Turning survey results into a mathematical model involves several steps. The process can be broken down into the following key stages:

1. Define the Research Question and Objectives:

- Clearly define the research question or problem you want to address with your mathematical model.

- Identify the specific objectives you aim to achieve with the model.

2. Data Collection and Analysis:

- Conduct the survey and gather relevant data from respondents.

- Analyze the survey data to identify patterns, correlations, and trends. Common statistical techniques, such as regression analysis or factor analysis, may be employed.

3. Conceptualize the Mathematical Model:

- Determine the variables that will be part of your mathematical model. These variables should be based on the survey data and their relevance to the research question.

- Decide on the type of mathematical model you want to create (e.g., linear regression, logistic regression, differential equations, agent-based models, etc.) based on the nature of your data and research objectives.

4. Model Formulation:

- Translate the relationships observed in your survey data into mathematical equations or expressions.

- Define the parameters of your model, which are values that influence the behavior of the model and may be estimated using the survey data.

5. Parameter Estimation:

- Use statistical techniques or optimization methods to estimate the parameters of your mathematical model.

- This step may involve fitting your model to the survey data to find the best-fit parameters.

6. Model Validation:

- Evaluate the performance of your mathematical model by comparing its predictions to independent data or by using techniques such as cross-validation.

- Ensure that your model accurately represents the real-world phenomena you are studying.

7. Sensitivity Analysis:

- Perform sensitivity analysis to assess how changes in model parameters or input data affect the model's outcomes.

- Identify which variables or parameters have the most significant impact on the model's results.

8. Model Interpretation:

- Interpret the results of your mathematical model in the context of your research question and objectives.

- Draw conclusions and insights from the model's predictions.

9. Model Implementation:

- Implement your mathematical model in a suitable software environment or programming language. This may involve writing code to simulate the model or using specialized modeling software.

10. Model Use and Decision-Making:

- Utilize the mathematical model to make predictions, test hypotheses, or inform decision-making processes based on the survey results.

Remember that building a mathematical model from survey data can be a complex process, and the specific steps and techniques used may vary depending on the nature of the survey, the research question, and the type of model you intend to create. Collaboration with experts in data analysis and modeling can be valuable in this endeavor.

Simply, you can use coding for the answers of the survey's questions, mainly when they are selection from choices. For example: strong agree (5), agree (4), not agree nor disagree (3), disagree (2), strongly disagree (1). Keep in mind that such analysis can be done using advanced professional software like SPSS, Minitab, and even Excel (but it need further calculations and data preparation for statistical analysis results).

Another note, when you organize the answers of the survey to be dealt with the selected program (software), then you have another selection to choose: Regression, Factorial Analysis or Response Surface Methodology. Each one will give you its own results different from the other. If you want more clarification, you are welcome to contact me directly.

Good luck