About the Workshop:
The workshop was held on October 4-9, 2021 (3pm - 6.30pm) via online medium (through Zoom).
Focus of the Workshop:
Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, or random-effects models) are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., employees nested under one team leader). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined. Multilevel models can be used on data with many levels, although 2-level models are the most common.
This workshop builds on the insights and knowledge of regressions to provide theoretical understanding of multilevel modeling for data having hierarchical/nested structure. The workshop will be based on a combination of theoretical and practice-oriented sessions and will provide a hands-on training on the methods of multilevel modeling in R and SPSS.
Upon the completion of this workshop, the participants would learn:
- Understand the mathematical formulation of a 2-level multilevel model.
- Know the technical and substantive difference between fixed and random effects.
- Understand random intercepts, random coefficients, and crossed random effects models and know when to use each one
- Understand the concepts of aggregation statistics (rwg, ICC1, ICC2) and the process of variance decomposition (random-slope/random-intercept models).
- Know how to combine the strengths of random-effects and fixed-effects approaches into a single “between-within” model
- Know how to estimate these models and interpret the results.
- Get hands-on training on multilevel analysis using R and SPSS.
Who should attend and prerequisites?
This workshop is designed for researchers working in social sciences (Psychology, OB, HRM, Marketing, Strategy, Communication, etc.) who want to apply multilevel analysis in their own research projects. A previous background in regression and structural equation modeling is necessary. Participants should have a good working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the basic theory and practice of linear regression. The empirical examples and exercises in this course will emphasize use of R and SPSS. Participants will be expected have their laptops loaded with R, R-Studio and SPSS.
The workshop will start on October 4 at 3pm and will end on October 9 at around 7 pm. The detailed agenda of the workshop can be found here.
Registration for this workshop is now closed.
Dr. Manish Panchasara
About the Workshop Faculty (Prof. Vishal Gupta):
Vishal Gupta is an Associate Professor in the Organizational Behavior Area at the Indian Institute of Management Ahmedabad, India. He obtained his doctorate in Human Resource Management from the Indian Institute of Management Lucknow, India, in 2013. His PhD thesis on ‘Leadership in Public Sector R&D Organizations’ was awarded the ‘Outstanding Doctoral Dissertation’ award in the year 2013-14 in Leadership & Organizational Development category by European Federation for Management Development and the Emerald Group Publishing. He was recognized as an ‘Emerging Psychologist’ by the National Academy of Psychology, India in 2014. In 2016, he was conferred the ‘Young Scientist Award’ by the National Academy of Sciences India and Scopus (Elsevier). His webpage is: www.iima.ac.in/~vishal