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## Catálogo de Disciplinas

### Introduction to Psychometrics using Mplus

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1 | Introduction to Psychometrics using Mplus | Psiquiatria e Psicologia Médica | 15/04/2019 a 18/04/2019 | |

Inscrição: 30/01/2019 a 01/03/2019 Período: 15/04/2019 a 18/04/2019 Responsável: Prof. Dr. Hugo Cogo-Meira Créditos: 3 Resumo: This 5-day short course (morning) is focused on the analysis of multivariate data using a latent variable approach using Mplus (https://www.statmodel.com/). Código: IPUSM Programa: 33009015032P3 Psiquiatria e Psicologia Médica Co-responsável: - Local: à definir Dias e horários: CURSO CANCELADO Vagas: 25 Créditos: 3 Critérios de ingresso: Researchers are expected to have a basic knowledge of regression analysis, discussed in most books on multivariate statistics (e.g., Andy Field: Discovering Statistics; Tabachnick and Fidell: Using multivariate statistics). Some knowledge of SEM and SEM may be helpful, but it is not mandatory. For those with no experience with SEM, the following paper is highly recommended: Hox, J. J. & Bechger, T.M. (2007). An introduction to structural equation modelling. Family Science Review, 11, 354-373. KNOWLEDGE IN MPLUS IS NOT REQUIRED.
Carga horária teórica: 20 Carga horária prática: 20 Carga horária total: 40 Ementa/Programação: Nome da disciplina Introduction to Psychometrics using Mplus
Local de matrícula
Responsáveis Prof. Dr. Hugo Cogo Moreira (PPG Psiquiatria-UNIFESP)
Data Inicial 15/04/2019
Data Final 18/04/2019
Horário Monday, Tuesday, Wed from 08h00 to 12h30; Thursday 8:00 to 18:00.
Carga Horária 40 horas
Dia da Semana Uma semana, todos os dias pela manhã (Em inglês)
Créditos 03
Número de Vagas 30
Obrigatório NÃO
Avaliação No evaluation will be conducted
Período de inscrição Dê 30/01/2019 até 01/03/2019
Local de aula
Sinopse:
This 5-day short course (morning) is focused on the analysis of multivariate data using a latent variable approach using Mplus (https://www.statmodel.com/). Statistical models will be applied to clinical and basic sciences data, and the interpretation of statistical models will be discussed. Using latent variables offers several advantages for the biological and clinical sciences. For instance, some (latent) phenomena are not directly observed (e.g. depression, inflammation) but rather they are directly evaluated by items in a scale designed to measure depression (i.e., Beck scale for depression) or different interleukins (e.g. inflammation). Although the intention is to assess the latent phenomena, we have only the observed measures. Each of these measures includes some degree of measurement error within them. The structural equation modeling (SEM) can be overcome and test theories by evaluating and creating a latent variable that underlies the observed measured variables, separating what is important in the observed indicators collected (i.e. common variance) from what is residual (i.e. noise). SEM is a general framework that allows empirical testing of research hypotheses in ways not otherwise possible. For instance, “principle components analysis” can reduce multiple measures into a single measure, but it cannot test the hypothesis that these measures inform the same underlying construct. In this workshop we provide an initial exploration of SEM ranging from basic principles to applications. This 5-day course will introduce the basics of SEM, and offer hands-on training in the use Mplus, a popular and very robust SEM software package, in your work. No previous knowledge in Mplus is required.
OBJETIVOS:
1) Hands-on experience with MPlus software
2) Learn fundamental concepts about latent variable modeling
3) Understand the strengths and weaknesses of different model approaches and their applications to cross-sectional and longitudinal data
4) Understand how structural equation modeling can address your specific research questions; specification and adaptation of models
5) Understand the statistical, practical and clinical considerations underlying how to formulate, compare, and evaluate models
6) Learn how “item parcels” can be used as alternative indicators to model latent phenomena
CONTEÚDO:
1) Measurement model specification & fir índices
2) Confirmatory factor analysis and Item Response theory (with 1 and 2 parameters)
3) Confirmatory factor analysis under multidimensional models
4) New reliability indices and old problems– Thanks Coefficient Alpha, We’ll Take it From Here
5) Invariance testing: are you comparing apples with pineapples?
Prerequisitos
Researchers are expected to have a basic knowledge of regression analysis, discussed in most books on multivariate statistics (e.g., Andy Field: Discovering Statistics; Tabachnick and Fidell: Using multivariate statistics). Some knowledge of SEM and SEM may be helpful, but it is not mandatory. For those with no experience with SEM, the following paper is highly recommended: Hox, J. J. & Bechger, T.M. (2007). An introduction to structural equation modelling. Family Science Review, 11, 354-373. KNOWLEDGE IN MPLUS IS NOT REQUIRED.
Referências: CONTEÚDO:
1) Measurement model specification & fir índices
2) Confirmatory factor analysis and Item Response theory (with 1 and 2 parameters)
3) Confirmatory factor analysis under multidimensional models
4) New reliability indices and old problems– Thanks Coefficient Alpha, We’ll Take it From Here
5) Invariance testing: are you comparing apples with pineapples?
Endereço dessa página web: http://epmpg.info/disciplinas.php?tp=d&id=334 Endereço web auxiliar: Inscrição: http://www.psiquiatriaunifesp.posgrad.com.br/ E-mail: pgpsiquiatria@gmail.com Telefone: 5576-4990 voip 1285 |

(CC) 2015-2019 EPMpg.info 1.05 Câmara de Pós-Graduação e Pesquisa da Escola Paulista de Medicina UNIFESP.

Desenvolvido por Ivan Pisa e Valdice Pereira. Licença Creative Commons - Atribuição-CompartilhaIgual 4.0 Internacional.

Informação errada ou incompleta: favor contatar diretamente secretaria do programa relacionado.

Acesso 18/06/2019 às 17:45. Ip 54.226.102.115. #241300 acessos.