In 2012, more than three million students dropped out from high school. At this pace, we will have more than 30 million Americans without a high school degree by 2022 and relatively high dropout rates among Hispanic and African American students. We have developed and analysed a data-driven mathematical model that includes multiple interacting mechanisms and estimates of parameters using data from a specifically designed survey applied to a certain group of students of a high school in Chicago to understand dynamics of dropouts. Our analysis suggests students' academic achievement is directly related to the level of parental involvement more than any other factors in our study. However, if the negative peer influence (leading to lower academic grades) increases beyond a critical value, the effect of parental involvement on the dynamics of dropouts becomes negligible.

At a time when OECD and partner countries are trying to figure out how to reduce burgeoning debt and make the most of shrinking public budgets, spending on education is an obvious target for scrutiny. Education officials, teachers, policy makers, parents and students struggle to determine the merits of shorter or longer school days or school years, how much time should be allotted to various subjects, and the usefulness of after-school lessons and independent study. This report focuses on how students use learning time, both in and out of school. What are the ideal conditions to ensure that students use their learning time efficiently? What can schools do to maximise the learning that occurs during the limited amount of time students spend in class? In what kinds of lessons does learning time reap the most benefits? And how can this be determined? The report draws on data from the 2006 cycle of the Programme of International Student Assessment (PISA) to describe differences across and within countries in how much time students spend studying different subjects, how much time they spend in different types of learning activities, how they allocate their learning time and how they perform academically.

From 2006 to 2022, the rate of high school dropouts in the United States has significantly decreased. In 2022, the high school drop out rate was five percent, a notable decrease from 9.7 percent in 2006.

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center

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.

“cct_correction_dropout_date_an.dta”: database created to input corrections to the school visit data about dropout dates. When dropout dates were not consistent between two visits, data were manually checked to determine which data was the most accurate.

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.

Number and proportion of persons aged 15 and over in private households with a high school diploma or equivalency certificate, by sex, age group and selected demographic characteristics for Canada, provinces and territories. High school completion rate is measured using the variable "Secondary (high) school diploma or equivalency certificate". Selected demographic characteristics include, immigrant status, visible minority and Aboriginal identity.

This research aimed to help two project countries (Malawi and Lesotho) increase access to learning for students living in high HIV prevalence areas who were at risk of grade repetition or school drop-out, through (i) complementing classroom teaching with self-study learner guides to provide more open, distance and flexible delivery of the curriculum and (ii) strengthening community support for learning. The research objectives were: (1) To increase understanding of how open, distance and flexible learning (ODFL) can be used to address the factors that disrupt schooling by conducting research with school teachers and community members; (2) To design and implement an intervention in primary schools (Grade 6) in Malawi and Junior secondary schools (Grade B) in Lesotho over one school year (January to November 2009); (3)To evaluate the effectiveness of the intervention in reducing student absenteeism, drop-out and grade-repetition using an experimental design; (4) To disseminate the new knowledge gained to enable appropriate, evidence informed policy development to better integrate and more open and flexible curriculum delivery into schools and strengthen community support for vulnerable learners. ODFL initiatives, structures and networks that are already in place to implement HIV/AIDS policies were firstly identified through analyses of secondary data. Case studies were developed in contrasting communities severely affected by HIV and AIDS to identify contextual factors that can lead to exclusion from conventional schooling and dropping out. The case studies are complemented by data collected using a range of approaches such as semi-structured interviews, focus group discussions, informal discussions with family members, participatory activities and observation. Based on this formative research, a pilot intervention will then be made through secondary schools to identify and trial a small-scale ODFL intervention package designed to overcome the barriers to conventional schooling identified in the case studies. The intervention will be evaluated qualitatively and also quantitatively using an experimental design. The impact was evaluated in a randomized controlled trial. In each country there were 20 schools in the intervention group and 20 schools in the control group. Data to evaluate the impact of the programme on school attendance, drop-out and grade repetition were collected before and after the intervention. Student achievement was assessed by testing children in Mathematics and English before and after the intervention. The study was conducted in 4 stages: (1) Sampling and randomization of schools; (2) Intervention design (informed by synthesizing existing knowledge, generating new knowledge and inviting critical comment from all stakeholders); (3) Intervention implementation; (4) Intervention evaluation. This study aimed to increase access to education and learning for young people living in high HIV prevalence areas in Malawi and Lesotho, by developing a new, more flexible model of education that uses open, distance and flexible learning (ODFL) to complement and enrich conventional schooling. The findings showed that in Malawi, the programme reduced overall student drop-out by 42% (OR=0.58). This effect was not significantly different among at-risk children targeted by the program and those not targeted in their class suggesting the intervention had spillover effects beyond the intended beneficiaries. There were improvements in mathematics scores for at risk students and a history of grade repetition was a better predictor of future drop-out than orphan-hood. In Lesotho the intervention reduced absenteeism and improved Mathematics and English scores. These findings suggest that the intervention reached the most vulnerable and was effective in increasing access to education and learning. The data collection includes: (I)Quantitative data from the intervention group schools and the control group schools in each of the two project countries to evaluate the impact of the intervention on school attendance, school drop-out and progression to the next grade;the quantitative data set for the Malawi data contains 438 variables for 3275 individuals(40 schools in 2 districts). The quantitative data set for the Lesotho data contains 56 variables for 5528 individuals(34 schools in 2 locations-high altitude and low altitude). Data ware collected from the intervention and the control schools during the pre-intervention baseline survey in October 2008, monthly monitoring forms and the post-intervention follow-up survey in November 2009. Data were collected using the following instruments: (1)pre-intervention pupil questionnaire to gather data on pupil characteristics; (2)pre-and post intervention tests in Mathematics and English;(3) a school checklist to collate data on attendance and progression from school records and monthly SOFIE monitoring forms) with additional questions included for intervention schools to collect data on process indicators during the mid-term and post intervention school visits); (4) pupil tracking records to maintain up-to-date information on pupil educational status. (II)Qualitative data were collected help explain the findings from the quantitative data by providing information on the implementation process and on how the intervention was received. These data were collected through SSIs with intervention class teachers, youth club leaders, school heads and members of the school management committee; FGDs with community members; workshops with ‘at-risk’ pupils to explore their views on schooling and on the intervention; and follow up interviews with workshop participants. (3) Diaries of Teacher's and Club-leader's(Scanned Documents) . The entities under study were in Malawi: primary school students in grade 6 and in Lesotho: junior secondary school students in class B (second year).

The master dataset contains comprehensive information for all government schools in NSW. Data items include school locations, latitude and longitude coordinates, school type, student enrolment numbers, electorate information, contact details and more.

This dataset is publicly available through the Data NSW website, and is used to support the School Finder tool.

Data Notes:

• Data relating to healthy canteen is no longer up to date as it is no longer updated by the Department, this data can be sourced through NSW health.

• Student enrolment numbers are based on the census of government school students undertaken on the first Friday of August; and LBOTE numbers are based on data collected in March.

• School information, such as addresses and contact details, are updated regularly as required, and are the most current source of information.

• Data is suppressed for indigenous and LBOTE percentages where student numbers are equal to, or less than five indicated by "np".

• NSSC out of scope schools will not have an enrolment figure.

• NSSC and LBOTE figures are updated annually in December.

• ICSEA values are updated every February with the previous year's ICSEA values. Small schools, SSPs and Senior Secondary schools do not have their ICSEA values published by ACARA.

• Family Occupation and Educational Index (FOEI) is a school-level index of educational disadvantage. Data is extracted in May and values are updated annually in December.

• Following the introduction of part-time study in secondary schools in 1993, student enrolments are generally reported in full-time equivalent units (FTE). The FTE for students studying less than 10 units, the minimum workload, is determined by the formula: 0.1 x the number of units studied and represented as a proportion of the full-time enrolment of 1.0 FTE.

Data Source:

• Education Statistics and Measurement. Centre for Education Statistics and Evaluation.

Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an assessment, in whichever manner the school is collecting • Student participation in an online forum, chat log, or discussion thread • Student/family phone call, email or response to teacher email • Phone, email, and/or other digital communication with a family member which confirms student interaction/engagement • Other evidence of participation as determined by the principal. Interactions data are attributed to students' school of record on a given day. A student participating in a Shared Instruction (SHIN) model may have recorded interactions at multiple schools on a given day, but only one record is counted for the interaction rate, attributed to students' school of record for that day. Due to the shift to hybrid learning, attendance data for the 2020-21 school year include both in-person and remote instruction. Total days, days absent, and days present fields include both in-person and remote attendance.

More information on attendance policies can be found here: https://www.schools.nyc.gov/school-life/rules-for-students/attendance

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.

Number of persons aged 15 and over in private households with or without a high school diploma or equivalency certificate, and high school completion rate (measured using the variable Secondary (high) school diploma or equivalency certificate) by sex, age group and selected demographic characteristics, Canada, provinces and territories.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Abstract copyright UK Data Service and data collection copyright owner.The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables. The Active Lives Children and Young People Survey, 2019-2020 began as the usual school-based survey (i.e. completed at school as part of lessons). From 20 March 2020, schools, colleges and nurseries were closed in the UK due to the COVID-19 pandemic and remained closed until 1 June 2020, when there was a phased reopening for reception, and Years 1 and 6. The Active Lives survey fieldwork in Spring term finished two weeks early before the end of term, in line with the school closures. Due to the closure of schools, the survey had to be adapted for at home completion. The adaptions involved minor questionnaire changes (e.g. to ensure the wording was appropriate for both the new lockdown situation and to account for the new survey completion method at home) and communication changes. For further details on the changes, please see the accompanying technical report. The circumstances and adaptations resulted in a delay to survey fieldwork re-starting. This means that the data does not cover the full lockdown period, and instead re-starts from mid-May 2020 (when the survey was relaunched). Sample targets were also reduced as a result of the pandemic, resulting in a smaller proportion of summer term responses for 2019-20 when compared to previous years. As part of Sport England’s official publication, an additional Coronavirus report was produced, which outlines changes during the ‘easing restrictions’ phase of lockdown from mid-May to the end of July, comparing the summer term in 2020 with summer 2019. Due to the reduced summer term sample, it is recommended to analyse within term and/or school phase for academic year 2019-20. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year. The following datasets have been provided: Main dataset: includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).Year 1-2 dataset: includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).Teacher dataset – this .sav file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable. For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.Latest edition informationFor the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

Over 400 simulated datasets (across six synthetic networks and four curated Boolean models) originally used for benchmarking algorithms for gene regulatory network inference.

Ground Truth

An edge weight of +1 represents activation, -1 represents inhibition.

Size: Groups of curated and synthetic datasets for gene expressions from single cell data

Number of features: 6-19

Ground truth: Yes

Type of Graph: directed, mixed

Synthetic datasets networks

Contains 6 synthetic networks: dyn-BF (Bifurcating), dyn-BFC (Bifurcating Converging), dyn-CY (Cycle), dyn-LI (Linear), dyn-LL (Linear Long) and dyn-TF (Trifurcating).

To simulate the data for each network a BoolODE approach was used. For each gene in a GRN, BoolODE requires a Boolean function that specifies how that gene’s regulators combine to control its state. Each Boolean function was represented as a truth table, which was converted into a nonlinear ordinary differential equation (ODE). This approach provides a reliable method to capture the logical relationships among the regulators precisely in the components of the ODE. Noise terms were added to make the equation stochastic. For each network, BoolODE was applied by sampling ODE parameters ten times and generating 5,000 simulations per parameter set. Five datasets were created per parameter set, one each with 100, 200, 500, 2,000 and 5,000 cells by sampling one cell per simulation, to obtain 50 different expression datasets.

Datasets from synthetic networks

Contains four published Boolean models: mammalian cortical area development (mCAD), ventral spinal cord (VSC), hematopoietic stem cell (HSC) differentiation and gonadal sex determination (GSD).

BoolODE was used to create ten different datasets with 2,000 cells for each model. For each dataset, one version was generated with a dropout rate of q = 50 and another with a rate of q = 70.

More information about the dataset is contained in BEELINE_description.html file.

There has been remarkably little attention to using the high resolution provided by genotyping-by-sequencing (i.e. RADseq and similar methods) datasets for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of dataset that could lead to downward-biased, yet precise, estimates of relatedness. Here we assess the applicability of genotyping-by-sequencing datasets for relatedness inferences given their relatively high genotyping error rates. Individuals of known relatedness were simulated under genotyping error, allelic dropout, and missing data scenarios based on an empirical ddRAD dataset, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (1996) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP dataset with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite dataset of tens of markers. The simulation-based approach used here can be easily implemented by others on their own genotyping-by-sequencing datasets to confirm the most appropriate and powerful estimator for their dataset.