While analyzing internal data, you encounter a scenario where employees’ feedback on a survey is not consistent. One employee’s rating of “8” for satisfaction is different from another’s “8.” What is this an example of? Group of answer choices
a. Challenges in gathering data from diverse sources
b. Variability in qualitative data
c. Problems with survey design
d. Issues around quantifying human behavior and opinion
The correct answer and explanation is:
Correct Answer: d. Issues around quantifying human behavior and opinion
Explanation:
In the scenario provided, employees are asked to rate their satisfaction on a numerical scale (e.g., 1 to 10), and the same numerical rating (“8”) appears to mean different things to different individuals. This reflects a challenge in how human perceptions and subjective experiences are translated into numerical data—a classic issue in quantifying human behavior and opinion.
When people rate feelings like satisfaction, happiness, or stress, they do so based on their personal benchmarks, cultural context, personality, and expectations. For example, one person might rate an “8” because they are genuinely very happy but rarely use the top of the scale. Another person might also choose “8” but do so only when they feel their expectations were merely met, not exceeded. In both cases, the numerical value is the same, but the subjective meanings behind the numbers are not.
This issue reveals the limitations of using quantitative scales to represent qualitative feelings. It makes interpreting and comparing results difficult, especially when making decisions based on survey data. It’s not a flaw in data collection from diverse sources (choice a), nor strictly about qualitative data variability (choice b), and while survey design (choice c) can influence outcomes, the core problem here is inherent subjectivity in how people interpret and express their opinions numerically.
To address this, organizations might use additional methods like follow-up interviews, open-ended responses, or anchored rating scales (where each number is explained with a descriptive label) to add context and improve data reliability.
Therefore, the best and most precise answer is:
d. Issues around quantifying human behavior and opinion.