Data Investigator provides professional data preparation and modification services to prepare datasets for statistical analysis. Our services include data grouping, variable creation and computation, data coding and recoding, reverse coding, and variable transformation according to the objectives and requirements of each analysis. We support academic research, medical and public health research, market research, government research, and business and organisational data analysis, including Independent Study (IS), Thesis and Dissertation projects. Our team has over 15 years of experience in statistical data analysis and provides consultation and support in both Thai and English. Free initial consultation and quotation are available. (Click here to contact us via LINE.)

What is Data Preparation and Modification?

• Data preparation and modification involve organising, creating or transforming variables within an existing dataset to make the data suitable for statistical analysis. This may include grouping continuous variables, calculating scores from multiple questionnaire items, reverse coding negatively worded items, recoding values, or creating new variables from existing data. The appropriate method should be determined by the research objectives, variable definitions and statistical analyses to be performed.

What Our Data Preparation Services Cover:

• Data Grouping — grouping continuous variables such as age or income into appropriate categories for analysis.

• Variable Creation — creating new variables from existing data according to the objectives of the analysis.

• Variable Computation — calculating total scores, mean scores or composite variables from multiple items.

• Reverse Coding — reversing the scoring of negatively worded questionnaire items where appropriate.

• Data Coding & Recoding — assigning or modifying numerical codes to prepare variables for statistical analysis.

• Variable Transformation — restructuring or transforming variables according to the characteristics of the data and analytical requirements.

​Why Data Preparation Matters Before Statistical Analysis:

• Properly structured variables and datasets are an important foundation for statistical analysis. Incorrect coding, score computation, grouping or reverse coding can affect analytical results and their interpretation. Reviewing and preparing the data before analysis helps ensure that variables are structured consistently with the research objectives and the statistical methods selected.

What is the Difference Between Data Cleaning and Data Modification?

• Data Cleaning focuses on identifying and reviewing potential problems in a dataset, such as missing data, data entry errors, out-of-range values and inconsistencies. Data Modification and Preparation, on the other hand, focuses on organising or transforming variables for analysis, such as data grouping, recoding, reverse coding and variable computation. In many research projects, these processes are performed sequentially before statistical analysis.
   

Our Data Preparation Process:

• Review the objectives: We review the research objectives, variables and intended statistical analyses.

• Review the dataset structure: The structure, variable formats and existing coding are reviewed.

• Determine appropriate modifications: Appropriate procedures such as grouping, recoding, reverse coding or variable computation are identified.

• Prepare and modify the data: The required modifications are performed according to the agreed criteria.

• Prepare the dataset for statistical analysis: The resulting dataset is prepared for the subsequent analytical stage.

Supporting Research and Business Data:

• Our data preparation and modification services support academic research, Independent Study (IS), Thesis and Dissertation projects, medical and public health research, market research, government research, and business and organisational data analysis. The appropriate data preparation procedures are determined according to the characteristics of each dataset and the objectives of the project.