This report explores the educational performance and attitudes of males and females during childhood and adolescence. It opens with a general summary of gender differences measured outside of the PISA assessment programme and then considers the knowledge gained about gender-related issues from PISA 2000, PISA 2003 and PISA 2006 when reading, mathematics and science respectively were the major domains of assessment. Among the key findings: in reading in PISA 2000, females significantly outscored males in all countries; in mathematics in PISA 2003, males outscored females somewhat; in the combined science scale in PISA 2006, there was no overall significant difference observed between males and females. However, when examining the various science competencies, knowledge components and attitudes to science, there were some marked differences.

Student enrolment in elementary and secondary schools across the province, aggregated by gender and school board or school authority. Includes: * board number * board name * elementary male enrolment * elementary female enrolment * secondary male enrolment * secondary female enrolment * total male enrolment * total female enrolment Enrolment data is reported by schools to the Ontario School Information System (OnSIS), October Submissions. The following school types are included: * public * Catholic To protect privacy, numbers are suppressed in categories with less than 10 students. Note: * Starting 2018-2019, enrolment numbers have been rounded to the nearest five. * Where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals. ## Related * College enrolment * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

The right to educate girls nation wide within the ages of 1-18 and 18- 25.

Administrative data used in regression analyses to test for interactions among student gender, teacher gender, and performance on a state civics and economics exam. Data were provided by the North Carolina Education Research Data Center. To maintain privacy of the individuals involved in the study, all of whom were public high school students, we are not permitted to share our dataset. We have instead posted a copy of the data use agreement and a Stata do file and codebook for the main regression analysis used in the paper.

By Bastian Herre, Pablo Arriagada, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell and Max Roser.

About dataset:

Women’s rights are human rights that all women have. But in practice, these rights are often not protected to the same extent as the rights of men.

Among others, women’s rights include: physical integrity rights, such as being free from violence and making choices over their own body; social rights, such as going to school and participating in public life; economic rights, such as owning property, working a job of their choice, and being paid equally for it; and political rights, such as voting for and holding public office.

The protection of these rights allows women to live the lives they want and to thrive in them.

On this page, you can find data on how the protection of women’s rights has changed over time, and how it differs across countries.

There are 6 dataset in here.

1- Female to male ratio of time devoted to unpaid care work. 2- Share of women in top income groups atkinson casarico voitchovsky 2018. 3- Ratio of female to male labor force participation rates ilo wdi. 4- Female to male ratio of time devoted to unpaid care work. 5- Maternal mortality 6- Gender gap in average wages ilo

In each one, there are some topics and variables that we can analysis and visualize them.

This data set is part of the endline study for girls education project titled as Leave No Girl Behind (LNGB). The project is being implemented by ACTED through Foreign, Commonwealth and Development Office (FCDO) support in Sindh and Khyber Pakhtunkhwa provinces of Pakistan. The project is supporting girls education through two learning streams i.e. through a primary Accelerated Learning Programme (ALP) will be provided to 1100 girls (10-13 years old), and basic Literacy and Numeracy (L&N) skills course will be provided to almost 4400 girls (14-19 years old). Additionally, vocational training will be provided to 200 selected girls (picked from amongst 4400) enrolled in L&N course. This particular endline study (and the datasets) covers the L&N Cohort implemented in Sindh province. Written consents were obtained, the consent form included aspects such as voluntary participation of the respondent in the study, and use of anonymised datasets and findings for further research utilization. Educational measurements and tests were implemented.

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

This impact evaluation uses random assignment at the village level to estimate impacts of the IMAGINE program on enrollment, attendance, learning and other education outcomes for primary school-age children in Niger. IMAGINE follow-up data were collected in 2011. NECS Wave 1 data (which were also used to estimate longer term impacts of IMAGINE) were collected in 2013.

After one year (using the data collected in 2011) the Impact Evaluation of Niger's IMAGINE program found that IMAGINE had a 4.3 percentage point positive impact on primary school enrollment, no impact on attendance, and no impact on math and French test scores. The program impacts were generally larger for girls than for boys. For girls, the program had an 8 percentage point positive impact on enrollment and a 5.4 percentage point impact on attendance. The program had no impact on girls’ math scores, though there is suggestive evidence it may have had a positive impact of 0.09 standard deviations on girls’ French test scores. No significant impacts were detected for boys’ enrollment, attendance, or test scores. Finally, impacts were larger for younger children (ages 7-10), than for those between the ages of 10 and 12.

After four years (using data collected in 2013 during the NECS Wave 1 data collection), the Niger IMAGINE Long-Term Evaluation found that IMAGINE had a 8.3 percentage point positive impact on enrollment and a 7.9 percentage point negative impact on absenteeism. On average, children in treatment villages scored 0.13 standard deviations higher on the math assessment than children in control villages (significant at the 5 percent level). Test scores in French for children in treatment villages were higher than in control villages, but were not statistically significant. The evaluation found large and significant impacts of the program on enrollment, attendance, and math scores for females, compared to more modest and less significant impacts for males.

Goal 5Achieve gender equality and empower all women and girlsTarget 5.1: End all forms of discrimination against all women and girls everywhereIndicator 5.1.1: Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sexSG_LGL_GENEQLFP: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 1: overarching legal frameworks and public lifeSG_LGL_GENEQVAW: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 2: violence against womenSG_LGL_GENEQEMP: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 3: employment and economic benefitsSG_LGL_GENEQMAR: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 4: marriage and familyTarget 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitationIndicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by ageVC_VAW_MARR: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrenceTarget 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilationIndicator 5.3.1: Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18SP_DYN_MRBF18: Proportion of women aged 20-24 years who were married or in a union before age 18 (%)SP_DYN_MRBF15: Proportion of women aged 20-24 years who were married or in a union before age 15 (%)Indicator 5.3.2: Proportion of girls and women aged 15–49 years who have undergone female genital mutilation/cutting, by ageSH_STA_FGMS: Proportion of girls and women aged 15-49 years who have undergone female genital mutilation/cutting, by age (%)Target 5.4: Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriateIndicator 5.4.1: Proportion of time spent on unpaid domestic and care work, by sex, age and locationSL_DOM_TSPDCW: Proportion of time spent on unpaid care work, by sex, age and location (%)SL_DOM_TSPDDC: Proportion of time spent on unpaid domestic chores, by sex, age and location (%)SL_DOM_TSPD: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%)Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public lifeIndicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governmentsSG_GEN_PARLN: Number of seats held by women in national parliaments (number)SG_GEN_PARLNT: Current number of seats in national parliaments (number)SG_GEN_PARL: Proportion of seats held by women in national parliaments (% of total number of seats)SG_GEN_LOCGELS: Proportion of elected seats held by women in deliberative bodies of local government (%)Indicator 5.5.2: Proportion of women in managerial positionsIC_GEN_MGTL: Proportion of women in managerial positions (%)IC_GEN_MGTN: Proportion of women in senior and middle management positions (%)Target 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesIndicator 5.6.1: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health careSH_FPL_INFM: Proportion of women who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women aged 15-49 years)SH_FPL_INFMSR: Proportion of women who make their own informed decisions regarding sexual relations (% of women aged 15-49 years)SH_FPL_INFMCU: Proportion of women who make their own informed decisions regarding contraceptive use (% of women aged 15-49 years)SH_FPL_INFMRH: Proportion of women who make their own informed decisions regarding reproductive health care (% of women aged 15-49 years)Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationSH_LGR_ACSRHE: Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education (%)SH_LGR_ACSRHEC1: (S.1.C.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 1: Maternity Care (%)SH_LGR_ACSRHEC10: (S.4.C.10) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 10: HIV Counselling and Test ServicesSH_LGR_ACSRHEC11: (S.4.C.11) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 11: HIV Treatment and Care Services (%)SH_LGR_ACSRHEC12: (S.4.C.12) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 12: HIV Confidentiality (%)SH_LGR_ACSRHEC13: (S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 13: HPV Vaccine (%)SH_LGR_ACSRHEC2: (S.1.C.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 2: Life Saving Commodities (%)SH_LGR_ACSRHEC3: (S.1.C.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 3: AbortionSH_LGR_ACSRHEC4: (S.1.C.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 4: Post-Abortion Care (%)SH_LGR_ACSRHEC5: (S.2.C.5) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 5: Contraceptive Services (%)SH_LGR_ACSRHEC6: (S.2.C.6) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 6: Contraceptive Consent (%)SH_LGR_ACSRHEC7: (S.2.C.7) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 7: Emergency Contraception (%)SH_LGR_ACSRHEC8: (S.3.C.8) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 8: Sexuality Education Curriculum Laws (%)SH_LGR_ACSRHEC9: (S.3.C.9) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 9: Sexuality Education Curriculum Topics (%)SH_LGR_ACSRHES1: (S.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 1: Maternity Care (%)SH_LGR_ACSRHES2: (S.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 2: Contraceptive and Family Planning (%)SH_LGR_ACSRHES3: (S.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 3: Sexuality Education (%)SH_LGR_ACSRHES4: (S.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 4: HIV and HPV (%)Target 5.a: Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources,

Background: The positive learning effects of academic risk taking (ART) in higher education has been discussed since the 1980's. However, this may not apply equally for all social groups. Men and women may differ in the way they use ART to construct their gender identity. Students with different socioeconomic status (SES) may differ in their ability to navigate academic risks due to differences in available cultural capital. Aims: This study examines gender and SES disparities in ART and their impact on learning success. It explores if ART mediates and is moderated by gender and SES effects. Additionally, it assesses if ART directly predicts learning success. Sample: A sample of N = 381 German university students was used. Methods: Data was analyzed following a structural equation modeling approach. Results: Men show more ART on the seminar group dimension, whereas women show more ART on the peer dimension. Being male indirectly predicts higher learning success via the seminar group dimension of ART. Furthermore, SES and gender moderate the effect between ART and learning success. Both ART dimensions directly predict students’ learning success. Conclusions: Our research contributes to understanding the mechanisms of social disparities within higher education and offers implications for the development of inclusive teaching strategies and research on aspects of intersectionality.

Boy Child VS Girls Child Enrollment comparison at Primary school level by County

Sector: 04. Provide quality, equitable and inclusive education and promote learning opportunities for all Algorithm: Percentage of upper secondary school class III students not achieving a sufficient level of literacy - Females Territorial comparisons: South Tyrol, Italy

These are the necessary do-files and guidance ("READ ME") to replicate all tables in the article "Adolescent Girls’ Safety In and Out of School: Evidence on Physical and Sexual Violence from across Sub-Saharan Africa," by David K. Evans, Susannah Hares, Peter Holland, and Amina Mendez Acosta, published in the Journal of Development Studies in 2023. The article principally relies on publicly available data from the Demographic and Health Surveys (DHS) and the Violence against Children Surveys (VACS), which we do not have permission to post. But the included documentation indicates which datasets need to be downloaded to then apply the included do-files to in order to generate the results report in the article.

This dataset has been replaced with a new version and will no longer be updated. New version of the value-added indicators for general and technological secondary schools

The high school added value indicators are a set of indicators that aim to assess the specific action of each high school to ensure the success of the students it welcomes, in terms of success in the baccalaureate and support throughout its schooling in high school.

IVALs allow a diagnosis that goes beyond that which can be made solely on the basis of the ‘raw’ success rates at the examination. To give a picture of the contribution of each high school, the statistical calculation tries to eliminate the impact of factors of academic success outside the high school, to try to preserve what is due to its own action. In order to judge the effectiveness of a high school, the success of each of its pupils must therefore be compared with that of comparable pupils enrolled in comparable high schools. Indicators in "value added" thus accompany the "gross" indicators. For each high school, the added value corresponds to the difference between the results obtained and the results expected, taking into account the educational and socio-demographic characteristics of the pupils received. The analysis combines individual factors of pupils (age and gender, level of schooling at entrance to high school, social profile) and factors related to the structure of the school (percentage of girls, share of pupils late in school, social profile of pupils and average score obtained at the national diploma of the certificate).

Added value is a relative, not an absolute, approach. If the added value is positive, there is every reason to believe that the school has made its pupils more successful than expected given the profile of the pupils it welcomed. If negative, this means that the institution’s results are below the average of similar institutions’ results.

IVALs are broadcast only for public and private high schools under contract.

Access rates and their added value are calculated only for high schools that offer a full cycle, i.e. that receive pupils from 2nd, 1st and Tale. Finally, results in terms of added value and the number of successful candidates per mention are not disseminated where there are fewer than 20 candidates in the GT series or fewer than 10 candidates in the PRO series.

The results sheets for secondary schools can be consulted from the IVAL distribution page on the Ministry’s website: https://www.education.gouv.fr/les-Indicateurs-de-resultats-des-colleges-et-des-lycees-377729

"https://www.education.gouv.fr/les-indices-de-resultats-des-lycees-1118"> Bibliography: Evain F., 2020, Indicators of added value of high schools From internal management to general public dissemination. Insee, Courier des statistiques n°5.

The data collection started in 2012 when students in grade 8 answered a questionnaire. The students were followed up after 3 months and after 12 months. Additional data collections are planned when the students are in high school.

The study was performed in grade 8 (students aged 13–15 years, median 14 years) in six municipalities in southern Sweden representing rural and urban areas with a total population of 120 000. There were 23 schools with grade 8 students in the included municipalities, and at 14 schools, a mental health program (the DISA program) was offered in the regular school context. At nine schools, the program was offered to girls only; at two schools, it was offered to girls and boys in separate groups; and at three schools, the program was offered in mixed groups.

The intervention had been delivered at the intervention schools for 2 years on average, with a range of 1–13 years. Three of the control schools had conducted the intervention before but did not do so during the study period. The reasons for this were staff turnover in two schools and priority of the curricular subject in the third school. Schools without this mental health program in their curriculum were recruited as control schools. At 17 of the schools, all students in grade 8 answered the study questionnaires, but at six schools, only girls participating in the mental health program completed the questionnaires due to school administration reasons, and two schools declined to participate. The gender inequity in the intervention and control groups is thus due to that the mental health program is offered to more girls than boys. The questionnaires were completed by 972 students at baseline.

Two data collections were conducted in grade 8, with a response rate of 75%. The questionnaires were completed by 972 students at baseline. At the 12-month follow-up, when students were in grade 9, the response rate was 80%. Further data collection took place during the students' highschool years.

This study of violent incidents among middle- and high-school students focused not only on the types and frequency of these incidents, but also on their dynamics -- the locations, the opening moves, the relationship between the disputants, the goals and justifications of the aggressor, the role of third parties, and other factors. For this study, violence was defined as an act carried out with the intention, or perceived intention, of physically injuring another person, and the "opening move" was defined as the action of a respondent, antagonist, or third party that was viewed as beginning the violent incident. Data were obtained from interviews with 70 boys and 40 girls who attended public schools with populations that had high rates of violence. About half of the students came from a middle school in an economically disadvantaged African-American section of a large southern city. The neighborhood the school served, which included a public housing project, had some of the country's highest rates of reported violent crime. The other half of the sample were volunteers from an alternative high school attended by students who had committed serious violations of school rules, largely involving illegal drugs, possession of handguns, or fighting. Many students in this high school, which is located in a large city in the southern part of the Midwest, came from high-crime areas, including public housing communities. The interviews were open-ended, with the students encouraged to speak at length about any violent incidents in school, at home, or in the neighborhood in which they had been involved. The 110 interviews yielded 250 incidents and are presented as text files, Parts 3 and 4. The interview transcriptions were then reduced to a quantitative database with the incident as the unit of analysis (Part 1). Incidents were diagrammed, and events in each sequence were coded and grouped to show the typical patterns and sub-patterns in the interactions. Explanations the students offered for the violent-incident behavior were grouped into two categories: (1) "justifications," in which the young people accepted responsibility for their violent actions but denied that the actions were wrong, and (2) "excuses," in which the young people admitted the act was wrong but denied responsibility. Every case in the incident database had at least one physical indicator of force or violence. The respondent-level file (Part 2) was created from the incident-level file using the AGGREGATE procedure in SPSS. Variables in Part 1 include the sex, grade, and age of the respondent, the sex and estimated age of the antagonist, the relationship between respondent and antagonist, the nature and location of the opening move, the respondent's response to the opening move, persons present during the incident, the respondent's emotions during the incident, the person who ended the fight, punishments imposed due to the incident, whether the respondent was arrested, and the duration of the incident. Additional items cover the number of times during the incident that something was thrown, the respondent was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, cut, or bruised, was threatened with a knife or gun, or a knife or gun was used on the respondent. Variables in Part 2 include the respondent's age, gender, race, and grade at the time of the interview, the number of incidents per respondent, if the respondent was an armed robber or a victim of an armed robbery, and whether the respondent had something thrown at him/her, was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, was threatened with a knife or gun, or had a knife or gun used on him/her.

Despite past research demonstrating a strong link between teenage marriage and high school dropout for teenage girls, mechanisms underlying the relation are not well-understood. Drawing from family life-course perspective and its growing literature, this narrative review found teenage girls’ marriage most likely to occur in the poor families was strongly linked to their early high school dropout, via early family formation, role transition, and school risk behavior. Longitudinal mediating research is needed to understand teenage marriage and high school dropout via early family formation, role transition & high school risk behavior among poor teenage girls in Bangladesh. Keywords: Teenage marriage, high school dropout, family formation, family role transition, high school risk behavior.

This dataset tracks annual black student percentage from 2016 to 2023 for Girls Athletic Leadership School High School vs. Colorado and School District No. 1 In The County Of Denver And State Of C

This dataset tracks annual asian student percentage from 2016 to 2023 for Girls Athletic Leadership School High School vs. Colorado and School District No. 1 In The County Of Denver And State Of C