Research - Data collection: Nursing

Nurse Jenna works in the imaging department at a local hospital. She noticed that clients who are scheduled for MRIs report high stress levels when compared to clients scheduled for other diagnostic testing, such as X-ray and CT scans. Nurse Jenna asks the nurse researcher, Nurse Chad, about this observation and he says, “How about we research stress levels in clients scheduled for MRIs compared to CT scans and X-rays? I’d be happy to assist you in the data collection process!” Nurse Jenna and Nurse Chad plan to measure stress levels in a sample of clients scheduled for MRIs, CT scans, and X-rays at their hospital by collecting interview data as well as biological data through measuring cortisol levels in the participant’s saliva. They will use what they know about data collection to gather both types of information.

Okay, so research is a systematic process of validating, refining, and generating knowledge. Data are pieces of information collected during research. Nurses use data all the time to care for clients and communities. Data can be as simple as a single blood glucose level to determine an insulin dose for a client, or as complex as international vaccine statistics to guide vaccination education campaigns. In addition to informing practice, data is also important in research. In research, data collection is done by systematically gathering data to be used to answer a research question and advance knowledge around a specific topic.

Now, when conducting a research study, researchers can either use existing data, such as information contained in medical records, or they collect new data, like Nurse Jenna, who is collecting information on stress levels in clients scheduled for MRIs. New data can be collected in several ways, like through observation, such as watching clients for signs of stress; self-reporting, such as through interviews or surveys about stress; or through physiological data, like collecting saliva for cortisol samples which is a measurement of stress.

Alright, several factors can affect data collection, such as bias, time, cost, and instrument. These are important for researchers to consider since they can impact the accuracy of research findings.

Bias refers to an error that causes data to become distorted, causing the results to be altered.. Bias can be introduced intentionally, like if Nurse Jenna purposefully only selected clients who are more prone to stress and anxiety to include in her study. It can also happen unintentionally when a researcher asks a question which influences the responses of the participants. For example, if Nurse Jenna asked participants, “Don’t you think that MRIs are a very stressful experience?” This question would introduce bias into the study since it could lead the participants to answer a question in a particular way. Instead, to eliminate bias, Nurse Jenna could say, “Tell me your thoughts about having an MRI.”

Time also affects data collection because data can change depending on the time period when it was collected. For example, stress levels may fluctuate depending on if Nurse Jenna interviews clients 4 days before a scheduled MRI or 4 minutes before an MRI. In addition, the time it takes a participant to complete a survey can affect data collection. So if Nurse Jenna uses a survey about stress that is 100 questions long, the participants might lose interest and decide not to finish it.