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Using Multilevel Statistical Models to Address Representativeness and Data at Different Spatial and Temporal Scales

EPA Grant Number: R826763
Title: Using Multilevel Statistical Models to Address Representativeness and Data at Different Spatial and Temporal Scales
Investigators: Berk, Richard , Ambrose, Richard , DeLeeuw, Jan , Gould, Robert , Turco, Richard
Current Investigators: Berk, Richard , Ambrose, Richard
Institution: University of California - Los Angeles
EPA Project Officer: Smith, Jonathan
Project Period: October 1, 1998 through September 30, 2000
Project Amount: $414,149
RFA: Regional Scale Analysis and Assessment (1998)
Research Category: Ecological Indicators/Assessment/Restoration

Description:

We will consider "representativeness" when probability sampling cannot be employed. From this examination, we will then extend multilevel statistical models to provide new techniques for working scientists. The extensions include: 1) multiple response variables, 2) nom-linear functional forms, 3) missing data, 4) disturbance covariance matrices allowing for temporal and spatial dependencies, and 5) latent variables. In so doing, we will not only provide better tools to determine "representativeness" but also a convenient means to properly analyze data at different spatial and temporal scales (e.g. satellite data and survey data). We will write software for the extended multilevel statistical models. Finally, we will illustrate the use of these models with three very different data sets, two of which were collected as part of an EPA-funded project to study the Los Angeles basin watershed.

Approach:

The proposed research builds on a number of rich traditions in statistics. While we will need to do some original statistical work, we will primarily be assembling and integrating a number of existing techniques into the multilevel statistical framework and then writing the necessary software. The empirical examples will exploit data that are already available in machine readable form.

Expected Results:

We expect to provide new statistical procedures working scientists can use to better generalize their results. With better statistical procedures for generalizing, the risk assessment generalizations will be on more sound footing.

Publications and Presentations:

Publications have been submitted on this project: View all 1 publications for this project

Supplemental Keywords:

statistical inference, external validity, hierarchical models , Ecosystem Protection/Environmental Exposure & Risk, Economic, Social, & Behavioral Science Research Program, RFA, Environmental Statistics, Regional/Scaling, risk assessment, multilevel statistical model, regional survey data, representativeness studies, multiple response variable, data synthesis, spatial-temporal methods, multiple response variables, survey data, external validity, regional scale impacts, remotely sensed data, statistical models, representativeness, spatial and temporal scales, statistical methods, data analysis, ecosystem assessment, environmental risks, innovative statistical models, regional environmental data, satellite data, disturbance covariance, data models, hierarchical statistical analysis, modeling, hierarchical statistical inference

Progress and Final Reports:
1999 Progress Report
2000 Progress Report
Final Report

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The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.

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