Description: Three multivariate statistical methods for the analysis of aquaculture experiment data are introduced and their application is demonstrated on different datasets. The methods are based on multiple regression and path analysis (causal analysis). The two multiple regression methods are termed the 'extended Gulland-and-Holt method' and the 'extended Bayley method', where the latter is derived and presented here for the first time. With these methods, the variables controlling fish growth rate in aquaculture experiments can be identified and their effects quantified in form of empirical, multiple regression models. Both methods permit the derivation of parameters of the von Bertalanffy growth function. These can be used for growth prediction and decision making in fish farm management and production under a wide range of environmental and treatment conditions. By computing the index of growth performance,ϕ' (phi-prime), the obtained growth parameters can be compared. The application of the presented methods is demonstrated on different types of datasets: 1) integrated livestock-fish farming experiments with mixed-sex populations of Nile tilapia (Oreochromis niloticus) conducted in the Philippines, 2) chicken manure-fed Nile tilapia culture experiments at Dor Station in Israel, 3) all-male hybrid tilapia, grown in polyculture at commercial farms in Israel, 4) pig and duck manure-fed Nile tilapia culture trials in Lima, Peru, and 5) experiments with mixed-sex populations of Oreochromis andersonii in Zambia, Africa. Depending on the amount of variables available in the datasets, and the precision of their measurement, models of different accuracy could be derived. The effect of the different variables was investigated with sensitivity analysis. Differences between the methods were studied. From the understanding gained by analyzing different types of datasets, recommendations are given for future applications of the methods, and for the design of new experiments that are to be analyzed with these methods.