size computation. The audience for this paper includes novice researchers as well as ViSta’s user interested on applying effect size measures. http://www.tqmp.org/Content/vol05-1/p025/p025.pdf Stastitics effect size means difference eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2009-03-01 5 1 35 39 article Jim Lemon jim.lemon@unsw.edu.au 1 National Drug and Alcohol Research Centre, Australia The assumptions underlying the categorization of numeric measurements are examined and it is concluded that some numeric data that are measured by categories might better be obtained by direct estimates. Statistical tests are performed on artificially generated data of normal, triangular and empirically measured distributions, and on various categorizations of these data. It is shown that categorization can markedly affect the outcome of significance tests, in some cases leading to both Type I and Type II errors. When high local densities of values are numerically separated by categorization, test statistics can be substantially inflated from the uncategorized values. It is recommended that response categorization be subjected to the same critical analysis as data transformation techniques like arbitrary dichotomization. http://www.tqmp.org/Content/vol05-1/p035/p035.pdf Stastitics Conversion to a categorical scale