A modelling approach to the analysis of complex survey data
- Authors: Dlangamandla, Olwethu
- Date: 2021-10-29
- Subjects: Sampling (Statistics) , Linear models (Statistics) , Multilevel models (Statistics) , Logistic regression analysis , Complex survey data
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192955 , vital:45284
- Description: Surveys are an essential tool for collecting data and most surveys use complex sampling designs to collect the data. Complex sampling designs are used mainly to enhance representativeness in the sample by accounting for the underlying structure of the population. This often results in data that are non-independent and clustered. Ignoring complex design features such as clustering, stratification, multistage and unequal probability sampling may result in inaccurate and incorrect inference. An overview of, and difference between, design-based and model-based approaches to inference for complex survey data has been discussed. This study adopts a model-based approach. The objective of this study is to discuss and describe the modelling approach in analysing complex survey data. This is specifically done by introducing the principle inference methods under which data from complex surveys may be analysed. In particular, discussions on the theory and methods of model fitting for the analysis of complex survey data are presented. We begin by discussing unique features of complex survey data and explore appropriate methods of analysis that account for the complexity inherent in the survey data. We also explore the widely applied logistic regression modelling of binary data in a complex sample survey context. In particular, four forms of logistic regression models are fitted. These models are generalized linear models, multilevel models, mixed effects models and generalized linear mixed models. Simulated complex survey data are used to illustrate the methods and models. Various R packages are used for the analysis. The results presented and discussed in this thesis indicate that a logistic mixed model with first and second level predictors has a better fit compared to a logistic mixed model with first level predictors. In addition, a logistic multilevel model with first and second level predictors and nested random effects provides a better fit to the data compared to other logistic multilevel fitted models. Similar results were obtained from fitting a generalized logistic mixed model with first and second level predictor variables and a generalized linear mixed model with first and second level predictors and nested random effects. , Thesis (MSC) -- Faculty of Science, Statistics, 2021
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- Date Issued: 2021-10-29
Generalized linear models, with applications in fisheries research
- Authors: Sidumo, Bonelwa
- Date: 2018
- Subjects: Western mosquitofish , Analysis of variance , Fisheries Catch effort South Africa Sundays River (Eastern Cape) , Linear models (Statistics) , Multilevel models (Statistics) , Experimental design
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/61102 , vital:27975
- Description: Gambusia affinis (G. affinis) is an invasive fish species found in the Sundays River Valley of the Eastern Cape, South Africa, The relative abundance and population dynamics of G. affinis were quantified in five interconnected impoundments within the Sundays River Valley, This study utilised a G. affinis data set to demonstrate various, classical ANOVA models. Generalized linear models were used to standardize catch per unit effort (CPUE) estimates and to determine environmental variables which influenced the CPUE, Based on the generalized linear model results dam age, mean temperature, Oreochromis mossambicus abundance and Glossogobius callidus abundance had a significant effect on the G. affinis CPUE. The Albany Angling Association collected data during fishing tag and release events. These data were utilized to demonstrate repeated measures designs. Mixed-effects models provided a powerful and flexible tool for analyzing clustered data such as repeated measures data and nested data, lienee it has become tremendously popular as a framework for the analysis of bio-behavioral experiments. The results show that the mixed-effects methods proposed in this study are more efficient than those based on generalized linear models. These data were better modeled with mixed-effects models due to their flexibility in handling missing data.
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- Date Issued: 2018
An assessment of inland fisheries in South Africa using fisheries-dependent and fisheries-independent data sources
- Authors: McCafferty, James Ross
- Date: 2012
- Subjects: Fisheries -- South Africa , Fishery management -- South Africa , Fisheries -- Economic aspects -- South Africa , Food security -- South Africa , Poverty -- South Africa , Economic development -- South Africa , Fishing -- South Africa , Fisheries -- Catch effort -- South Africa , Fish stock assessment -- South Africa , Fish populations -- South Africa , Linear models (Statistics)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5229 , http://hdl.handle.net/10962/d1005072 , Fisheries -- South Africa , Fishery management -- South Africa , Fisheries -- Economic aspects -- South Africa , Food security -- South Africa , Poverty -- South Africa , Economic development -- South Africa , Fishing -- South Africa , Fisheries -- Catch effort -- South Africa , Fish stock assessment -- South Africa , Fish populations -- South Africa , Linear models (Statistics)
- Description: The role of inland fisheries as contributors to local and national economies in developing African countries is well documented. In South Africa, there is increasing interest in inland fisheries as vehicles for achieving national policy objectives including food security, livelihoods provision, poverty alleviation and economic development but there is surprisingly little literature on the history, current status, and potential of inland fishery resources. This lack of knowledge constrains the development of management strategies for ensuring the biological sustainability of these resources and the economic and social sustainability of the people that are dependent on them. In order to contribute to the knowledge base of inland fisheries in South Africa this thesis: (1) presents an exhaustive review of the available literature on inland fisheries in South Africa; (2) describes the organisation of recreational anglers (the primary users of the resource); (3) compiles recreational angling catch records and scientific gill net survey data, and assesses the applicability of these data for providing estimates of fish abundance (catch-per-unit effort [CPUE]); and finally, (4) determines the potential for models of fish abundance using morphometric, edaphic, and climatic factors. The literature review highlighted the data-poor nature of South African inland fisheries. In particular information on harvest rates was lacking. A lack of knowledge regarding different inland fishery sectors, governance systems, and potential user conflicts was also found. Recreational anglers were identified as the dominant user group and catch data from this sector were identified as potential sources of fish abundance and harvest information. Formal freshwater recreational angling in South Africa is a highly organised, multi-faceted activity which is based primarily on angling for non-native species, particularly common carp Cyprinus carpio and largemouth bass Micropterus salmoides. Bank anglers constituted the largest number of formal participants (5 309 anglers affiliated to formal angling organisations) followed by bass anglers (1 184 anglers affiliated to formal angling organisations). The highly structured nature of organised recreational angling and dominant utilisation of inland fisheries resources by this sector illustrated not only the vested interest of anglers in the management and development of inland fisheries but also the role that anglers may play in future decision-making and monitoring through the dissemination of catch data from organised angling events. Generalised linear models (GLMs) and generalised additive models (GAMs) were used to standardise CPUE estimates from bass- and bank angling catch records, which provided the most suitable data, and to determine environmental variables which most influenced capture probabilities and CPUE. Capture probabilities and CPUE for bass were influenced primarily by altitude and conductivity and multiple regression analysis revealed that predictive models incorporating altitude, conductivity, surface area and capacity explained significant (p<0.05) amounts of variability in CPUE (53%), probability of capture (49%) and probability of limit bag (74%). Bank angling CPUE was influenced by conductivity, surface area and rainfall although an insignificant (p>0.05) amount of variability (63%) was explained by a predictive model incorporating these variables as investigations were constrained by small sample sizes and aggregated catch information. Scientific survey data provided multi-species information and highlighted the high proportion of non-native fish species in Eastern Cape impoundments. Gillnet catches were influenced primarily by species composition and were less subject to fluctuations induced by environmental factors. Overall standardised gillnet CPUE was influenced by surface area, conductivity and age of impoundment. Although the model fit was not significant at the p<0.05 level, 23% of the variability in the data was explained by a predictive model incorporating these variables. The presence of species which could be effectively targeted by gillnets was hypothesised to represent the most important factor influencing catch rates. Investigation of factors influencing CPUE in impoundments dominated by Clarias gariepinus and native cyprinids indicated that warmer, younger impoundments and smaller, colder impoundments produced higher catches of C. gariepinus and native cyprinids respectively. A predictive model for C. gariepinus abundance explained a significant amount of variability (77%) in CPUE although the small sample size of impoundments suggests that predictions from this model may not be robust. CPUE of native cyprinids was influenced primarily by the presence of Labeo umbratus and constrained by small sample size of impoundments and the model did not adequately explain the variability in the data (r² = 0.31, p>0.05). These results indicate that angling catch- and scientific survey data can be useful in providing predictions of fish abundance that are biologically realistic. However, more data over a greater spatial scale would allow for more robust predictions of catch rates. This could be achieved through increased monitoring of existing resource users, the creation of a centralised database for catch records from angling competitions, and increased scientific surveys of South African impoundments conducted by a dedicated governmental function.
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- Date Issued: 2012