Closed questions, also known as fixed-response questions, offer respondents a predetermined set of answers to choose from. They are a cornerstone of quantitative research, allowing for efficient data collection and streamlined statistical analysis. Designing well-crafted closed questions is crucial for obtaining accurate and meaningful data. Poorly designed questions can lead to biased results, inaccurate interpretations, and ultimately, flawed conclusions. This lesson will focus on key considerations for designing effective closed questions for use in surveys, questionnaires, and other research instruments.
Clarity and Specificity in Question Wording
The language used in closed questions must be crystal clear and unambiguous. Avoid jargon, technical terms, or overly complex sentence structures that respondents may not understand. Each question should focus on a single, specific issue. Double-barreled questions, which ask about two different things simultaneously (e.g., "Do you find our product both useful and affordable?"), should be avoided as they can confuse respondents and yield unreliable data. Vague or abstract concepts need to be defined clearly. For example, if asking about "satisfaction," specify what aspects of the service or product the satisfaction refers to. Pilot testing your questions with a small sample group is crucial to identify potential areas of confusion and refine the wording accordingly.
Exhaustive and Mutually Exclusive Answer Options
A fundamental principle of closed question design is ensuring that the provided answer options are both exhaustive and mutually exclusive. Exhaustive means that the response options cover all possible answers that a respondent might have. If necessary, include an "Other" option with a space for respondents to provide a written answer. This allows for the capture of responses that may not have been anticipated during the question design phase. Mutually exclusive means that each answer option is distinct and does not overlap with any other option. Overlapping options force respondents to choose the option that is closest to their actual answer, potentially leading to inaccurate data. For example, age ranges should not overlap (e.g., avoid "20-30" and "30-40"; instead, use "20-29" and "30-39").
Appropriate Response Scales and Categories
The choice of response scales and categories directly impacts the type of data collected and the types of statistical analyses that can be performed. Likert scales, which measure agreement or disagreement with a statement (e.g., strongly agree, agree, neutral, disagree, strongly disagree), are commonly used for measuring attitudes and opinions. Numerical scales, such as rating scales from 1 to 10, are suitable for measuring intensity or frequency. Categorical scales, which offer distinct categories without inherent order (e.g., marital status, occupation), are used for demographic data. The number of points on a scale can also influence the results. Generally, a 5- or 7-point Likert scale is preferred, providing sufficient granularity while remaining manageable for respondents. The categories used should be relevant to the target population and the research question.
Ordering and Presentation of Answer Options
The order in which answer options are presented can sometimes influence respondents' choices. This is known as response order effects. To minimize these effects, randomize the order of answer options whenever possible, especially for questions where there is no logical order. For scales that have a natural order (e.g., Likert scales), the order should follow that natural progression (e.g., from strongly disagree to strongly agree). Visual presentation also plays a role. Ensure that the answer options are clearly aligned and easy to read. Use consistent formatting throughout the questionnaire to maintain a professional and user-friendly appearance. If the questionnaire is administered online, ensure that the answer options are displayed correctly on various devices and screen sizes.
Now let's see if you've learned something...
⇦ 2 Cross-Tabulation Techniques 4 Interpreting Results and Drawing Conclusions ⇨