Description:

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.

Start: Monday, 16 January 2023 @ 10:00

End: Friday, 20 January 2023 @ 16:00

Duration: 5 Days

Timezone: Melbourne

Venue: Online

 Country: Australia

Prerequisites:

No prior knowledge of multilevel analysis is required nor is it assumed that participants have had experience with Mplus – the Mplus programming language will be taught as part of the course. However, it is assumed that all participants will have a thorough understanding of regression analysis and factor analysis. Furthermore, it is assumed that all participants have completed a course in Structural Equation Modeling (SEM) or have had equivalent SEM experience.

Learning Objectives:

This course is designed to take participants from an introductory level up to an intermediate level of multilevel analysis. That is, the course assumes that participants have had no prior experience with multilevel modeling, (or have only a basis understanding), and takes participants through the basics up to an intermediate level. Although there are several programs that can be used to conduct multilevel analysis, in this course we will use the Mplus program.

Eligibility:
  • Open to all

Organiser: ACSPRI

Contact: info@acspri.org.au

Host institution: ACSPRI

Keywords: multilevel modelling, SEM, Statistical Methods, Statistics

Fields: MEDICAL AND HEALTH SCIENCES, EDUCATION, ECONOMICS, COMMERCE, MANAGEMENT, TOURISM AND SERVICES, STUDIES IN HUMAN SOCIETY, PSYCHOLOGY AND COGNITIVE SCIENCES

Capacity: 12

Event type:
  • Workshop
Tech Requirements:

Your own computer
Zoom
A copy of Mplus (full version - not trial version)

Cost Basis: Cost incurred by all

Multi-level Analysis using Mplus: Online https://dresa.org.au/events/multi-level-analysis-using-mplus-online 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. 2023-01-16 10:00:00 UTC 2023-01-20 16:00:00 UTC ACSPRI Online, Australia Online Australia ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all multilevel modellingSEMStatistical MethodsStatistics