Analysis unit

Persons and households Intercensal survey

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building and usually live together and eat together from one kitchen. One kitchen means that the daily needs are managed and provided by one budget. - Group quarters: Not applicable for public use sample

Universe

Permanent residents. Special census blocks and institutions are not included.

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE SIZE (person records): 605858.

SAMPLE DESIGN: Multistage sample of census blocks using urban/rural status and population density of the province.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire with dwelling information and social and demographic characteristics of individuals.

Analysis unit

Persons and households Intercensal survey; excludes Aceh

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building and usually live together and eat together from one kitchen. One kitchen means that the daily needs are managed and provided by one budget. - Group quarters: Not applicable for public use sample

Universe

Permanent residents

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE SIZE (person records): 1090892.

SAMPLE DESIGN: Multistage sample of census blocks using urban/rural status of each regency/municipality.

Mode of data collection

Face-to-face [f2f]

Research instrument

One multi-part questionnaire with dwelling information and social and demographic characteristics of individuals.

National Survey on Drug Use and Health (NSDUH) for Years 2015-2019

The National Survey on Drug Use and Health (NSDUH) is the "leading source of statistical information on the use of illicit drugs, alcohol, and tobacco and mental health issues in the United States" (SAMHSA). The abundance of Yes/No questions regarding the usage of illicit drugs make this dataset valuable for binary classification problems. During 2015, the survey received a partial redesign, creating "broken trends" from pre-2015 and post-2015. This is dataset contains every year of the NSDUH survey after the major restructuring in 2015.

Column Descriptions

All column names are identical to the Question Index found in the NSDUH documentation. The values in each column are codes that correspond to a particular answer in the survey. You can reference each question's meaning in the documentation, found here. Be sure to account for these codes before performing any analyses.

Additionally, some questions are not asked across ALL years, and will instead have an NA value.

Sources

All of the data used to create this dataset was obtained from the Substance Abuse & Mental Health Data Archive. You can access the data for separate years here.

n =  Numbers of responses. Test statistics for Wilcoxon rank sum test (W), Student's t-test (t), and χ2-test (χ2). Mean, median and ranges calculated from raw data before imputation.

Abstract

The Sierra Leone Core Welfare Indicators Questionnaire (CWIQ) survey provides information for management of the Sierra Leone economy and society. It embodies the results of a household survey designed to produce indicators for social welfare in a cheaper and more regular way to provide instruments for continuous monitoring of the poverty alleviation programme. The CWIQ survey produces information for measuring key changes in social indicators for different population groups in particular Indicators of access, use and satisfaction with social services. The overall objective of the Sierra Leone CWIQ survey, 2007 was to provide timely information for monitoring the implementation of the Sierra Leone Poverty Reduction Strategy and to begin a process of capacity building for the design, implementation, processing and analysis of household surveys within Statistics Sierra Leone (SSL) to strengthen the Poverty Reduction Strategy Monitoring and Evaluation System. This report presents the major findings of the CWIQ survey carried out from 5 April-10 May 2007 by SSL. A sample size of 7,800 households, covering rural and urban areas, in all nineteen Local Councils of the four administrative regions of the country was selected from a total of 520 Enumeration Areas. Detailed information was collected on most aspects of poverty such as demographic characteristics, education, health, employment, household assets, household amenities, poverty predictors, children under five, maternal child health and agriculture. The major findings of the survey are summarized in the order of the relevant chapters of the report.

Geographic coverage

The CWIQ Survey covered all four Sierra Leone administrative regions and nineteen Local Councils. Five hundred and twenty (520) Enumeration Areas (E.A.s) covering rural and urban areas in each of the Local Councils were sampled. Fifteen households were sampled in each EA and resulted in an overall sample of 7,800 households.

Analysis unit

The survey design was based on a stratified two-stage sample design using existing SSL sampling frame (2004 Population and Housing Census). E.A.'s served as primary sampling units while households served as secondary sampling units. The survey design enabled reporting of results at Local Council, Regional and National levels.

Universe

The survey covered sampled households and all household members country-wide.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 7,797 households were enumerated from a sample of 7,800 households, in 520 Enumeration Areas, giving the survey coverage rate of 98.4 percent (Table 1.2 of Appendix 1).For each enumeration area a reserve list of three household was selected for replacement due to refusals, respondents not at home, households not located, moved away among others. Only three Local Councils Kenema District, Kenema Town and Kambia District had 100% completed of the original households in the sample. The rest of the Local Council areas had some household replaced due to refusals or not found. The Southern Region had the highest level of replacement households of 3.3% and the Eastern Region had the lowest level of replacement of 0.1%. Nationally, 4,905 households were covered in the rural areas while 2,892 households were covered in the urban areas.

Sampling deviation

For each enumeration area a reserve list of three household was selected for replacement due to refusals, respondents not at home, households not located, moved away among others.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instruments included the modified generic scannable CWIQ questionnaire; the interviewer's manual and supervisor's manual. CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 IMPORTANT Create a reference number by combining the household and questionnaire numbers. Write this number NOW on the top of all pages. Page 1 of 14 Q.1 INTERVIEWER'S NAME A.1 CLUSTER A.2 HOUSEHOLD A.3 SEQ. A.4 INTERVIEWER A.5 DATE A.6 TIME A.7 RESPONDENT Q.2 NAME OF HEAD OF HOUSEHOLD Q.3 PROVINCE/REGION Q.4 DISTRICT Statistics Sierra Leone A.J. Momoh Street Freetown, Sierra Leone Comments A - INTERVIEW INFORMATION C W I Q Core Welfare Indicators Questionnaire I Q.5 LOCAL COUNCIL Q.6 CHIEFDOM/WARD Quest. N o. Hour Min. AM PM Member N o. A.8 RESULT Complete with selected household Complete with replacement - refusal Complete with replacement - not found Incomplete 1 2 3 4 A.9 INTERVIEW END Hour Min. AM PM PRINTING AND SHADING INSTRUCTIONS For optimum accuracy, please print carefully and avoid contact with the edges of the box. The following will serve as an example: Tel: 022-223287 Q.7 SECTION Q.8 VILLAGE/LOCALITY Day Mon th Y ear Reference Number 8862244644 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 IF RESPONSE IS NO OR DON'T KNOW GO TO NEXT PERSON MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Head B Page 2 of 14 - LIST OF HOUSEHOLD MEMBERS WRITE DOWN THE NAMES OF ALL PERSONS WHO NORMALLY LIVE AND EAT TOGETHER IN THIS HOUSEHOLD, STARTING WITH THE HEAD. B.1 Is [NAME] male or female? Male Female B.2 How long has [NAME] been away in the last 12 months? Never Less than 6 months 6 months or more B.3 What is [NAME]'s relationship to the head of household? Head Spouse Child Parent Other relative Not related B.4 How old was [NAME] at last birthday? (RECORD AGE IN COMPLETED YEARS.) B.9 Is [NAME]'s mother living in the household? Yes No What is [NAME]'s marital status? Never married Married(monogamous) Married(polygamous) Divorced Separated Widowed Is [NAME]'s father alive? Yes No Don't know B.7 Is [NAME]'s father living in the household? Yes No B.8 Is [NAME]'s mother alive? Yes No Don't know M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N IF RESPONSE IS NO OR DON'T KNOW GO TO B.8 M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N B.5 IF PERSON IS UNDER AGE 10 GO TO B.6 B.6 IF PERSON IS AGED 18 OR ABOVE GO TO NEXT PERSON Reference Number 7226244647 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 GO TO NEXT PERSON GO TO NEXT PERSON MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Page 3 of 14 C - EDUCATION Can [NAME] read and write in any language? Yes No C.2 Has [NAME] ever attended school? Yes No Why has [NAME] not started school? (YOU MUST MARK AT LEAST ONE ANSWER) a Too young b Too far away c Too expensive d Is working (home or job) e Useless/uninteresting f Illness g Other C.3 What is the highest grade [NAME] completed? C.4 Did [NAME] attend school last year? Yes No C.5 Is [NAME] currently in school? Yes No C.6 What is the current grade [NAME] is attending? C.7 Who runs the school [NAME] is attending? Government Religious organization Private Community Other C.8 Did [NAME] have any problems with school? (YOU MUST MARK AT LEAST ONE ANSWER) a No problem (satisfied) b Lack of books/supplies c Poor teaching d Not enough teachers e Teachers often absent f Lack of space g Facilities in bad condition h High fees i Other problem C.9 Why is [NAME] not currently in school? (YOU MUST MARK AT LEAST ONE ANSWER) a Completed school b Too far away c Too expensive d Is working (home or job) e Illness f Drug related problem g Pregnancy h Got married i Useless/uninteresting j Failed exam k Awaiting admission l Dismissed m Other C3 - Highest grade completed 00 None 01 Pre-school 11 P1 21 JSS1 31 University 12 P2 22 JSS2 41 Vocational 13 P3 23 JSS3 42 Teacher training 14 P4 24 SSS1 43 Technical 15 P5 25 SSS2 16 P6 26 SSS3 C6 - Current grade attending 01 Pre-school 11 P1 21 JSS1 31 University 12 P2 22 JSS2 41 Vocational 13 P3 23 JSS3 42 Teacher training 14 P4 24 SSS1 43 Technical 15 P5 25 SSS2 16 P6 26 SSS3 Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y IF C5 RESPONSE IS NO GO TO C.9 IF C2 IS NO AND [NAME]IS BELOW 19 YEARS GO TO C.10; ELSE GO TO NEXT PERSON C.10 ASK C10 IF PERSON IS BELOW 19 YEARS C.1 ASK C.1 IF PERSON IS AGE 15 OR ABOVE OTHERWISE GO TO C.2 Reference Number 2017244640 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Page 4 of 14 D - HEALTH D.1 Is [NAME] physically or mentally handicapped or disabled? Include person only if handicap prevents him or her from maintaining a significant activity or schooling. What sort of sickness/injury did [NAME] suffer? (YOU MUST MARK AT LEAST ONE ANSWER) D.5 What kind of health provider did [NAME] see? D.6 How did [NAME] pay for the consultation? a No need b Too expensive c Too far d Lack of confidence e Other D.8 Why did [NAME]

Analysis unit

Persons and households Intercensal survey; excludes all provinces in Kalimantan, Sulawesi, Maluka and Papua

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building and usually live together and eat together from one kitchen. One kitchen means that the daily needs are managed and provided by one budget. - Group quarters: Not applicable for public use sample

Universe

Permanent residents

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE SIZE (person records): 718837.

SAMPLE DESIGN: Multistage sample of households with first stage selection of enumeration areas, within which households were sampled from selected segments.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire with dwelling information and social and demographic characteristics of individuals.

Date created: July 12, 2022Update frequency: Not applicable. This was a one-time survey. Accuracy: This was a voluntary survey. Survey respondents self-selected into demographic categories. The results do not reflect a representative sample of Ottawa’s residents. For some questions, there was an “Unsure” option that respondents could select. As such, the sum of “Yes” and “No” responses may be less than the total number respondents who provided an answer to the question.Attributes: DemographicsWhat age category do you belong to? Age 18-29: Respondents who indicated that their age was between 18 - 29 years at the time of the survey.Age 30-39: Respondents who indicated that their age was between 30 - 39 years at the time of the survey.Age 40-49: Respondents who indicated that their age was between 40 - 49 years at the time of the survey.Age 50-64: Respondents who indicated that their age was between 50 - 59 years at the time of the survey.Age 65+: Respondents who indicated that their age was between 65 years or older at the time of the survey. Which language(s) do you speak at home? Check all that apply. Language En: Respondents who indicated they speak English at home.Language: Fr: Respondents who indicated they speak French at home.Language Both: Respondents who indicated they speak both English and French at home. Where were you born? Birthplace Canada: Respondents who indicated they were born in Canada.Birthplace Outside Canada: Respondents who indicated they were born outside of Canada and became a resident of Canada at any point in the past. What is your gender identity? The City of Ottawa recognizes that people may identify along the gender continuum. You may check all that apply.Gender Woman: Respondents who indicated that they identify as a woman.Gender Man: Respondents who indicated that they identify as a man.Gender Non-binary: Respondents who indicated that they identify as non-binary. Do you identify as a person with a disability? Disability Yes: Respondents who indicated that they identify as someone who has any type of disability.Disability No: Respondents who indicated that they do not identify as having any type of disability. Which of the following describes your household? Check all that apply Household Nuclear family: Respondents who identified their household as a "nuclear family", consisting of parents with their biological, adopted, and/or step children.Household Couple without children: Respondents who identified their household as a "couple without children".Household Single person: Respondents who identified their household as a "single person".Household Empty nesters: Respondents who identified their household as "emptynesters", meaning their children have grown up and now live elsewhere.Household Parents with adult children (age 18+) living in their home: Respondents who identified their household as "parents with adult children (18+) living in their home.Household One parent family: Respondents who identified their household as "one parent family".Household Multigenerational: Respondents who identified their household as "multigenerational", consisting of grandparents living with their children and grandchildren.Household Blended family: Respondents who identified their household as a "blended family", consisting of parents living with their children from previous relationships.Household Roommates: Respondents who identified their household as "roommates". Do you identify with one or more of the terms represented by or related to the 2SLGBTQQIA+ (Twospirited, Lesbian, Gay, Bi-sexual, Trans, Queer, Questioning, Intersex, Asexual) acronym?2SLGBTQQIA+ Yes: Respondents who indicated that they identify with any part of the 2SLGBTQQIA+ acronym.2SLGBTQQIA+ No: Respondents who indicated that they do not identify with any part of the 2SLGBTQQIA+ acronym. Do you have children age 18 and under in your household? What are their age(s)?Children No: Respondents who indicated that they do not have children under 18 years old in their household.Children 0-5:Respondents who indicated that they have at least one child between the ages of 0 and 5 in their household.Children 6-12:Respondents who indicated that they have at least one child between the ages of 6 and 12 in their household.Children 12-17:Respondents who indicated that they have at least one child between the ages of 13 and 17 in their household.FieldsResponses The number of respondents in each demographic category. How have your recreational and cultural activities changed since March 2020?Actvities_Responses: The number of respondents in each demographic category who responded to any part of the questions about how their activity participation changed during the COVID-19 pandemic.Activities_Virtual_More: The number of respondents in each demographic category who indicated that they have been participating in virtual activities more often.Activities_Virtual_Same: The number of respondents in each demographic category who indicated that they have been participating in virtual activities about the same as before.Activities_Virtual_Less: The number of respondents in each demographic category who indicated that they have been participating in virtual activities less often.Activities_Outdoor_More: The number of respondents in each demographic category who indicated that they have been participating in outdoor activities more often.Activities_Outdoor_Same: The number of respondents in each demographic category who indicated that they have been participating in outdoor activities about the same as before.Activities_Outdoor_Less: The number of respondents in each demographic category who indicated that they have been participating in outdoor activities less often.Activities_Individual_More: The number of respondents in each demographic category who indicated that they have been participating in individual activities more often.Activities_Individual_Same: The number of respondents in each demographic category who indicated that they have been participating in individual activities about the same as before.Activities_Individual_Less: The number of respondents in each demographic category who indicated that they have been participating in individual activities less often.Activities_SmallGroup_More: The number of respondents in each demographic category who indicated that they have been participating in small group activities more often.Activities_SmallGroup_Same: The number of respondents in each demographic category who indicated that they have been participating in small group activities about the same as before.Activities_SmallGroup_Less: The number of respondents in each demographic category who indicated that they have been participating in small group activities less often. Since March 2020, have you discovered new activities that you are enjoying participating in (e.g. virtual fitness classes, baking, crafts, hiking, leisure activities, etc.)?New_Activities_Responses: The number of respondents in each demographic category who responded to the question about discovering new activities during the COVID-19 pandemic.New_Activities_Yes: The number of respondents in each demographic category who indicated that they have discovered new activities.New_Activities_No: The number of respondents in each demographic category who indicated that they have not discover new activities. Have you participated in any City of Ottawa recreation, fitness or cultural programs since March 2020? If yes, What City of Ottawa recreation, fitness and cultural programs have you participated in or attended since March 2020? (Please check all that apply)City_Prog_Responses: The number of respondents in each demographic category who responded to any part of the questions about participating in City of Ottawa recreation, fitness, or cultural programs since March 2020.City_Prog_Yes: The number of respondents in each demographic category who indicated that they have participated in City recreation, fitness, or cultural programs since March 2020.City_Prog_No: The number of respondents in each demographic category who indicated that they have not participated in City recreation, fitness, or cultural programs since March 2020.City_Prog_Virtual: The number of respondents in each demographic category who indicated that they have participated in City virtual recreation, fitness, or cultural programs since March 2020.City_Prog_Registered: The number of respondents in each demographic category who indicated that they have participated in City registered recreation, fitness, or cultural programs since March 2020.City_Prog_Membership: The number of respondents in each demographic category who indicated that they have participated in City membership recreation, fitness, or cultural programs since March 2020.City_Prog_Drop-In: The number of respondents in each demographic category who indicated that they have participated in City drop-in recreation, fitness, or cultural programs since March 2020.City_Prog_Fitness: The number of respondents in each demographic category who indicated that they have participated in City fitness programs since March 2020.City_Prog_Swim: The number of respondents in each demographic category who indicated that they have participated in City swim programs since March 2020.City_Prog_MuseumGallery: The number of respondents in each demographic category who indicated that they have participated in City museum or gallery exhibitions or programs since March 2020.City_Prog_Indoor: The number of respondents in each demographic category who indicated that they have participated in City indoor recreation, fitness, or cultural programs since March 2020.City_Prog_Outdoor: The number of respondents in each demographic category who indicated that they have participated in City outdoor recreation, fitness, or cultural programs since March 2020. Would you like to continue using an online pre-booking system?OnlineBooking_Responses: The number of respondents in each

The data set provides insightful information based on a survey related to the socioeconomic demographics of drug-free parents living in high-risk environments and their involvement in drug prevention programs. The survey involved 200 families living in high-risk drug environments located in East Coast states of Peninsular Malaysia. The data includes a significant group of variables (A) socioeconomic demographic including; gender, age, race, marital status, place of current residents, number of children, number of children in primary school, number of children in secondary school, number of working children, number of children with disabilities, number of household members, level of education and employment categories, (B) parents involvement in drug prevention programs organised by various agencies including National Anti-Drug Agency, Ministry of Education, Ministry of Youth and Sport, The National Population and Family Development Board (LPPKN), Department of Information (Ministry of Communications and Multimedia), Department of National Unity and National Integration (Ministry of National Unity) and Non-Government Organisation (NGOs), parents’ interest towards drug prevention program and reasons for involvement. Each question regarding parent involvement is rated on a nominal scale in such a way that scores are given for 'Yes' for parents who involved in drug prevention programs and 'No' for parents who never involved in any drug prevention programs. Further question for parents’ interest towards drug prevention programs is rated by Yes because of seek new information, increase knowledge, availability and No because of commitment and health condition

Description

The research employed a mixed methods online survey to understand better the meaning, use, and development of academic research software at the University of Illinois Urbana-Champaign. Other objectives include understanding academic research software support and training needs to make projects successful at Illinois, as well as investigating the use of generative AI tools in using and creating research software.

At the beginning of the survey, all participants gave informed consent. The University of Illinois Urbana-Champaign Institutional Review Board (IRB Protocol no.: Project IRB24-0989) reviewed the study and gave it an exempt determination.

Data collection took place from August 2024 to October 2024. Prior to data analysis, identifiable respondent details were removed during the data cleaning process. Not Applicable and Unsure style responses were used for descriptive statistics, but these responses were excluded for inferential statistics.

Survey design

At the beginning of the online survey, a consent form was provided based on guidelines from the University of Illinois Institutional Review Board to the respondents stating the aims of the study, its benefits and risks, ethical guidelines, being a voluntary survey for participation and withdrawal, privacy and confidentiality, data security, estimated time for survey completion, and contact information of researchers for asking questions. Respondents clicked to indicate their consent. Survey questions were divided into four parts: demographic information, using software for research, creating software for research, and the protocol of citing software for research. The survey had to stop points, whereby not all questions applied to respondents, which led to different sample sizes at the stop points. At the opening of the survey, the number of respondents was 251 with the funding demographic question being answered by all respondents, while other demographic questions had between 225 and 228 respondents answering them. For the first stop question, using research software in their research, the total respondents was 212, and at the last stop question, respondents considering themselves to be research developers, the total number of respondents was 74. The last question of the survey was answered by 71 respondents. Respondents may also have left the survey for other reasons. The questions were primarily closed-type questions with single choice, multiple choice, or Likert scale, as well as a few open-ended questions. Likert scale responses were created utilizing validated scales from Vagias' (2006) Likert Type Scale Response Anchors.

Sampling

Survey Respondents’ Demographics

While most respondents were Tenure Track Faculty (34.7%, f=227), other key categories included Principal Investigator (22.4%, f=227) and Research Scientist (12.1%, f=227). Computer Science, Information Science, Mathematics, and Engineering fields combined for 16% (f=228) of the respondents surveyed, but it should be noted the remaining respondents were from various academic fields across campus from various arts, humanities, and social science fields (25%, f=228) to agriculture (10%, f=228), education (5%, f=228), economics (3%, f=228), medical sciences (4%, f=228), and politics and policy/law (1%, f=228). Most respondents were likely to receive funding from various government agencies. A more detailed breakdown of the demographic information can be found in the supplemental figures. Of the 74 respondents who answered whether they were a research software developer, most respondents did not consider themselves a research software developer, with respondents stating Not at All (39%, n=74) and Slightly (22%, n=74). In addition, open-ended questions asked for further detail about research software titles used in research, research software developer challenges, how generative AI assisted in creating research software, and how research software is preserved (e.g., reproducibility).

Table 1: Survey Respondents’ Demographics

Characteristics

Respondent (%)

Age

 18-24

 25-34

 35-44

 45-54

 55-64

 Over 64

 Preferred Not Answer

3%

14%

33%

27%

14%

7%

2%

Gender

 Woman

 Man

 Non-binary / non-conforming

 Prefer not to answer

49%

44%

2%

4%

Race

 Asian

 Black or African American

 Hispanic or Latino

 Middle Eastern or North African (MENA; new)

 White

 Prefer not to answer

 Other

12%

5%

6%

1%

67%

8%

1%

Highest Degree

 Bachelors

 Masters

 Professional degree (e.g., J.D.)

 Doctorate

6%

19%

5%

70%

Professional Title

 Tenure Track Faculty

 Principal Investigator

 Research Scientist

 Staff

 Research Faculty

 Other

 Teaching Faculty

 Postdoc

 Research Assistant

 Research Software Engineer

35%

22%

12%

8%

7%

4%

4%

4%

2%

2%

Academic Field

 Biological Sciences

 Other

 Agriculture

 Engineering

 Psychology

 Earth Sciences

 Physical Sciences

 Education

 Medical & Health Sciences

 Computer Science

 Library

 Chemical Sciences

 Human Society

 Economics

 Information Science

 Environment

 Veterinary

 Mathematical Sciences

 History

 Architecture

 Politics and Policy

 Law

18%

10%

10%

9%

8%

6%

6%

5%

4%3%

3%

3%

3%

3%

2%

2%

2%

2%

1%

1%

1%

0%

Years Since Last Degree

 Less than 1 Year

 1-2 Years

 3-5 Years

 6-9 Years

 10-15 Years

 More than 15 Years

4%

8%

11%

14%

24%

40%

Receive Funding

 Yes

 No

73%

27%

Funders for Research

 Other

 National Science Foundation (NSF)

 United States Department of Agriculture (USDA)

 National Institute of Health (NIH)

 Department of Energy (DOE)

 Department of Defense (DOD)

 Environmental Protection Agency (EPA)

 National Aeronautics and Space Administration (NASA)

Bill and Melinda Gates Foundation

Advanced Research Projects Agency - Energy (ARPA-E)

Institute of Education Sciences

Alfred P. Sloan Foundation

W.M. Keck Foundation

Simons Foundation

Gordon and Betty Moore Foundation

Department of Justice (DOJ)

National Endowment for the Humanities (NEH)

Congressionally Directed Medical Research Programs (CDMRP)

Andrew W. Mellon Foundation

22%

18%

18%

11%

9%

5%

4%

4%

2%

2%

1%

1%

1%

1%

1%

1%

0%

0%

0%

Table 2: Survey Codebook

QuestionID

Variable

Variable Label

Survey Item

Response Options

1

age

Respondent’s Age

Section Header:

Demographics Thank you for your participation in this survey today! Before you begin to answer questions about academic research software, please answer a few demographic questions to better contextualize your responses to other survey questions.

What is your age?

Select one choice.

Years

1-Under 18

2-18-24

3-25-34

4-35-44

5-45-54

6-55-64

7-Over 64

8-Prefer not to answer

2

gender

Respondent’s Gender

What is your gender?

Select one choice.

1-Female

2-Male

3-Transgender

4-Non-binary / non-conforming

5-Prefer not to answer

6-Other:

3

race

Respondent’s Race

What is your race?

Select one choice.

1-American Indian or Alaska Native

2-Asian

3-Black or African American

4-Hispanic or Latino

5-Middle Eastern or North African (MENA; new)

6-Native Hawaiian or Pacific Islander

7-White

8-Prefer not to answer

9-Other:

4

highest_degree

Respondent’s Highest Degree

What is the highest degree you have completed?

Select one choice.

1-None

2-High school

3-Associate

4-Bachelor's

5-Master's

6-Professional degree (e.g., J.D.)

7-Doctorate

8-Other:

5

professional_title

Respondent’s Professional Title

What is your professional title?

Select all that apply.

1-professional_title_1

Principal Investigator

2-professional_title_2

Tenure Track Faculty

3-professional_title_3

Teaching Faculty

4-professional_title_4

Research Faculty

5-professional_title_5

Research Scientist

6-professional_title_6

Research Software Engineer

7-professional_title_7

Staff

8-professional_title_8

Postdoc

9-professional_title_9

Research Assistant

10-professional_title_10

Other:

6

academic_field

Respondent’s most strongly identified Academic Field

What is the academic field or discipline you most strongly identify with (e.g., Psychology, Computer Science)?

Select one choice.

1-Chemical sciences

2-Biological sciences

3-Medical & health sciences

4-Physical sciences

5-Mathematical sciences

6-Earth sciences

7-Agriculture

8-Veterinary

9-Environment

10-Psychology

11-Law

12-Philosophy

13-Economics

14-Human society

15-Journalism

16-Library

17-Education

18-Art & Design Management

19-Engineering

20-Language

21-History

22-Politics and policy

23-Architecture

24-Computer Science

25-Information science

26-Other:

7

years_since_last_degree

Number of years since last respondent’s last degree

How many years since the award of your last completed degree?

Select one choice.

1-Less than 1 year

2-1-2 years

3-3-5 years

4-6-9 years

5-10-15 years

6-More than 15 years

8

receive_funding_for_research

Whether respondent received funding for research

Do you receive funding for your research?

1-Yes

0-No

9

funders_for_research

Respondent’s funding sources if they answered yes in Question 8

Who funds your research or work (e.g., NIH, Gates Foundation)?

Select all that apply.

1-funders_for_research_1

United States Department of Agriculture (USDA)

2-funders_for_research_2

Department of Energy (DOE)

3-funders_for_research_3

National Science

LimeSurvey application, where only Pirate Party members had access to the survey via a unique URL sent in an e-mail invitation that allowed members to fill out the questionnaire once. The poll ran from 22 February to 14 March 2024. 213 members out of a total of 1,179 party members completed the 19-question poll in full, a response rate of 18.6%. 16 research questions were Yes or No answers, 3 questions were socio-demographic questions focusing on gender, age and educational attainment. 47 women, 146 men and 20 respondents did not classify themselves as male or female. The age group 18-30 years included 27 respondents, 31-40 years included 93 respondents, 41-50 years included 56 respondents, 51-60 years included 24 respondents, 61-70 years included 11 respondents, 71 years and above included 2 respondents. In terms of highest completed education of the respondents, 60 have high school degree, 27 have bachelor's degree, 97 have master's degree, 13 have doctoral degree and 16 have other degree.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle.."Internet access" refers to whether or not a household uses or connects to the Internet, regardless of whether or not they pay for the service to do so. Data about Internet access was collected by asking if the respondent or any member of the household accessed the Internet. The respondent then selected one of the following three categories: "Yes, by paying a cell phone company or Internet service provider"; "Yes, without paying a cell phone company or Internet service provider"; or "No access to the Internet at the house, apartment or mobile home". Only respondents who answered "Yes, by paying a cell phone company or Internet service provider" were asked the subsequent question about the types of service they had access to such as dial-up, broadband (high speed) service such as cable, fiber-optic, or DSL, a cellular data plan, satellite or some other service..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most noticeable are increases in overall computer ownership or use, the total of Internet subscriptions, satellite subscriptions, and cellular data plans for a smartphone or other mobile device. For more detailed information about these changes, see the 2016 American Community Survey Content Test Report for Computer and Internet Use located at https://www.census.gov/library/working-papers/2017/acs/2017_Lewis_01.html or the user note regarding changes in the 2016 questions located at https://www.census.gov/programs-surveys/acs/technical-documentation/user-notes/2017-03.html..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural...

European Health Survey: Population with some chronic or long-term illness or health problem, according to sex and Autonomous Community. Population aged 16 years old and over. Autonomous Community.

This dataset contains student achievement data for two Portuguese high schools. The data was collected using school reports and questionnaires, and includes student grades, demographics, social, parent, and school-related features.

Two datasets are provided regarding performance in two distinct subjects: Mathematics and Portuguese language. I have cleaned the original datasets so that they are easier to read and use.

Attributes for both student_math_cleaned.csv (Math course) and student_portuguese_cleaned.csv (Portuguese language course) datasets:

1. school - student's school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira)
2. sex - student's sex (binary: "F" - female or "M" - male)
3. age - student's age (numeric: from 15 to 22)
4. address_type - student's home address type (binary: "Urban" or "Rural")
5. family_size - family size (binary: "Less or equal to 3" or "Greater than 3")
6. parent_status - parent's cohabitation status (binary: "Living together" or "Apart")
7. mother_education - mother's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
8. father_education - father's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
9. mother_job - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
10. father_job - father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
11. reason - reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")
12. guardian - student's guardian (nominal: "mother", "father" or "other")
13. travel_time - home to school travel time (ordinal: "<15 min.", "15 to 30 min.", "30 min. to 1 hour", or 4 - ">1 hour")
14. study_time - weekly study time (ordinal: 1 - "<2 hours", "2 to 5 hours", "5 to 10 hours", or ">10 hours")
15. class_failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16. school_support - extra educational support (binary: yes or no)
17. family_support - family educational support (binary: yes or no)
18. extra_paid_classes - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19. activities - extra-curricular activities (binary: yes or no)
20. nursery - attended nursery school (binary: yes or no)
21. higher_ed - wants to take higher education (binary: yes or no)
22. internet - Internet access at home (binary: yes or no)
23. romantic_relationship - with a romantic relationship (binary: yes or no)
24. family_relationship - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25. free_time - free time after school (numeric: from 1 - very low to 5 - very high)
26. social - going out with friends (numeric: from 1 - very low to 5 - very high)
27. weekday_alcohol - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28. weekend_alcohol - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29. health - current health status (numeric: from 1 - very bad to 5 - very good)
30. absences - number of school absences (numeric: from 0 to 93)

These grades are related with the course subject, Math or Portuguese:

1. grade_1 - first period grade (numeric: from 0 to 20)
2. grade_2 - second period grade (numeric: from 0 to 20)
3. final_grade - final grade (numeric: from 0 to 20, output target)

Important note: the target attribute final_grade has a strong correlation with attributes grade_2 and grade_1. This occurs because final_grade is the final year grade (issued at the 3rd period), while grade_1 and grade_2 correspond to the 1st and 2nd period grades. It is more difficult to predict final_grade without grade_2 and grade_1, but these predictions will be much more useful.

Additional note: there are 382 students that belong to both datasets, though the ID's do not match. These students can be identified by searching for identical attributes that characterize each student.

Please include this citation if you plan to use this database: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

Abstract

Household Income and Expenditure Survey (HIES) collects a wealth of information on HH income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (as a proxy for HH food consumption and nutrition analysis), and gender.

The Pacific Islands regionally standardized HIES instruments and procedures were adopted by the Government of Tokelau for the 2015/16 Tokelau HIES. These standards were designed to feed high-quality data to HIES data end users for:

1. deriving expenditure weights and other useful data for the revision of the consumer price index;
2. supplementing the data available for use in compiling official estimates of various components in the System of National Accounts;
3. supplementing the data available for production of the balance of payments; and
4. gathering information on poverty lines and the incidence of poverty in Tokelau.

The data allow for the production of useful indicators and information on the sectors covered in the survey, including providing data to inform indicators under the UN Sustainable Development Goals (SDGs). This report, the above listed outputs, and any thematic analyses of HIES data, collectively provide information to assist with social and economic planning and policy formation.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The universe of the 2015/16 Tokelau Household Income and Expenditure Survey (HIES) is all occupied households (HHs) in Tokelau. HHs are the sampling unit, defined as a group of people (related or not) who pool their money, cook and eat together. It is not the physical structure (dwelling) in which people live. The HH must have been living in Tokelau for a period of six months, or have had the intention to live in Tokelau for a period of twelve months in order to be included in the survey.

Household members covered in the survey include: -usual residents currently living in the HH; -usual residents who are temporarily away (e.g., for work or a holiday); -usual residents who are away for an extended period, but are financially dependent on, or supporting, the HH (e.g., students living in school dormitories outside Tokelau, or a provider working overseas who hasn't formed or joined another HH in the host country) and plan to return; -persons who frequently come and go from the HH, but consider the HH being interviewed as their main place of stay; -any person who lives with the HH and is employed (paid or in-kind) as a domestic worker and who shares accommodation and eats with the host HH; and -visitors currently living with the HH for a period of six months or more.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015/16 Tokelau Household Income and Expenditure Survey (HIES) sampling approach was designed to generate reliable results at the national level. That is, the survey was not designed to produce reliable results at any lower level, such as for the three individual atolls. The reason for this is partly budgetary constraint, but also because the HIES will serve its primary objectives with a sample size that will provide reliable national aggregates.

The sampling frame used for the random selection of HHs was from December 2013, i.e. the HH listing updated in the 2013 Population Count.

The 2015/16 Tokelau HIES had a quota of 120 HHs. The sample covered all three populated atolls in Tokelau (Fakaofo, Nukunonu and Atafu) and the sample was evenly allocated between the three atoll clusters (i.e., 40 HHs per atoll surveyed over a ten-month period). The HHs within each cluster were randomly selected using a single-stage selection process.

In addition to the 120 selected HHs, 60 HHs (20 per cluster) were randomly selected as replacement HHs to ensure that the desired sample was met. The replacement HHs were only approached for interview in the case that one of the primarily selected HHs could not be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for this Household Income and Expenditure Survey (HIES) are composed of a diary and 4 modules published in English and in Tokelauan. All English questionnaires and modules are provided as external resources.

Here is the list of the questionnaires for this 2015-2016 HIES: - Diary: week 1 an 2; - Module 1: Demographic information (Household listing, Demographic profile, Activities, Educational status, Communication status...); - Module 2: Household expenditure (Housing characteristics, Housing tenure expenditure, Utilities and communication, Land and home...etc); - Module 3: Individual expenditure (Education, Health, Clothing, Communication, Luxury items, Alcohonl & tobacco); - Module 4: Household and individual income (Wages and salary, Agricultural and forestry activities, Fishing gathering and hunting activities, livestock and aquaculture activities...etc).

Cleaning operations

All inconsistencies and missing values were corrected using a variety of methods: 1. Manual correction: verified on actual questionnaires (double check on the form, questionnaire notes, local knowledge, manual verifications) 2. Subjective: the answer is obvious and be deducted from other questions 3. Donor hot deck: the value is imputed based on similar characteristics from other HHs or individuals (see example below) 4. Donor median: the missing or outliers were imputed from similar items reported median value 5. Record deletion: the record was filled by mistake and had to be removed.

Several questions used the hotdeck method of imputation to impute missing and outlying values. This method can use one to three dimensions and is dependent on which section and module the question was placed. The process works by placing correct values in a coded matrix. For example in Tokelau the “Drink Alcohol” questions used a three dimension hotdeck to store in-range reported data. The constraining dimensions used are AGE, SEX and RELATIONSHIP questions and act as a key for the hotdeck. On the first pass the valid yes/no responses are place into this 3-dimension hotdeck. On the second pass the data in the matrix is updated one person at a time. If a “Drink Alcohol” question contained a missing response then the person's coded age, sex and relationship key is searched in the “valid” matrix. Once a key is found the result contained in the matrix is imputed for the missing value. The first preferred method to correct missing or outlying data is the manual correction (trying to obtain the real value, it could have been miss-keyed or reported incorrectly). If the manual correction was unsuccessful at correcting the values, a subjective approach was used, the next method would be the hotdeck, then the donor median and the last correction is the record deletion. The survey procedure and enumeration team structure allow for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: Person level record - characteristics of every (household) HH member, including activity and education profile; HH level record - characteristics of the dwelling and access to services; Final aggregated income - all HH income streams, by category and type; Final aggregated expenditure - all HH expenditure items, by category and type.

The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database.

Response rate

Overall, 99% of the response rate objective was achieved.

Sampling error estimates

Refer to Appendix 2 of the Tokelau 2015/2016 Household Income and Expenditure Survey report attached as an external resource.

This dataset contains Iowa population by Hispanic or Latino Origin, and race for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B03002.

Hispanic includes total, yes and no. Yes = Hispanic or Latino; No = Not Hispanic or Latino

Race includes: White Alone, Black or African American Alone, American Indian and Alaska Native, Asian Alone, Native Hawaiian and Other Pacific Islander Alone, Some Other Race, Two or More Races, and Two Races Excluding Some Other Race and Three or More Races.

Abstract

During October 1996 Statistics South Africa recorded the details of people living in more than nine million households in South Africa, as well as those in hostels, hotels and prisons. Census 1996 was the first nation wide census since the splitting up of the country under apartheid after 1970 and sought to apply the same methodology to everyone: visiting the household, and obtaining details about all its members from a representative who was either interviewed, or else filled in the questionnaire in their language of choice.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered households and household members in households in the nine provinces of South Africa.

Kind of data

Sample survey data

Sampling procedure

A sample of 1600 Enumerator Areas (EA's) was produced in conjunction with the sample for the 1996 Population Census post-enumeration survey. A two stage sampling procedure was applied in the following manner.

The first stratification was done by province, as well as by type of EA (formal or informal urban areas, commercial farms, traditional authority areas or other non-urban areas). Originally eight hundred EA's were allocated to each strata by province proportionately. Later some adjustments were made to ensure adequate representation of smaller provinces such as the Northern Cape. Independent systematic samples of EA's were drawn for each stratum within each province. The sampling frame that was used was constructed from the preliminary database of EA's which was established during the demarcation and listing phase of the 1996 population census. In the second phase 10 households were drawn from each EA on the western and eastern side of the EA drawn for the post enumeration survey. This meant 10 households per EA in 1600 different EA's, that is 16 000 households in total.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data files in the October Household Survey 1996 (OHS 1996) correspond to the following sections in the questionnaire:

House: Data from FLAP, Section 1 and Section 7 Person: Data from Section 2 Worker: Data from Section 3 Migrant: Data from Section 4 Death: Data from Section 5 Births: Data from Section 6 - This data had a considerable number of problems and will not be published. Income: Data from Section 7 (included in House) Domestic: Data from Section 8

Data appraisal

Questionnaire: The October Household Survey 1996 questionnaire had incorrect FLAP data. No Population Group question was indicated on the FLAP. DataFirst notified Statistics SA who supplied a corrected questionnaire which is the one now available with the dataset.

Household IDs: In the previous version of the 1996 October Household Survey dataset archived by DataFirst the HHID were not unique. This was corrected in the first version disseminated by DataFirst, version 1. Version 1.1 keeps this correction, but data users should check versions not obtained from DataFirst and replace these with the latest version available from DataFirst.

Linking Files: The Metadata for the OHS 1996 provides an explanation for merging the files in the files in the OHS 1996 dataset: "The data from different files can be linked on the basis of the record identifiers. The record identifiers are composed of the first few fields in each file. Each record contains the three fields Magisterial District, Enumeration area, and Visiting point number. These eleven digits together constitute a unique household identifier. All records with a given household identifier, no matter which file they are in, belong to the same household. For individuals, a further two digits constituting the Person number, when added to the household identifier, creates a unique individual identifier. Again, these can be used to link records from the PERSON and WORK files. The syntax needed to merge information from different files will differ according to the statistical package used (October Household Survey 1996: Metadata: General Notes: 2).” According to the above, to generate household IDs it is necessary to use a combination of magisterial district number (mdnumber), enumeration area number (eanumber) and visiting point number (vpnumber). To generate person IDs it is necessary to use the above with the person number (personnu).

These variables are named as such in the OHS 1996 House, OHS 1996 Births, OHS 1996 Migrant, OHS 1996 Deaths, OHS 1996 Household Income Other, OHS 1996 Other, OHS 1996 Domestic and OHS 1996 Flap data files. However, in the OHS 1996 Worker and OHS 1996 Person data files the variable for magisterial district number is “distr”, the variable for Enumeration Area is “ea” and the variable for visiting point number is called "visp”. The variable for person number in these files is called “respno”.

The metadata provided to DataFirst with this dataset does not discuss these changes.

October Household Survey 1996 Births file: Births data was collected by Section 6 of the OHS 1996 questionnaire, completed for all women younger than 55 years who had ever given birth. The metadata for this survey from Statistics SA states that “This data had a considerable number of problems and will not be published” The dataset provided by DataFirst therefore does not include the original “births” file. Those in possession of this file from unofficial versions of the dataset should note the following problems with the data in the OHS 1996 births file:

Variable name: eegender Question 6.2: Is/was (the child) a boy or a girl? Valid range: 1 (boy) - 2 (girl) Data quality issue: There is a third response value of 0 with no description

Variable name: livinghh Question 6.4: If alive: Is (the child) currently living with this household? Valid range: 1 (yes) - 2 (no) Data quality issue: This variable has an additional response value (0), which has no description

Variable name: agealive Question 6.5: If alive: How old is he/she? This question was asked of all women younger than 55 years who have ever given birth to provide the age of their living children. Data quality issue: responses range from 0-77 for age of child (assuming age 99 is for missing responses) which is outside the plausible range.

Variable name: agenaliv Question 6.6: If dead: How old was (the child) when he/she died? Data quality issue: The format of the age at death variable is not clear

Variable name: datebirt Question 6.7: [All children]: In what year and month was (the child) born? Data quality issue: There are problems with the format of the date of birth variable

Variable name: wherebor Question 6.8: [All children]: Where was (the child) born? Data quality issue: There are only three options for the place of birth in the questionnaire (in a hospital, in a clinic and elsewhere), but the data has 10 response values (0-9) with no explanation for this in the metadata.

Variable name: regstere Question 6.9 [All children] Was the birth registered? Valid range: 1(yes) - 2 (no) Data quality issue: There are 4 response values (0-3) for this variable

Updated Dataset Description

Title: LGTBI Survey in Peru 2017 - INEI Dataset

Dataset Description

This dataset contains responses from the 2017 survey conducted by the National Institute of Statistics and Informatics (INEI) in Peru. The survey provides insights into the demographics, socio-economic conditions, identity, and experiences of individuals within the LGTBI community in Peru.

Key Features:

• Demographics: Age, region of birth, gender identity, sexual orientation, and educational level.
• Socioeconomic Status: Employment, occupation, and housing conditions.
• Identity: Self-identified gender and sexual orientation.
• Experiences: Discrimination and societal attitudes.

This dataset is invaluable for researchers and policymakers interested in the rights and well-being of the LGTBI community in Latin America. Additionally, the dataset is suitable for training machine learning models for classification and multi-class prediction tasks.

Suggested Machine Learning Applications

The dataset can be used for various machine learning and statistical modeling tasks, including:

1. Classification Tasks

• Predicting Experiences of Discrimination: Use demographic features (edad, depa, p114) to classify whether an individual has experienced discrimination (p117_1, p117_2).
• Employment Prediction: Predict current employment status (cond_act) based on education level (p101), gender identity (p114), and sexual orientation (p113).

2. Multi-Class Prediction

• Gender Identity Prediction: Develop models to predict p114 (gender identity) using features like age (edad), region of residence (depa), and sexual orientation (p113).
• Occupation Classification: Use p130 (employment type) as a target variable and features like education level (p101), age (edad), and department of residence (depa).

3. Exploratory Data Analysis and Insights

• Understand relationships between variables, such as the correlation between discrimination experiences (p117_1 to p117_7) and education level (p101).
• Analyze regional disparities in experiences and opportunities by cross-tabulating depa with cond_act or p117.

Example Research Questions

1. Discrimination Patterns: What demographic or socioeconomic factors most strongly predict experiences of discrimination in the LGTBI community?
2. Employment Disparities: Are there significant differences in employment types between different gender identities or sexual orientations?
3. Regional Variations: How do experiences of discrimination and access to employment differ across regions in Peru?
4. Educational Attainment and Opportunities: How does educational attainment influence employment and discrimination experiences within the LGTBI community?

Dictionary of Variables

Below is a sample dictionary of variables with their descriptions. Both English and Spanish labels are included for categorical values:

Variable	Description	Values (Spanish)	Values (English)
p113	Sexual orientation	Asexual, Pansexual, Lesbiana, Gay, Heterosexual	Asexual, Pansexual, Lesbian, Gay, Heterosexual
p114	Gender identity	Trans masculino, mujer trans, etc.	Trans male, trans female, etc.
p116	Self-identified as trans (Yes/No)	Sí, No	Yes, No
p117_1	Experienced discrimination (Yes/No)	Sí, No	Yes, No
depa	Department of residence	Lima, Junín, etc.	Lima, Junín, etc.
edad	Age	Numerical	Numerical
cond_act	Current employment status	Ocupados, No pea	Employed, Not in labor force
p130	Employment type	Empleado(a), Trabajador(a) independiente	Employee, Self-employed
p101	Educational attainment	Sin nivel educativo, Superior Universitaria	No education, Higher education

How to Use

1. Data Preparation: Clean and preprocess the dataset, handling missing or inconsistent data.
2. Exploratory Analysis: Use tools like Pyt...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..Internet access refers to whether or not a household uses or connects to the Internet, regardless of whether or not they pay for the service to do so. Data about Internet access was collected by asking if the respondent or any member of the household accessed the Internet. The respondent then selected one of the following three categories: "Yes, by paying a cell phone company or Internet service provider"; "Yes, without paying a cell phone company or Internet service provider"; or "No access to the Internet at the house, apartment or mobile home". Only respondents who answered "Yes, by paying a cell phone company or Internet service provider" were asked the subsequent question about the types of service they had access to such as dial-up, broadband (high speed) service such as cable, fiber-optic, or DSL, a cellular data plan, satellite or some other service..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is con...

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: no - Households: Dwelling places with fewer than five persons unrelated to a household head, excluding institutions and transient quarters. - Group quarters: Institutions, transient quarters, and dwelling places with five or more persons unrelated to a household head.

Universe

Residents of the 50 states (not the outlying areas).

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: U.S. Census Bureau

SAMPLE SIZE (person records): 1799888.

SAMPLE DESIGN: 1-in-100 national random sample drawn by the U.S. Census Bureau

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1960 census used a machine-readable household form. Separate forms were used for each housing unit. Housing questions were included on the same form as the population items. Every fourth enumeration unit received a "long form," containing supplemental sample questions that were asked of all members of the unit. Sample questions are available for all individuals in every unit. Of the units receiving a long form, four-fifths received one version (the 20% questionnaire), and one-fifth received a second version with the same population questions but slightly different housing questions (the 5% questionnaire).

Early in the pandemic, the spread of the emerging virus SARS-CoV-2 was causing mild illness lasting less than two weeks for most people, with a small proportion of people developing serious illness or death. However, as the pandemic progressed, many people reported suffering from symptoms for weeks or months after their initial infection. Persistence of COVID-19 symptoms beyond one month, or what is known as long COVID-19, is recognized as a risk of acute infection. Up to date, information on long COVID-19 among Jordanian patients has not been reported. Therefore, we sought to conduct this cross-sectional study utilizing a self-administered survey. The survey asks a series of questions regarding participant demographics, long COVID-19 symptoms, information about pre-existing medical history, supplements, vaccination history, and symptoms recorded after vaccination. Chi square analysis was conducted on 990 responders, and the results showed a significant correlation (P<0.05) between ..., , , # Long COVID-19 in Jordan

The data represent responses to a self-reporting questionnaire that was designed to address long COVID-19 status and factors that may associate with it among Jordanians. It included questions regarding COVID-19 symptoms, pre-existing medical history, treatment and supplements, COVID-19 vaccination history, and symptoms recorded after vaccination. We adopted the definition of long COVID-19 that refers to individuals experiencing at least one symptom longer than four weeks.

Description of the data and file structure

The data were entered into SPSS data file and organized as follows: Demographic data (columns B-H) are sex (Male:0, Female: 1), age (18-34:2, 35-44:3, 45-54:4, >55: 5), marital status (single:1, married:2, other:3), smoking (No:0, Yes:1), employment status (not:0, goverment:1, private:2), and obesity (non- obese:0, obese:1), hospitalization required (column I, No:0, Yes:1), number of times of infected with COVID-19 (column J, 1,2,3 times), ...