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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Sampling methods and considerations in quality improvement: a scoping review.

Lucylynn Lizarondo1, Vanesa Bochkezanian1, Alexa McArthur1

  • 1JBI, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia.

JBI Evidence Implementation
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Quality improvement (QI) sampling prioritizes practical, non-probability methods for timely decisions. Clearer guidance is needed for transparent, context-sensitive sampling in healthcare.

Keywords:
PDSAevaluationimprovement sciencequality improvementsample sizesampling

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Area of Science:

  • Healthcare Quality Improvement
  • Implementation Science
  • Research Methodology

Background:

  • Quality improvement (QI) uses data-driven methods to enhance healthcare.
  • Effective sampling is crucial for feasible and relevant QI data collection.
  • Current QI sampling methods are often pragmatic, inconsistently reported, and lack justification.

Purpose of the Study:

  • To identify and synthesize sampling strategies for QI initiatives.
  • To examine sampling frameworks and sample size considerations in QI.
  • To situate QI sampling within the broader context of evidence implementation and implementation science.

Main Methods:

  • Scoping review following JBI methodology.
  • Searched peer-reviewed and gray literature (2000-2024) across multiple databases and organizational websites.
  • Included sources offering conceptual/methodological insights into QI sampling; excluded empirical QI studies.

Main Results:

  • Ten sources were included, focusing on sampling for decision-making, not statistical inference.
  • Non-probability methods (judgment, purposive, convenience) dominated due to contextual fit and feasibility.
  • Limitations like bias and unclear sample size guidance were noted, with few actionable frameworks.

Conclusions:

  • QI sampling balances pragmatism with statistical rigor.
  • There is a need for clearer, fit-for-purpose guidance for QI sampling.
  • Improved guidance can support transparent, context-sensitive, and methodologically sound sampling decisions.