Abstract
Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.
Highlights
Sample surveys, where data from a subset, or sample, of a population are used to make inferences about that population, are a traditional research methodology which has been widely used in veterinary research and investigation [1,2]
In this era of “big data”, with modern techniques such as machine learning, bioinformatics, or other computer-based technologies being increasingly used in veterinary research [3] across areas such as animal behaviour [4] and disease detection [5] and prediction [6], the sample survey is in danger of appearing “old fashioned” and “out-dated”
In a well-planned sample survey the target population can be framed in advance and followed by a well-designed sampling process so that the samples are representative of the population [7]
Summary
Sample surveys, where data from a subset, or sample, of a population are used to make inferences about that population, are a traditional research methodology which has been widely used in veterinary research and investigation [1,2]. In a well-planned sample survey the target population can be framed in advance and followed by a well-designed sampling process so that the samples are representative of the population [7] This representativeness is often not achieved during the passive “big data” collection process, with data often being collected only from a particular subset of the target population—e.g., Revilla, et al [8] analysed more than 10.5 million measurements from ~13,000 pigs obtained using automatic feeding systems. This dataset was collected from only one boar testing station, making generalisation to the wider population potentially difficult. The aim of this review article is to provide a comprehensive introduction to the design-based approach for analysing survey data by (1) describing the analytical methods for elementary probability sampling methods, including simple random sampling, stratified random sampling, and cluster sampling, and (2) to demonstrate the key ideas necessary to understand and interpret those analytical methods, as well as how those ideas can be used to develop methods for any specific complex survey design
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