The Welfare Quality protocol for dairy cows is a mainly animal-based welfare assessment, which has limited application due to its lengthy assessment time. Assessing physical and behavioural health, the measures included in the protocol are combined into an overall classification. This thesis aims to investigate ways of increasing the feasibility of animal-based welfare assessments through applying statistical methodologies to, 1) optimise the selection of measures in the Welfare Quality protocol, and 2) to investigate sequential sampling methods. For 1) a dataset of 92 Welfare Quality assessed UK dairy farms was used. The data were collected as part of a real world trial of the Welfare Quality protocol, which revealed several challenges associated with its implementation. As tests of correlation were unable to identify any potential 'iceberg indicators', cross-validated modelling -methodologies were then used to show that the Welfare Quality score for Absence of Prolonged Thirst was able to predict the overall Welfare Quality classification (Enhanced or Acceptable) 88% of t he time. This was interpreted as an unintended consequence of the measure aggregation system. With t he Welfare Quality overall classification being related to scope of assessment, the possibility of expanding scope of welfare assessment more generally to include positive welfare is explored through the Qualitative Behaviour Assessment and provision of behavioural opportunities. For 2) using a dataset of 80 fully locomotion scored dairy farms, three two-stage sequential sampling schemes were developed to classify farms as having good or bad welfare according to a lameness prevalence threshold. The preferred scheme, which attached greater certainty to decisions of bad welfare, demonstrated equal levels of accuracy to the Welfare Quality sampling scheme (88.6%) but at a reduced average sample size (69.8 vs 54.4). Limited to using indicators in isolation however, the challenge remains to increase the feasibility of a comprehensive welfare assessment.