Number of divorces and various divorce indicators (crude divorce rate, divorce rate for married persons, age-standardized divorce rate, total divorce rate, mean and median duration of marriage, median duration of divorce proceedings, percentage of joint divorce applications), by place of occurrence, 1970 to most recent year.

The graph displays the divorce rate per 1,000 people in the United States from 2000 to 2022. The x-axis represents the years, labeled from '00 to '22, while the y-axis indicates the divorce rate per 1,000 individuals. The divorce rate starts at 4.0 per 1,000 in 2000 and 2001, which are the highest values in the dataset. Over the years, there is a general downward trend, with the rate decreasing to 2.3 per 1,000 in 2020, the lowest point recorded. In 2021 and 2022, the rate slightly fluctuates, rising to 2.5 and then decreasing to 2.4 per 1,000 respectively. The data highlights a consistent decline in the divorce rate over the 22-year period.

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.

Annual divorce numbers and rates, by duration of marriage, sex, to whom granted and reason, that took place in England and Wales.

Ages of husband and wife at marriage, and analyses of the percentage of marriages ending in divorce by year of marriage and anniversary, and proportions of men and women who had ever divorced by year of birth and age.

Abstract

Ever been heart broken and/or wondered what makes a lasting relationship? This dataset may help you.

About this dataset

This dataset contains data about 150 couples with their corresponding Divorce Predictors Scale variables (DPS) on the basis of Gottman couples therapy. The couples are from various regions of Turkey wherein the records were acquired from face-to-face interviews from couples who were already divorced or happily married. All responses were collected on a 5 point scale (0=Never, 1=Seldom, 2=Averagely, 3=Frequently, 4=Always).

How to use

• Predict divorce events
• Explore predictive factors that lead to divorce
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

Yöntem, M , Adem, K , İlhan, T , Kılıçarslan, S. (2019). DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS. Nevşehir Hacı Bektaş Veli University SBE Dergisi, 9 (1), 259-273. (link)

License

License was not specified at the source.

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Analysis of ‘Divorce Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/divorce-prediction on 30 September 2021.

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

Abstract

Ever been heart broken and/or wondered what makes a lasting relationship? This dataset may help you.

About this dataset

This dataset contains data about 150 couples with their corresponding Divorce Predictors Scale variables (DPS) on the basis of Gottman couples therapy. The couples are from various regions of Turkey wherein the records were acquired from face-to-face interviews from couples who were already divorced or happily married. All responses were collected on a 5 point scale (0=Never, 1=Seldom, 2=Averagely, 3=Frequently, 4=Always).

How to use

• Predict divorce events
• Explore predictive factors that lead to divorce
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

Yöntem, M , Adem, K , İlhan, T , Kılıçarslan, S. (2019). DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS. Nevşehir Hacı Bektaş Veli University SBE Dergisi, 9 (1), 259-273. (link)

License

License was not specified at the source.

Splash icon

Icon by Freepik available on Flaticon

Splash banner

Photo by Kelly Sikkema on Unsplash

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

Mean age and median age at divorce and at marriage, for persons who divorced in a given year, by sex or gender and place of occurrence, 1970 to most recent year.

Number and age of children in families where the parents divorce.

This data set shows the number of labour force by marital status for all states in Malaysia for year 1982 until 2021. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach. Labour force refers to those who during the reference week of LFS, are in the 15-64 years age group and who are either employed or unemployed. Marital status is categorised as follows: a. Never married Refers to those who have never been married at the time of interview. b. Married Refers to persons who are currently married at the time of interview. The term, ‘married’ includes those married by law or by religious rites or are living together by mutual agreement. c. Widowed Refers to those who have not remarried after the death of the spouses at the time of interview. d. Divorced/permanently separated Refers to those whose marriages were annulled through divorce by law or religious arrangement or separated for a long duration without any possibility of reconciliation. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. Total includes unknown marital status No. of Views : 769

Abstract

The Marriages and Divorces (MD) dataset is one of three primary sources of marriage and divorce statistics in South Africa. Unlike the other two sources (population censuses and household sample surveys), the MD dataset is compiled from administrative data and based on continuous recording (i.e., from civil registration systems and administrative records). Statistics South Africa (Stats SA) regularly publishes a series of data on marriages and divorces, with the first dataset in the series beginning in 2006. The most recent dataset in the series is MD 2019.

Marriage data: Data on marriages for citizens and permanent residents are obtained from registered marriage records that are collected through the civil registration systems of the Department of Home Affairs (DHA). South Africa recognizes three types of marriages by law: civil marriages, customary marriages, and civil unions. Before 2008, marriage data only covered civil marriages. The registration of customary marriages and civil unions began in 2003 and 2007 respectively. However, from 2008 onwards, Stats SA began publishing available data on customary marriages and civil unions.

Divorce data: Data on divorces are obtained from various regional courts that deal with divorce matters. The data are based on successful divorce cases that have been issued with a decree of divorce by the Department of Justice and Constitutional Development (DoJCD). Divorce cases come from marriages that were registered in different years as well as divorce cases that were filed in different years but whose divorce decrees were granted in the relevant year of collection.

NOTE: although both the data on marriages and divorces are collected in the same year, the data sets are not linked to each other.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all civil marriages that were recorded by the Department of Home Affairs and all divorce applications that were granted by the Department of Justice and Constitutional Development in 2019 in South Africa.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Data appraisal

Geography is problematic in this dataset as not all the data files have geographic data. The Civil Marriages and Civil Unions data files include a Province of Registration variable but the Customary Marriages data file does not. There is also no geographical data in the Divorces file. As this data file includes divorce data from only a subset of divorce courts, this lack of geographical information compromises its usability.

This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons 14 years old and over. Also included are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. In addition, data pertaining to marital history and fertility are included in the file. Men who were ever married (currently widowed, divorced, separated, or married) aged 15 and over were asked the number of times married and if the first marriage ended in widowhood or divorce. Ever married women aged 15 and over were asked the number of times married, date of marriage, date of widowhood or divorce, and if divorced the date of separation of the household for as many as three marriages. Questions on fertility were asked of ever married women 15 years and over and never married women 18 years and over. These questions included number of liveborn children, and date of birth, sex, and current residence for as many as five children. In addition, women between the ages of 18 and 39 were asked how many children they expect to have during their remaining childbearing years. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08899.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Annual population estimates by marital status or legal marital status, age and sex, Canada, provinces and territories.

This data set shows the number of employed persons by marital status for all states in Malaysia from year 1982 until 2021. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach. Employed persons are those between the working age of 15-64 years old who at any time during the reference week of LFS had worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker). Marital status is categorised as follows: a. Never married Refers to those who have never been married at the time of interview. b. Married Refers to persons who are currently married at the time of interview. The term, ‘married’ includes those married by law or by religious rites or are living together by mutual agreement. c. Widowed Refers to those who have not remarried after the death of the spouses at the time of interview. d. Divorced/permanently separated Refers to those whose marriages were annulled through divorce by law or religious arrangement or separated for a long duration without any possibility of reconciliation. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. Footnote: Total includes unknown marital status. Source: Department of Statistics Malaysia No. of Views : 428

Explore Saudi Arabia's population breakdown by age groups, gender, and marital status. This dataset includes information on males and females, along with those who are divorced, married, never married, or widowed.

Males, Divorced, Married, Never Married, Females, Widowed, Marital Status, Gender, Male, Female, Age group

Saudi Arabia

Abstract copyright UK Data Service and data collection copyright owner.

The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been surveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.

Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42, 46, and 51. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics. Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.

BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets
Secure Access versions of BCS70 have more restrictive access conditions than versions available under the standard End User Licence (EUL).

1970 British Cohort Study: Partnership Histories, 1986-2016:
Data on live-in relationships lasting one month or more have been collected in all BCS70 sweeps from sweep 6 (age 30) as well as data on current live-in relationship at sweep 5 (age 23). The purpose of the Partnership Histories dataset is to merge all data on live-in relationships in successive sweeps into one longitudinal dataset.

The focus of the questions asked at each sweep are about the relationship start date; whether married/became civil partner (sweep 8 and later) to this partner and if so the marriage/civil partnership dates; whether still together with this partner and if not the date that the relationship ended; how the relationship ended; if relevant whether divorced and divorce dates; the sex, marital status and age at start of relationship of the partner.

For the fourth edition (March 2021), both data files have been updated to include partnership data from the latest BCS70 data sweep (2016). Following Sweep 10 (2016, age 46), longitudinal datasets have been streamlined by removing cases which have never participated in any main sweep survey and are no longer being issued.

• BCS70 Partnership dataset: One record is recorded for each partnership. Cohabitation transformed into marriage is recorded as two separate partnerships
• BCS70 Partner dataset: One record is recorded for each partner. A cohabitation which has transformed into marriage is treated as one partner

This data set shows the number of employed persons by marital status for all states in Malaysia. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach.

Employed persons are those between the working age of 15-64 years old who at any time during the reference week of LFS had worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker).

Marital status is categorised as follows:

a. Never married Refers to those who have never been married at the time of interview.

b. Married Refers to persons who are currently married at the time of interview. The term, ‘married’ includes those married by law or by religious rites or are living together by mutual agreement.

c. Widowed Refers to those who have not remarried after the death of the spouses at the time of interview.

d. Divorced/permanently separated Refers to those whose marriages were annulled through divorce by law or religious arrangement or separated for a long duration without any possibility of reconciliation.

W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards.

LFS was not conducted during the years 1991 and 1994.

Disclaimer: This is an artificially generated data using a python script based on arbitrary assumptions listed down.

The data consists of 100,000 examples of training data and 10,000 examples of test data, each representing a user who may or may not buy a smart watch.

----- Version 1 -------

trainingDataV1.csv, testDataV1.csv or trainingData.csv, testData.csv The data includes the following features for each user: 1. age: The age of the user (integer, 18-70) 1. income: The income of the user (integer, 25,000-200,000) 1. gender: The gender of the user (string, "male" or "female") 1. maritalStatus: The marital status of the user (string, "single", "married", or "divorced") 1. hour: The hour of the day (integer, 0-23) 1. weekend: A boolean indicating whether it is the weekend (True or False) 1. The data also includes a label for each user indicating whether they are likely to buy a smart watch or not (string, "yes" or "no"). The label is determined based on the following arbitrary conditions: - If the user is divorced and a random number generated by the script is less than 0.4, the label is "no" (i.e., assuming 40% of divorcees are not likely to buy a smart watch) - If it is the weekend and a random number generated by the script is less than 1.3, the label is "yes". (i.e., assuming sales are 30% more likely to occur on weekends) - If the user is male and under 30 with an income over 75,000, the label is "yes". - If the user is female and 30 or over with an income over 100,000, the label is "yes". Otherwise, the label is "no".

The training data is intended to be used to build and train a classification model, and the test data is intended to be used to evaluate the performance of the trained model.

Following Python script was used to generate this dataset

import random
import csv

# Set the number of examples to generate
numExamples = 100000

# Generate the training data
with open("trainingData.csv", "w", newline="") as csvfile:
  fieldnames = ["age", "income", "gender", "maritalStatus", "hour", "weekend", "buySmartWatch"]
  writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

  writer.writeheader()

  for i in range(numExamples):
    age = random.randint(18, 70)
    income = random.randint(25000, 200000)
    gender = random.choice(["male", "female"])
    maritalStatus = random.choice(["single", "married", "divorced"])
    hour = random.randint(0, 23)
    weekend = random.choice([True, False])

    # Randomly assign the label based on some arbitrary conditions
    # assuming 40% of divorcees won't buy a smart watch
    if maritalStatus == "divorced" and random.random() < 0.4:
      buySmartWatch = "no"
    # assuming sales are 30% more likely to occur on weekends.
    elif weekend == True and random.random() < 1.3:
      buySmartWatch = "yes"
    elif gender == "male" and age < 30 and income > 75000:
      buySmartWatch = "yes"
    elif gender == "female" and age >= 30 and income > 100000:
      buySmartWatch = "yes"
    else:
      buySmartWatch = "no"

    writer.writerow({
      "age": age,
      "income": income,
      "gender": gender,
      "maritalStatus": maritalStatus,
      "hour": hour,
      "weekend": weekend,
      "buySmartWatch": buySmartWatch
    })

----- Version 2 -------

trainingDataV2.csv, testDataV2.csv The data includes the following features for each user: 1. age: The age of the user (integer, 18-70) 1. income: The income of the user (integer, 25,000-200,000) 1. gender: The gender of the user (string, "male" or "female") 1. maritalStatus: The marital status of the user (string, "single", "married", or "divorced") 1. educationLevel: The education level of the user (string, "high school", "associate's degree", "bachelor's degree", "master's degree", or "doctorate") 1. occupation: The occupation of the user (string, "tech worker", "manager", "executive", "sales", "customer service", "creative", "manual labor", "healthcare", "education", "government", "unemployed", or "student") 1. familySize: The number of people in the user's family (integer, 1-5) 1. fitnessInterest: A boolean indicating whether the user is interested in fitness (True or False) 1. priorSmartwatchOwnership: A boolean indicating whether the user has owned a smartwatch in the past (True or False) 1. hour: The hour of the day when the user was surveyed (integer, 0-23) 1. weekend: A boolean indicating whether the user was surveyed on a weekend (True or False) 1. buySmartWatch: A boolean indicating whether the user purchased a smartwatch (True or False)

Python script used to generate the data:

import random
import csv

# Set the number of examples to generate
numExamples = 100000

with open("t...

The dataset consists of 17 texts, written by men aged 25-64 who were living without a steady relationship. In the invitation to write, men who were living without a steady relationship either of their own will or for other reasons were asked to describe their experiences and perceptions regarding the matter as extensively as possible. The participants could, for example, be divorced or widowed, or have no experience of steady relationships. The participants were asked to discuss how they experienced living without a steady relationship and what sources of joy and sorrow they had in their life. They were also asked to consider what they thought the reasons were for them living without a steady relationship. Additionally, the writing guidelines prompted the respondents to describe how they would want society to take into account those who are living without a steady relationship. Background information included the respondent's gender, age and how long they had been living without a steady relationship. The data were organised into an easy to use HTML version at FSD. The dataset is only available in Finnish.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by legal partnership status, by sex and by age. The estimates are as at Census Day, 21 March 2021.

Separate estimates by opposite and same-sex partnerships for the marital status categories “Separated”, “Divorced/dissolved” and “Widowed/surviving partners” are not available. This is because quality assurance showed the figures for some of the categories were unreliable. Read more about this quality notice.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Marital and civil partnership status

Classifies a person according to their legal marital or registered civil partnership status on Census Day 21 March 2021.

It is the same as the 2011 census variable "Marital status" but has been updated for Census 2021 to reflect the revised Civil Partnership Act that came into force in 2019.

In Census 2021 results, "single" refers only to someone who has never been married or in a registered civil partnership.

Sex

This is the sex recorded by the person completing the census. The options were “Female” and “Male”.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.