The primary author(s) responsible for maintaining this repository are:
- Craig H. Faunce ([email protected])
- Debra Duarte ([email protected])
- Geoff Mayhew (NOAA-AFSC)
Addressing observer effects, wherein the act of observation has influence on the phenomenon being observed, is of great importance to science because its presence indicates that the results from data collected from observation (the sample) are biased and cannot be used to infer the properties of unobserved nature (the population). The presence of an observer effect in partial coverage fisheries means that the data from monitored trips (the sample) are not representative of the entire fleet (the population), and this bias can have broad implications to catch accounting and stock assessments.
Analysts of the Fisheries Monitoring and Analysis Division (AFSC) have developed a model-based method to test for observer effects in the catch of multiple species from the partial coverage fleet (Appendix A; NMFS and AKRO, 2025. The method tests different multivariate generalized linear models (MvGLM) with permutation to evaluate the similarity of species abundances from landed catch between trips that were monitored with EM or observers and those that were not. Because the method employs a MvGLM to detect observer effects, it is abbreviated for convenience as MOE (Multivariate Observer Effects), and the model as mvglm_obs.
Despite its development, the relative performance of the method has not been tested. Its relative gains over other methods are based solely on reported advantages from other sources used in its development (Wharton et al. 2012).
The work aims to iterate the work of Duerte and Cadrin (2004) to include MOE in its evalution of the relative performance of methods used to measure observer effects in fisheries.
The repo mvobservr associated with conducting MOE will be used to source the most up to date code for this project. Potential comparisons to this method are slim, because MOE uses a matrix as a response. PERMANOVA is one for sure however.
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