219371 Modern Approaches to Analyze Time-Series-Cross-Section Data: An Application to the Analysis of State-Level Longitudinal Data

Wednesday, November 10, 2010 : 9:30 AM - 9:50 AM

Jangho Yoon, PhD , Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA
     
Time-series-cross-section (TSCS) data are characterized by having repeated observations on the same fixed units such as states. The analysis of such data is becoming more common in social sciences including public health research. However, standard techniques for the analysis of TSCS data often fail to fully account for the temporal and spatial properties of TSCS data, thereby producing inaccurate standard errors leading to misleading conclusions in common research situations. This talk will discuss innovative, modern approaches for correct inferences in the analysis of TSCS data. It will also discuss the treatment of TSCS data to control for the possibility of non-spherical errors in the time and cross-sectional dimensions. Examples from published research will be used to contrast various analytical techniques applicable to TSCS data.

Learning Areas:
Public health or related research

Learning Objectives:
1. To discuss the treatment of Time-series-cross-section (TSCS) data to control for possibility of non-spherical errors in TSCS dimensions 2. To demonstrate modern approaches in the analysis of TSCS data to make correct inferences.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified as I am currently working as Assistant Professor in Health Policy and Management at Georgia Southern University.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 5087.0: Statistics at the State level