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Barbara A. Cleary

Six Sigma

Using Check Sheets to Improve Data Analysis

The lowly check sheet represents a critical tool in effective data collection if it is used correctly.

Published: Tuesday, June 16, 2009 - 23:00

Specific techniques for data collection, fundamental to accurate analysis, are sometimes overlooked in the need to see outcomes or trends in data. The lowly check sheet represents a critical tool in effective data collection if it is used correctly.

Because check sheets are such simple tools, they are sometimes associated with quick-and-dirty, penciled notations that record data as it is collected. But creating an effective check sheet involves thinking, understanding why the data is being collected, how it will be used, who will gather it, where it will be gathered, and when it will be gathered. It is, fundamentally, a matter of design. By designing the data collection process rather than simply barging into it, one assures that the data itself will be useful and accurate.

Almost everyone, after all, uses check sheets. Teachers record grades, hospitals list infection types, cooks make grocery lists, families record children’s behaviors, inspectors identify defects in products—all with check sheets in various forms. To be sure that a check sheet will be truly useful, it’s important to go back to that thinking process:

Why is data being collected?—Before any improvements or changes can be made to a system or process, it is essential to establish a baseline by collecting data in an organized way (otherwise, how will one know that improvement has indeed ensued after changes have been made?) Check sheets are useful in this step, as well as in the ongoing monitoring of improvement efforts, since they provide a consistent way to collect data regardless of who is collecting it.

Example: If a problem in on-time delivery of medications to hospital patients demands improvement in the delivery process, it will be important to collect data on the medication delivery as it currently stands. Knowing whether medications are 10 minutes late on average, four hours late, or two days late helps to focus the improvement process. In this case, the purpose of data collection is to establish a baseline from which improvement can be considered. It would be important to collect data not only by number of incidents, but by time of day, length of delay, and perhaps type of medication.

How is this data to be used?—Whether the analysis will focus on improvement or on monitoring processes in an ongoing way, knowing the ways that it will be used will help to create check sheets that contribute to the outcome, providing enough data for analysis to be complete, but not extraneous or irrelevant data.

Example: Because the data related to scratches on finished manufactured products will be monitored over time even after the process has been improved, a check sheet that is designed to record types of scratches, location of scratches on the product, and the point in the process in which scratches occur most frequently, as well as the number of scratches that are identified, will give information that can be used on an ongoing basis. Analysis can drill down to distinguish sources of problems that occur over time.

Who will gather the data?—If a number of different people will be collecting data, it is essential to design a check sheet that will give consistent records. If, on the other hand, collection will be the responsibility of only one staff member, that consistency can be achieved by seeing the data in the same way each time.

Example: In a classroom, students might record their own spelling test results on individual check sheets that reflect the number of words misspelled each week, or the percentage of correctly-spelled words, or perhaps the specific types of errors (double consonants, suffixes, etc.). Each student’s check sheet will give him or her the information that will be most useful in bringing about improvement. But if a check sheet is created for all members of the class, it will be important that each one collects the same kind of data, or that all members collect data in the same way.

Where and when will data be collected?—This question is partly answered by determining the purpose for which data collection is undertaken, as described above. In order to garner the most useful information from the data that is collected, advance planning is critical. Depending on the purpose of the data collection, it might be important to include data for different shifts, different problems (wrong label, prescription not filled to specified amount, etc.) times of day, areas of production, even specific operators. Is it important to collect data once a day? Once a week? Every half hour? This decision depends, again, on the purpose for which the data will be used.

 

In answering these and other questions that are to be addressed before launching a data collection process, one step to be taken involves listing all the needs that are anticipated for this data. If someone wants to discover how many infections are occurring in inpatient care, brainstorming might yield other related questions:

  • What kinds of infections are manifested? (List all possibilities.)
  • What are the ages of patients who are infected?
  • How do infections affect patients by gender?
  • What are the numbers of infections by month or season?
  • What treatment options are exercised?

 

While each additional question adds complexity to the data collection process, it is clear that gathering the most data with respect to specific areas of the process renders the data more useful and the process more efficient.

A simple check sheet for only one of these areas of interest might look like this:

Infection
By unit

M

4/7

T

4/8

W

4/9

Th

4/10

F

4/11

S

4/12

S

4/13

Delivery

 

 

 

 

 

 

 

Intensive Care

 

 

 

 

 

 

 

Coronary Care

 

 

 

 

 

 

 

Respiratory Care

 

 

 

 

 

 

 

Surgical

 

 

 

 

 

 

 

On the other hand, it is possible to collect data relating to a number of different factors, using a single check sheet, if that check sheet has been designed with careful planning, so as the following:

 

Clinic Patients: Dr. E. Kolai

Data collected by_____________________

Date

Appointment time

Age

Gender

Symptoms/condition to be treated

Wait time 15 min. or less

Another appt. scheduled? Y/N

Lab work required?

10/7/08

 

 

 

 

 

 

 

10/14/08

 

 

 

 

 

 

 

10/21/08

 

 

 

 

 

 

 

10/28/08

 

 

 

 

 

 

 

11/4/08

 

 

 

 

 

 

 

11/11/08

 

 

 

 

 

 

 

11/10/08

 

 

 

 

 

 

 

11/18/08

 

 

 

 

 

 

 

11/25/08

 

 

 

 

 

 

 

12/2/08

 

 

 

 

 

 

 

12/9/08

 

 

 

 

 

 

 

12/16/08

 

 

 

 

 

 

 

12/23/08

 

 

 

 

 

 

 

1/6/09

 

 

 

 

 

 

 

1/13/09

 

 

 

 

 

 

 

1/20/09

 

 

 

 

 

 

 

1/27/09

 

 

 

 

 

 

 

2/3/09

 

 

 

 

 

 

 

2/10/09

 

 

 

 

 

 

 

 

What this example demonstrates is that a check sheet does not have to be restricted to checked items. Information can be entered in simple terms (e.g., gender designation) that can be analyzed after it has been collected.

To summarize, this flexible tool will support further analytical tools and approaches and render them more useful—but only if some thought is given to its construction. To get the most out of check sheets, in short, you need to think like an investigative reporter and ask the who, what, when, where, why, and how questions before you begin to gather data.

Discuss

About The Author

Barbara A. Cleary’s picture

Barbara A. Cleary

Barbara A. Cleary, Ph.D., is a teacher at The Miami Valley School, an independent school in Dayton, Ohio, and has served on the board of education in Centerville, Ohio, for eight years—three years as president. She is corporate vice president of PQ Systems Inc., an international firm specializing in theory, process, and quality management. She holds a masters degree and a doctorate in English from the University of Nebraska. Cleary is author and co-author of five books on inspiring classroom learning in elementary schools using quality tools and techniques (i.e., cause and effect, continuous improvement, fishbone diagram, histogram, Pareto chart, root cause analysis, variation, etc.), and how to think through problems and use data effectively. She is a published poet and a writer of many articles in professional journals and magazines including CalLab, English Journal, Quality Progress, and Quality Digest.