ranks_read_genres: Reading Genres Data (partial rankings with covariates)
| ranks_read_genres | R Documentation |
Reading Genres Data (partial rankings with covariates)
Description
The Reading Genres dataset was collected through an on-line survey conducted in Italy to investigate reading preferences in the context of the 2019 project Patto per la lettura – Conta chi legge. The questionnaire was administrated by the municipality of Latina (Latium, Italy), in collaboration with Sapienza University of Rome and the School of Government of the University of Tor Vergata. A sample of N=507 respondents provided their partial top-5 rankings of n=11 reading genres according to their personal preferences. The reading genres are: 1 = Classic, 2 = Novel, 3 = Thriller, 4 = Fantasy, 5 = Biography, 6 = Teenage, 7 = Horror, 8 = Comics, 9 = Poetry, 10 = Essay and 11 = Humor. The dataset also includes several covariates concerning respondents' socio-demographics characteristics and other free time activities.
Usage
data(ranks_read_genres)Format
A data frame gathering N=507 partial top-5 rankings of the reading genres in the first n=11 columns (rank 1 = most preferred item) and individual covariates in the remaining columns. Missing positions are coded as NA. The variables are detailed below:
- Classic
Rank assigned to Classic.
- Novel
Rank assigned to Novel.
- Thriller
Rank assigned to Thriller.
- Fantasy
Rank assigned to Fantasy.
- Biography
Rank assigned to Biography.
- Teenage
Rank assigned to Teenage.
- Horror
Rank assigned to Horror.
- Comics
Rank assigned to Comics.
- Poetry
Rank assigned to Poetry.
- Essay
Rank assigned to Essay.
- Humor
Rank assigned to Humor.
- Gender
Gender.
- Region
Italian region of residence.
- Age
Age (years).
- N_children
Number of children.
- Education
Education level.
- Final_mark
Final grade of the education degree, scaled in the interval [6,10].
- N_books
Number of books read in the last 12 months.
References
Crispino M, Mollica C, Astuti V and Tardella L (2023). Efficient and accurate inference for mixtures of Mallows models with Spearman distance. Statistics and Computing, 33(98), DOI: 10.1007/s11222-023-10266-8.
Mollica (2019). On-line questionnaire of the Italian 2019 project Patto per la lettura – Conta chi legge available at https://form.jotformeu.com/90275118835359.
Examples
str(ranks_read_genres)
head(ranks_read_genres)
