Why Is The Five-Point Likert Scale Used For Footwear Purchase Intentions? Mastering Data-Driven Consumer Insights

The Five-point Likert scale serves as the essential translation layer between subjective consumer sentiment and objective data analysis. It is primarily used to convert vague, qualitative perceptions—such as trust in the quality of safety shoes or acceptance of delivery times—into quantifiable, discrete values. This standardization is critical because it meets the precise data input requirements necessary for advanced statistical tools like Generalized Linear Models (GLM).

The core value of the Likert scale in footwear research is its ability to turn abstract human feelings into structured data, allowing for the rigorous mathematical modeling required to predict purchase behavior accurately.

From Abstract Feeling to Concrete Data

Eliminating Ambiguity

Consumer perceptions are naturally qualitative and often vague. When a customer considers a footwear product, their feelings about "trust" or "value" are difficult to measure directly.

The Five-point Likert scale solves this by forcing these abstract feelings into a standardized framework. By providing specific options ranging from 'strongly disagree' to 'strongly agree', researchers eliminate the uncertainty inherent in open-ended descriptions.

Creating Discrete Values

Statistical models cannot process emotions; they process numbers. The Likert scale assigns a discrete value to a consumer's sentiment.

This conversion process turns a feeling of "I generally trust this brand" into a specific data point (e.g., a "4"). This distinct numerical value is what allows for the aggregation and comparison of data across large groups of consumers.

Powering Advanced Statistical Models

Meeting GLM Requirements

The primary technical reason for using this scale is to satisfy the input requirements of Generalized Linear Models (GLM). GLM is a standard framework used in market research to predict outcomes based on various input variables.

GLM requires precise, quantifiable inputs to function correctly. The Likert scale provides the necessary discrete values to populate these models, ensuring the resulting analysis is mathematically sound.

Enabling Structural Equation Modeling

Beyond basic regression, the standardized data from Likert scales facilitates high-precision analysis using software like Partial Least Squares (PLS).

This approach allows researchers to evaluate "latent variables"—hidden factors like perceived value or product involvement—that cannot be measured directly. By quantifying these factors, analysts can map complex causal relationships between product features and the final intention to purchase.

Understanding the Trade-offs

The Constraints of Standardization

While the Likert scale offers the precision needed for GLM, it does so by simplifying human emotion. It forces complex, nuanced feelings into one of five rigid categories.

Potential for Nuance Loss

There is a risk that the "distance" between a 3 and a 4 is not the same for every respondent. However, this trade-off is accepted in market research because the benefit of statistical compatibility outweighs the loss of qualitative nuance.

Making the Right Choice for Your Research

To effectively measure purchase intention, you must align your methodology with your analytical goals.

If your primary focus is Predictive Modeling (GLM): Use the Five-point Likert scale to ensure your qualitative data is converted into the discrete values required for mathematical precision.
If your primary focus is Causal Analysis (PLS): Rely on Likert-derived data to quantify latent variables, allowing you to map how specific product features directly influence consumer attitudes.

By standardizing subjective perceptions, you transform vague consumer opinions into actionable, rigorous market intelligence.

Summary Table:

Feature	Benefit for Footwear Research	Statistical Application
Subjective Conversion	Turns vague feelings (trust, value) into discrete numbers	Standardizes data for GLM input
Standardized Framework	Eliminates ambiguity from open-ended consumer descriptions	Enables aggregation of large-scale data
Latent Variables	Measures hidden factors like product involvement	Powers Structural Equation Modeling (PLS)
Predictive Accuracy	Maps causal relationships between features and sales	Optimizes production based on purchase intent

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References

Larisa Ivaşcu, Codruța Daniela Pavel. Psychological and Behavior Changes of Consumer Preferences During COVID-19 Pandemic Times: An Application of GLM Regression Model. DOI: 10.3389/fpsyg.2022.879368

This article is also based on technical information from 3515 Knowledge Base .