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1 event found
Keywords: multilevel modelling
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Multi-level Analysis using Mplus: Online
20 - 24 January 2025
Multi-level Analysis using Mplus: Online https://www.acspri.org.au/summer-program-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
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Multi-level Analysis using Mplus: Online
20 - 24 January 2025
Multi-level Analysis using Mplus: Online https://www.acspri.org.au/summer-program-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
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