Dataset

Variables

All variables were generated following specific constraints that could provide a broader look at statistical analysis through their characteristics variability. Therefore, this approach enables a large type of possible example analysis the reader can find throughout the book.

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  id: “id” is a numerical type variable that provides identification of an individual. Its value is unique for each individual covered under the universe of the dataset.

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  Age: “Age” is a numerical type of variable providing the current age of each individual.

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  Gender: “Gender” is a nominal variable providing the gender of each individual. This variable has two possible values, “Male” or “Female”.

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  Python_user: “Python_user” is a nominal type of variable and has the information if the individual is a frequent user of Python language in his or her data analysis task. This variable has two possible values, “Yes” or “No”.

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  R_user: “R_user” is a nominal type of variable and has the information if the individual is a frequent user of R software in his or her data analysis task. This variable has two possible values, “Yes” or “No”.

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  Publications: “Publications” is a numeric type of variable and its value indicates the number of publications the individual data analyst made until the date of the data collection.

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  Tasks: “Tasks” is a nominal type of variable and its values indicate the position or functions the researcher performs in his institution. The three possible values for this variable are:

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    Phd Student,

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    Postdoctoral Research,

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    PhD Supervisor.

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  Q1 to Q10: Variables “Q1” to “Q10” are the results of a survey’s questionnaire presented to our researchers, subjected to this study. The presented survey was:

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    Q1: I feel that research tools (software, hardware, books, and others) I currently use are enough to achieve my research goals.

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    Q2: I understand that my research area provides the opportunity to achieve excellent productivity (published papers, book chapters, books, etc.).

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    Q3: My scientific productivity increased in the last year.

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    Q4: I feel I can improve some of my research methods.

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    Q5: My research methods changed very much with time.

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    Q6: I quickly adapted to new research tools throughout time when I needed.

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    Q7: I am receptive to learn new research tools that might appear in the future.

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    Q8: I am sure my research methods are directly related to my scientific productivity.

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    Q9: I would change my research tools if I were given a chance to do that.

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    Q10: I feel that my research tools improved in the last few years.