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 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.

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.

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 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.

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.

Chapter 48, Title 2, of the Texas Human Resources Code (HRC) and Chapter 705 of the Texas Administrative Code (TAC) authorizes APS to investigate abuse and financial exploitation of a person age 65 or older or an adult with a disability when the person responsible for the maltreatment is a: • caretaker; • paid caretaker; • family member; or • person who has an ongoing relationship with the alleged victim. Examples include a personal friend, paramour, or roommate. In the case of neglect, the perpetrator may also be the victim himself or herself. This is called "Self-neglect". In cases of family violence, a protective order can be obtained from a court that prohibits a member of a family or household from remaining in the household, and from contacting or coming near the victim. The purpose of the order is to prevent that person from committing further acts of family violence against the victim. The statutes governing family violence protective orders are set forth in Texas Family Code Chapters 71-87. This order is only available when family violence has been committed by a family member, member of the household, or in some circumstances by a person the victim has dated. Each victim may have more than one perpetrator in an investigation. Visit dfps.state.tx.us for information on all DFPS programs.

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.

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.

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.

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.

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.

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.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..When information is missing or inconsistent, the Census Bureau uses a method called imputation to assign values. Responses assigned using the Census Bureau's imputation method are called imputed values. The "Percent Imputed" section is the percent of respondents who received an imputed value for a particular subject. ..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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2015 American Community Survey 1-Year Estimates

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_d9aecb2954fe4e5128985c7621091f59/view

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...

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

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.

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

These histograms represent our calibration of conductance of a volcanic geothermal field (with a clay cap) and the observed steam flow rates. Darajat is a vapor geothermal field located in West Java, Indonesia. First production from the field was started in 1994 and additional capacity was added in 2000 and 2007 to bring the total production capacity to 271 MW from three power plants. Please refer to Rejeki et al. (2010) for geologic and modeling background. The steam flow measurements are the average production over one year for 27 different wells. Four of these wells were drilled near to or outside of the geothermal field and are characterized by production rates of < 5kg/s. Rejeki, S., Rohrs, D., Nordquist, G., and Fitriyanto, A., 2010, Geologic Conceptual Model Update of the Darajat Geothermal Field , Indonesia, in Proceedings World Geothermal Congress 2010, p. 25-29. Further details will be contained in the following paper to be published in the fall of 2015: Trainor-Guitton, Hoversten,Nordquist, Intani, Value of information analysis using geothermal field data: accounting for multiple interpretations & determining new drilling locations. SEG Abstracts 2015.