TITLE:
Maximizing Sampling Efficiency
AUTHORS:
Harmon S. Jordan
KEYWORDS:
Sampling; Finite Population Correction; FPC; Finite Population; Infinite Population
JOURNAL NAME:
Applied Mathematics,
Vol.4 No.11,
November
5,
2013
ABSTRACT: Background and Goals: Although health care quality improvement has traditionally involved extensive work with paper records, the adoption of health information technology has increased the use of electronic record and administrative systems. Despite these advances, quality improvement practitioners now and for the foreseeable future need guidance in defining populations of individuals for study and in selecting and analyzing sample data from such populations. Statistical data analysis in health care research often involves using samples to make inferences about populations. The investigator needs to consider the goals of the study, whether sampling is to be used, and the type of population being studied. While there are numerous sampling strategies designed to conserve resources and yield accurate results, one of these techniques—use of the finite population correction (FPC)—has received relatively little attention in health care sampling contexts. It is important for health care quality practitioners to be aware of sampling options that may increase accuracy and conserve resources. This article describes common sampling situations in which the issue of the finite population correction decision often arises. Methods: This article describes 3 relevant sampling situations that influence the design and analysis phases of a study and offers guidance for choosing the most effective and efficient design. Situation 1: The study or activity involves taking a sample from a large finite target population for which enumerative inferences are needed. Situation 2: The population is finite and the study is enumerative. A complete enumerative count of “defects” in the process is needed so that remediation can occur. Here, statistical inference is unnecessary. Situation 3: The target population is viewed as infinite; such populations are “conceptual populations” [1] or “processes”. Results: The article shows how savings in resources can be achieved by choosing the correct analytic framework at the conceptualization phase of study design. Choosing the right sampling approach can produce accurate results at lower costs. Several examples are presented and the implications for health services research are discussed. Conclusion: By clearly specifying the objectives of a study and considering explicitly whether the data are a sample or a population, the practitioner may be able to design a more efficient study and thereby conserve resources. This article provides a conceptual framework in the form of three situations, several examples, and an algorithm (Figure 1) to help the intervention planner determine how to classify the study and when to apply the FPC.