# Dataset

DOI: 10.4018/978-1-68318-016-6.ch003
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## Abstract

In this chapter, we present the dataset used in the course of this book. Our case study is built upon fictional data. This process implied “collecting” data, and we will explain each of the chosen variables with more detail.
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## 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.

• 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.

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

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

• 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”.

• 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”.

• 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.

• 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:

• o

Phd Student,

• o

Postdoctoral Research,

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PhD Supervisor.

• 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.

• o

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

• o

Q3: My scientific productivity increased in the last year.

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

• o

Q5: My research methods changed very much with time.

• o

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

• o

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.

• o

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

• o

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

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