23 datasets found
  1. l

    Cleaned spouse and marriage data - Malawi

    • kpsmw.lshtm.ac.uk
    Updated Oct 25, 2022
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    Professor Amelia (Mia) Crampin (2022). Cleaned spouse and marriage data - Malawi [Dataset]. https://kpsmw.lshtm.ac.uk/nada/index.php/catalog/12
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    Dataset updated
    Oct 25, 2022
    Dataset authored and provided by
    Professor Amelia (Mia) Crampin
    Area covered
    Malawi
    Description

    Abstract

    The do-file marital_spouselinks.do combines all data on people's marital statuses and reported spouses to create the following datasets: 1. all_marital_reports - a listing of all the times an individual has reported their current marital status with the id numbers of the reported spouse(s); this listing is as reported so may include discrepancies (i.e. a 'Never married' status following a 'Married' one) 2. all_spouse_pairs_full - a listing of each time each spouse pair has been reported plus summary information on co-residency for each pair 3. all_spouse_pairs_clean_summarised - this summarises the data from all_spouse_pairs_full to give start and end dates of unions 4. marital_status_episodes - this combines data from all the sources to create episodes of marital status, each has a start and end date and a marital status, and if currently married, the spouse ids of the current spouse(s) if reported. There are several variables to indicate where each piece of information is coming from.

    The first 2 datasets are made available in case people need the 'raw' data for any reason (i.e. if they only want data from one study) or if they wish to summarise the data in a different way to what is done for the last 2 datasets.

    The do-file is quite complicated with many sources of data going through multiple processes to create variables in the datasets so it is not always straightforward to explain where each variable come from on the documentation. The 4 datasets build on each other and the do-file is documented throughout so anyone wanting to understand in great detail may be better off examining that. However, below is a brief description of how the datasets are created:

    Marital status data are stored in the tables of the study they were collected in: AHS Adult Health Study [ahs_ahs1] CEN Census (initial CRS census) [cen_individ] CENM In-migration (CRS migration form) [crs_cenm] GP General form (filled for various reasons) [gp_gpform] SEI Socio-economic individual (annual survey from 2007 onwards) [css_sei] TBH TB household (study of household contacts of TB patients) [tb_tbh] TBO TB controls (matched controls for TB patients) [tb_tbo & tb_tboto2007] TBX TB cases (TB patients) [tb_tbx & tb_tbxto2007] In many of the above surveys as well as their current marital status, people were asked to report their current and past spouses along with (sometimes) some information about the marriage (start/end year etc.). These data are stored all together on the table gen_spouse, with variables indicating which study the data came from. Further evidence of spousal relationships is taken from gen_identity (if a couple appear as co-parents to a CRS member) and from crs_residency_episodes_clean_poly, a combined dataset (if they are living in the same household at the same time). Note that co-parent couples who are not reported in gen_spouse are only retained in the datasets if they have co-resident episodes.

    The marital status data are appended together and the spouse id data merged in. Minimal data editing/cleaning is carried out. As the spouse data are in long format, this dataset is reshaped wide to have one line per marital status report (polygamy in the area allows for men to have multiple spouses at one time): this dataset is saved as all_marital_reports.

    The list of reported spouses on gen_spouse is appended to a list of co-parents (from gen_identity) and this list is cleaned to try to identify and remove obvious id errors (incestuous links, same sex [these are not reported in this culture] and large age difference). Data reported by men and women are compared and variables created to show whether one or both of the couple report the union. Many records have information on start and end year of marriage, and all have the date the union was reported. This listing is compared to data from residency episodes to add dates that couples were living together (not all have start/end dates so this is to try to supplement this), in addition the dates that each member of the couple was last known to be alive or first known to be dead are added (from the residency data as well). This dataset with all the records available for each spouse pair is saved as all_spouse_pairs_full.

    The date data from all_spouse_pairs_full are then summarised to get one line per couple with earliest and latest known married date for all, and, if available, marriage and separation date. For each date there are also variables created to indicate the source of the data.
    As culture only allows for women having one spouse at a time, records for women with 'overlapping' husbands are cleaned. This dataset is then saved as all_spouse_pairs_clean_summarised.

    Both the cleaned spouse pairs and the cleaned marital status datasets are converted into episodes: the spouse listing using the marriage or first known married date as the beginning and the last known married plus a year or separation date as the end, the marital status data records collapsed into periods of the same status being reported (following some cleaning to remove impossible reports) and the start date being the first of these reports, the end date being the last of the reports plus a year. These episodes are appended together and a series of processes run several times to remove overalapping episodes. To be able to assign specific spouse ids to each married episode, some episodes need to be 'split' into more than one (i.e. if a man is married to one woman from 2005 to 2017 and then marries another woman in 2008 and remains married to her till 2017 his intial married episode would be from 2005 to 2017, but this would need to be split into one from 2005 to 2008 which would just have 1 idspouse attached and another from 2008 to 2017, which would have 2 idspouse attached). After this splitting process the spouse ids are merged in.
    The final episode dataset is saved as marital_status_episodes.

    Analysis unit

    Individual

    Mode of data collection

    Face-to-face [f2f]

  2. Number of persons who married in a given year and marriage rate per 1,000...

    • datasets.ai
    • canwin-datahub.ad.umanitoba.ca
    • +3more
    21, 55, 8
    Updated Sep 7, 2024
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    Statistics Canada | Statistique Canada (2024). Number of persons who married in a given year and marriage rate per 1,000 unmarried persons, by age group and legal marital status [Dataset]. https://datasets.ai/datasets/cc589a5a-e8ac-4348-a5b5-8b50af7b1503
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    8, 21, 55Available download formats
    Dataset updated
    Sep 7, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Authors
    Statistics Canada | Statistique Canada
    Description

    Number of persons who married in a given year and age-specific marriage rate per 1,000 unmarried persons, by legal marital status, gender (when available) and place of occurrence, 1991 to most recent year.

  3. t

    MARITAL STATUS - DP02_DES_T - Dataset - CKAN

    • portal.tad3.org
    Updated Nov 18, 2024
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    (2024). MARITAL STATUS - DP02_DES_T - Dataset - CKAN [Dataset]. https://portal.tad3.org/dataset/marital-status-dp02_des_t
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    Dataset updated
    Nov 18, 2024
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES MARITAL STATUS - DP02 Universe - Population 15 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The marital status question is asked to determine the status of the person at the time of interview. Many government programs need accurate information on marital status, such as the number of married women in the labor force, elderly widowed individuals, or young single people who may establish homes of their own. The marital history data enables multiple agencies to more accurately measure the effects of federal and state policies and programs that focus on the well-being of families. Marital history data can provide estimates of marriage and divorce rates and duration, as well as flows into and out of marriage. This information is critical for more refined analyses of eligibility for program services and benefits, and of changes resulting from federal policies and programs.

  4. Total first marriage rates and age-specific first marriage rates per 1,000...

    • open.canada.ca
    • www150.statcan.gc.ca
    • +1more
    csv, html, xml
    Updated Jan 17, 2023
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    Statistics Canada (2023). Total first marriage rates and age-specific first marriage rates per 1,000 males, all marriages, inactive [Dataset]. https://open.canada.ca/data/en/dataset/91a68b5f-067e-4cbb-bab6-856a8dd2b884
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    csv, html, xmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Total first marriage rates and age-specific first marriage rates per 1,000 males, all marriages, by place of occurrence, 2000 to 2004.

  5. G

    Number of persons who divorced in a given year and divorce rate per 1,000...

    • open.canada.ca
    • www150.statcan.gc.ca
    • +1more
    csv, html, xml
    Updated Nov 8, 2023
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    Statistics Canada (2023). Number of persons who divorced in a given year and divorce rate per 1,000 married persons, by age group and sex or gender [Dataset]. https://open.canada.ca/data/dataset/19d4409d-25dd-4d17-a4f9-4a4bdc8d8add
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    xml, csv, htmlAvailable download formats
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Number of persons who divorced in a given year and age-specific divorce rates per 1,000 legally married persons, by sex or gender and place of occurrence, 1970 to most recent year.

  6. Total number of Spouses, by Marital Status of Spouses

    • open.canada.ca
    • datasets.ai
    csv, html
    Updated Jun 18, 2025
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    Government of Ontario (2025). Total number of Spouses, by Marital Status of Spouses [Dataset]. https://open.canada.ca/data/en/dataset/21f042c5-d6c4-44e2-9c03-f71c39b3a0e8
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    csv, htmlAvailable download formats
    Dataset updated
    Jun 18, 2025
    Dataset provided by
    Government of Ontariohttps://www.ontario.ca/
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Time period covered
    Jan 1, 2012 - Dec 31, 2022
    Description

    This data set contains counts of the total number of marriages by year, by the previous marital status of each spouse. The Office of the Registrar General (ORG) is responsible for maintaining vital statistics for the province of Ontario. The data provided represents the total number of completed registrations as of a specific date, tabulated or filtered by the given variables, and includes both residents and non-residents of Ontario (unless otherwise stated). This information is released in compliance with the Vital Statistics Act R.S.O. 1990, c. V. 4. Please note that the ORG does not guarantee the suitability, completeness or accuracy of the information.

  7. u

    Number of persons who married in a given year and marriage rate per 1,000...

    • data.urbandatacentre.ca
    • beta.data.urbandatacentre.ca
    Updated Oct 1, 2024
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    (2024). Number of persons who married in a given year and marriage rate per 1,000 unmarried persons, by age group and legal marital status - Catalogue - Canadian Urban Data Catalogue (CUDC) [Dataset]. https://data.urbandatacentre.ca/dataset/gov-canada-cc589a5a-e8ac-4348-a5b5-8b50af7b1503
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    Dataset updated
    Oct 1, 2024
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    Canada
    Description

    Number of persons who married in a given year and age-specific marriage rate per 1,000 unmarried persons, by legal marital status, gender (when available) and place of occurrence, 1991 to most recent year.

  8. D

    NL, BE and DE first marriages and lifespans 1600-1899 (GO 924 set)

    • test.dataverse.nl
    • dataverse.nl
    bin +1
    Updated Nov 2, 2016
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    Corry Gellatly; Charlotte Störmer; Tine De Moor; Corry Gellatly; Charlotte Störmer; Tine De Moor (2016). NL, BE and DE first marriages and lifespans 1600-1899 (GO 924 set) [Dataset]. http://doi.org/10.34894/IMTAAR
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    text/plain; charset=us-ascii(2965), bin(8069940)Available download formats
    Dataset updated
    Nov 2, 2016
    Dataset provided by
    DataverseNL (test)
    Authors
    Corry Gellatly; Charlotte Störmer; Tine De Moor; Corry Gellatly; Charlotte Störmer; Tine De Moor
    License

    https://tdvnl.dans.knaw.nl/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34894/IMTAARhttps://tdvnl.dans.knaw.nl/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34894/IMTAAR

    Time period covered
    1600 - 1899
    Description

    A data set of first marriages (including marriage location and ages of spouses at marriage) and lifespans of spouses (including year and location of births and deaths, where known), for marriages conducted between 1600 and 1899 in the Netherlands, Belgium and Germany. Also included is a binary indicator for whether the marriage, birth or death locations were urban or rural between 1600 and 1800, according to the coding system used by Bosker et al. 2013 [Rev. Econ. Stat., 95(4), 1418-1437 doi:10.1162/REST_a_00284]. The data set is derived from a genealogical database, which was constructed from family tree (GEDCOM) files contributed by users of www.genealogieonline.nl. The genealogical data from contributed files was error-checked before being combined into a single database using the TreeChecker application. From the initial pool of >1600 f iles contributed by the users of www.genealogieonline.nl, 924 files were included in the database after an assessment of the percentage of errors in each file, hence the database is known as the GO 924 set.Duplicate marriages were identified by the year of marriage and the first 7 letters of each spouse surname, whereupon a random duplicate was selected for inclusion in the data set. Note that names of individuals and exact dates of marria ges, births and deaths are excluded from this data set to prevent identification of individuals, as the genealogical data was provided to our research group on the basis that it would only be published in an aggregated or anonymised format. Access to the un-anonymised data may be granted subject to confidentiality agreements, please contact the authors for further information. Marriages were only included where the place of marriage had been checked and geocoded with latitude and longitude coordinates, where (as far as we could ascertain) it was the first marriage of the spouse, age at marriage was > 13 for both spouses, all lifespans were < 111 and no estimated dates were used to calculate spouse lifespan.The dataset is in a long format, in which there is a separate record for each spouse. It can be determined whether the spouse is the husba nd or wife (and conversely whether the other is the wife or husband) by the spouse_sex variable. A description of each variable is included in the text file accompanying the csv data file.

  9. g

    Total number of Spouses, by Marital Status of Spouses | gimi9.com

    • gimi9.com
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    Total number of Spouses, by Marital Status of Spouses | gimi9.com [Dataset]. https://gimi9.com/dataset/ca_21f042c5-d6c4-44e2-9c03-f71c39b3a0e8/
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    Description

    This data set contains counts of the total number of marriages by year, by the previous marital status of each spouse. The Office of the Registrar General (ORG) is responsible for maintaining vital statistics for the province of Ontario. The data provided represents the total number of completed registrations as of a specific date, tabulated or filtered by the given variables, and includes both residents and non-residents of Ontario (unless otherwise stated). This information is released in compliance with the Vital Statistics Act R.S.O. 1990, c. V. 4. Please note that the ORG does not guarantee the suitability, completeness or accuracy of the information.

  10. g

    Marital offspring reached from the first marriages by year of marriage and...

    • gimi9.com
    Updated Dec 17, 2024
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    (2024). Marital offspring reached from the first marriages by year of marriage and exact duration. | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_a6e230ac44e73fe249c4cfb4b85f42a129435e59
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    Dataset updated
    Dec 17, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The count and description of the families of the Basque Country is done using various statistical sources. On the one hand, families are counted through the Population and Housing Census; on the other hand, the operation Demographic Survey (ED), of an intercensal and five-year periodicity, offers information on the formation of families, family size, preferences on the number and spacing of children, as well as numerous other characteristics, using the method of retrospective approximation to demographic phenomena. The count and description of the families of the Basque Country is done using various statistical sources. On the one hand, families are counted through the Population and Housing Census; on the other hand, the operation Demographic Survey (ED), of an intercensal and five-year periodicity, offers information on the formation of families, family size, preferences on the number and spacing of children, as well as numerous other characteristics, using the method of retrospective approximation to demographic phenomena.

  11. u

    Total number of Spouses, by Marital Status of Spouses - Catalogue - Canadian...

    • data.urbandatacentre.ca
    • beta.data.urbandatacentre.ca
    Updated Sep 30, 2024
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    (2024). Total number of Spouses, by Marital Status of Spouses - Catalogue - Canadian Urban Data Catalogue (CUDC) [Dataset]. https://data.urbandatacentre.ca/dataset/gov-canada-21f042c5-d6c4-44e2-9c03-f71c39b3a0e8
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    Dataset updated
    Sep 30, 2024
    Description

    This data set contains counts of the total number of marriages by year, by the previous marital status of each spouse. The Office of the Registrar General (ORG) is responsible for maintaining vital statistics for the province of Ontario. The data provided represents the total number of completed registrations as of a specific date, tabulated or filtered by the given variables, and includes both residents and non-residents of Ontario (unless otherwise stated). This information is released in compliance with the Vital Statistics Act R.S.O. 1990, c. V. 4. Please note that the ORG does not guarantee the suitability, completeness or accuracy of the information.

  12. o

    Registered marriage officiants

    • data.ontario.ca
    • gimi9.com
    • +1more
    csv, xls
    Updated Jul 4, 2025
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    Government and Consumer Services (2025). Registered marriage officiants [Dataset]. https://data.ontario.ca/dataset/registered-marriage-officiants
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    xls(2142720), csv(1517601)Available download formats
    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    Government and Consumer Services
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Time period covered
    Jul 4, 2025
    Area covered
    Ontario
    Description

    Registered marriage officiants in Ontario.

    The dataset includes:

    • first name, last name and location of the marriage officiant
    • affiliation
    • a date stamp at the end indicating the date when the changes were made by the operations team

    To search: Use the Control+F buttons to find a specific city or person.

    Note: Registrations and cancellations of marriage officiants are generally updated within 4 weeks of notification.

    This data is related to:

  13. D

    Life histories of persons marrying, between 1600 and 1999, and dying, in the...

    • dataverse.nl
    csv, txt
    Updated Nov 15, 2017
    + more versions
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    Corry Gellatly; Corry Gellatly; Charlotte Störmer; Anita Boele; Tine De Moor; Charlotte Störmer; Anita Boele; Tine De Moor (2017). Life histories of persons marrying, between 1600 and 1999, and dying, in the Netherlands [GO924] [Dataset]. http://doi.org/10.34894/MILU3S
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    txt(7102), csv(239421126)Available download formats
    Dataset updated
    Nov 15, 2017
    Dataset provided by
    DataverseNL
    Authors
    Corry Gellatly; Corry Gellatly; Charlotte Störmer; Anita Boele; Tine De Moor; Charlotte Störmer; Anita Boele; Tine De Moor
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Netherlands
    Description

    A data set based on marriages conducted between 1600 and 1999 in the Netherlands. Includes information on lifespans and marriage ages of spouses (focal spouse and other), also location data for births, marriages and deaths. Includes information on lifespans and death locations of parents of the spouses. Includes information on whether the marriage, birth and death locations were urban or rural between 1600 and 1800, based on whether a place contained 5000 or more inhabitants (using a database developed by Bosker et al. 2013 [Rev. Econ. Stat., 95(4), 1418-1437 doi:10.1162/REST_a_00284]). The data set is derived from a genealogical database, which was constructed from family tree (GEDCOM) files contributed by users of www.genealogieonline.nl, via a program in which genealogists who were registered with the website were invited to contribute their family trees for scientific research purposes. The genealogical data from contributed files was error-checked before being combined into a single database using the TreeChecker application (http://www.treechecker.net). From the initial pool of >1600 contributed files, 924 files were included in the database after an assessment of the percentage of errors in each file, hence the database is known as the GO 924 set. Duplicate marriages were identified by the year of marriage and the first 7 letters of each spouse surname, whereupon a random duplicate was selected for inclusion in the data set. Note that names of individuals and exact dates of marriages, births and deaths are excluded from this data set to prevent identification of individuals, as the genealogical data was provided to our research group on the basis that it would only be published in an aggregated or anonymised format. Access to the un-anonymised data may be granted subject to confidentiality agreements, please contact the authors for further information. Marriages were only included where the place of marriage was in the Netherlands, marriage age of the spouse was > 13 and the spouse died in the Netherlands. The dataset is in a long format, in which there is a separate record for each spouse. It can be determined whether the spouse is the husband or wife (and conversely whether the other is the wife or husband) by the spouse_sex variable. A description of each variable is included in the accompanying text file: GO924_married_and_dying_in_NL_VARIABLES.txt.

  14. g

    Number of Spouses, by Age Groups of Spouses | gimi9.com

    • gimi9.com
    Updated Jun 12, 2024
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    (2024). Number of Spouses, by Age Groups of Spouses | gimi9.com [Dataset]. https://gimi9.com/dataset/ca_46df90d5-0425-4472-b30c-c225dcd64f1e/
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    Dataset updated
    Jun 12, 2024
    Description

    This data set contains a summary of the ages of spouses for marriages registered with the province. The data is organized by year. The Office of the Registrar General (ORG) is responsible for maintaining vital statistics for the province of Ontario. The data provided represents the total number of completed registrations as of a specific date, tabulated or filtered by the given variables, and includes both residents and non-residents of Ontario (unless otherwise stated). This information is released in compliance with the Vital Statistics Act R.S.O. 1990, c. V. 4. Please note that the ORG does not guarantee the suitability, completeness or accuracy of the information.

  15. u

    Number of persons who divorced in a given year and divorce rate per 1,000...

    • data.urbandatacentre.ca
    • beta.data.urbandatacentre.ca
    Updated Oct 1, 2024
    + more versions
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    (2024). Number of persons who divorced in a given year and divorce rate per 1,000 married persons, by age group and sex or gender - Catalogue - Canadian Urban Data Catalogue (CUDC) [Dataset]. https://data.urbandatacentre.ca/dataset/gov-canada-19d4409d-25dd-4d17-a4f9-4a4bdc8d8add
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    Dataset updated
    Oct 1, 2024
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    Canada
    Description

    Number of persons who divorced in a given year and age-specific divorce rates per 1,000 legally married persons, by sex or gender and place of occurrence, 1970 to most recent year.

  16. d

    Connecticut Vital Records — Index of Marriages, 1897-2001

    • catalog.data.gov
    • data.ct.gov
    Updated May 31, 2025
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    data.ct.gov (2025). Connecticut Vital Records — Index of Marriages, 1897-2001 [Dataset]. https://catalog.data.gov/dataset/connecticut-vital-records-index-of-marriages-1897-2001
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    Dataset updated
    May 31, 2025
    Dataset provided by
    data.ct.gov
    Area covered
    Connecticut
    Description

    Connecticut Vital Records — Index of Marriages, 1897-2001 This index allows researchers to find the name, place and date of marriage for couples who were married in Connecticut between 1897 through 2001. The information from this index can be used to request a copy of a marriage certificate from the Town/City Clerk of the municipality listed in the search results. Order forms for vital records requests can be found at this link. There is no guarantee that the information contained in the index is accurate and the State of Connecticut has no legal liability for any claims resulting from reliance on this information.

  17. A

    ‘Measuring Belief In Conspiracy Theories’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 4, 2017
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2017). ‘Measuring Belief In Conspiracy Theories’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-measuring-belief-in-conspiracy-theories-00ad/ae059d69/?iid=001-189&v=presentation
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    Dataset updated
    Jan 4, 2017
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Measuring Belief In Conspiracy Theories’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/measuring-belief-in-conspiracy-theories on 14 February 2022.

    --- Dataset description provided by original source is as follows ---

    Introduction

    from openpsychometrics.org: The Generic Conspiracist Beliefs Scale (GCBS) was created for use in researching conspiracy theories. Research had typically measures beliefs in conspiracies by asking questions about specific conspiracy theories. The GCBS attempts to correct problems with this approach by asking broad questions about assumption that are presumed to underlie such beliefs. The GCBS measures an overall score and five facet scores. The scale was developed by Robert Brotherton, Christopher C. French and Alan D. Pickering of Goldsmiths University of London in 2013.

    Procedure

    The GCBS has 15 questions. In each questions the taker must rate how much they agree with a given statement on a five point scale where 1=Disagree, 3=Neutral and 5=Agree.

    The Dataset

    This dataset contains ~2,495 questionnaire answers collected online by Open Psychometrics.

    Dataset Content

    This data was collected through an interactive on-line version of the Generic Conspiracist Beliefs Scale in 2016.

    Visitors completed the test primarily for personal amusement. At the end of the test but before the results were displayed, users were asked if they would be willing to complete an additional survey and allow their responses to be saved for research. Only users who agreed yes are in this dataset. Individuals with age < 13 were not recorded.

    • The responses to the GCBS are in (question numbers match to items in TABLE A1 of Brotherton, et. al. 2013.):

    Q1 - Q15

    • The time spent answering each question was also recorded, and are stored in variables

    E1 - E15

    • The other following time elapses were also recorded:

    introelapse The time spent on the introduction/landing page (in seconds) testelapse The time spent on the GCBS questions surveyelapse The time spent answering the rest of the demographic and survey questions

    The Ten Item Personality Inventory was administered (see Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A Very Brief Measure of the Big Five Personality Domains. Journal of Research in Personality, 37, 504-528.):

    TIPI1 Extraverted, enthusiastic. TIPI2 Critical, quarrelsome. TIPI3 Dependable, self-disciplined. TIPI4 Anxious, easily upset. TIPI5 Open to new experiences, complex. TIPI6 Reserved, quiet. TIPI7 Sympathetic, warm. TIPI8 Disorganized, careless. TIPI9 Calm, emotionally stable. TIPI10 Conventional, uncreative.

    The TIPI items were rated "I see myself as:" _ such that

    1 = Disagree strongly 2 = Disagree moderately 3 = Disagree a little 4 = Neither agree nor disagree 5 = Agree a little 6 = Agree moderately 7 = Agree strongly

    The following items were presented as a check-list and subjects were instructed "In the grid below, check all the words whose definitions you are sure you know":

    VCL1 boat VCL2 incoherent VCL3 pallid VCL4 robot VCL5 audible VCL6 cuivocal VCL7 paucity VCL8 epistemology VCL9 florted VCL10 decide VCL11 pastiche VCL12 verdid VCL13 abysmal VCL14 lucid VCL15 betray VCL16 funny

    A value of 1 is checked, 0 means unchecked. The words at VCL6, VCL9, and VCL12 are not real words and can be used as a validity check.

    A bunch more questions were then asked:

    education "How much education have you completed?", 1=Less than high school, 2=High school, 3=University degree, 4=Graduate degree urban "What type of area did you live when you were a child?", 1=Rural (country side), 2=Suburban, 3=Urban (town, city) gender "What is your gender?", 1=Male, 2=Female, 3=Other engnat "Is English your native language?", 1=Yes, 2=No age "How many years old are you?" hand "What hand do you use to write with?", 1=Right, 2=Left, 3=Both religion "What is your religion?", 1=Agnostic, 2=Atheist, 3=Buddhist, 4=Christian (Catholic), 5=Christian (Mormon), 6=Christian (Protestant), 7=Christian (Other), 8=Hindu, 9=Jewish, 10=Muslim, 11=Sikh, 12=Other orientation "What is your sexual orientation?", 1=Heterosexual, 2=Bisexual, 3=Homosexual, 4=Asexual, 5=Other race "What is your race?", 1=Asian, 2=Arab, 3=Black, 4=Indigenous Australian, Native American or White***, 5=Other voted "Have you voted in a national election in the past year?", 1=Yes, 2=No married "What is your marital status?", 1=Never married, 2=Currently married, 3=Previously married familysize "Including you, how many children did your mother have?"
    major "If you attended a university, what was your major (e.g. "psychology", "English", "civil engineering")?"

    Source

    Brotherton, Robert, Christopher C. French, and Alan D. Pickering. "Measuring belief in conspiracy theories: the generic conspiracist beliefs scale." Frontiers in psychology 4 (2013). http://dx.doi.org/10.3389/fpsyg.2013.00279

    --- Original source retains full ownership of the source dataset ---

  18. w

    Ukraine - Demographic and Health Survey 2007 - Dataset - waterdata

    • wbwaterdata.org
    Updated Mar 16, 2020
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    (2020). Ukraine - Demographic and Health Survey 2007 - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/ukraine-demographic-and-health-survey-2007
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    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Ukraine
    Description

    The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

  19. w

    Marriage Transitions in Malawi 2007-2009, Panel Data - Malawi

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Jun 6, 2019
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    Kathleen Beegle (2019). Marriage Transitions in Malawi 2007-2009, Panel Data - Malawi [Dataset]. https://microdata.worldbank.org/index.php/catalog/3462
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    Dataset updated
    Jun 6, 2019
    Dataset authored and provided by
    Kathleen Beegle
    Time period covered
    2007 - 2009
    Area covered
    Malawi
    Description

    Abstract

    The Marriage Transitions in Malawi Project (MTM) consists of a longitudinal dataset on young women and men in central Malawi, one of the poorest countries in the world, where marriage is nearly universal, and most women and men marry before the age of 20. The data are intended to support the study of the social and economic influences on the timing of key life events among young people, such as leaving school, engaging in sex for the first time, and marrying. These pivotal moments and experiences shape future life trajectories. The project also sought to identify whether and how socioeconomic conditions and gender might influence an individual’s chances of acquiring HIV. The rich MTM panel data can contribute to understanding the forces that drive young lives and inform policy interventions.

    Geographic coverage

    Salima Malawi and, after baseline, other parts of Malawi to which respondents migrated.

    Analysis unit

    Young men and women and their households in Salima, Malawi

    Universe

    At baseline, unmarried women ages 13–21 and men ages 18–25.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A random sample of 60 enumeration areas of a possible 215 in Salima defined by the National Statistical Office were selected.

    A complete household listing was undertaken within each enumeration area to draw the sample of core respondents. Households with no age-eligible members were excluded by design. From the listing of all age-eligible women (ages 13-21) and men (ages 18-25) in each enumeration area, 10 women and 10 men were randomly selected. Table 1 of the Summary of Baseline Data from Marriage Transitions in Malawi Project shows the age distribution among the target sample by sex and by the actual sample size. In a few enumeration areas that had an insufficient number of women and men in a target age category who had never married, respondents were randomly selected from an adjacent age category.

    Replacements were used when the field team was not able to interview the original selected respondent. There were 315 replacements for a variety of reasons. In 144 cases, the information from the household listing was inaccurate: the respondent did not actually reside in the household [58], had ever been married [35], wrong age was reported [46], and wrong gender [5]). In 127 cases, the individual was unavailable: away temporarily and not returning during the baseline field work [63], attending boarding school [30], difficult to meet due to work [30], parent away and unable to give consent for minors [2], or in police custody [2]. There were 28 refusals (14 by the respondent and 14 by a parent of a minor). Finally, there were 16 other cases: illness [10], household located on police quarters and required special permission [1], mentally ill [4], and other language [1].

    The final sample consisted of 1,183 men and women (core respondents), who resided in 1,059 households; in a few households, more than one person per household was selected for inclusion in the survey.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Data collection for the MTM project consisted of five rounds over 26 months. Three annual rounds were conducted in 2007, 2008, and 2009. Two interim survey rounds, called partnership interviews, were conducted midway between the annual surveys. The sample for the partnership interviews was a random selection of two-thirds of the overall sample. The annual household survey and the partnership interviews were developed based on other surveys in Malawi and other countries as well as new content specific to the MTM. All instruments were extensively pretested in Malawi.

    Household survey: The questionnaire of the MTM household survey, which was conducted in three annual rounds, consisted of three parts. Part 1 included household information, such as a household roster and household economic variables. It was administered to the household head or other knowledgeable member of the household. Part 2 was fielded to the core respondents and included modules on parental background, the characteristics of sexual partnerships, social capital, risk perceptions pertaining to HIV, health, fertility and desired fertility, and aspirations and expectations (such as for marriage, schooling, and so on). Part 3 surveyed new spouses of core respondents and collected information similar to that collected through part 2. Part 3 was administered in 2008 and 2009.

    Partnership interviews: Two rounds of partnership interviews were administered at the midway point of the three annual rounds of the household surveys, roughly six months following the 2007 household survey and six months following the 2008 household survey. In these much shorter interviews, core respondents and new spouses were asked about important life events and experiences that had occurred since their last interviews. The information gathered focused on relationships and partnerships, such as any newly acquired partners, the frequency of sexual intercourse, and the presence of sexually transmitted infections. Changes in key life events were also documented, such as leaving school, moving, pregnancies, births, changes in households, or marriage. The close-ended questions in the partnership interviews were similar and, in many cases, identical to parts 1 and 3 of the household survey, but also included open-ended, conversational-style questions, more well suited for sensitive topics. Respondents were permitted to answer in their own words.

    Response rate

    See Table 3 in Beegle and Poulin (2017) in the journal Studies in Family Planning.

  20. w

    Pakistan - Demographic and Health Survey 1990-1991 - Dataset - waterdata

    • wbwaterdata.org
    Updated Mar 16, 2020
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    (2020). Pakistan - Demographic and Health Survey 1990-1991 - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/pakistan-demographic-and-health-survey-1990-1991
    Explore at:
    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Pakistan
    Description

    The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975. The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS). MAIN RESULTS Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education. There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent). The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme. The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts. The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence. The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin. Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas. In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education. Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference. The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme. Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education. The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister. One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education. Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for antenatal care generally. Eighty-five percent of the births occurring during the five years preceding the survey were delivered

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Professor Amelia (Mia) Crampin (2022). Cleaned spouse and marriage data - Malawi [Dataset]. https://kpsmw.lshtm.ac.uk/nada/index.php/catalog/12

Cleaned spouse and marriage data - Malawi

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Dataset updated
Oct 25, 2022
Dataset authored and provided by
Professor Amelia (Mia) Crampin
Area covered
Malawi
Description

Abstract

The do-file marital_spouselinks.do combines all data on people's marital statuses and reported spouses to create the following datasets: 1. all_marital_reports - a listing of all the times an individual has reported their current marital status with the id numbers of the reported spouse(s); this listing is as reported so may include discrepancies (i.e. a 'Never married' status following a 'Married' one) 2. all_spouse_pairs_full - a listing of each time each spouse pair has been reported plus summary information on co-residency for each pair 3. all_spouse_pairs_clean_summarised - this summarises the data from all_spouse_pairs_full to give start and end dates of unions 4. marital_status_episodes - this combines data from all the sources to create episodes of marital status, each has a start and end date and a marital status, and if currently married, the spouse ids of the current spouse(s) if reported. There are several variables to indicate where each piece of information is coming from.

The first 2 datasets are made available in case people need the 'raw' data for any reason (i.e. if they only want data from one study) or if they wish to summarise the data in a different way to what is done for the last 2 datasets.

The do-file is quite complicated with many sources of data going through multiple processes to create variables in the datasets so it is not always straightforward to explain where each variable come from on the documentation. The 4 datasets build on each other and the do-file is documented throughout so anyone wanting to understand in great detail may be better off examining that. However, below is a brief description of how the datasets are created:

Marital status data are stored in the tables of the study they were collected in: AHS Adult Health Study [ahs_ahs1] CEN Census (initial CRS census) [cen_individ] CENM In-migration (CRS migration form) [crs_cenm] GP General form (filled for various reasons) [gp_gpform] SEI Socio-economic individual (annual survey from 2007 onwards) [css_sei] TBH TB household (study of household contacts of TB patients) [tb_tbh] TBO TB controls (matched controls for TB patients) [tb_tbo & tb_tboto2007] TBX TB cases (TB patients) [tb_tbx & tb_tbxto2007] In many of the above surveys as well as their current marital status, people were asked to report their current and past spouses along with (sometimes) some information about the marriage (start/end year etc.). These data are stored all together on the table gen_spouse, with variables indicating which study the data came from. Further evidence of spousal relationships is taken from gen_identity (if a couple appear as co-parents to a CRS member) and from crs_residency_episodes_clean_poly, a combined dataset (if they are living in the same household at the same time). Note that co-parent couples who are not reported in gen_spouse are only retained in the datasets if they have co-resident episodes.

The marital status data are appended together and the spouse id data merged in. Minimal data editing/cleaning is carried out. As the spouse data are in long format, this dataset is reshaped wide to have one line per marital status report (polygamy in the area allows for men to have multiple spouses at one time): this dataset is saved as all_marital_reports.

The list of reported spouses on gen_spouse is appended to a list of co-parents (from gen_identity) and this list is cleaned to try to identify and remove obvious id errors (incestuous links, same sex [these are not reported in this culture] and large age difference). Data reported by men and women are compared and variables created to show whether one or both of the couple report the union. Many records have information on start and end year of marriage, and all have the date the union was reported. This listing is compared to data from residency episodes to add dates that couples were living together (not all have start/end dates so this is to try to supplement this), in addition the dates that each member of the couple was last known to be alive or first known to be dead are added (from the residency data as well). This dataset with all the records available for each spouse pair is saved as all_spouse_pairs_full.

The date data from all_spouse_pairs_full are then summarised to get one line per couple with earliest and latest known married date for all, and, if available, marriage and separation date. For each date there are also variables created to indicate the source of the data.
As culture only allows for women having one spouse at a time, records for women with 'overlapping' husbands are cleaned. This dataset is then saved as all_spouse_pairs_clean_summarised.

Both the cleaned spouse pairs and the cleaned marital status datasets are converted into episodes: the spouse listing using the marriage or first known married date as the beginning and the last known married plus a year or separation date as the end, the marital status data records collapsed into periods of the same status being reported (following some cleaning to remove impossible reports) and the start date being the first of these reports, the end date being the last of the reports plus a year. These episodes are appended together and a series of processes run several times to remove overalapping episodes. To be able to assign specific spouse ids to each married episode, some episodes need to be 'split' into more than one (i.e. if a man is married to one woman from 2005 to 2017 and then marries another woman in 2008 and remains married to her till 2017 his intial married episode would be from 2005 to 2017, but this would need to be split into one from 2005 to 2008 which would just have 1 idspouse attached and another from 2008 to 2017, which would have 2 idspouse attached). After this splitting process the spouse ids are merged in.
The final episode dataset is saved as marital_status_episodes.

Analysis unit

Individual

Mode of data collection

Face-to-face [f2f]

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