During a 2021 survey conducted in the United States, it was found that ** percent of children aged between eight and 18 years had a smartphone in their home, up from ** percent recorded two years before. The share of children subscribing to streaming services also increased, reaching ** percent. The increase in children’s use of technology Recently, the number of children with access to different kinds of electronic devices has increased. Between 2019 and 2021, subscription streaming devices and VR headsets have become more popular, and in 2021, ** percent and ** percent of American households owned them, respectively. Furthermore, not only do children have access to such devices at home, but they also own their own electronic devices. For instance, in that same year, ** percent of children aged between eight and ** had a tablet or a computer. Smartphones are the most common devices among children and teens: in 2021, over ** percent of American 12-year-olds had a smartphone. Activities performed on electronic devices American children perform many media activities on electronic devices, such as watching TV, playing games, and video chatting. In 2021, children aged between eight and ** spent around *** hours and ** minutes watching TV on average. Furthermore, social media usage is prolific, particularly TikTok and Snapchat, on which they spend an average of around ** and ** minutes per day, respectively.

Capturing variability in use of commercial technologies by children with autism can inform future learning and support technology design. Survey data were collected from parents (n = 388) in the UK, Spain, and Belgium, and includes information about individuals with a range of ages and ability levels. We found a comparable pattern of access and usage across age groups, though higher reading and language ability was linked to use of more devices and interfaces. Reported worries about technology correlated with longer time spent using technology. Autistic people use mainstream technologies for a broad range of recreational uses. The data suggest that technologies developed with therapeutic goals in mind may need to achieve a high standard of design to engage users.

According to a 2020 survey of parents in the United States, 97 percent of the respondents with children 8 years and younger had smartphones. During the same survey, it was found that 75 percent of respondents had tablets at home.

The barometer was collected as part of a research project funded by the Ministry of Education and Culture of Finland and it aimed to investigate children's media use and their relationship with the media in the year 2013. The data were collected from families that had children aged 0-8. The parents of the children responded to the survey. In the survey, media include television, the Internet, digital games, mobile phone, the radio and music, and print media. The data was collected in a manner that makes comparisons with Children's Media Barometer 2010 possible. The respondents were asked how well they knew the media contents their children used and which media devices their children had access to at home. The respondents were asked whether their children used different media (television and films, the Internet, digital games, mobile phone, radio and music, and print media), in which format / on which device and how often, and what kind of content they accessed using each medium. Additional questions concerning digital media charted who accompanied the children when they used each medium and how often the children were accompanied by a parent when they accessed different media content. With regard to media use in the family, the respondents were asked how often they discussed media use or media content with their children, how often they did things not related to media together with the child (e.g. went shopping, did sports, visited friends or family), and to what extent they agreed with a number of statements related to media use in the family (e.g. "Playing digital games together is a nice way to spend time with the children", "The child often asks me to stop watching TV or using the computer"). The respondents' own media use was charted by asking them how often they used used different media (television and films, the Internet, digital games, mobile phone, radio and music, and print media), in which format / on which device and how often, what kind of content they accessed using digital media, and how often they kept in contact with different people through media. Finally, the respondents were asked how often they met different people face-to-face. Bakcground variables included the respondent's role (mother/stepmother, father/stepfather, other guardian), age, economic activity, education, major region, and type of neighbourhood of residence as well as the number of adults in the household, and ages and genders of children. In addition, there were background variables charting information on the child on whose behalf the parent responded, which included the child's year of birth, gender, and whether the child had a room of his/her own.

According to a survey of parents and children in the United Kingdom (UK) conducted between October and December 2023, 97 percent of children between 16 and 17 years old were using their mobile phones to go online, while only 28 percent of respondents from this age group were using desktop computers for the same purpose. The survey found that tablets were the most popular devices used to navigate the web among children between five and seven years old.

Using the revised bioecological model, we examined whether three broad factors predict adolescents’ nonacademic media use, with the exception of TV: (a) process factors that highlight a child’s fundamental and proximal interactional activities (e.g., eating meals together); (b) person factors (e.g., age, sex, ethnicity); and (c) contextual factors that delineate a child’s immediate physical and social environments (such as family, school, and community). By analyzing a nationally representative cohort (N = 22,454) of U.S. parents/primary caregivers who completed surveys regarding their children, we identified specific process-person-contextual factors that predict adolescents’ nonacademic screen time. Factors that positively predict screen time include, e.g., age, sex, ethnicity, BMI, anxiety. Those that negatively predict screen time include, e.g., sleep, physical activity, father’s physical health, mother’s mental health, eating meals together, sharing ideas with parents, the child’s active participation in school activities and community service, school safety, and emotional support for parents. Further, we found one age-related developmental process; the beneficial impact of meal sharing on media use was more pronounced in younger adolescents. This underscores the importance of exploring not only individual characteristics but also the broader process and contextual factors that shape adolescents’ nonacademic media use. Prior research on adolescents’ screen time primarily examined risk or protective factors at the individual level. In contrast, understanding the nuanced interplay among individual, familial, and broader contextual factors in shaping nonacademic media consumption is limited. We identified a comprehensive but understudied group of process, personal, and contextual factors and their intricate interactions that are pivotal in adolescents’ media use. We also made critical theoretical contributions regarding family functioning in the promotion of healthy media practices. Our results have important implications for effective and holistic interventions that support healthy media-use practices in adolescents. These include the promotion of adolescents’ self-regulatory skills, healthy family lifestyles at home, and diverse activities at school and within the community.

This project investigated parent perceptions of COVID19 Schooling from home based on a national survey of parents. Survey questions are listed below:• What is your usual employment?• How many hours a week are you currently employed?• What is your age?• What is your gender?• Country of residence• State• Postcode• How many children are currently under your care?• How many children are you currently schooling at home?• What is your child’s age?• What year of school is your child in?• What is your child’s gender?• Does your child have any special learning needs, and if so, what are they?• What type of school does your child attend?• In what area is your child’s school located?• What sort of technology or device does your child most often use for schooling at home (e.g. iPad, Chromebook, ACER laptop, Samsung phone, none)?• Which would best describe the access that your child has to a device or technology in order to undertake schooling at home?• Approximately how many weeks in total have you schooled your child from home since the beginning of the COVID-19 pandemic?• Approximately how many hours a week do you personally support your child to undertake schooling at home?• Approximately how many hours a week does another adult or adults support your child to undertake schooling at home?• Please rate your agreement with the following questions:- Schooling at home has been stressful for me.- Schooling at home has been difficult for my child.• What has been most stressful and difficult for you and your child about homeschooling, and why?• What has worked well/has been beneficial for you or your child during homeschooling, and why?• How many days each week does your child undertake schooling at home?• On each schooling at home day, approximately how many hours does your child spend schooling at home?• Are you generally aware of how your child spends their time completing schooling at home?• Approximately how many minutes each day (on average) would you estimate your child spends completing each of the following schooling-related activities?- Paper based activities (e.g. printed worksheets)- Offline tactile activities (e.g., exercise, science experiments)- Web-conferencing with a teacher (e.g. via Zoom)- Online learning games (e.g. Mathletics, Reading Eggs)- Digital worksheets completed online (e.g. fill-in-the-blank)- Reading online resources (e.g. links to websites)- Watching videos (teacher created)- Watching videos (general public domain)- Digital creativity tasks (e.g. creating essays, videos, posters)- Other online tasks (e.g. Google Classroom, Moodle chats)- Other:• If you could change anything about your child’s online and offline schooling at home activities, what would it be?• Does your child learn more, the same or less when schooling from home compared to when learning at school?• How much more or less do you estimate your child is learning during schooling at home compared to their normal learning when at school?• Please rate your agreement with the following questions:- My child is able to learn independently using technology- I am satisfied with the homeschooling support being offered by my child’s school• Compared to the first time during the pandemic that you had to do schooling at home, how would you rate schooling at home now?• Please explain the reasons for your answer to the previous question.

According to a survey of parents and children in the UK conducted in 2024, ** percent of children between 16 and 17 years old owned a smartphone, while ** percent of respondents aged between ***** and **** did not have a mobile phone.

Electronic devices available to children Mobile phones are not the only devices children are exposed to daily. At home, indeed, they have access to all kinds of electronic devices, such as TVs, gaming consoles, and radios. For instance, in 2020, ** percent of children had access to a smart TV, and ** percent had a game console. Furthermore, ** percent of children in the UK had access to a PC, laptop, or netbook with an internet connection. Children’s online activities British children perform many different activities online, with mobile phones being the most used devices to go online. Among the most recurring online activities were playing games and watching videos, especially on YouTube. Furthermore, children in the UK appear to spend quite some time on social media platforms, like TikTok and Snapchat, where they spend on average ** and ** minutes daily, respectively.

Positive relationships between the home literacy environment and children’s language and literacy development are well-established. However, existing literature has overlooked the potential contributions of the home music environment. Initial evidence indicates positive relationships between the home music environment and children’s emerging language and literacy skills, yet it remains unclear whether and how children’s home music and literacy environments may be related. Furthermore, parents’ sense of self-efficacy is known to impact the home environment provided for their children. Despite being linked with the home literacy environment, parental self-efficacy has not been directly investigated in relation to the home music environment. In the present study, 124 caregivers of preschoolers completed a one-time online survey about their children’s home music environment, home literacy environment, and parental self-efficacy. Partial correlations and hierarchical regressions reveal that children’s amount of music exposure is associated with qualitative (not quantitative) aspects of the home literacy environment, specifically parents’ use of interactive techniques during shared reading. Moreover, parental self-efficacy is associated with children’s amount of exposure to music. Overall, these findings support the need to further examine how the home music environment may meaningfully contribute to an enriching learning environment, especially to support language and literacy development.

Abstract

The objective of the endline surveys in 2016 were to gather household, biomedical, and cognition data in order to evaluate the long-term impact of home supplementation with micronutrient powders (MNP), when combined with seasonal malaria chemoprevention (SMC) and early stimulation, delivered through community preschools and parenting sessions, on the health and cognitive development of children during the first five years of life.

The trial consisted of 3 arms. First, 60 villages with established Early Childhood Development centres (ECD) were randomised to 1 of 2 arms:

1) Children living in villages in the ECD control arm received SMC as part of national health programming and a national parenting intervention delivered by ECD center staff trained and supported by Save the Children, with ALL resident children eligible to participate in the interventions irrespective of enrolment in ECD program (ECD Control group).

2) Children living in villages in the intervention arm also received the SMC and parenting interventions described above, but additionally were eligible to receive home supplementation with micronutrient powders (MNP intervention arm).

3) Second, a third non-randomised arm was recruited comprised of children living in 30 randomly selected villages where there were no ECD centers in place and thus both the parenting interventions and MNPs were absent. These children received SMC only, as part of national health programming (non-ECD comparison arm).

Trial arm and Interventions received:

T1. MNP intervention arm: 30 villages with ECD centre (randomised); MNP-Yes, Parenting-Yes, SMC-Yes C1. ECD control arm: 30 villages with ECD centre (randomised); MNP-No, Parenting-Yes, SMC-Yes C2. Non-ECD comparison arm: 30 villages without ECD centre (not randomised); MNP-No, Parenting-No, SMC-Yes

Three cross-sectional endline surveys took place during the period May-August 2016, three years after the original MNP intervention began, and consisted of the following questionnaires and assessments in two age groups of children, 3 year olds and 5 year olds:

i) A household questionnaire was used to collect data from the primary adult caregiver of the child on home environment, exposure to the interventions, and reported practice outcomes of relevance to the parenting intervention.

ii) Biomedical outcomes were measured in children through laboratory and clinical assessment.

iii) A battery of tests were used to assess cognitive performance and school readiness in childen, using a different age-specific test battery for each age group adapted for local language and culture.

Note: Household and cognitive performance data were gathered from participants in all three arms. Biomedical data were only collected from children in the two randomised arms, to evaluate impact of MNP supplementation on anaemia (primary biomedical outcome) in children who received MNPs and those who did not, using a robust study design.

Geographic coverage

Districts (cercles) of Sikasso and Yorosso, Region of Sikasso

Analysis unit

Individuals and communities

Universe

Random sample of target population for the intervention in the 90 communities that consented to participate in the trial, namely pre-school children 0-6 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population for the interventions comprised all children aged 3 months to 6 years, who were resident in the 90 study communities participating in the trial; the primary sampling unit is the individual child.

Sample Frame:

To identify the number of target beneficiaries, a complete census of all children of eligible age was carried out in the 90 study villages in August 2013. The census listing from 2013 thus defined the population of children who are eligible to have received the interventions every year for the three years between 2013-2016; and was used as the sampling frame of children in whom the impact after three years of implementation of the interventions was evaluated. The intention was to evaluate study outcomes in the same child one year after the start of the MNP intervention (May 2014) and again after three years of the intervention (2016).

A random sample of children was drawn from all children listed in the census for each community participating in the trial, according to the following age criteria:

Date of Birth, or Age in August 2013 (Age group in 2016 surveys) (i) Born between 1 Jan 2013 – 30 June 2013, or aged <1 year in 2013 census if DOB not known (3 years) (ii) Born between 1 May 2010 – 30 April 2011, or aged 2 years in census if DOB not known (5 years)

Thus, all children previously randomly selected and enrolled in the evaluation cohort in 2014 were, if still resident in the village and present on the day of the survey, re-surveyed in May 2016.

Sample Size:

Power analysis was undertaken for a comparison of two arms, taking account of clustering by community. Survey data on biomedical and cognitive outcomes collected in 2014 were used to inform sample size assumptions, including prevalence of primary outcomes, intraclass correlation (ICC) and number of children recruited per cluster. Prevalence of anaemia amongst 3-year old children in 2014 was found to be 61.6% and 64.0% in the intervention and control arms respectively (p=0.618) and 53.8% and 51.9% respectively amongst 5-year old children (p=0.582). The observed ICC for anaemia endpoint at baseline was 0.08 in 3-year old children and 0.06 in 5-year old children. Observed ICC for cognitive outcomes measured in 2014 was 0.09, ranging from 0.05 to 0.16 for individual tasks within the cognitive battery.

Sample Size Estimation for Health Outcomes:

Approximately 20-25 children per cluster were recruited into each age cohort in 2013. Power calculations for anaemia (primary endpoint) were undertaken for three alternative scenarios at endline: (i) to allow for the possibility of up to 20% loss to follow up between 2014 and 2016, power calculations were performed for a sample size at endline of 16 children per cluster; (ii) a smaller cluster size of 14 children sampled per village, under a scenario of 30% loss to follow-up; and (iii) unequal clusters, to allow for the possibility that variation in losses to follow-up between villages could result in an unequal number of children sampled in each village. In this case, cluster size is the mean number of children sampled per cluster.

Thus, assuming a conservative prevalence of anaemia of 50% in the control group and ICC of 0.08, a sample size of 30 communities per arm with 14-20 children sampled per community, will under all of these scenarios provide 80% power to detect a reduction in anemia of at least 28% at 5% level of significance.

Sample Size Estimation for Cognitive Outcomes:

Power calculations for cognitive outcomes explored: (i) a smaller cluster size of 14 children sampled per village, for example resulting from a higher than expected loss to follow-up of 30%; (ii) statistical analysis of differences between arms which does not adjust for baseline - a scenario which allows for the possibility to increase the sample size to compensate for losses to follow-up by increased recruitment of new children for whom no baseline data would be available; and (iii) effect of unequal clusters. Thus, for cognitive-linguistic skills, a sample size of 30 communities per arm with 14-20 children in each age cohort sampled per community will provide 80% power to detect an effect size between 0.27-0.29 at 5% level of significance, assuming an (ICC) of 0.10 and individual, household and community-level factors account for at least 25% of variation in cognitive foundation skills. Whilst for a similar sample size of 30 communities per arm with 14-20 children sampled per community and ICC of 0.10, a statistical analysis which does not adjust for baseline will provide 80% power to detect an effect size between 0.28-0.30 at 5% level of significance.

The sample at endline in May 2016 thus comprised a total of up to 600 children aged 3y and 600 children aged 5y at endline in each arm: T1 Intervention group (with ECD): 30 communities, with approx. 40 randomly selected children in each community (20 aged 3y; 20 aged 5y). C1 ECD control group (with ECD): 30 communities, with approx. 40 randomly selected children in each community (20 aged 3y; 20 aged 5y). C2 Comparison group (without ECD): 30 communities, with approx. 40 randomly selected children in each community (20 aged 3y; 20 aged 5y).

Strategy for Absent Respondents/Not Found/Refusals:

Every effort was made to trace children previously recruited into the evaluation cohort. Since some losses-to-follow-up (for example to due to child deaths, outward migration) were expected between 2014 and 2016, the primary strategy was to oversample in 2014. However, for villages where loss-to-follow-up was higher than expected and it was not possible to trace sufficient number of children remaining from the original sample to meet the required sample size per cluster, additional children were recruited into the evaluation survey in 2016. New recruits were selected at random from the children listed as resident in the village at the time of the original census in 2013. All new recruits had thus been resident in the village and exposed to the interventions throughout the three preceding years.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. Household questionnaire (Form_Parent_MaliSIEF_2016_french.pdf ; Form_Parent_MaliSIEF_2016_english.pdf)

The questionnaires for the parent interview were structured questionnaires. A questionnaire was administered to the child’s primary caregiver

The survey focused on media use and experiences of media education of children aged 7 - 11 in Finland. Media devices in the respondents' room or elsewhere at home and amount of television, videos or other recorded programmes watched were investigated. Some questions covered habits of playing video games (online, on pc or game console), frequency of Internet use, reading habits and how often an adult read to the respondents. The respondents were asked how often they read comics, newspapers and magazines and listened to radio and music from other sources (CD, mp3 etc). Internet use was examined with questions focusing on the use of different websites and services online (e.g. search engines, social media, email) and the extent to which they agreed with some statements about Internet use (e.g. "using social media is important in order to keep up with what happens in one's group of friends", "I often have arguments over Internet use with my parents") The respondents were asked whether they had rules at home for using the Internet, playing video games or watching television, DVDs etc, whether the rules were appropriate, whether they were allowed to watch some material or play some games with older siblings and whether there were any parental controls at home. Further questions covered the respondents' assigned bedtimes and whether they had been punished for misbehaviour by forbidding the use of media devices. One theme pertained to whether the respondents had learned to use different services online, send picture messages by phone and use video camera and who had taught them. Experiences of providing content online (e.g. uploading videos, blogging, commenting on a forum) were charted. The respondents were asked if they had seen or read something that had disturbed or frightened them when watching programmes or reading, who they had told about it, what an adult had done when hearing about it and how long seeing the content had bothered them. The same questions were asked about the content in video games and Internet with an added question on why they had been frightened or disturbed (e.g. bullying, a piece of news, sexual content). Some questions explored whether the respondents felt that their parents or other adults at home were interested in their favourite media and how often they talked to adults about various media. Habits of browsing webshops and stores for desired products, reading advertisements for children's products and using money on online games or communities were investigated. The final set of questions surveyed whether the respondents had followed news about the 2011 parliamentary elections, what channels they had used and who they had discussed these news with. The same set of questions was asked about important foreign news. The Children's Media Barometer 2011 project also studied whether guided questionnaires and face-to-face interviews conducted by young people can be a viable method when studying children. The fifth-graders filled in the questionnaires themselves in a guided situation in class, while the first- and third-graders were interviewed by older students who filled in the questionnaires for them. In the data, the responses of the first-, third- and fifth-graders have been divided into separate variables and differentiated with initial letters a, b and c, respectively. Background variables included the respondent's grade at school, age, gender, household composition, number and age of children in the household (also including potential other home), own room at home, languages spoken at home, and school and respondent id.

Abstract

To support national goals of educational access and equity, Senegal has launched PAQEEB 2013-2017 (Projet d’Amelioration de la Qualité et de l’Equité dans l’Education de Base), which is a comprehensive government strategic plan to improve school governance, as well as increase equity and access to formal education. This is a collaborative effort of the Ministry of Education (MoE) of Senegal, the World Bank (WB), and other International agencies to improve the quality and equity of basic education (World Bank, 2013). A sub-component of this wide initiative is the objective to reach children who do not typically access formal education and are enrolled in religious education in Koranic schools known as Daaras. With Muslims comprising around 95% of the Senegalese population, a vast majority of Senegalese males would have attended Daaras at one time or another, and it is estimated that between 800,000 and one million children and youth attend Daaras (D’Aoust, 2013; as cited in Goensch, 2016). In Senegal, a “Traditional Daara” is dedicated only to memorization of the Koran and advanced studies (Islamic law, etc.) and do not offer any additional instruction in science, math, French or other core courses under the official curriculum. “Modern Daaras”, on the other hand, train students not only in religious education like memorization of Koran but also in Math and French as per the official curriculum.

This subcomponent of the PAQEEB project aims to upgrade and improve Traditional Daaras to have language and math curricula like the Modern Daaras. This is an innovative intervention that provides pedagogical support through disbursement of “grants for results.” In return for this funding, project schools commit to perform the following activities: (1) to implement the specific “Modern Daaras” math and French curriculum; and (2) to ensure that students achieve learning results as reported through indicators measuring their levels of proficiency in reading and mathematics. The project stakeholders selected 100 Daaras in 20 counties based on the lowest gross enrollment ratios out of the 46 counties in Senegal (effectively, counties with gross enrollment ratios between 29 percent and 69 percent) to pilot the Daara modernization efforts (Bureau des Statistiques Scolaires et Universitaires, 2007).

This survey was used to evaluate this sub-component of the larger PAQEEB project that provides “grants for results” to selected Daaras. The survey consists of three distinct instruments that collected relevant data from teachers, caretakers of students, and students.

Geographic coverage

Rural and peri-urban areas only.

Analysis unit

Individuals, households, and schools.

Universe

Primary school and Daara teachers, students, and caretakers of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

Identifying Eligible Treatment and Control Schools The first step in the sampling implementation process was to identify a list of eligible treatment and control schools for the sample. To this effect, The World Bank and the Inspections des Daara committee provided IMPAQ with lists of Daaras that participated in the selection process for the Daara sub-component of the PAQEEB program within each of the 20 included districts. These lists included details on the ranking assigned to each candidate Daara and which Daaras were selected into the program (treatment Daaras) based on those rankings. Using this data on school rankings and characteristics, as well as information gathered during an initial visit to candidate Daaras, IMPAQ began the sample selection process by disqualifying schools from the sample that have previously been deemed ineligible for program allocation based on PAQEEB guidelines.

Selecting from Eligible Treatment and Comparison Schools In order to decrease spillover effects between individuals in treatment school communities and those in comparison school communities, IMPAQ used GPS data to apply a set of minimum distance criteria to all eligible comparison schools and remove any that were too close to treatment schools. More specifically, comparison Daaras were removed from the sample if they were less than 2 kilometers from a treatment school. This decision was based on Theunynck (2009), who shows that distance to school is inversely related to the probability of being enrolled in school in Senegal. Additionally, Theunynck explains that evidence from multiple countries in Africa shows that enrollment and retention decline significantly when students must walk more that 1 to 2 kilometers to get to school. This trend is particularly strong among younger children. Thus, at a distance of two kilometers, we should see minimal interference between treatment and comparison Daaras.

Additionally, in order to be able to distinguish the communities around comparison schools, comparison Daaras were removed from the sample if they were less than ½ kilometer away from other comparison school. In these cases, one school out of the two was randomly chosen to remain as eligible for selection. The radius around comparison schools is smaller because there is no concern of spillover effects between these Daaras. Rather, this radius ensured the research team that they were not measuring the outcomes of two comparison Daaras within the same community. The concern that children from comparison communities may enroll in other nearby comparison Daaras is not considered a major source of bias in the ITT estimate, as the comparison Daaras are generally considered to be of similar quality, making it less likely for a child in a comparison community to commute to a Daara in a different comparison community.

Remaining eligible Daaras were selected for inclusion in the sample based on their ranking in the PAQEEB program selection process. Specifically, Daaras included in the PAQEEB program that were ranked closest to (just above) the program selection threshold were identified as treatment Daaras. Daaras not included in the PAQEEB program that were ranked closest to (just below) the program selection threshold were identified as comparison Daaras. In this way, IMPAQ ensured that treatment and comparison Daaras were as similar as possible concerning the key criteria used for program selection. In the event that multiple comparison schools received equivalent rankings, a random number generator was used to select among them for inclusion into the sample. If an appropriate comparison school could not be identified within a given IEF, all schools from that IEF were dropped from the sample. In most IEFs, IMPAQ selected 3 treatment Daaras and 3 comparison Daaras into the sample.

Selecting Eligible Secondary Comparison Schools In addition to the comparison Daaras, IMPAQ included a second comparison group consisting of formal government schools. These schools were selected based on proximity to treatment Daaras, while still meeting the minimum distance criteria outlined above for comparison Daaras (i.e. 2-kilometer distance).

Household and Child Selection IMPAQ performed a house-listing census of all households with children under the age 16 within a 1-kilometer perimeter (school catchment areas) of each Daara and formal school selected into the sample. For details on this house listing please see section 6.3.3 below. Once all households within the established perimeter of a selected school that had at least one child aged 7-10 were identified, IMPAQ randomly selected 15 households with at least one girl aged 7-10 and 15 households with at least one boy aged 7-10 for inclusion in the study. Only one child of each gender was selected from a given household in order to minimize the influence of larger households on the study outcome. Lastly, if a selected household had more than one child aged 7-10 of a single gender, IMPAQ randomly selected which of those children would be included in the sample, in order to prevent any bias in the selection of children within households.

Mode of data collection

Face-to-face [f2f]

Research instrument

All instruments were originally developed in French, but have been translated to English as well.

Instruments The baseline survey consisted of three unique instruments: A caretaker survey, a child survey and academic assessment, and a teacher survey.

Caretakers’ instrument (Enquête sur les personnes qui s’occupent des enfants) The caretaker survey was designed to learn about the decisions and opinions within each household in the sample. A caretaker was defined as “the person who takes care of the child and makes decisions about what he/she eats and how he/she spends his/her time.” The survey instrument was divided into a schooling section and a household information section. Within the schooling section, caretakers were asked about schools and Daaras in their community, last year’s schooling choices, this year’s schooling choices, their opinions about education, and the child’s school participation/attendance. The household information section briefly captured some basic household characteristics, such as household size, number of children, education levels, and household assets.

Children’s instrument (Enquête sur les enfants) The children’s survey begins with a few questions for the child’s caretaker, which are used to confirm the child’s name, age, and the school he or she attends. The rest of the survey is addressed to the child. First, the enumerator spent 3 to 5 minutes speaking with the child and setting him/her at ease. Next, the child answers questions about the school/Daara they attend. There are different sets of questions depending on whether he/she attends

Abstract

This study aimed to (a) investigate the impacts of offering an additional year of pre-primary education in Bangladesh on child development outcomes (cognitive and social-emotional) and (b) examine the benefits relative to the costs of the program. The study also examined the mechanisms through which the Early Year Pre-School Program affected the outcomes of interest (e.g., children's school readiness) and the operational and community conditions for program implementation. This study provides evidence for the government of Bangladesh on how and how much the additional year of preschool benefits children, and at what cost. In addition to informing future policy in Bangladesh, this information may be useful for other countries considering similar programming. This survey provides endline findings for the evaluation and incorporates information from the baseline (2017) and midline (2018) surveys.

Geographic coverage

District of Meherpur

Analysis unit

Individuals, schools, and communities

Kind of data

Sample survey data [ssd]

Sampling procedure

We conducted a randomized controlled trial (RCT) of the EYPP to determine its impacts on children's learning and development. In 2016, we randomly assigned 100 schools in the Meherpur district of Bangladesh to either a treatment group receiving the EYPP (n = 50) or a no-program control group (n = 50). In October 2017, we conducted a census of the area around all 100 schools to identify children who lived within a 15-minute walk of the school and were in the target age range-that is, children expected to enroll in a typical government pre-primary in 2019 and enter Grade 1 in 2020. In the 50 treatment school catchment areas, children selected for the study were invited to participate in the EYPP at their local school during the 2018 school year and then would go on to government pre-primary as usual in 2019. In the 50 control school catchment areas, children selected for the study would be eligible to enroll in the government pre-primary as usual in 2019 but did not have the EYPP available to them the year before.

Sampling of Children: The target sample for our study included all children in the census areas born from January 1, 2013 - December 31, 2013 (because on-time enrollment in government pre-primary school for these children would be in January 2019). In most cases (exact figure unknown but in a substantial majority), children's dates of birth were verified with the Extended Program of Immunization (EPI) card or a birth certificate. If these documents were unavailable (even after parents were encouraged to search), enumerators recorded what the parent reported as the child's date of birth. We identified a total of 1,986 children born in 2013. We did not exclude any age-eligible children based on any other criteria (for example, children with disabilities were included in our sample pool).

AIR agreed with the World Bank that we would sample an average of 20 children in each of the 100 study communities. Many communities had fewer than 20 eligible children. Because EYPP centers will typically enroll up to 25 children, for both treatment and control communities with 25 or fewer children, we included all eligible children in the study (with parental consent). In the 20 communities (14 treatment and 6 control) with over 25 children in the target age range, we drew a random subsample of 25 for inclusion in this sample.

For this longitudinal study, we collected baseline, midline, and endline data. The midline and endline samples included schools, children, and families enrolled in the study at baseline; we did not add any new participants after baseline. Of the 1,856 enrolled children and families, 1,801 (97%) participated at all three timepoints.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

We administered the family questionnaire at baseline, midline, and endline. Its purpose was to gather information on the characteristics of the study children and their home environments and, at midline and endline, to determine whether and how the intervention affected the home learning environment. Nearly all items on this questionnaire were already used widely in Bangladesh as part of national household surveys. To administer this tool, enumerators read questions and response options aloud to respondents (parents or guardians of the study children). For some questions about family background, we asked the question only at baseline because the answers were unlikely to change across time and were unrelated to the intervention.

At each timepoint, we measured children's school readiness with the IDELA, which has been used widely in Bangladesh. A trained enumerator administered the assessment to children one on one. At endline, we also added subtasks from the Early Grade Reading Assessment (EGRA) and the Early Grade Mathematics Assessment (EGMA) as used in Bangladesh. Because the EGRA and EGMA were designed for children in Grade 1 and higher, we did not expect the study children to perform well, but wanted to ensure that we were prepared should we have ceiling issues with children's performance on the IDELA.

The endline parent questionnaire can be found under the 'Documentation' tab. To obtain a free copy of the IDELA questionnaire please go to https://idela-network.org/the-idela-tool/ and register.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: - Office editing and coding - During data entry - Structure checking and completeness - Secondary editing - Structural checking of STATA data files

Response rate

97% (n = 1801 children)

Abstract

The Quality Preschool for Ghana Impact Evaluation 2016, Midline survey (QP4G-ML 2016) was approved by the Strategic Impact Evaluation Fund (SIEF) of the World Bank on August 2015 in the Great Accra Region of Ghana. The official project name is called "Testing and scaling-up supply- and demand-side interventions to improve kindergarten educational quality in Ghana”, known as “Quality Preschool for Ghana (QP4G)”.

The project seeks to increase the quality of preschool education during the two years of universal Kindergarten (KG) in Ghana through intervening in the supply-side (i.e., teacher in-service training) and the demand side (i.e., increasing parental awareness for developmentally appropriate quality early education).

The primary goal of the impact evaluation is to test the efficacy of a potentially scalable (8-day) in-service teacher training to improve the quality of KG teacher practices and interactions with children and to improve children’s development, school readiness and learning in both private and public preschools in the Greater Accra Region of Ghana. Additional goals of this evaluation are: to test the added value of combining a scalable (low-cost) parental awareness intervention with teacher in-service training; to compare implementation challenges in public and private schools; and to examine several important sources of potential heterogeneity of impact, primarily impacts in public vs. private schools.

The current submission is for the Midline Survey, conducted with 3 types of respondents across two phases – School survey and Caregiver [household] surveys. The school survey was conducted from May to July 2016 and consisted of collecting the following data: (a) direct assessments of children’s school readiness, (b) surveys of KG teachers, (c) direct observation of inventory of facilities within KG classrooms [environmental scan]; videotaping of KG classroom processes, teaching, and learning (not being submitted); as well as video coding of KG classroom video recordings using Teacher Instructional Practices and Processes Systems (instrument not being submitted). The caregiver survey was conducted via phone from August to September 2016 on primary caregivers of KG children. The caregiver survey sought information on caregivers’ background, poverty status, involvement or participation in school and home activities, and perception about ECD. Overall, the Midline Survey was conducted from May to September 2016 for all respondents.

Geographic coverage

Urban and Peri-Urban Districts, Greater Accra Region

Analysis unit

Units of analysis include individuals (KG teachers, children, caregivers), KG classrooms and preschools.

Universe

The survey universe is 6 poor districts in the Greater Accra Region. We sampled 240 schools, 108 public (Govt.) schools and 132 private schools. The population of interest is KG teachers and children in KG 1 and KG 2 classrooms in these schools, as well as the caregivers of sampled students.

Kind of data

Sample survey data [ssd]

Sampling procedure

This impact evaluation applies a cluster-randomized design. Eligible schools were randomly selected to participate in the study. The eligible population was schools with KG 1 and KG 2 classrooms (the two years of universal preprimary education) in six districts in the Greater Accra Region. In these six districts, we have sampled 240 schools; 108 public schools and 132 private schools in total.

The unit of randomization for this randomized control trial (RCT) is schools, whereby eligible schools (stratified by public and private sector schools) are randomly assigned to: (1) in-service teacher-training program only; (2) in-service teacher-training program plus parental awareness program; or (3) control (current standard operating) condition.

The sampling frame for this study was based on data in the Education Management Information System (EMIS) from the Ghana Education Service. This data was verified in a 'school listing exercise' conducted in May 2015.

Sample selection was done in four stages: The first stage involved purposive selection of six districts within the region based on two criteria: (a) most disadvantaged (using UNICEF's District League Table scores, out of sixteen total districts); and (b) close proximity to Accra Metropolitan for travel for the training of the KG teachers. The six selected municipals were La Nkwantanang-Madina Municipal, Ga Central Municipal, Ledzokuku-Krowor Municipal, Adentan Municipal, Ga South Municipal and Ga East Municipal.

The second stage involved the selection of public and private schools from each of the selected districts in the Accra region. We found 678 public and private schools (schools with kindergarten) in the EMIS database. Of these 361 schools were sampled randomly (stratified by district and school type) for the school listing exercise, done in May 2015. This was made up of 118 public schools and 243 private schools. The sampling method used for the school listing exercise was based on two approaches depending on the type of school. For the public schools, the full universe of public schools (i.e., 118) were included in the school listing exercise. However, private schools were randomly sampled using probability proportional to the size of the private schools in each district. Specifically, the private schools were sampled in each district proportionate to the total number of district private schools relative to the total number of private schools. In so doing, one school from the Ga South Municipal was removed and added to Ga Central so that all districts have a number of private schools divisible by three. This approach yielded 122 private schools. Additionally, 20 private schools were randomly selected from each of the districts (i.e., based on the remaining list of private schools in each district following from the first selection) to serve as replacement lists. The replacement list was necessary given the potential refusals from the private schools. There were no replacement lists for the public schools since all public schools would automatically qualify for participation.

The third stage involved selecting the final sample for the evaluation using the sampling frame obtained through the listing exercise. A total of 240 schools were randomly selected, distributed by district and sector. Schools were randomized into treatment groups after the first round of baseline data collection was completed.

The survey respondents were sampled using different sampling techniques: a. KG teachers: The research team sampled two KG teachers from each school; one from KG1 and KG2. KG teachers were sampled using purposive sampling method. In schools where there were more than two KG classes, the KG teachers from the "A" stream were selected. For the treatment schools, all KG teachers were invited to participate in the teacher training program. b. KG child-caregiver pair: The research team sampled KG children and their respective caregivers using simple random sampling method. Fifteen KG children-caregivers pair were sampled from each school. For schools with less than 15 KG children (8 from KG1, 7 from KG2 where possible), all KG children were included in the survey. KG children were selected from the same class as the selected KG teacher. The survey team used the class register to randomly select KG children who were present on the day of the school visit. Sampling was not stratified by gender or age. The caregivers of these selected child respondents were invited to participate in the survey. The research team sought informed consent from the school head teacher, caregivers, as well as child respondents.

Mode of data collection

Other [oth]

Research instrument

Data were collected at Midline Survey using structured questionnaires or forms.

Child Direct Assessment: The KG Child Assessment was conducted using the International Development and Early Learning Assessment (IDELA) tool designed by Save the Children. IDELA was adapted based on extensive pre-testing and piloting by different members of the evaluation team. The adapted version measured five indicators of ECD. The indicators were early numeracy skills, language/literacy skills and development, physical well-being and motor development, socio-emotional development, and approaches to learning. IDELA contained 28 items. In addition, one task was added – the Pencil Tap – to assess executive function skills. Apart from the English language, IDELA was translated and administered into three local languages, namely, Twi, Ga, and Ewe. These local language versions had gone through rigorous processes of translation and back translation. The IDELA tool has not been shared as Save the Children have proprietary rights over this.

KG Class Environmental Scan: The KG classroom observation involved taking inventories of the KG classrooms [environmental scan] and conducting video recordings of the classroom processes. The KG Class Environmental Scan tool was designed to take inventories of the facilities in the KG classrooms. The classroom video recordings have not been shared as they contain PIIs.

TIPPS: The video recordings taken during the classroom observations were coded using an early childhood education adapted version of Teacher Instructional Practices and Processes Systems (TIPPS). Seidman, Raza, Kim, and McCoy (2014) of New York University developed the TIPPS instrument. TIPPS observes nineteen key concepts of teacher practices and classroom processes that influence children’s cognitive and social-emotional development. The concept sheet was used to code the kindergarten classroom videos. The TIPPS tool has not been shared as

The home environment has a significant influence on children’s physical activity and obesity risk. Our understanding of this environment is limited by current measurement tools. The Home Self-administered Tool for Environmental assessment of Activity and Diet addresses this gap. This paper describes the development and psychometric testing of its family physical activity and screen media practices and beliefs survey. Methods: Survey development was guided by the Analysis Grid for Environments Linked to Obesity (ANGELO) framework and informed by a literature review, expert opinion, and cognitive interviews. Parents of children ages 3–12 years (n = 129) completed the HomeSTEAD survey three times over 12–18 days. Additionally, parents reported on child behaviors and trained staff measured parent and child height and weight. Five exploratory factor analyses were conducted after categorizing items into: control of physical activity, control of screen media, explicit modeling, implicit modeling, and perceived barriers and facilitators. Scales with 3 or more items underwent scale reduction. Psychometric testing evaluated internal consistency (Chronbach’s alphas), test-retest reliability (analysis of variance and intraclass correlations (ICC)), and construct validity (correlations with child BMI, physical activity, screen time). An integrated conceptual model of parent physical activity and screen media practices and beliefs was developed based on recent literature to aid in the identification and naming of constructs. Results: Final scales demonstrated good internal consistency (median Cronbach’s alpha = 0.81, IQR = 0.74–0.85), test-retest reliability (median ICC = 0.70, IQR = 0.66–0.78), and construct validity (with correlations between scale score and children’s behaviors generally in the expected direction). Comparison with the integrated conceptual model showed that most identified constructs were captured. Conclusions: The family physical activity and screen media practices survey advances the measurement of the home environment related to children’s physical activity, screen time, and weight. The integrated conceptual model provides a useful framework for researchers studying both physical activity and screen media parenting practices.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the Young Lives website. School surveys were introduced into Young Lives in 2010 in order to capture detailed information about children's experiences of schooling, and to improve our understanding of: the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups. school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems. equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education The survey allows us to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. Findings are all available on our Education theme pages and our publications page. Further information is available from the Young Lives School Survey webpages.

The main objective of the SIMCUR research project is to uncover the processes underlying developmental resilience in children from immigrant families during the transitions to primary and secondary education in three European countries. These processes are examined at the levels of the individual, the family, the school, and the community. By comparing children in Germany, the Netherlands, and Norway, the study also elucidates the impact of broader societal influences. In a longitudinal cohort design based on the two school transitions, we studied 880 migrant families with origins in Turkey allowing across- country comparisons. Mastering major educational transitions is a critical indicator of social integration and is related to individual psychosocial adaptation. For the primary school transition, 364 children from Turkish migrant families (cohort 1) were assessed at ages 5, 6, and 7 in the three participating countries. For the secondary school transition, we assessed 256 children in a second cohort of children at ages 12, 13, and 14. Because this transition takes place earlier in Germany, this country had an extra cohort of 147 children assessed at ages 9, 10, and 11. At each assessment, variables from all levels of functioning are measured using multiple methods (behavioral observation, interviews, tests, and surveys), obtained from multiple sources (mothers, fathers and children).

Parents 1. Mother questionnaire:

Category 1: Background Family situation: relation to the child / father; caregiver; number of children, marital status; health; education, work and economic situation: years of schooling, ISCED; gainful employment; working hours; total income; neighborhood: managing to make it (length of residence), NICHD (National Institute of Child Health and Human Development); Collective Efficacy Scale; activities at home: literacy and media at home; language use in reading and watching TV; language: language proficiency Turkish and majority language; importance of child language use; language use in Turkish and majority language; culture: MEIM-R (Multigroup Ethnic Identity Measure - revised); questions from ICSEY study (International Comparative Study on Ethnocultural Youth; acculturation stress; discrimination; religion: religious affiliation; role of religion in parenting.

Category 2: Child Behavior: CBQ (Child Behavior Questionnaire), EATQ-R (Early Adolescent Temperament Questionnaire - revised); parenting: discipline; EMBU (Egna Minnen Beträffande Uppfostran (´My memories of upbringing´); aspirations and expectations: schooling you would lke/actually expect your child to complete; school: child preschool attendance; parent-teacher involvement, parent-teacher responsibility, confidence in school/teacher; get ready for school; transition; strengths and difficulties: SDQ (Strength and Difficulties Questionnaire); friends: number of friends; frequency of playing; language: child´s language use.

Category 3: Yourself Your life: SWLS (The Satisfaction with Life Scale); task division; social network: Oslo 3-item social support scale; relationship to neighbors; daily life: daily hassles; relationship: VGP (Vragenlijist voor Gezins Problemen); feelings: CES-D 10 (Center for Epidemiologic Studies Short Depression Scale); family: FAD (Family Assessment Device); values: perceived achievement values; familiy collectivist values.

Mother interview: Family history: family tree, reason for migration; legal status; neighborhood: NICHD (National Institute of Child Health and Human Development); managing to make it (Subscale: community services); car/driver´s license; living situation: living space; daily schedule: daily schedules; spare time; activities at home: media use at home.

1. Father questionnaire:

Category 1: Background Language: language proficiency Turkish and majority language; importance of child; language use in Turkish and majority language; culture: MEIM-R (Multigroup Ethnic Identity Measure - revised); questions from ICSEY study (International Comparative Study on Ethnocultural Youth); acculturation stress; discrimination; religion: religious affiliation; role of religion in parenting.

Category 2: Child Parenting: EMBU (Egna Minnen Beträffande Uppfostran (´My memories of upbringing´); school: parent-teacher-responsibility; SDQ (Strength and Difficulties Questionnaire).

Category 3: Yourself Parenting: task division; relationship: VGP (Vragenlijist voor Gezins Problemen, Subscale: partner relationship/...

Abstract

The 2013 NDHS is part of the worldwide Demographic and Health Surveys (DHS) programme funded by the United States Agency for International Development (USAID). DHS surveys are designed to collect data on fertility, family planning, and maternal and child health; assist countries in monitoring changes in population, health, and nutrition; and provide an international database that can be used by researchers investigating topics related to population, health, and nutrition.

The overall objective of the survey is to provide demographic, socioeconomic, and health data necessary for policymaking, planning, monitoring, and evaluation of national health and population programmes. In addition, the survey measured the prevalence of anaemia, HIV, high blood glucose, and high blood pressure among adult women and men; assessed the prevalence of anaemia among children age 6-59 months; and collected anthropometric measurements to assess the nutritional status of women, men, and children.

A long-term objective of the survey is to strengthen the technical capacity of local organizations to plan, conduct, and process and analyse data from complex national population and health surveys. At the global level, the 2013 NDHS data are comparable with those from a number of DHS surveys conducted in other developing countries. The 2013 NDHS adds to the vast and growing international database on demographic and health-related variables.

Geographic coverage

National coverage

Analysis unit

• Households
• Children aged 0-5
• Women aged 15 to 49
• Men aged 15 to 64

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The primary focus of the 2013 NDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas. In addition, the sample was designed to provide estimates of most key variables for the 13 administrative regions.

Each of the administrative regions is subdivided into a number of constituencies (with an overall total of 107 constituencies). Each constituency is further subdivided into lower level administrative units. An enumeration area (EA) is the smallest identifiable entity without administrative specification, numbered sequentially within each constituency. Each EA is classified as urban or rural. The sampling frame used for the 2013 NDHS was the preliminary frame of the 2011 Namibia Population and Housing Census (NSA, 2013a). The sampling frame was a complete list of all EAs covering the whole country. Each EA is a geographical area covering an adequate number of households to serve as a counting unit for the population census. In rural areas, an EA is a natural village, part of a large village, or a group of small villages; in urban areas, an EA is usually a city block. The 2011 population census also produced a digitised map for each of the EAs that served as the means of identifying these areas.

The sample for the 2013 NDHS was a stratified sample selected in two stages. In the first stage, 554 EAs-269 in urban areas and 285 in rural areas-were selected with a stratified probability proportional to size selection from the sampling frame. The size of an EA is defined according to the number of households residing in the EA, as recorded in the 2011 Population and Housing Census. Stratification was achieved by separating every region into urban and rural areas. Therefore, the 13 regions were stratified into 26 sampling strata (13 rural strata and 13 urban strata). Samples were selected independently in every stratum, with a predetermined number of EAs selected. A complete household listing and mapping operation was carried out in all selected clusters. In the second stage, a fixed number of 20 households were selected in every urban and rural cluster according to equal probability systematic sampling.

Due to the non-proportional allocation of the sample to the different regions and the possible differences in response rates, sampling weights are required for any analysis using the 2013 NDHS data to ensure the representativeness of the survey results at the national as well as the regional level. Since the 2013 NDHS sample was a two-stage stratified cluster sample, sampling probabilities were calculated separately for each sampling stage and for each cluster.

See Appendix A in the final report for details

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered in the 2013 NDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from the standard DHS6 core questionnaires to reflect the population and health issues relevant to Namibia at a series of meetings with various stakeholders from government ministries and agencies, nongovernmental organisations, and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organised by the MoHSS from September 25-28, 2012, in Windhoek. The questionnaires were then translated from English into the six main local languages—Afrikaans, Rukwangali, Oshiwambo, Damara/Nama, Otjiherero, and Silozi—and back translated into English. The questionnaires were finalised after the pretest, which took place from February 11-25, 2013.

The Household Questionnaire was used to list all usual household members as well as visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, parents’ survival status was determined. In addition, the Household Questionnaire included questions on knowledge of malaria and use of mosquito nets by household members, along with questions regarding health expenditures. The Household Questionnaire was used to identify women and men who were eligible for the individual interview and the interview on domestic violence. The questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods. The results of tests assessing iodine levels were recorded as well.

In half of the survey households (the same households selected for the male survey), the Household Questionnaire was also used to record information on anthropometry and biomarker data collected from eligible respondents, as follows: • All eligible women and men age 15-64 were measured, weighed, and tested for anaemia and HIV. • All eligible women and men age 35-64 had their blood pressure and blood glucose measured. • All children age 0 to 59 months were measured and weighed. • All children age 6 to 59 months were tested for anaemia.

The Woman’s Questionnaire was also used to collect information from women age 50-64 living in half of the selected survey households on background characteristics, marriage and sexual activity, women’s work and husbands’ background characteristics, awareness and behaviour regarding AIDS and other STIs, and other health issues.

The Man’s Questionnaire was administered to all men age 15-64 living in half of the selected survey households. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

CSPro—a Windows-based integrated census and survey processing system that combines and replaces the ISSA and IMPS packages—was used for entry, editing, and tabulation of the NDHS data. Prior to data entry, a practical training session was provided by ICF International to all data entry staff. A total of 28 data processing personnel, including 17 data entry operators, one questionnaire administrator, two office editors, three secondary editors, two network technicians, two data processing supervisors, and one coordinator, were recruited and trained on administration of questionnaires and coding, data entry and verification, correction of questionnaires and provision of feedback, and secondary editing. NDHS data processing was formally launched during the week of June 22, 2013, at the National Statistics Agency Data Processing Centre in Windhoek. The data entry and editing phase of the survey was completed in January 2014.

Response rate

A total of 11,004 households were selected for the sample, of which 10,165 were found to be occupied during data collection. Of the occupied households, 9,849 were successfully interviewed, yielding a household response rate of 97 percent.

In these households, 9,940 women age 15-49 were identified as eligible for the individual interview. Interviews were completed with 9,176 women, yielding a response rate of 92 percent. In addition, in half of these households, 842 women age 50-64 were successfully interviewed; in this group of women, the response rate was 91 percent.

Of the 5,271 eligible men identified in the selected subsample of households, 4,481 (85 percent) were successfully interviewed.

Response rates were higher in rural than in urban areas, with the rural-urban difference more marked among men than among women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview

This statistic displays the percentage of children who have access to TV sets outside of the home in the United Kingdom (UK) in 2015, by location and device type. During the survey period, it was found that **** percent of responding parents or caregivers reported that their children had access to an standard TV set at their grandparent's house.

The Head Start Family and Child Experiences Survey (FACES) is a periodic, ongoing longitudinal study of program performance. Successive nationally representative samples of Head Start children, their families, classrooms, and programs provide descriptive information on the population of children and families served; staff qualifications, credentials, and opinions; Head Start classroom practices and quality measures; and child and family outcomes. FACES includes a battery of child assessments across multiple developmental domains (cognitive, social, emotional, and physical). FACES 2009 is the latest FACES cohort study and followed children from Head Start entry in fall 2009 through one or two years of program participation and to kindergarten.

For nearly a decade, the Office of Head Start, the Administration for Children and Families, other federal agencies, local programs, and the public have depended on FACES for valid and reliable national information on (1) the skills and abilities of Head Start children, (2) how Head Start children's skills and abilities compare with preschool children nationally, (3) Head Start children's readiness for and subsequent performance in kindergarten, and (4) the characteristics of the children's home and classroom environments. The FACES study is designed to enable researchers to answer a wide range of research questions that are crucial for aiding program managers and policymakers. Some of the questions that are central to FACES include:

1. What are the demographic characteristics of the population of children and families served by Head Start? How has the population served by Head Start changed?
2. What are the experiences of families and children in the Head Start program? How have they changed?
3. What are the cognitive and social skills of Head Start children at the beginning and end of their first year in the program? Has Head Start program performance improved over time?
4. Do the gains in cognitive and social skills that Head Start children achieve carry over into kindergarten? Do larger gains (or greater declines in problem behavior) translate into higher achievement at the end of kindergarten?
5. What are the qualifications of Head Start teachers in terms of education, experience, and credentials? Are average teacher education levels rising in Head Start?
6. What is the observed quality of Head Start classrooms as early learning environments, including the level and range of teaching and interactions, provisions for learning, emotional and instructional support, and classroom organization? How has quality changed over time? What program- and classroom-level factors are related to observed classroom quality? How is observed quality related to children's outcomes and developmental gains?

In response to recent trends and mandates, FACES 2009 expanded the information collected on families and children who speak a primary language other than English and the information collected on children who are homeless. Earlier cohorts of FACES gathered information on the languages spoken in the home and used for classroom instruction. Given the growth in the population of Hispanic/Latino preschoolers (Hernandez 2006), FACES 2009 placed additional emphasis on Dual Language Learners (DLLs). In addition, given the 2007 Head Start Act's focus on children and families who are homeless, FACES 2009 expanded coverage on the enrollment of such children, how the program ensures that they enroll in Head Start, and the special services available to such children and their families.

FACES 2009 carefully balanced the need for consistent measurement of outcomes against the need for improvements in instrumentation and techniques. In some instances, new instruments were added to obtain more comprehensive information on Head Start children. For example, the Expressive One-Word Picture Vocabulary Test was added to assess children's expressive language, which is related to later reading achievement even more so than receptive language (National Early Literacy Panel 2008). A measure of phonemic awareness from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) preschool wave was also added to assess children's knowledge of beginning and ending sounds in words. Further, FACES 2009 included a direct assessment of executive functioning-a