As I continue my research project, it is crucial for me to understand the types of data I will collect and how I will analyse them (Creswell and Creswell, 2018). The core focus of my research revolves around creating a web based tool that filters recipes based on allergen restrictions, providing a convenient solution for people with food allergies.

Data Gathering

There are two types of data that I will gather for my project:

Quantitative Data: This type of data consists of information which will be collected through website analytics, such as the number of users, duration of usage and user interactions. Additionally, I will assess the performance metrics of the tool in terms of accuracy and speed when filtering recipes.

Qualitative Data: This form of data is non-numerical. It can be obtained through user feedback surveys, interviews or reviews. It offers insights into users’ experiences with the tool, potential areas for improvement and overall satisfaction levels.

Data Analysis

Once the data has been collected, the next step involves analysing it to derive insights. Here’s how I plan to approach this process:

Quantitative Data Analysis: To summarise the website analytics and system performance data effectively I will employ statistics such as mean, median, mode and standard deviation. When it comes to determining relationships between variables, inferential statistics like correlation coefficients can be helpful (Field, 2013). For example, we can use them to understand the connection between usage duration and user satisfaction.

Qualitative Data Analysis: This involves identifying, analysing and reporting patterns or themes within the data. For instance, if user reviews consistently mention that the user interface is intuitive, that would be seen as a theme.

Ensuring Quality and Rigour

Ensuring the quality and reliability of my research findings is of importance. To achieve this, I will follow these steps:

Triangulation: By incorporating data collection methods such as analytics, user surveys and interviews, I can cross verify my findings and minimise bias.

Transparency: I will maintain a record of both the data collection and analysis processes so that others can easily comprehend and replicate my research.

Piloting and Iteration: Prior to commencing the study I will conduct a pilot test to assess the effectiveness of both the tool used and the chosen data collection methods. This will allow me to refine these aspects based on feedback received in order to ensure higher quality data for the study.

The journey towards conducting research relies heavily on obtaining quality data.

By strategising my approach to collecting and analysing data I can guarantee the dependability, authenticity, and ultimately, the significance of my findings. Not only will the data shed light on the project’s success, but it will also offer valuable insights for future improvements and potential avenues in creating a more inclusive digital landscape for individuals with food allergies.

References

Creswell, J.W. and Creswell, J.D. (2018) Research design: qualitative, quantitative, and mixed methods approaches. Fifth edition. Los Angeles: SAGE.

Field, A.P. (2013) Discovering statistics using IBM SPSS statistics: and sex and drugs and rock ‘n’ roll. 4th edition. Los Angeles: Sage.