FROM NEUROSCIENCE TO SOCIETY: A MULTI-DISCIPLINARY STUDY OF HUMAN PERCEPTION AND COGNITION
Grant number: FT140100629 | Funding period: 2014 - 2021
Social science research on the effects of income on wellbeing is substantial, however, this research has largely missed five crucial factors. These factors include: the work people do to generate income; how they use income; effects on physical and social wellbeing; effects at the household level; and causal effects at multiple levels. This project aims to address all of these factors with a new model of wellbeing, the Work-Income-Spending-Effects (WISE) model, and utilises the Household, Income and Labour Dynamics in Australia database. This project also aims to demonstrate trade-offs among work, income, spending, and multiple types of wellbeing within a new paradigm for engaging the Austra..View full description
Related publications (7)
Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata
Jinjing Li, Michael J Zyphur, George Sugihara, Patrick J Laub
How can social and health researchers study complex dynamic systems that function in nonlinear and even chaotic ways? Common metho..
From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
Michael J Zyphur, Paul D Allison, Louis Tay, Manuel C Voelkle, Kristopher J Preacher, Zhen Zhang, Ellen L Hamaker, Ali Shamsollahi, Dean C Pierides, Peter KOVAL, Ed Diener
This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal ..
From Data to Causes II: Comparing Approaches to Panel Data Analysis
Michael J Zyphur, Manuel C Voelkle, Louis Tay, Paul D Allison, Kristopher J Preacher, Zhen Zhang, Ellen L Hamaker, Ali Shamsollahi, Dean C Pierides, Peter KOVAL, Ed Diener
This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic..
Income Inequality and White-on-Black Racial Bias in the United States: Evidence From Project Implicit and Google Trends
M Zyphur, P Connor, V Sarafidis, K Dacher, S Chen
Several theories predict that income inequality may produce increased racial bias, but robust tests of this hypothesis are lacking..