Training and Events
General Linear Model with applications to ANOVA, Regression Analysis and Factor Analysis
Dr John Mallett, Prof Gary Adamson and Prof Jamie Murphy
04/09/2019 - 06/09/2019
View in Google Maps (BT52 1SA)
Synopsis of the course
This three day short course provides participants with a firm working knowledge of a wide range of statistical models – many of which are the most commonly used statistical models in the behavioural and social sciences. These models also serve as the fundamental building blocks for advanced statistical models and will be particularly useful for those participants wishing to take more advanced short-courses e.g. the Latent Variable Modelling course.
The course begins by exploring the general linear model and its application in Anova, Ancova, Manova and Mancova with repeated measures models. The short-course will describe simple bivariate regression and correlation and build gradually to the multivariate case, which incorporates a number of predictor variables. In addition to examining regression models with a continuous outcome variable, time will be devoted to data situations in which the outcome variable is either dichotomous or polytomous, i.e. binary and multinomial logistic regression models. Moreover, exploratory factor analysis (EFA) will be covered in some depth, with the focus on its usefulness as a data reduction method: the EFA model primarily involve reducing a large number of observed variables to a lesser number of latent factors, the purpose of which is to explain the structural relationship between the observed variables parsimoniously. The short-course will conclude with an introduction to the Confirmatory Factor Analysis models and its applications using advanced statistical software. The assumptions underpinning the use of all techniques will be considered throughout the short-course, together with identifying some strategies for assessing potential violations.
Each element of the short-course will begin with a lecture to provide participants with a sound conceptual understanding of each statistical model and its application. However, greater emphasis will be placed on practical activity, with participants gaining experience using a hands-on approach to reinforce the learning concepts and to ensure that participants are able to perform the desired analysis and appropriately interpret the output. Days 1 and 2 will be taught primarily using SPSS software with Day 3 using both SPSS and Mplus.
No prior knowledge is assumed, but some experience of descriptive statistics and hypothesis testing would be helpful.
Intermediate (some prior knowledge)
Fees (including lunch and refreshments)
Website and registration
Quantitative Data Handling and Data Analysis
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