README file
Data Set Title: “CATCH-EyoU. Processes in Youth’s Construction of Active EU Citizenship. Italian Wave 1 Questionnaires. EXTRACT. Profiles of Citizenship Orientations”
Data Set Authors: Elvira Cicognani (University of Bologna), https://orcid.org/0000-0002-8653-290X; Peter Noack (University of Jena); Petr Macek (Masaryk University).
Data Set Contributors: Iana Tzankova (University of Bologna), https://orcid.org/0000-0001-7172-2009; Gabriele Prati (University of Bologna), https://orcid.org/0000-0002-0749-183X.
Data Set Contact Person: Iana Tzankova (University of Bologna), https://orcid.org/0000-0001-7172-2009, iana.tzankova2@unibo.it.
Data Set License: this data set is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/.  
Publication Year: 2021
Project Info: CATCH EyoU (Constructing AcTive CitizensHip with European YOUth: Policies, Practices, Challenges and Solutions), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 649538; http://www.catcheyou.eu/.  
Data set Contents
The data set consists of:
•	1 quantitative file saved in .csv format
CatchEyoU_WP7_Italian_W1_ProfilesCitizenship.csv. It contains a spreadsheet with 33 items taken from the Italian CATCH-EyoU Wave 1 data (see https://doi.org/10.5281/zenodo.1313624). 
•	1 README file saved in .rtf format
README_CatchEyoU_WP7_Italian_W1_ProfilesCitizenship.rtf
Data set Documentation
Abstract
This dataset contains the underlying data of the following article published in the Journal YOUNG:
	Tzankova, I., Prati, G., & Cicognani, E. (2021). Profiles of Citizenship Orientations among Youth.YOUNG  
The dataset consists of correlational quantitative data collected in Italy within Work Package 7 of the research project CATCH EyoU (Constructing AcTive CitizensHip with European YOUth: Policies, Practices, Challenges and Solutions). The sample consists of 1732 Italian adolescents and young adults between 15 and 30 years old. They participated in the study on a voluntary basis and filled paper and online questionnaires, after receiving an invitation from the researchers and a presentation of the aims of the study. The data was used in the analysis of patterns of citizenship orientations identified by civic and political participation, political interest and political trust. In particular, the following variables are included: age group (adolescent vs. young adults), age, gender, school track, perceived family income, minority background, participation (online, civic, conventional political and protest), political interest, political trust.
This data is made available for open access in compliance with H2020 Program regulation, following the guidelines stipulated by the Data Management Plan adopted by the CATCH-EyoU research project. 
File specifics
The dataset file was generated through IBM SPSS Software.
Data source
The Italian CATCH-EyoU Wave 1 data.
Codebook of variables 
Variable name	Variable description (item)	Values	
ID	Participants’ unique identification code	88; 99 = Missing values	
Cohort	Age groups	0 = 15-19 years old (adolescents); 
1 = 20-30 years old (young adults);
88; 99 = Missing values	
A_Age	How old are you?	Age in years
88; 99 = Missing values	
A_Gender	Gender	0= female
1= male
2= other	
A_Income	Does the money your household has cover everything your family needs?	0 = not at all; 
1 = partly;
2= mostly;
3= fully; 
88; 99 = Missing values	
A_Minor	National/ethnic majority or minority	0= majority;
1= minority;
2= double identification;
88; 99 = Missing values	
A_Track	What school track are you attending?	1= Lower track
2= Higher track
88; 99 = Missing values	
A_Part1	Signed a petition	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part3	Boycotted or bought certain products for political, ethical or environmental reasons	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part5	Volunteered or worked for a social cause	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part6	Participated in a concert or a charity event for a social or political cause	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part7	Donated money to a social cause	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part8	Shared news or music or videos with social or political content with people in my social networks	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part9	Discussed social or political issues on the internet	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part10	Participated in an internet-based protest or boycott	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part11	Joined a social or political group on Facebook (or other social networks)	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part12	Painted or stuck political messages or graffiti on walls	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part13	Taken part in an occupation of a building or a public space	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part14	Taken part in a political event where there was a physical confrontation with political opponents or with the police	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part15	Worked for a political party or a political candidate	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part16	Contacted a politician or public official	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part17	Donated money to support the work of a political group or organization	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Part18	Created political content online	1 = “No”;
2 = “Rarely”;
3 = “Sometimes”;
4 = “Often”;
5 = “Very often”;
88; 99 = Missing values	
A_Polint1	How interested are you in politics?	1 = “Not at all”;
2 = “Hardly”;
3 = “Somewhat”;
4 = “Very”;
5 = “Extremely”;
88; 99 = Missing values	
A_Polint2	How interested are you in what is going on in society?	1 = “Not at all”;
2 = “Hardly”;
3 = “Somewhat”;
4 = “Very”;
5 = “Extremely”;
88; 99 = Missing values	
A_Polint3	How interested are you in European Union related topics?	1 = “Not at all”;
2 = “Hardly”;
3 = “Somewhat”;
4 = “Very”;
5 = “Extremely”;
88; 99 = Missing values	
A_Polint4	How interested are you in national politics?	1 = “Not at all”;
2 = “Hardly”;
3 = “Somewhat”;
4 = “Very”;
5 = “Extremely”;
88; 99 = Missing values	
A_Itrust1	I trust the European Union.	1 = “Strongly disagree”;
2 = “Mostly disagree”;
3 = “Neither disagree nor agree”;
4 = “Mostly agree”;
5 = “Strongly agree”;
88; 99 = Missing values	
A_Itrust2	I trust the national government.	1 = “Strongly disagree”;
2 = “Mostly disagree”;
3 = “Neither disagree nor agree”;
4 = “Mostly agree”;
5 = “Strongly agree”;
88; 99 = Missing values	
A_Alien2	It does not matter who wins the European elections, the interests of ordinary people do not matter.	1 = “Strongly disagree”;
2 = “Mostly disagree”;
3 = “Neither disagree nor agree”;
4 = “Mostly agree”;
5 = “Strongly agree”;
88; 99 = Missing values	
A_Alien4	It does not matter who wins the /country/ elections, the interests of ordinary people do not matter.	1 = “Strongly disagree”;
2 = “Mostly disagree”;
3 = “Neither disagree nor agree”;
4 = “Mostly agree”;
5 = “Strongly agree”;
88; 99 = Missing values