PROGRAMA

MODELLINGDATA

MARCELLO GALLUCCIUNIVERSIT Y OF MILANBICOCCA

16 JUNHO 2015 - CLASS 1A MODERATED VIEW OF THE LINEAR MODEL3 hours – theor y basedThis class reviews the most interesting and advanced parts of the General Linear Model(GLM). In particular, moderation with continuous and categorical independent variables, contrast analysis , mediation and moderated mediation. It also lays down the basic principles of the genera l izat ion of the GLM to be tack led in the other c lasses .

17 JUNHO 2015 - CLASS 2A MIX WITHIN3 hours – theor y basedThe mixed l inear models – a lso cal led random e�ec ts models , h ierarchica l l inear models ormult i level models – is analyzed with part icular at tent ion to i t s appl icat ions to repeatedmeasures des igns and longi tudina l des igns . The a im of the c lass is to general ize the GLM to advanced and complex ly s t ruc tured research des igns .

18 JUNHO 2015 - CLASS 3PRACTICE MAKES PERFECT3 hours – prac t ica l sk i l l s – computer roomThe GLM and the mixed linear model applications are shown with examples of real data and analyzed by students with the available software (SPSS). The class is “handson” so the aim is to overview practical problems and best practices of the analysis of data.

COUNT ON ME3 hours – theor y basedIn th is c lass the genera l ized l inear model i s d iscussed . Th is a l lows s tudents to fami l iar ize with logis t ic models , mult inomial and Poisson analyses, in order to analyze data with categorical and count dependent var iables . The a im is to show how the concepts s tudied in the GLM eas i ly apply to research problems involv ing categor ica l var iab les .

19 JUNHO 2015 - CLASS 4PRACTICE MAKES PERFECT 23 hours – prac t ica l sk i l l s – computer roomThe genera l ized l inear model and i ts random e�ec ts vers ion is d iscussed wi th example of rea l data and analyzed by s tudents wi th the available software (SPSS). The class is “handson”so the a im is to over v iew prac t ica l problems and bes t prac t ices of the analys is of data .

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