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.
data(ranks_read_genres)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:
Rank assigned to Classic.
Rank assigned to Novel.
Rank assigned to Thriller.
Rank assigned to Fantasy.
Rank assigned to Biography.
Rank assigned to Teenage.
Rank assigned to Horror.
Rank assigned to Comics.
Rank assigned to Poetry.
Rank assigned to Essay.
Rank assigned to Humor.
Gender.
Italian region of residence.
Age (years).
Number of children.
Education level.
Final grade of the education degree, scaled in the interval [6,10].
Number of books read in the last 12 months.
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.
str(ranks_read_genres)
head(ranks_read_genres)
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