1. Home
  2. Events
Register event
Show past events
2 events found

Keywords: SEM 

  • Multi-level Analysis using Mplus: Online

    20 - 24 January 2025

    Multi-level Analysis using Mplus: Online https://dresa.org.au/events/multi-level-analysis-using-mplus-online-a161d07c-5e81-4efd-b300-32b2cf1a0ae9 This course is designed as an introduction to the concepts and techniques required to analyse data that is multi-level in nature. (That is, data that is derived from subjects who are nested within groups or data that involves repeated measures that are nested within subjects) In conventional regression analysis it is assumed that subjects are randomly selected and, therefore, all the variance in your dependent variables is due solely to variation amongst individuals. However, in most studies, subjects are clustered within a group and multiple groups are sampled. For example, in an education study, we may have students clustered within multiple classes; in a human resourcing study, we may have employees clustered within multiple work units or teams. In such sampling, although some of the variance in your dependent variables will be due to variation amongst individuals, some the variance in your dependent variables will also be due to variation amongst the groups themselves. In such cases, multilevel analysis (MLA) should be employed to account for the different levels of variation. Repeated measure designs should also be analysed using multilevel analysis because the repeated observations are nested within subjects. For example, in a marketing study, we may have repeated measures of consumers’ attitudes to a brand over the life of a marketing campaign; in an epidemiology study, we may have repeated measures of a health outcome over the life of a drug treatment regime. In such studies, although some of the variance in your dependent variables will be due to variation across the various time occasions, some the variance in your dependent variables will also be due to variation amongst individuals themselves. Again, in such cases, multilevel analysis (MLA) should be employed to account for the different levels of variation. 2025-01-20 10:00:00 UTC 2025-01-24 16:00:00 UTC ACSPRI Online, Australia Online Australia ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all multilevel modellingSEMStatistical MethodsStatistics

  • Advanced Structural Equation Modelling: Online

    10 - 14 February 2025

    Advanced Structural Equation Modelling: Online https://dresa.org.au/events/advanced-structural-equation-modelling-online Introductory courses typically cover path analysis amongst observed variables, confirmatory factor analysis, and full SEM models with latent variables. This course covers a number of more complex models including models with mediating variables, models with interactions (moderation), ANOVA and ANCOVA models for latent outcomes, multi-level models (including repeated measures models) and mixture models. Furthermore, introductory courses usually deal only with continuous, normally distributed variables. This course addresses the treatment of non-normal data and covers the analysis of observed categorical variables including ordered categorical (ordinal) variables such as Likert scales and unordered categorical (nominal) variables that may be binary (Male/Female, Problem Gambler/Non Gambler, Smoker/Non-Smoker, etc.) or polynomial (Australian/Indonesian/South African, etc.) Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course. The target audience for this course is existing AMOS and/or Mplus users. 2025-02-10 10:00:00 UTC 2025-02-14 16:00:00 UTC ACSPRI Online, Australia Online Australia ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all Structural equation modellingAdvancedSEMMplusAMOS

  • Multi-level Analysis using Mplus: Online

    20 - 24 January 2025

    Multi-level Analysis using Mplus: Online https://dresa.org.au/events/multi-level-analysis-using-mplus-online-a161d07c-5e81-4efd-b300-32b2cf1a0ae9 This course is designed as an introduction to the concepts and techniques required to analyse data that is multi-level in nature. (That is, data that is derived from subjects who are nested within groups or data that involves repeated measures that are nested within subjects) In conventional regression analysis it is assumed that subjects are randomly selected and, therefore, all the variance in your dependent variables is due solely to variation amongst individuals. However, in most studies, subjects are clustered within a group and multiple groups are sampled. For example, in an education study, we may have students clustered within multiple classes; in a human resourcing study, we may have employees clustered within multiple work units or teams. In such sampling, although some of the variance in your dependent variables will be due to variation amongst individuals, some the variance in your dependent variables will also be due to variation amongst the groups themselves. In such cases, multilevel analysis (MLA) should be employed to account for the different levels of variation. Repeated measure designs should also be analysed using multilevel analysis because the repeated observations are nested within subjects. For example, in a marketing study, we may have repeated measures of consumers’ attitudes to a brand over the life of a marketing campaign; in an epidemiology study, we may have repeated measures of a health outcome over the life of a drug treatment regime. In such studies, although some of the variance in your dependent variables will be due to variation across the various time occasions, some the variance in your dependent variables will also be due to variation amongst individuals themselves. Again, in such cases, multilevel analysis (MLA) should be employed to account for the different levels of variation. 2025-01-20 10:00:00 UTC 2025-01-24 16:00:00 UTC ACSPRI Online, Australia Online Australia ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all multilevel modellingSEMStatistical MethodsStatistics

  • Advanced Structural Equation Modelling: Online

    10 - 14 February 2025

    Advanced Structural Equation Modelling: Online https://dresa.org.au/events/advanced-structural-equation-modelling-online Introductory courses typically cover path analysis amongst observed variables, confirmatory factor analysis, and full SEM models with latent variables. This course covers a number of more complex models including models with mediating variables, models with interactions (moderation), ANOVA and ANCOVA models for latent outcomes, multi-level models (including repeated measures models) and mixture models. Furthermore, introductory courses usually deal only with continuous, normally distributed variables. This course addresses the treatment of non-normal data and covers the analysis of observed categorical variables including ordered categorical (ordinal) variables such as Likert scales and unordered categorical (nominal) variables that may be binary (Male/Female, Problem Gambler/Non Gambler, Smoker/Non-Smoker, etc.) or polynomial (Australian/Indonesian/South African, etc.) Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course. The target audience for this course is existing AMOS and/or Mplus users. 2025-02-10 10:00:00 UTC 2025-02-14 16:00:00 UTC ACSPRI Online, Australia Online Australia ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all Structural equation modellingAdvancedSEMMplusAMOS

Note, this map only displays events that have geolocation information in DReSA.
For the complete list of events in DReSA, click the grid tab.