Course Syllabus

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Il corso si propone di introdurre al concetto di variabile latente e ai più importanti modelli con variabili latenti. A lezioni frontali si alterneranno lezioni in laboratorio con SAS in modo da permettere di imparare l’utilizzo di pacchetti statistici e poter quindi applicare le tecniche apprese.

• Significato di causalità in statistica Correlazione spuria
• Modelli strutturali con variabili osservate
• Path analysis Diversi significati di variabile latente
• Modello fattoriale
• Analisi delle componenti principali e modello fattoriale
• Modelli strutturali con variabili latenti

Introduzione al significato di causalità in statistica
Spurious correlation
Modelli strutturali con variabili osservate. path analysis. Esempi ed esercitazioni con SAS
Diversi significati di variabile latente
Modello fattoriale: Ipotesi. Comunalità : significato e calcolo. Metodi per ricavare
soluzioni: significato e metodi.Metodi di stima dei fattori Interpretazione dei risultati .Metodi per ricavare punteggi fattoriali. Non unicità soluzioni fattoriali: non identificabilità dei parametri e indeterminatezza dei punteggi fattoriali. Metodi di stima e Verifica di ipotesi.Esempi ed esercitazioni con SAS
Differenze di analisi dei fattori con metodo delle componenti principali
Modelli strutturali con variabili latenti. Nessi con path analysis e analisi dei fattori. Soluzione e loror interpretazione. Non unicità soluzioni. Metodi di stima e verifica di ipotesi. Esempi ed esercitazioni con SAS

Nessuna propedeuticità formale. Si richiede però la conoscenza dei contenuti degli insegnamenti di Calcolo delle probabilità, Statistica 1, Laboratorio di Informatica, Analisi statistica multivariata., Base di dati

Si alterneranno lezioni in cui verranno impartire le nozioni teoriche con molte esemplificazioni pratiche a esercitazioni in laboratorio in cui con l'ausilio di SAS si utilizzeranno le tecniche apprese su dati reali

Esame scritto, 2 domande di teoria e 1 esercizio di analisi dei dati svolto mediante SAS

Slides e appunti del corso
Alwin, Duane F.and Robert M. Hauser (1975) “The decomposition of effects in path analysis”. American Sociological Review, vol. 40 ( February ): 37-40.
factor pattern. Rotazione delle
Blalock, Hubert M., Jr. (1965) “Path analysis: sociological examples”. American Journal of Sociology 72 (July): 1-16.
Blalock, Hubert M., Jr. (1971)“Path analysis: sociological examples (addenda)”. Pp. 136-8 in Hubert M. Blalock, Jr., (ed.), Causal Models in the Social Sciences. Chicago: Aldine-Atherton.
Duncan, Otis Dudley, David L. Featherman and Beverly Duncan (1971) Socioeconomic Background and Achievement. New York: Academic Press.
Land, Kenneth C., (1969.) “Principles of path analysis”. Pp. 3-37 in Edgar F. Borgatta (ed.), Sociological Methodology, San Francisco: Jossey-Bass.
Saris, W.E., & Stronkhorst, L.H.. (1984). Causal Modelling in Nonexperimental Research. Sociometrical Research Foundation. Amsterdam, The Nederlands.
Werts, C. E., & Linn. R. L. (1970) Path analysis: Psychological examples. Psychological Bulletin, ,67, 193-212. Lawley, D. N. and Maxwell, A.E. (1971) Factor Analysis as a Statistical Method, American Elsevier, New York.
Hauser, R. M., Goldberger,A. S. The treatment of unobservable variables in path analysis. In H. L. Costner (Ed.), Sociological methodology. London: Jossey-Bass, 1971. In K. G. Jöreskog, & H. Wold (Eds.). Sustems under indirect observation Causality structure prediction New York: North Holland
Lawley, D. N. and Maxwell, A. E. (1971) Factor Analysis as a Statistical Method, American Elsevier, New York.
Hauser, R. M., Goldberger,A. S. The treatment of unobservable variables in path analysis. In H. L. Costner (Ed.), Sociological methodology. London: Jossey-Bass, 1971.
In K. G. Jöreskog, & H. Wold (Eds.). Systems under indirect observation Causality structure prediction New York: North Holland

2 semestre 2023 - secondo ciclo

Italiano

SALUTE E BENESSERE
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The course aims to introduce the concept of latent variable and the most important models with latent variables. Lectures will be alternated with lectures in the laboratory with SAS so as to enable students to learn the use of statistical packages and thus be able to apply the techniques learned.

  • Meaning of causality in statistics Spurious correlation
  • Structural models with observed variables
  • Path analysis Different meanings of latent variable
  • Factorial model
  • Principal component analysis and factorial model
  • Structural models with latent variables

Introduction to the meaning of causality in statistics
Spurious correlation
Structural models with observed variables. path analysis. Examples and exercises with SAS
Different meanings of latent variable
Factorial model: Assumptions. Communality: meaning and computation. Methods for deriving solutions: meaning and methods.
Methods of estimating factors Interpretation of results.
Methods of deriving factorial scores. Non-uniqueness of factorial solutions: non-identifiability of parameters and indeterminacy of factorial scores. Estimation methods and Hypothesis Testing. Examples and exercises with SAS
Differences in factor analysis with principal component method.
Structural models with latent variables. Relationships with path analysis and factor analysis. Solution and their interpretation. Non-uniqueness of solutions. Estimation methods and hypothesis testing. Examples and exercises with SAS

No formal prerequisites. However, knowledge of the contents of the teachings of Probability Calculus, Statistics 1, Computer Laboratory, Multivariate Statistical Analysis., Database is required

Lectures will alternate between lectures in which the theoretical concepts will be imparted with many practical exemplifications and laboratory exercises in which with the help of SAS the techniques learned will be used on real data

Written exam, 2 theory questions and 1 data analysis exercise carried out using SAS

Slides and course notes
Alwin, Duane F. and Robert M. Hauser (1975) "The decomposition of effects in path analysis." American Sociological Review, vol. 40 ( February ): 37-40.
factor model. Rotation of the
Blalock, Hubert M., Jr. (1965) "Path analysis: sociological examples." American Journal of Sociology 72 ( July ): 1-16.
Blalock, Hubert M., Jr. (1971) "Path analysis: sociological examples (addenda)." Pp. 136-8 in Hubert M. Blalock, Jr. (ed.), Causal Models in the Social Sciences. Chicago: Aldine-Atherton.
Duncan, Otis Dudley, David L. Featherman and Beverly Duncan (1971) Socioeconomic Background and Achievement. New York: Academic Press.
Land, Kenneth C., (1969) "Principles of path analysis." Pp. 3-37 in Edgar F. Borgatta (ed.), Sociological Methodology, San Francisco: Jossey-Bass.
Saris, W.E., & Stronkhorst, L.H. (1984). Causal modeling in nonexperimental research. Sociometrical Research Foundation. Amsterdam, The Netherlands.
Werts, C. E., & Linn. R. L. (1970) Path analysis: psychological examples. Psychological Bulletin, ,67, 193-212. Lawley, D. N. and Maxwell, A. E. (1971) Factor Analysis as a Statistical Method, American Elsevier, New York.
Hauser, R. M., Goldberger,A. S. The treatment of unobservable variables in path analysis. In H. L. Costner (ed.), Sociological methodology. London: Jossey-Bass, 1971. In K. G. Jöreskog, & H. Wold (Eds.). Sustems under indirect observation Causality structure prediction New York: North Holland.
Lawley, D. N. and Maxwell, A. E. (1971) Factor Analysis as a Statistical Method, American Elsevier, New York.
Hauser, R. M., Goldberger,A. S. The treatment of unobservable variables in path analysis. In H. L. Costner (ed.), Sociological methodology. London: Jossey-Bass, 1971.
In K. G. Jöreskog, & H. Wold (Eds.). Systems under indirect observation Causality structure prediction New York: North Holland.

2 semester 2023 - second cycle

Italian

GOOD HEALTH AND WELL-BEING
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Key information

Field of research
SECS-S/01
ECTS
6
Term
Second semester
Activity type
Mandatory to be chosen
Course Length (Hours)
47
Degree Course Type
Degree Course
Language
Italian

Staff

    Teacher

  • DS
    Daniele Spinelli
  • GV
    Giorgio Vittadini

Students' opinion

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Bibliography

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Enrolment methods

Manual enrolments
Self enrolment (Student)

Sustainable Development Goals

GOOD HEALTH AND WELL-BEING - Ensure healthy lives and promote well-being for all at all ages