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Data Collection and Analysis Tools

Quality Glossary Definition: Data collection and analysis tools

Data collection and analysis tools are defined as a series of charts, maps, and diagrams designed to collect, interpret, and present data for a wide range of applications and industries. Various programs and methodologies have been developed for use in nearly any industry, ranging from manufacturing and quality assurance to research groups and data collection companies.

Data Analysis Tools, Charts, and Diagrams

Use the following tools to collect or analyze data:

Box and whisker plot: A tool used to display and analyze multiple sets of variation data on a single graph.

Check sheet: A generic tool that can be adapted for a wide variety of purposes, the check sheet is a structured, prepared form for collecting and analyzing data.

Control chart: A graph used to study how a process changes over time. Comparing current data to historical control limits leads to conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).

Design of experiments (DOE): A method for carrying out carefully planned experiments on a process. Usually, design of experiments involves a series of experiments that start by looking broadly at a great many variables and then focus on the few critical ones.

Histogram: The most commonly used graph for showing frequency distributions, or how often each different value in a set of data occurs.

Scatter diagram: A diagram that graphs pairs of numerical data, one variable on each axis, to look for a relationship.

Stratification: A technique that separates data gathered from a variety of sources so that patterns can be seen.

Survey: Data collected from targeted groups of people about their opinions, behavior or knowledge. 

Data Collection Tools & Templates

Case Studies and Articles

Child Protective Services Agencies Turn Data Into Action Using Quality Tools (PDF) The Children’s Research Center’s SafeMeasures® service uses quality tools to help navigate data fog and provide meaningful analysis. Results from agencies using the service demonstrate how the availability of timely and useful data has dramatically improved the documentation, delivery and monitoring of child protective services.

Improving Child Protective Services Using Quality Tools (PDF) With the help of an ASQ Community Good Works grant, the Children’s Research Center is piloting a training curriculum to teach data-driven improvement techniques to social service agency workers in Santa Cruz County, California. By teaching a basic 10-step process for improvement, the center equips social workers with the tools needed to overcome barriers created by complex state and federal regulations and inefficient case management systems.

Outperforming Completely Randomized Designs (Journal of Quality Technology) Split-plot designs should be considered as an alternative to completely randomized designs even if running a completely randomized design is affordable.

Which Control Chart Do I Use? (World Conference on Quality and Improvement) It is important to choose the appropriate control chart. The type of control chart chosen can positively or negatively affect the outcomes. By selecting the appropriate control chart the economic control of quality is accomplished, which helps to minimize mistakes that can be made in deciding the fate of a process on the basis of a sample.

Adapted from The Quality Toolbox, Second Edition, ASQ Quality Press.
Adapted from The Quality Toolbox, Second Edition, ASQ Quality Press.

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