Embarking on a new research endeavor can be a daunting task. User guides, books, and published articles are written for an audience that already has some background experience in the field. Undergraduate students like you, who are at the very beginning of their research careers, often struggle to make sense of these documents. Furthermore, students like you often attempt to do so while balancing heavy course loads. Thus, I have written this document to help ease the burden so that you have more time to ponder the interesting scientific questions instead of digging through pages upon pages of documentation. I assume that you already have some basic familiarity with R before starting this project. I also assume that you have a well devised plan for your experimental design, including the variables you want to collect, the sampling scheme, and of course the questions of interest. Finally, I assume that you have taken a basic course in statistics and have a research mentor that can assist you with more advanced statistical methods. This is not an exhaustive user manual, but rather a guide to help you get started on your journey with geometric morphometrics.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2012 will continue the work started through CSES 2004 and the annual CSES 2007 and 2008 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives:

Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2012 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2012"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2012 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2014 will continue the work started through CSES 2004 and the annual CSES 2007 to 2013 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan. In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural 1) Banteay Meanchey 2) Kampong Cham/Tbong Khmum 3) Kampong Chhnang
4) Kampong Speu 5) Kampong Thom 6) Kandal
7) Kratie
8) Phnom Penh
9) Prey Veng
10) Pursat
11) Siem Reap
12) Svay Rieng
13) Takeo
14) Otdar Meanchey
15) Battambang/Pailin
16) Kampot/Kep
17) Preah Sihanouk/Koh Kong 18) Preah Vihear/Stung Treng
19) Mondul Kiri/Ratanak Kiri

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2014 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2014"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2014 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average

Context

Overwatch is a 6v6 FPS (first-person shooter) team game with great variety between heroes who can be played as. Overwatch League (OWL) is the professional esports league of Overwatch. When watching the OWL matches this year, I noticed the power-rankings and predictive statistics by IBM Watson extremely intriguing, so I determined to introduce the datasets into Kaggle. I, myself, really want to replicate the predictions and rankings, then testing with the stats lab.

Content

The datasets include players, head-to-head match-ups, and maps. The player historical statistics should contain OWL games from 2018 till now. It's centered around each player, and player's picked hero, its team name, performance, match IDs, etc.

Acknowledgements

Overwatch League Stats Lab has updated and downloadable csv files. And here are some interesting and inspiring questions to look at: https://overwatchleague.com/en-us/news/23303225.

Inspiration

Other than the power rankings and outcome predictions, I plan to look at teamfight stats, first elimination, and first death to compare the team's power.

For Players: 1. Match Report dashboard 2. Rate Ranks dashboard: Who led the league in solo kills/10 mins in 2018 as Junkrat? (min. 60 mins played) 3. Career Totals dashboard 4. Single Records dashboard

For Heroes: 1. Which 4 heroes did one play for 10% or more of his time on assault map attack rounds in the season? 2. Which hero increased in usage from 8% at the end of Stage 4 of 2018 to over 45% in the inaugural season playoffs?

For Matches: 1. Which team played the most matches that ended in a 3-2 score during the 2021 regular season? 2. Which team is entering the 2021 season on a 7-map loss streak? 3. Which team has the fastest completion time on Rialto?

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

Abstract

Violence against children under 18 years of age is a major human rights violation and social and health problem throughout the world. Generally, child abuse is divided into three major categories: physical, emotional, and sexual, all of which can have significant short- and long-term health consequences for children. These include injury, sexual and reproductive health problems, unintended pregnancy, increased risk of HIV, mental health issues, alcohol and drug abuse, social ostracism, and increased incidence of chronic disease in adulthood. Those who have experienced childhood violence are more likely to engage in risk behaviors as adolescents and adults, and may be more likely to become perpetrators themselves.

The key objectives of Cambodia VACS are:

• To estimate the national prevalence of physical, emotional and sexual violence perpetrated against boys and girls, including touching without permission, attempted sexual intercourse, physically forced sexual intercourse, and pressured sexual intercourse perpetrated against boys and girls prior to turning age 18 and more recently;

• To identify risk and protective factors for physical, emotional and sexual violence against children to inform stakeholders and guide prevention efforts;

• To identify the health and social consequences associated with violence against children;

• To assess the knowledge and utilization of medical, psychosocial, legal, and protective services available for children who have experienced sexual, emotional and physical violence;

• To identify areas for further research; and

• To make recommendations to the Government of Cambodia and international and local partners on developing, improving and enhancing prevention and response strategies to address violence against children as part of a larger, comprehensive, multi-sectoral approach to child protection.

Geographic coverage

National Urban and rural areas Twenty (20) domains:

1. Banteay Meanchey
2. Battambang
3. Kampong Cham
4. Kampong Chhnang
5. Kampong Speu
6. Kampong Thom
7. Kampot
8. Kandal
9. Kratie
10. Phnom Penh
11. Preah Vihear
12. Prey Veng
13. Pursat
14. Ratanak Kiri
15. Siem Reap
16. Sihanoukville
17. Stung Treng
18. Svay Rieng
19. Takeo
20. Oddar Meanchey

Analysis unit

Household Individual (Eligible from 13-24 years old)

Universe

Children aged 13-24 years old, male and female, who have been victims of physical, emotional, and sexual violence

Kind of data

Sample survey data [ssd]

Sampling procedure

VACS 2013 makes use of a four-stage cluster sample survey design. In the first stage, a total of 225 villages were selected using probability proportional to size with an allocation by urbanization (27% urban/ 73% rural). In stage 2, enumeration areas known as EAs - the primary sampling units based on geographical subdivisions in Cambodia determined by the department of demographic statistics, censuses and surveys - were selected. The 225 sample EAs were gendered (106 female and 119 male EAs) and one EA was randomly selected from each of the 225 sampled villages. In stage 3, a fixed number of 25 households were selected by equal probability systematic sampling from each selected EA. In stage 4, one eligible respondent (female or male depending on the EA) was randomly selected from the list of all eligible respondents (females or males) 13-24 years of age in each household.

The sampling frame was originally compiled by the National Institute of Statistics for the national population census in 2013. In preparation for several national surveys, the sampling frame was updated in 2012 and takes into account the 2011 reclassification of urban areas in Cambodia.

To calculate separate male and female prevalence estimates for violence victimization, a split sample was used. This means that the survey for females was conducted in different EAs than the survey for males. The split sample approach serves to protect the confidentiality of respondents, and eliminates the chance that a male perpetrator of a sexual assault and the female who was the victim of his sexual assault in the same community would both be interviewed. The design also eliminates the chance that a female perpetrator and a male victim of sexual violence from the same community would both be interviewed.

Prior to the implementation of the survey, a mapping and listing team, primarily composed of supervisors identified for the actual survey, visited all of the randomly selected EAs from the second stage of sampling. It was necessary to map and list all structures within each EA. After the list was constructed, a cluster of 25 households, based on sample size estimates, were selected using either simple random selection, or systematic selection with a random start.

During survey implementation, 25 households were randomly selected in each EA. Upon entering a randomly selected household, interviewers were tasked to identify the head of household or the person representing the head of household in order to introduce the study and complete a household list to determine eligibility of household members to participate in the study. The head of household were requested to participate in a short (15 minute) survey to assess the socio-economic conditions of the household (Appendices W/AA). When there was more than one eligible participant, the interviewer randomly selected one respondent using the Kish Method. If there was no eligible participant, the household was still requested to participate in the household questionnaire. In the case that the head of household is a female or male 13-24 years old, she or he was included in the household listing and may be selected as the respondent. In this case, she or he completed the household questionnaire and the respondent questionnaire. If the selected respondent was not available after three attempts or refused to participate, the household was skipped regardless of whether another eligible respondent existed in the household, thus, the household was not replaced.

For more details please refer to the technical document IRB Protocol VACS Cambodia Final.

Mode of data collection

Face-to-face [f2f]

Research instrument

The development of a standardized global questionnaire was led by CDC scientists with extensive external consultation. A broad range of academic background and subject-matter expertise is represented in the team at CDC and among the external consultants who developed this tool. The questionnaire draws questions and definitions from a number of well-respected survey tools which has the benefit of (a) being able to compare data on various measures with other studies as a useful validation and an interesting comparison and (b) using measures that have already been field tested in other studies. In addition, the questionnaire has been previously implemented in five other countries (i.e. Swaziland, Tanzania, Kenya, Zimbabwe and Haiti) after being adapted based on vital country-level review by stakeholders.

The following international and violence surveys helped to inform the questionnaire: - Cambodia Demographic and Health Survey (CDHS) - National Intimate Partner and Sexual Violence Surveillance System (NISVSS) - The Child Sexual Assault Survey (CSA) - Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) - ISPCAN Child Abuse Screening Tool (ICAST) - HIV/AIDS/STD Behavioral Surveillance Surveys (BSS) - Youth Risk Behavior Survey (YRBS) - National Longitudinal Study of Adolescent Health (Add Health) - World Health Organization (WHO) Multi-country Study on Women's Health and Domestic Violence against Women - Behavioral Risk Fact Surveillance System (BRFSS) - Hopkins Symptoms Checklist - ISPCAN Child Abuse Screening Tool (ICAST)

The questionnaire has been further adapted for Cambodia (Appendices W/AA, X/BB, Y/CC). Consultation with key informants from Cambodia and input from stakeholders participating in the Technical Working Group on Questionnaire Development (part of the Steering Committee), who are familiar with the problem of violence against children, child protection, and the cultural context, helped to further adapt the questionnaire and survey protocol for Cambodia.

The questionnaire includes the following topics: demographics; parental relations, family, friends and community support, school experiences, sexual behavior and practices; physical, emotional, and sexual violence; perpetration of sexual violence, health outcomes associated with exposure to violence; and utilization and barriers to health services. The background characteristics of the study respondents and the head of household survey include questions that assess age, socio-economic status, marital status, work status, education, and living situation. The sexual behavior and HIV/AIDS component utilizes questions from the CDHS, BSS, and WHO Multi-country study. Sexual behavior questions are divided among the following topics: sexual behavior, including sex in exchange for money or goods, pregnancy, and HIV/AIDS testing. The sexual violence module, the primary focus of the study, includes questions on the types of sexual violence experienced and important information on the circumstances of these incidents, such as the settings where sexual violence occurred and the relationship between the victim and perpetrator. This information will be collected on the first and most recent incidents of sexual violence, which will include a question on whether sexual violence occurred within the past 12-months. In addition, we developed several questions assessing potential risk and protective factors, including attitudes around sexual violence. Some of these questions were based on DHS, YRBS, and Add Health. We also ask