8,081 responses from 386 respondents to 21 items.
One of 4 tables from the same source; this one is the movie_preferences table. The others are listed under Related tables.
| Description | Liking ratings for 21 film genres, 1-5. 386 respondents x 21 items = 8081 responses. |
|---|---|
| Reference | Manolika, M. (2021). The Big Five and Beyond: Which Personality Traits Do Predict Movie and Reading Preferences? [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/23NDKX |
| Licence | CC0 1.0 |
| Source data | https://doi.org/10.7910/DVN/23NDKX |
| Responses | 8,081 |
|---|---|
| Respondents | 386 |
| Items | 21 |
| Response categories | 5 |
| Responses per respondent | 20.935 |
| Responses per item | 384.810 |
| Density | 0.997 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | gre |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Movie genre preferences (21 film genres; Manolika, 2022) |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 2 |
| Mean characters per response | 12.800 |
| Flesch-Kincaid grade level | -3.400 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, manolika_2021_preferences.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | Age |
cov_gender | Covariate | Gender |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("manolika_2021_movie_preferences")
# Python
pip install irw
import irw
df = irw.fetch("manolika_2021_movie_preferences")
@misc{https://doi.org/10.7910/dvn/23ndkx,
doi = {10.7910/DVN/23NDKX},
url = {https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/23NDKX},
author = {Manolika, Maria},
keywords = {Social Sciences, Big Five; Dark Triad; movie preferences; reading preferences; personality},
title = {The Big Five and Beyond: Which Personality Traits Do Predict Movie and Reading Preferences?},
publisher = {Harvard Dataverse},
year = {2022}
}
| IRW version | v566 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v43.0 |