A(n) __ is one of two types of scaling techniques in which each stimulus object is scaled independently of the others.
A) random scale
B) nonrandom scale
C) comparative scale
D) noncomparative scale
E) independence scale
The correct answer and explanation is :
The correct answer is: D) noncomparative scale
Explanation (300 words):
In marketing research and psychometrics, scaling techniques are methods used to assign numbers or labels to different objects, people, or events based on specific rules. These techniques help researchers measure attitudes, preferences, and perceptions in a structured way.
There are two major types of scaling techniques: comparative and noncomparative.
- Comparative scaling involves direct comparison of two or more objects. Respondents are asked to evaluate one object relative to another. This method provides ordinal data, meaning it can show the order of preferences but not the degree of difference between them. Examples include paired comparison, rank order, and constant sum scaling.
- Noncomparative scaling, also known as monadic scaling, is the type where each stimulus object is scaled independently of the others. Respondents evaluate a single item at a time, not in relation to others. This method allows for interval or ratio data, depending on the scale used. Noncomparative techniques include Likert scales, semantic differential scales, and staple scales.
In noncomparative scaling, since each item is assessed on its own merits, the data collected are generally more flexible and useful in statistical analysis. For instance, using a Likert scale (e.g., “strongly agree” to “strongly disagree”), a respondent might rate how much they agree with various statements independently.
This approach is advantageous when researchers want to measure absolute attitudes, satisfaction levels, or perceptions without requiring respondents to directly compare options. It’s especially useful in large surveys where comparing all possible pairs of items would be impractical.
In summary, a noncomparative scale is a scaling technique where each stimulus is assessed independently, making it suitable for absolute evaluation and statistical modeling.