Skip to main content


Digital Health

Subjective Outcomes: Questionnaires and Data Forms

By January 10, 2020No Comments
Conductscience Administrator
Conduct Science promotes new generations of tools for science tech transferred from academic institutions including mazes, digital health apps, virtual reality and drones for science. Our news promotes the best new methodologies in science.
Conductscience Administrator
Conduct Science promotes new generations of tools for science tech transferred from academic institutions including mazes, digital health apps, virtual reality and drones for science. Our news promotes the best new methodologies in science.
Latest Posts
  • SDS-Polyacrylamide Gel Electrophoresis at Neutral pH (NuPAGE)
  • Games as Research Tools - Featured Image
  • SDS-Polyacrylamide Gel Electrophoresis at Neutral pH (NuPAGE)
  • SDS-Polyacrylamide Gel Electrophoresis at Neutral pH (NuPAGE)

Questionnaires, Data Forms & Research

Medical research and data go hand in hand. Often, to collect health-related information, experts rely on surveys, data forms, and questionnaires. Such measurements are based on data about previous symptoms, demographics, and other vital details which objective methods and laboratory results may have missed. One of the biggest advantages of questionnaires and data forms is the fact that they are cost-effective, simple, and quick to administer. On top of that, such tools are focused on patients and their subjective opinion, which is the core topic of interest in the field of digital health.

However, developing a new questionnaire or a survey comes with many challenges. First, as medical research should be precise and valid, the first step in the development of a new tool is a detailed literature review. Also, experimental goals need to be clear: experts need to clarify what they will gain from the development of a new questionnaire and explore if there are not any other sources they can use to collect information (“A Step-by-Step Guide to Developing Effective Questionnaires and Survey Procedures for Program Evaluation & Research”). Consequently, research variables and coding schedules must be well-defined. All items must be able to detect subtle changes, and at the same time, they must be logical, organized, short, and simple to understand. To help the effective development of a new questionnaire, other experts need to be consulted: peer review, for instance, is a recommended technique (Peat, 2011). Most of all, pilot testing must be conducted to ensure good validity, reliability, and generalizability.


    Choosing the Mode of Administration

    One of the first aspects to consider is the mode of administration. There are different modes of administration: self-administered questionnaires, surveys administered by a caregiver or a family member, and researcher-administered forms – each with its various benefits and disadvantages (Peat, 2011). For instance, self-administered questionnaires are time and cost-effective, and as such, they are extremely beneficial in large samples. However, self-administered forms are prone to unclarity and low response rates. Surveys administered by a family member or a caregiver can be used in pediatric populations or for adults who cannot respond for themselves. However, they can refer only to observed symptoms, such as vomiting – simply because some outcomes, such as morning stiffness, can be known only to the patients. On the other hand, surveys administered by a researcher can help experts collect more complex data, which is paramount in rare diseases. Unfortunately, such tools are more expensive and prone to interpretation bias.

    Another important factor is the procedure itself: surveys can be done in person, via the phone and post or web-based. Recently, health technology has been established as an effective tool, and online surveys have slowly replaced paper forms. No matter what type of administration experts choose, it’s extremely important for experimenters to be consistent throughout the whole study, which will increase the internal validity and reliability of the new test.

    Creating the Right Questions

    Creating new research questions is perhaps one of the most exciting and challenging parts of research. As explained above, a literature review is crucial to help experts gain a better understanding of the topic of interest and the existing measurements. Knowing if there are other similar and valid surveys is vital, and in fact, it can save precious time (Peat, 2011).

    All questions should be easy to understand and relevant. Therefore, the main characteristics of each sample (including size) should be considered. In fact, when it comes to participants, the sampling procedures (randomized, etc.) also need to be established. On top of that, researchers must decide if the questionnaire will be confidential for follow-up purposes or completely anonymous.

    The content, the wording, the order and the length itself are other vital aspects researchers need to focus on. Note that sometimes the same question can be asked twice but in a different way to double check responses and social desirability.

    We should mention that, usually, research questions tackle two types of medical information: qualitative (which is used to generate hypotheses) and quantitative (which is needed to test hypotheses) (“Questionnaire Design”). For qualitative data, exploratory and open questions might be better. Although they are more difficult to code and analyze, open questions widen the scope of research and help experts generate new ideas (Peat, 2011). On the other hand, standardized close-ended items are needed for collecting quantitative information. They also come with lots of challenges, such as attracting random responses. Still, they help researchers collect data quickly, via fixed and pre-coded replies (Peat, 2011).

    In any case, experts should always try to reduce unambiguity. If one could measure exposures, confounders, outcomes, and demographics at the same time, this would be the ultimate testing tool. Therefore, focus groups can be extremely valuable to collect new ideas, census forms to help research generate questions, and peer review to establish internal validity.

    Note that when sensitive information, such as ethnicity or income, is needed, surveys become more complicated. Do not forget that such questions may reduce the response rate, so they can be excluded or added at the end of the survey.  In fact, using wording similar to the national census, for instance, is a good method to make participants feel more comfortable (Peat, 2011).

    The Power of Wording

    As explained above, questions should be relevant, simple, valid, and responsive to change. Therefore, the wording is a paramount aspect of the development of a new survey.

    • Positive wording is more attractive and generates better response rates. Therefore, experts should avoid medical jargon and terms.
    • The construction of the items should be simple to understand and easy to answer, code, and analyze. Experts must ask only one question at the time. In case it’s needed, capitalization of words can be applied to clarify the question and help subjects focus better.
    • Pilot studies are a must. They can help experts deal with unambiguity. During a pilot study, experts can ask participants to rephrase questions they don’t understand, and consequently, achieve some new and better wording.
    • If there’s one correct answer, the response options should be clear. If there are multiple-response categories, these different groups should have the same meaning for everyone. For instance, timing responses, such as ‘seldom’ or ‘rare,’ may mean different things to different people.
    • ‘Don’t know’ options should be avoided. People are more willing to choose ‘Don’t know,’ especially when the meaning of the question is unclear. What’s more, this effect can lead to low validity and generalizability of the tool.
    • Dealing with missing data is also vital and can help experts increase generalizability. In addition, researchers should decide if missing answers can be used as negative responses in the analysis.