This statistic shows the results of a survey conducted in the United States in 2015 which shows the importance of romance in a relationship. Results show that 74 percent of respondents agree that relationships can't last long without romance.

This statistic shows the results of a survey conducted in the United States in 2015 which shows the longest time period people have been in a long distance relationship. Results show that 38 percent of respondents state to never have been in a long distance relationship. 11 percent of respondents have been in a long distance relationship of 5 years or more.

Sub-table of Table 92. It includes the subset ‘marriage/partnership/family including total family members’ broken down by family and other relatives

This statistic shows the percentage of employees in the U.S. who stated the stress from their job affected their relationship with friends or family from 2015 to 2017. During this time period, only 19 percent of respondents stated that work-related stress rarely or never affected their relationship with their friends or family.

This statistic shows the results of a survey among American teenagers aged 13 to 17 on their experience with love, romance and relationships. According to the source, 64 percent of teenagers have never been in a romantic relationship as of 2015.

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs. The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status. Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements. Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual. Average household size is rounded to the nearest hundredth. Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008. The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

Sub-table of Table 93. Breakdown of the subsets ‘live in the common household’ and ‘education/care ratio’.

Abstract (en): How Couples Meet and Stay Together (HCMST) surveyed how Americans met their spouses and romantic partners, and compared traditional to non-traditional couples. This collection covers data that was gathered over five waves. During the first wave, respondents were asked about their relationship status, including the gender, ethnicity, and race of their current partner, as well as the level of education of their parents. They were also asked about their living arrangements with their partner, the country, state, and city the respondent and/or the respondent's partner resided in most from birth to age 16, and whether the couple attended the same high school/college/university, or grew up in the same town. Information was collected on the legal status of the relationship, the city/state where the partnership was legalized, and how many times the respondent had previously been married. Additionally, respondents were asked about how often they visited with relatives, which gender they were most attracted to, their earned income in 2008, and the length of their current relationship. Finally, respondents were asked to recall how, when, and where they met their partner, how their parents felt about their partner, and to describe the perceived quality of their relationship. The second wave followed up with respondents one year after Wave 1. Information was collected on respondents' changes, if any, in marital status, relationship status, living arrangements, and reasons for separation where applicable. The third wave followed up with respondents one year after the second wave, and collected information on respondents' relationships reported in the first two waves, again including any changes in the status of the relationship and reasons for separation. The fourth wave followed up with respondents two years after Wave 3. In addition to information on relationship status and reasons for separation, Wave 4 includes the subjective level of attractiveness for the respondent and their partner. Wave 5 collected updated data on respondents' changes, if any, in marital status, relationship status, and reasons for separation where applicable. Information about respondents' sexual orientations, sex frequencies, and attitudes towards sexual monogamy were also collected. Demographic information includes age, race/ethnicity, gender, level of education, household composition, religion, political party affiliation, and household income. The data is being released in two parts: part one is available for public use and part two is available for restricted use. The public use data contains Waves 1-5, including the addition of nine variables collecting information such as race, household income, whether the respondent was born outside of the United States, zip code relative to rural area, and respondents' living arrangements between birth and 16 years of age. The restricted use data contains Waves 1-3, and differs from the public use data by including FIPS codes for state of marriage and state of residence, town or city where the respondent was raised, and qualitative variables revised by the Principal Investigator (Waves 1-5), consisting of respondent's answers to how they first met their partner, the quality of their relationship in their own words, why they broke up if applicable and if they have an open relationship. The survey was carried out by survey firm Knowledge Networks. The survey respondents were recruited from an ongoing panel. Panelists are recruited via random digit dial phone survey. Survey questions were mostly answered online; some follow-up surveys were conducted by phone. Panelists who did not have internet access at home were given an internet access device (WebTV). For further information about how the Knowledge Networks hybrid phone-internet survey compares to other survey methodology, see the accompanying documentation. The data are not weighted; however, this collection contains eight weight variables; WEIGHT1-WEIGHT7 and WEIGHT_COUPLES_CORESIDENT. Please refer to the ICPSR codebook for further information about weighting. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized...

Distance-power relationship data used in our automatic detection algorithm.

Add Health Parent Study (2015-2017) gathered social, behavioral, and health survey data in 2015-2017 on a probability sample of the "https://addhealth.cpc.unc.edu/" Target="_blank">Add Health parents who were originally interviewed in 1995. Data for 2,013 Wave I parents, ranging in age from 50-80 years and representing 2,244 Add Health sample members, are available. Add Health Parent Study Wave I Parents were the biological, adoptive, or stepparent of an Add Health child; not deceased or incarcerated at the time of Parents (2015-2017) sampling; and had at least one Add Health child who is also not deceased at the time of Parents (2015-2017) sampling. The Add Health Parent Study interview also gathered survey data on the current cohabiting Spouse or Partner of Wave I Parents who completed the interview. Nine hundred eighty-eight (988) current Spouse/Partner interviews are available. These data can be linked with Wave I parent data, and corresponding Add Health respondents at Waves I - V.

The Add Health Parent Study (2015-2017) interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In particular, the study was designed to improve the understanding of the role that families play through socioeconomic channels in the health and well-being of the older, parent generation and that of their offspring. This unique data set supports the analyses of intergenerational transmissions of (dis)advantage that have not been possible to date. Add Health Parent Study data permits the examination of both short-term and long-term linkages and interactions between parents and their adult children.

For more information, please visit the Add Health Parent Study official website "https://addhealth.cpc.unc.edu/about/#studies-satellite" Target="_blank">here.

This file is the small subset of family relationship data collected 2015-2017 from the Spouse or Partner of the Add Health Wave I Parent. The name of the file is "rsp2" on official Add Health "https://www.cpc.unc.edu/projects/addhealth/documentation/restricteduse/datasets#parent_study_files" Target="_blank">data documentation.

Abstract

How Couples Meet and Stay Together (HCMST) is a study of how Americans meet their spouses and romantic partners.

• The study is a nationally representative study of American adults.
• 4,002 adults responded to the survey, 3,009 of those had a spouse or main
  romantic partner.
• The study oversamples self-identified gay, lesbian, and bisexual adults
• Follow-up surveys were implemented one and two years after the main survey, to study couple dissolution rates. Version 3.0 of the dataset includes two follow- up surveys, waves 2 and 3.
• Waves 4 and 5 are provided as separate data files that can be linked back to the main file via variable caseid_new.

The study will provide answers to the following research questions:

1. Do traditional couples and nontraditional couples meet in the same way? What kinds of couples are more likely to have met online?
2. Have the most recent marriage cohorts (especially the traditional heterosexual same-race married couples) met in the same way their parents and grandparents did?
3. Does meeting online lead to greater or less couple stability?
4. How do the couple dissolution rates of nontraditional couples compare to the couple dissolution rates of more traditional same-race heterosexual couples?
5. How does the availability of civil union, domestic partnership or same-sex marriage rights affect couple stability for same-sex couples? This study will provide the first nationally representative data on the couple dissolution rates of same-sex couples.

Methodology

Universe:

The universe for the HCMST survey is English literate adults in the U.S.

**Unit of Analysis: **

Individual

**Type of data collection: **

Survey Data

**Time of data collection: **

Wave I, the main survey, was fielded between February 21 and April 2, 2009. Wave 2 was fielded March 12, 2010 to June 8, 2010. Wave 3 was fielded March 22, 2011 to August 29, 2011. Wave 4 was fielded between March and November of 2013. Wave 5 was fielded between November, 2014 and March, 2015. Dates for the background demographic surveys are described in the User's Guide, under documentation below.

Geographic coverage:

United States of America

Smallest geographic unit:

US region

**Sample description: **

The survey was carried out by survey firm Knowledge Networks (now called GfK). The survey respondents were recruited from an ongoing panel. Panelists are recruited via random digit dial phone survey. Survey questions were mostly answered online; some follow-up surveys were conducted by phone. Panelists who did not have internet access at home were given an internet access device (WebTV). For further information about how the Knowledge Networks hybrid phone-internet survey compares to other survey methodology, see attached documentation.

The dataset contains variables that are derived from several sources. There are variables from the Main Survey Instrument, there are variables generated from the investigators which were created after the Main Survey, and there are demographic background variables from Knowledge Networks which pre-date the Main Survey. Dates for main survey and for the prior background surveys are included in the dataset for each respondent. The source for each variable is identified in the codebook, and in notes appended within the dataset itself (notes may only be available for the Stata version of the dataset).

Respondents who had no spouse or main romantic partner were dropped from the Main Survey. Unpartnered respondents remain in the dataset, and demographic background variables are available for them.

**Sample response rate: **

Response to the main survey in 2009 from subjects, all of whom were already in the Knowledge Networks panel, was 71%. If we include the the prior initial Random Digit Dialing phone contact and agreement to join the Knowledge Networks panel (participation rate 32.6%), and the respondents’ completion of the initial demographic survey (56.8% completion), the composite overall response rate is a much lower .326*.568*.71= 13%. For further information on the calculation of response rates, and relevant citations, see the Note on Response Rates in the documentation. Response rates for the subsequent waves of the HCMST survey are simpler, using the denominator of people who completed wave 1 and who were eligible for follow-up. Response to wave 2 was 84.5%. Response rate to wave 3 was 72.9%. Response rate to wave 4 was 60.0%. Response rate to wave 5 was 46%. Response to wave 6 was 91.3%. Wave 6 was Internet only, so people who had left the GfK KnowledgePanel were not contacted.

**Weights: **

See "Notes on the Weights" in the Documentation section.

Usage

When you use the data, you agree to the following conditions:

1. I will not use the data to identify individuals.
2. I will not charge a fee for the data if I distribute it to others.
3. I will inform the contact person abo

The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File (FEATNAMES.dbf) contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines Shapefile (EDGES.shp), where applicable to the corresponding address range or ranges in the Address Ranges Relationship File (ADDR.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads Shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines Shapefile (EDGES.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File (FEATNAMES.dbf) through the Address Range / Feature Name Relationship File (ADDRFN.dbf).

      The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the
      U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents
      a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data
      set, or they can be combined to cover the entire nation.

Thematic map showing the median family after tax income, in 2015, by dissemination area for the Saint John census metropolitan area.Note: Family income is the sum of the incomes of all members of the family. A census couple family consists of a couple living together (married or common-law, including same-sex couples) living at the same address with or without children. Beginning in 2001, same-sex couples reporting as couples are counted as couple families.

This statistic presents the percentage of spousal caregivers in the U.S. whose relationship with their spouse has suffered as a result of their having to act as caregiver to their spouse as of 2015. It was found that over half of wives who acted as caregivers to their partner agreed their relationship with their spouse has suffered as a result of this caregiving role.

Sub-table of Table 93. Breakdown of the subset “in the health sector”.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Parent Study Public Use collection includes data gathered as part of the Add Health longitudinal survey of adolescents. The original Add Health survey is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. In Wave 1 of the Add Health Study (1994-1995), a parent of each Add Health Sample Member (AHSM) was interviewed. The Add Health Parent Study gathered social, behavioral, and health survey data in 2015-2017 from the parents of Add Health Sample members who were originally interviewed at Wave 1 (1994-1995). Wave 1 Parents were asked about their adolescent children, their relationships with them, and their own health. The Add Health Parent Study interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In addition, survey data contains cognitive assessments, a medications log linked to a medications database lookup table, and household financial information collection. The survey also includes permission for administrative data linkages and includes data from a Family Health History Leave-Behind questionnaire. Interviews were conducted with parents' spouse/partner when available. Research domains targeted in the survey and research questions that may be addressed using the Add Health Parent Study data include: Health Behaviors and Risks Many health conditions and behaviors run in families; for example, cardiovascular disease, obesity and substance abuse. How are health risks and behaviors transmitted across generations or clustered within families? How can we use information on the parents' health and health behavior to better understand the determinants of their (adult) children's health trajectories? Cognitive Functioning and Non-Cognitive Personality Traits What role does the intergenerational transmission of personality and locus of control play in generating intergenerational persistence in education, family status, income and health? How do the personality traits of parents and children, and how they interact, influence the extent and quality of intergenerational relationships and the prevalence of assistance across generations? Decision-Making, Expectations, and Risk Preferences Do intergenerational correlations in risk preferences represent intergenerational transmission of preferences? If so, are the transmission mechanisms a factor in biological and environmental vulnerabilities? Does the extent of genetic liability vary in response to both family-specific and generation-specific environmental pressures? Family Support, Relationship Quality and Ties of Obligation How does family complexity affect intergenerational obligations and the strength of relationship ties? As parents near retirement: What roles do they play in their children's lives and their children in their lives? What assistance are they providing to their adult children and grandchildren? What do they receive in return? And how do these ties vary with divorce, remarriage and familial estrangement? Economic Status and Capacities What are the economic capacities of the parents' generation as they reach their retirement years? How have fared through the wealth and employment shocks of the Great Recession? Are parents able to provide for their own financial need? And, do they have the time and financial resources to help support their children and grandchildren and are they prepared to do so?

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined etiological pathways to teen dating violence (TDV) in a sample of adolescents who had been followed since infancy and were at high-risk due to parental alcohol problems. Adolescents (M=17.68 years of age) who had been participating, along with their parents, in a longitudinal study of the effects of parental alcohol problems on child development completed an additional wave of survey data in 11-12th grades. Families (N=227) were initially recruited from county birth records when the child was 12 months of age and had been previously assessed at 12-, 18-, 24-, 36-months, kindergarten, 4th, 6th, and 8th grades. For the current wave of data collection, adolescent participants (n=185) used computer-assisted interviewing to complete questionnaires assessing their individual characteristics, family and peer relationships, substance use, dating behaviors and involvement in TDV as a victim or perpetrator.

The dating service sector is undergoing a dynamic transformation as digital technology reshapes consumer behavior and preferences. With the advent of mobile and online platforms, many relationship-seekers now opt for the convenience of digital interactions over traditional methods. This shift has led to a booming demand for mobile dating apps, which dominate user engagement because of their real-time and accessible nature. Leaders prioritize investments in digital innovations to secure market share and optimize revenue. The industry revenue has grown at a CAGR of 11.8% to $4.4 billion over the past five years, with a 0.0% rise in 2024 alone. Over the past five years, the industry has witnessed robust profitability driven by integrating innovative technologies and strategic pricing models. Companies have embraced mobile apps extensively, aligning with consumer expectations for seamless digital experiences. Subscription models have emerged as a key driver of financial growth, providing a steady revenue stream instead of one-time transactions. This evolution demands strategic planning for long-term user retention. While hosting, technology and skilled workforce expenses have risen, the emphasis on quality user experience justifies the investment. The sector is poised for further expansion over the next five years, fueled by technological advancements and increasing mobile internet accessibility. As digital interactions become more sophisticated, users will likely benefit from safer and more efficient matchmaking processes. Established companies are expected to acquire startups, incorporating cutting-edge features to stay competitive and cater to evolving user demands. Additionally, opportunities lie in targeting niche market segments and offering tailored services that resonate with specific demographics. However, as regulatory scrutiny intensifies, firms prioritizing data security and transparency will likely gain the upper hand in building user trust and loyalty. Emphasizing seamless and personalized experiences will remain critical for sustaining growth in a competitive landscape. Overall, industry revenue will stagnate at $4.4 billion through 2029, with 0.0% growth in CAGR.

Information on the geographical relationship of the residence of the German suspect to the scene of the crime (division by offence, total number of suspects, by sex, data on the place of residence)

Denmark Number of Family: Couples: with Children data was reported at 588,412.000 Unit in 2017. This records an increase from the previous number of 587,852.000 Unit for 2016. Denmark Number of Family: Couples: with Children data is updated yearly, averaging 592,436.000 Unit from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 601,022.000 Unit in 2000 and a record low of 584,183.000 Unit in 2015. Denmark Number of Family: Couples: with Children data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H011: Number of Family: by Family Type.