Why Do Conceptual and Operational Definitions Matter?
Do conceptual and operational definitions matter? A major flaw of most surveys utilized in health services research is failure to develop an appropriate level of specificity in defining what is to be measured. This causes healthcare organizations to use instruments that can be vague or confusing for both survey administrators – and for respondents. The resulting data is often difficult to analyze and a significant amount of time and resources may have been wasted if the final survey results are of minimal use to the organization.
Creating Accurate Questions for Hard-to-Measure Domains
After the survey design team has crafted a clear statement of research purpose, they must create data collection questions that will allow them to gather information that accurately reflects the assessment domains or variables indicated in the research questions. However, this is deceptively difficult because few assessment domains are easily measurable.
For example, while a person’s height or resting pulse rate can be measured objectively with little difficulty, variables like “healthy dietary habits” cannot be measured as easily. In addition, any given area to be assessed may have several dimensions (discussed in Part 2 of this series). This entry focuses on how survey designers prepare to properly measure abstract and complex variables. Survey designers engage in a two-step process to render abstract concepts measurable. These processes are the elaboration of conceptual and operational definitions.
A conceptual definition is very important for the accurate measurement of an abstract assessment domain. Conceptualization is the process by which the survey design team clearly defines what their assessment domain means to the healthcare organization. For example, to measure the success of a program focused on “healthy dietary habits”, a health care organization must know exactly what they mean by that term.
Does it simply mean, “Does the individual report eating a healthy diet?” It could also mean, “Has the individual increased his or her intake of fruits and vegetables?” Or it could even mean, “Has the individual decided to eat healthier?” Of course, a very complete conceptual definition of healthy dietary habits might include them all. Depending on the complexity of the definition of healthy dietary habits one uses, very different data collection questions are necessary.
Once a conceptual definition has been created, the next step for the survey design team is to elaborate an operational definition. Operationalization is the process by which researchers define exactly what indicates a presence or absence of the various elements of the conceptual definition they have created. Keeping healthy dietary habits as an example, the survey designer team can operationalize it several ways.
What data can be collected to demonstrate how much impact their program has had on the people involved? One survey design team may operationalize “healthy dietary habits” as “the individual’s consumption of calories falls within the recommended allowance at least 5 days a week,” while another may choose to operationalize it as “the individual has reduced intake of deep fried foods to an average of 1 day per week.”
What are the pitfalls of poorly created conceptual and operational definitions? If a survey design team simply writes a data collection question to measure healthy dietary habits that says, “Since enrolling in our program have you begun to eat healthier?” the individual is left to choose what it means to eat healthy. An individual who has increased intake of fruits, while making no other dietary changes, may answer “yes” to this while another who has completely stopped eating fried foods and snacking on candies may still answer “no” because they are not yet confident in the permanency of these dietary changes.
Poor conceptual and operational definitions can compromise the validity of the survey. Survey questions may not actually be measuring what they are intended to, and the entire survey could produce meaningless data. Proper conceptual and operational definitions are a major step to guaranteeing a valid survey that can provide reportable results.
Focusing specifically on developing surveys for Health Services Research studies designed to measure health outcomes, this blog series details the following:
// Various types of health-related surveys and outcomes that can be measured
// Creating meaningful research questions
// Conceptualizing and operationalizing variables
// Developing sophisticated survey questions
Aday, Lu Ann and Llewellyn J. Cornelius, 2006. “Designing and Conducting Health Surveys: A Comprehensive Guide,” Jossey-Bass: San Francisco, CA.
As an experienced health care professional, Susan (Sue) Dess brings a wide range of experiences to Crestline. Her 15 year administrative and executive management background spans the operations of both managed care and provider organizations.
Additionally, Sue spent 25 years as an Emergency Room and Intensive Care Registered Nurse, further rounding out her ability to understand the “big picture.” Sue is intimately involved with each Crestline project, collaborating closely with consultants and clients.