Dhere Processings and analysiss documentationna

To enhance the reproducibility of the study, particular care must be taken in documenting the steps involved in data processing and analysis. This includes recording and writing down in detail all the processes and activities involved in data processing and analysis. Properly described and documented data ensure that the datasets can be understood and used both by the researchers themselves and by others who may wish to replicate the study or use the data for further analysis.
Processing of research data is the process by which raw data (in English: raw data) is structured, transformed and prepared for analysis.
Often, when data is collected, it is not in the right format or organised in a way that allows analysis to start straight away. Data processing is therefore an integral and important step in research. It involves a variety of data handling activities, which may vary according to the type and complexity of the data.
Examples:
  • Quantitative data processing: survey variables are recoded (e.g. responses on a scale from "strongly disagree" to "strongly agree" are converted to numbers from 1 to 5); missing values are replaced by "NA"
  • Qualitative data processing: transcription of interview recordings – audio or video recordings are converted into text format; analogue research material is digitised
Analysis of research data is the stage at which the processed data are analysed to answer the research questions and/or test the hypotheses. At this stage, the researcher uses methods and analysis techniques appropriate to his/her field of research and the objectives of the study. A variety of tools and computer programs are used to analyse the data.

Recommendations reproducible data processingi and analysisi

Reproducible processing and analysis of research data means that another researcher, using the same data and clearly documented activities, can replicate . steps and obtain identical results. Reproducibility is essential for the transparency and reliability of science,– It helps to avoid accidental errors, allows the validity of results to be verified and promotes collaboration between researchers.

Recommendations Study Reproducibilityas to promote

  • Produce detailed documentation: make clear notes on each step of data processing and analysis. Describe how the data were transformed from raw data to a data set ready for analysis and what methods, parameters and software were used
  • Keep folders where data is stored in order: Organise folders and files in a clear, logical way and ensure version control according to the data management plan
  • Use tools that promote reproducibility: If possible, choose tools that provide scripting to document the entire processing and analysis process
  • Introduce a reproducibility check: Invite colleagues to check whether they are able to reproduce the results, using the documentation on the data processing and analysis steps that has been created

Dhere Processings and analysiss documentationna

To enhance the reproducibility of the study, particular care must be taken in documenting the steps involved in data processing and analysis. This includes recording and writing down in detail all the processes and activities involved in data processing and analysis. Properly described and documented data ensure that the datasets can be understood and used both by the researchers themselves and by others who may wish to replicate the study or use the data for further analysis.
Processing of research data is the process by which raw data (in English: raw data) is structured, transformed and prepared for analysis.
Often, when data is collected, it is not in the right format or organised in a way that allows analysis to start straight away. Data processing is therefore an integral and important step in research. It involves a variety of data handling activities, which may vary according to the type and complexity of the data.
Examples:
  • Quantitative data processing: survey variables are recoded (e.g. responses on a scale from "strongly disagree" to "strongly agree" are converted to numbers from 1 to 5); missing values are replaced by "NA"
  • Qualitative data processing: transcription of interview recordings – audio or video recordings are converted into text format; analogue research material is digitised
Analysis of research data is the stage at which the processed data are analysed to answer the research questions and/or test the hypotheses. At this stage, the researcher uses methods and analysis techniques appropriate to his/her field of research and the objectives of the study. A variety of tools and computer programs are used to analyse the data.

Recommendations reproducible data processingi and analysisi

Reproducible processing and analysis of research data means that another researcher, using the same data and clearly documented activities, can replicate . steps and obtain identical results. Reproducibility is essential for the transparency and reliability of science,– It helps to avoid accidental errors, allows the validity of results to be verified and promotes collaboration between researchers.

Recommendations Study Reproducibilityas to promote

  • Produce detailed documentation: make clear notes on each step of data processing and analysis. Describe how the data were transformed from raw data to a data set ready for analysis and what methods, parameters and software were used
  • Keep folders where data is stored in order: Organise folders and files in a clear, logical way and ensure version control according to the data management plan
  • Use tools that promote reproducibility: If possible, choose tools that provide scripting to document the entire processing and analysis process
  • Introduce a reproducibility check: Invite colleagues to check whether they are able to reproduce the results, using the documentation on the data processing and analysis steps that has been created