The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

The Health Information National Trends Survey (HINTS) collects nationally representative data about the American public's access to and use of cancer-related information. The 2007 HINTS survey is the third in an ongoing biannual series and provides information on the changing patterns, needs, and behavior in seeking and supplying cancer information and explores how cancer risks are perceived. Respondents were asked about the ways in which they obtained health information, their use of health care services, their views about medical information and research, and their beliefs about cancer. A series of questions specifically addressed cervical cancer, colon cancer, and the Human Papillomavirus (HPV). Information was also collected on physical and mental health status, diet, physical activity, sun exposure, history of cancer, tobacco use, and whether respondents had health insurance. Demographic variables include sex, age, race, education level, employment status, marital status, household income, number of people living in the household, ownership of residence, and whether respondents were born in the United States.

This dataset tracks the updates made on the dataset "Health Information National Trends Survey (HINTS)" as a repository for previous versions of the data and metadata.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

How high is the brand awareness of hint in the United States?When it comes to beverage online shop users, brand awareness of hint is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is hint in the United States?In total, *** of U.S. beverage online shop users say they like hint. However, in actuality, among the *** of U.S. respondents who know hint, *** of people like the brand.What is the usage share of hint in the United States?All in all, *** of beverage online shop users in the United States use hint. That means, of the *** who know the brand, *** use them.How loyal are the customers of hint?Around *** of beverage online shop users in the United States say they are likely to use hint again. Set in relation to the *** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around hint in the United States?In July 2022, about *** of U.S. beverage online shop users had heard about hint in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's some buzz around hint in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

The odds ratio and rate ratio for physician’s willingness to perform each examination and suspicion for central disease among otolaryngologists compared to non-otolaryngologists by multivariate analyses.

According to numerical simulations, stars are not always kept at their birth galactocentric distances but migrate. The importance of this radial migration in shaping galactic light distributions is still unclear. However, if it is indeed important, galaxies with different surface brightness (SB) profiles must display differences in their stellar population properties. We investigate the role of radial migration on the light distribution and the radial stellar content by comparing the inner colour, age and metallicity gradients for galaxies with different SB profiles. We define these inner parts avoiding the bulge and bar regions and up to around three disc scale lengths (type I, pure exponential) or the break radius (type II, downbending; type III, upbending). We analyse 214 spiral galaxies from the CALIFA survey covering different SB profiles. We make use of GASP2D and SDSS data to characterise their light distribution and obtain colour profiles. The stellar age and metallicity profiles are computed using a methodology based on full-spectrum fitting techniques (pPXF, GANDALF, and STECKMAP) to the IFS CALIFA data. The distributions of the colour, stellar age and stellar metallicity gradients in the inner parts for galaxies displaying different SB profiles are unalike as suggested by Kolmogorov-Smirnov and Anderson-Darling tests. We find a trend in which type II galaxies show the steepest profiles of all and type III the shallowest, with type I galaxies displaying an intermediate behaviour. These results are consistent with a scenario in which radial migration is more efficient for type III galaxies than for type I systems with type II galaxies presenting the lowest radial migration efficiency. In such scenario, radial migration mixes the stellar content flattening the radial stellar properties and shaping different SB profiles. However, in sight of these results we cannot further quantify its importance in shaping spiral galaxies, and other processes such as recent star formation or satellite accretion might play a role. Cone search capability for table J/A+A/604/A4/table1 (Galaxy characterisation and surface brightness)

Results of bivariate analysis in diagnosis by physician type.

The odds ratio and for physician’s willingness to perform each treatment and disposition among otolaryngologists compared to non-otolaryngologists by multivariate analyses.

Frequency and weighted percentages of socioeconomic and demographic variables among individuals in the HINTS 2003 survey.

Physician characteristics and knowledge.

Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers’ expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners.