Natural survivorship for the South African west coast rock lobster resource, Jasus lalandii, is estimated at two locations, Robben Island and Hout Bay. A size-based modelling approach is used, in which the model is fitted, to pristine catch size frequencies. The natural survivorship value for lobsters in the Robben Island region is estimated to be 0.94. The natural survivorship value for Hout Bay lobsters is estimated to be 0.87. The natural survivorship estimates are found to be very sensitive to rock lobster growth rate. The Robben Island estimate decreases from 0.968 for a decrease of 40% in the growth rate, to 0.9 7 for a increase of 40% in the growth rate. These estimates.are compared with those made for lobsters in more northern areas. The implications of these natural survivorship estimates are discussed. A simple, direct method is used to estimate natural survivorship for Robben Island and Hout Bay, and the results are compared to those obtained with the model. Includes bibliographical references (pages 64-68).

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Thesis advisor: Michael Russell / Intersectionality theory garners increased attention from researchers interested in understanding the many ways in which oppression impacts lived experiences. In any given present and evolving context, oppression leads to advantages for some social positions and disadvantages for others (Collins & Bilge, 2016; Crenshaw, 1989). Quantitative researchers have attempted to adapt statistical modeling methods to reflect intersectional identities as a proxy for oppression and advantage in their models (Bauer et al., 2021; Schudde, 2018). This dissertation expanded on existing knowledge about the statistical limitations of three methods of modeling intersectional analyses on a continuous outcome variable: 1) Interaction, 2) Categorical, and 3) MAIDHA (multilevel analysis of individual heterogeneity and individual accuracy). Using a Monte Carlo simulation, four demographic data characteristics were manipulated to explore the three models under different scenarios which manipulated: a) the number of demographic categories (and thus intersections); b) the proportion of the sample represented by each demographic group; c) the within-intersectional-group variance in the outcome variable of interest; d) overall sample size. Each scenario and model were replicated 1000 times; results summarized performance of the intersection estimates and effect detection using the outcomes: bias, accuracy, power, type 1 error, and confidence interval coverage. The fundamental questions that guided this dissertation were: 1. What are the statistical advantages and disadvantages of each model under different demographic data characteristics? 2. In what ways does each model perform differently from one another under each demographic data characteristic condition? The findings of this dissertation contribute to intersectional quantitative research methods by providing greater insight into how each model performs under more complex data scenarios. / Thesis (PhD) — Boston College, 2024. / Submitted to: Boston College. Lynch School of Education. / Discipline: Measurement, Evaluation, Statistics & Assessment.

No description available.

No description available.