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 number of Iranians getting divorced are on the rise, according to the latest data from the country's Statistics Center.

While the number of marriages in Iran, too, increased during the period between 2019 and 2020 by 4.4%, reaching 556,731 cases, the divorce rates rose by 3.6%, reaching 183,193 cases.

This means that in 2020, for every 100 registered marriages, 32.9 divorces occurred.

According to the statistics reported in Iran's Report on the Social and Cultural Status, published in the fall of 2020, some 51,270 marriages that ended in divorce lasted between one to five years.

Furthermore, 11,715 marriages ended in divorce in just one year. On the other hand, 7,809 marriages of around 29 years were terminated.

In the 2020 report, neither a complete national nor a granular provincial marriage and divorce data has been published therefore data on this topic had to be sourced from other reports.

The latest statistical report on Iran’s divorce and marriage, categorized by province, is related to the first half of last year. This information was taken from the Civil Registration Organization of Iran’s website. This article analyzes this data.

The highest number of marriages, in the first half of 2020, happened in Tehran. During this period, 34,451 marriages were recorded in this province while the lowest number of marriages, namely 1,914 marriages, was recorded in the same period in Semnan province.

The highest number of divorces, in the first half of 2020, was recorded in the Tehran province. During this period, 15,303 divorces were recorded.

Ilam province has the lowest number of divorces, in the same time period, which was 388 cases.

The numerical quantity of marriage and divorce cases is not a good basis for comparison because of the population differences between provinces. Accurate and proportional comparison requires careful consideration of the province’s population that are being compared.

Adding the population variable to this data changes some of the provincial marriage and divorce rankings.

Statistical source:

https://www.amar.org.ir/%D9%BE%D8%A7%DB%8C%DA%AF%D8%A7%D9%87-%D9%87%D8%A7-%D9%88-%D8%B3%D8%A7%D9%85%D8%A7%D9%86%D9%87-%D9%87%D8%A7/%D8%B3%D8%B1%DB%8C%D9%87%D8%A7%DB%8C-%D8%B2%D9%85%D8%A7%D9%86%DB%8C/agentType/ViewType/PropertyTypeID/1938

Divorce (also known as dissolution of marriage) is the process of terminating a marriage or marital union. Divorce usually entails the canceling or reorganizing of the legal duties and responsibilities of marriage, thus dissolving the bonds of matrimony between a married couple under the rule of law of the particular country or state. Divorce laws vary considerably around the world, but in most countries, divorce requires the sanction of a court or other authority in a legal process, which may involve issues of distribution of property, child custody, alimony (spousal support), child visitation/access, parenting time, child support, and division of debt. In most countries, monogamy is required by law, so divorce allows each former partner to marry another person.

According to a recent survey of 191 CDFA professionals from across North America, the three leading causes of divorce are "basic incompatibility" (43%), "infidelity" (28%), and "money issues" (22%).

In this dataset, we can spot facts about divorces, and trends across countries - continents. Mainly we have the marriage, divorce rates and the percentage score as well as numbers of divorces (given by the source and year as provided).

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.

This dataset explores marriage trends in India, comparing love marriages and arranged marriages across various demographic, social, and economic factors. I, capturing key aspects such as age at marriage, caste and religion dynamics, parental approval, dowry exchange, marital satisfaction, divorce rates, income levels, and urban-rural differences.

The dataset aims to provide valuable insights into changing marriage patterns, the role of tradition vs. modernity, and their impact on marital outcomes. Researchers, sociologists, and data analysts can use this dataset to study relationship trends, predict marriage success, and analyze social influences on marriage in India.

ID – Unique identifier

Marriage_Type – Love / Arranged

Age_at_Marriage – Age of the person at marriage

Gender – Male / Female

Education_Level – School / Graduate / Postgraduate / PhD

Caste_Match – Same / Different

Religion – Hindu / Muslim / Christian / Sikh / Others

Parental_Approval – Yes / No / Partial

Urban_Rural – Urban / Rural

Dowry_Exchanged – Yes / No / Not Disclosed

Marital_Satisfaction – Low / Medium / High

Divorce_Status – Yes / No

Children_Count – Number of children (0-5)

Income_Level – Low / Middle / High

Years_Since_Marriage – Number of years since marriage

Spouse_Working – Yes / No

Inter-Caste – Yes / No

Inter-Religion – Yes / No

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.

Content

Marriage

This folder contains data behind the story Marriage Isn’t Dead — Yet.

Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via IPUMS USA.

Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have ever been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is currently divorced, conditional on having ever been married.

Variable names are as follows. Number in variable names are age ranges, so all_2534 is the marriage rate for everyone ages 25 to 34.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

IntroductionDivorce is one of the harshest realities in Eastern societies worldwide because it is an intact component of the elementary social institution of the family. Grievously, divorce rates are escalating with profound ramifications for divorcees in Asia, including Pakistan. Therefore, exploring the challenges after divorce with gender-based dimensions in the Pakistani context was necessary, particularly in Hazara Division.Research methodologyThe study followed a pragmatic approach through snowball sampling and recruited 75 respondents. The data were collected through a semi-structured questionnaire and analysed using IBM SPSS 25. For descriptive statistics, frequencies of quantitative responses were determined using percentages, means, and standard deviations. Then Chi-Square Test of Independence, Principal Component Analysis, and Multivariate Analysis of Covariance were performed to find an association between the dependent and independent variables.ResultsResults demonstrated that most divorcees face children-related issues followed by economic, social, and psychological issues that impede post-divorce adjustment of divorcees. Results showed that the immaturity of divorcees and gender-specific violence specifically for women make it challenging for them to cope with the situation and impede their growth after divorce. Results revealed that more than half of women and a few of men have custody of children after divorce; however, fight over custody of children is another major cause of delayed adjustment. Results presented that gender significantly influences women’s intensity of post-divorce adjustment constraints.ConclusionTherefore, it is concluded that regardless of gender, ongoing conflicts with ex-spouses or in-laws made the post-divorce adjustment of divorcees difficult. Divorcees are in a constant tug-of-war between fighting internal dilemmas, pursuing individualistic ideals, and fulfilling societal norms, values, and expectations. This battle complicates and prolongs their adjustments after divorce. The study suggests that institutional, psychosocial, and family support is critical to proactively relieve divorcees from resources and their children.

United States Marriage Statistics

Marriage rates by gender, age group, and year in the United States (2005-2017)

By Throwback Thursday [source]

About this dataset

This dataset, titled United States Marriage Status 2005-2017, provides detailed information on marriage rates and population estimates in the United States. The data is sourced from the U.S. Census Bureau's American Community Survey 1-Year Estimates.

The dataset includes several key attributes that offer insights into different aspects of marriage status. These attributes include Year, Gender, Age Group, Metric, Estimated Percent, and Estimated Population.

The Year attribute represents the year in which the data was collected, spanning from 2005 to 2017. It allows for analysis of trends and changes in marriage rates over time.

The Gender attribute categorizes the population groups based on their gender. This information helps explore any variations or differences between male and female populations in terms of marital status.

Age Group attribute classifies individuals into specific age categories within the population. By segmenting the data based on age groups, it becomes possible to analyze how different age demographics contribute to overall marriage rates.

Metric serves as a descriptor for specific measurements or indicators being reported within this dataset. This attribute provides further context for understanding different aspects related to marriage status and its calculation methods.

Estimated Percent denotes the estimated percentage of a particular population group falling into a specific category related to marital status. It offers valuable insights into relative proportions within each demographic group.

Estimated Population showcases estimated count figures representing various subgroups' populations classified by gender, age groupings, and metric categories specified previously. These estimates allow researchers to explore potential correlations between population sizes and marriage rates across various segments of society over time period covered by this dataset.

Overall, this comprehensive United States Marriage Status dataset provides a valuable resource for analyzing trends in marriage rates while considering gender demographics, age distributions within these populations along with respective metrics indicating changes occurring over time periods marked since 2005 until 2017 (date-range excluding exact dates provided). By exploring relationships among these factors using reliable census data available through American Community Survey 1-Year Estimates, researchers can gain deep understanding of marriage status dynamics in the United States

How to use the dataset

• Understand the Columns:

  • Year: This column represents the year in which the data was collected. It provides a timeline for analyzing marriage trends over time.
  • Gender: This column categorizes individuals based on their gender, providing insights into marriage rates and patterns specific to each gender.
  • Age Group: This column categorizes individuals based on their age group. It allows for a detailed analysis of marriage rates and statistics among different age groups.
  • Metric: This column specifies the type of data or measurement being reported, providing clarity on what aspect of marriage is being analyzed.
  • Estimated Percent: This column represents the estimated percentage of individuals within a population group falling into a particular category. It quantifies marriage rates as percentages.
  • Estimated Population: This column provides an estimation of the total population count within a specific category, offering insights into the size and distribution of different population groups.

• Analyzing Trends: Use this dataset to analyze trends in US marriage statistics by leveraging various combinations of columns:

  • Gender vs Metric: Compare different metrics (e.g., number of marriages, divorce rate) between genders, allowing for an understanding of any gender-specific variations in marital trends.
  • Year vs Metric: Study changes in various metrics over time (e.g., changes in average age at first marriage), identifying trends and potential shifts in societal attitudes towards marriage.
  • Age Group vs Metric/Gender/Year: Examine how different age groups contribute to overall marital statistics (e.g., comparing divorce rates among different age groups or analyzing changes over time within specific age cohorts).

• Interpreting Results: When analyzing this dataset's results, keep these factors in mind:

  • Size Differences: Ensure you factor in the estimated population count for eac...

Linear regression estimation results for suicidal rate among urban men and urban women.

IntroductionDivorce is one of the harshest realities in Eastern societies worldwide because it is an intact component of the elementary social institution of the family. Grievously, divorce rates are escalating with profound ramifications for divorcees in Asia, including Pakistan. Therefore, exploring the challenges after divorce with gender-based dimensions in the Pakistani context was necessary, particularly in Hazara Division.Research methodologyThe study followed a pragmatic approach through snowball sampling and recruited 75 respondents. The data were collected through a semi-structured questionnaire and analysed using IBM SPSS 25. For descriptive statistics, frequencies of quantitative responses were determined using percentages, means, and standard deviations. Then Chi-Square Test of Independence, Principal Component Analysis, and Multivariate Analysis of Covariance were performed to find an association between the dependent and independent variables.ResultsResults demonstrated that most divorcees face children-related issues followed by economic, social, and psychological issues that impede post-divorce adjustment of divorcees. Results showed that the immaturity of divorcees and gender-specific violence specifically for women make it challenging for them to cope with the situation and impede their growth after divorce. Results revealed that more than half of women and a few of men have custody of children after divorce; however, fight over custody of children is another major cause of delayed adjustment. Results presented that gender significantly influences women’s intensity of post-divorce adjustment constraints.ConclusionTherefore, it is concluded that regardless of gender, ongoing conflicts with ex-spouses or in-laws made the post-divorce adjustment of divorcees difficult. Divorcees are in a constant tug-of-war between fighting internal dilemmas, pursuing individualistic ideals, and fulfilling societal norms, values, and expectations. This battle complicates and prolongs their adjustments after divorce. The study suggests that institutional, psychosocial, and family support is critical to proactively relieve divorcees from resources and their children.

Key Table Information.Table Title.Divorces in the Last Year by Sex by Marital Status for the Population 15 Years and Over.Table ID.ACSDT1Y2024.B12503.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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 na...

Contains covariate data for "The association between alcohol consumption per capita and suicide mortality across 30 European countries" which were extracted from the Pew Research Center (pewresearch.org), World Bank Group (worldbank.org), and Eurostat (ec.europa.eu/eurostat). Also contains dummy variables to represent: the 2008 global economic recession, changes from ICD-9 to ICD-10, and the COVID-19 pandemic. All covariates which were initially considered are included in this dataset. However, data were further cleaned according to methods described in the associated publication prior to analysis. Within the dataset: edu = Educational attainment (completion of post-secondary or equivalent) lit = Literacy, adult total (% of people ages 15 and above) unemp = Unemployment, total (% of total labor force) (modeled ILO estimate) divorce = Divorce rate migration = Net migration rate relig.muslim = Proportion of the population who identified as Muslim relig.buddhist = Proportion of the population who identified as Buddhist lff = Female labour force participation (% of total labor force) gdp = Gross domestic product based on purchasing power parity (GDP (PPP)) gini = Gini index density = Population density urban = Proportion of the population living in urban areas recession, covid, icd: Dummy variables detailed above.

Annual UK and constituent country figures for births, deaths, marriages, divorces, civil partnerships and civil partnership dissolutions.

PART I: Distribution table: Interval Frequency Cumulative Frequency Percentage distribution Cumulative percentage distribution 10-12 2 2 13.33 13.33 12.1-14 5 7 33.33 46.66 14.1-16 8 15 53.33 99.99 16.1-18 0 15 0 99.99

18.1 0 15 0 99.99

Majority of the countries, eight, fall in the 14.1-16 category. Five countries fall in the 12.1-14 category and two countries in the 10-12 bin. The remaining categories have zero entries. This means the data does not follow a normal distribution since most of the countries are concentrated at the highest peak. This data could be better visualized in a histogram.

Frequency distribution with revised interval: Interval Frequency Cumulative Frequency Percentage Frequency Cumulative percentage <12 2 2 13.33 13.33 12-12.9 1 3 6.67 20 13-13.9 4 7 26.67 46.67 14-14.9 4 11 26.67 73.34 15-15.9 3 14 20 93.34 16-16.9 1 15 6.67 100.01 17-17.9 0 15 0 100.01

18 0 15 0 100.01 Eight countries have between 14% and 18% of their population above age 65. The number of countries with 14% - 18% of their population above 65 years remain the same even after revising the interval. The percentage of countries that have between 14-18 percent of their population above age 65 is 53.33%.

PART II Q1. Time series chart for divorce rate in Netherlands

Q2. Describe divorce rate in Netherlands before and after 1970. There is a decline in divorce rate between 1950 and 1960. There is a moderate rise in divorce rate between 1960 and 1970, the rate steadily rises between 1970 and 1980 and thereafter exhibits a slight decline between 1980 and 1990. The rate shifts to a declining trend after the year 2000. The decline does not indicate negative number of divorces, this could be attributed to increased population size and fewer number of divorce cases filed. Q3. A bar graph would best display the divorce rate for each year, hence easy comparison. Q4. Bar graph The highest number of divorce cases were recorded in the year 2000, while the least number was observed in 1960.

Set 2: Show how different elements contributed to population change in 2018

Immigration contributed 34 percent of the change in population; births, Emigration, and deaths contributed almost equal change in population.

Q2. Elements of population growth

Immigration contributed the largest change in population growth compared to birth.

Q3. A time series to show changes in male and female population

Both populations show an increasing trend over the 4 years. We could also conclude there are more females than males in the country’s population.