15 Jan Tools for Multivariate Data Analysis
- 01/06/2021 - 03/06/2021
9:30 am - 4:30 pm
statistics course | level: advanced |
for questions related to this event, contact email@example.com
affiliation: Ghent University
The course will take place on three days. Each day will involve lecture-style presentations interchanged with practical hands-on sessions using software R for multivariate analysis. Attention will be given to theoretical aspects as well as to the interpretation of statistical results that are presented in real data examples.
day 1On the first day, the general features of Factor Analysis and Principal Component Analysis are explained. The general goal of both techniques is to summarize a large number of variables by a smaller number which retains the most important information. The differences and similarities between principal component analysis and (exploratory) factor analysis are discussed, and attention is given to important concepts such as communality, eigenvalue, uniqueness, factor loadings, and Kaiser-Meyer-Olkin criterion. These techniques will be introduced with built-in functions in R (e.g. princomp() and factanal()), but many features will be illustrated by means of the tools in the R package psych (and FactoMineR). The following methods will be covered:
• Data reduction techniques (e.g., scree plots)
• Extraction methods: Principal Axis Factoring, Maximum Likelihood
• Rotation methods: Varimax, Promax, Oblimin (with the R package GPArotation)
The first day will conclude with the first steps for a confirmatory factor analysis, which relates observed variables to latent variables (i.e. it tests a measurement model). This will be useful as an introduction to Structural Equation Modeling on the second day.
day 2On the second day the principles of Structural Equation Modeling are introduced using the R package lavaan. Structural equation modeling (SEM) is a general statistical modeling technique to study the relationships among observed and unobserved (latent) variables. In this way it spans a wide range of multivariate methods to analyze a large variety of statistical models, including factor analysis and path analysis. The general framework of SEM therefore builds upon the knowledge obtained in the first day. A brief summary of the second day is as follows:
• Basics of SEM
• Introduction to the R package lavaan
• Model estimation, model evaluation, and model re-specification in practice
• Reporting analysis
• A short note on mean structures, multiple groups, and measurement invariance
day 3On the third day one of the following topics can be discussed, depending on the interests of the participants:
• Multiple factor analysis and/or Homogeneity analysis
• EM clustering and Latent class analysis
• Item response theory
• Multilevel SEM
• Estimating average and conditional effects using SEM
• Causal analysis: mediation and instrumental variables
This course is targeted to researchers in social and behavioral sciences (e.g., psychology, education, economics, business, and sociology), life sciences and medicine (e.g., health sciences) and natural sciences, who are interested in getting practice to analyze multivariate data.
Fair understanding of linear statistical modelling and inference: linear regression, correlation, analysis of variance.
Basic/fair skills in R are mandatory. This is not an introduction to R! If you have no idea what the following commands mean, the course is too advanced for you:
mydata <- read.table (“c:/temp/rawdata.txt”,header=T, dec=”,”)
Prices• PhDs and post-docs of a Flemish university: free of charge
• Other academics: 180 €
• Non-profit/Social sector: 300 €
• Private sector: 600 €
Cristina Cametti & dr. Mariska Barendse
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