The Australian Early Development Census is a measure of how young children are developing in Australian communities. It involves collecting information to help create a snapshot of early childhood development in communities across Australia. Australian Early Development Census 2009-2015.

Australia Children Out of School: % of Primary School Age data was reported at 0.296 % in 2022. This records a decrease from the previous number of 0.368 % for 2021. Australia Children Out of School: % of Primary School Age data is updated yearly, averaging 1.419 % from Dec 1971 (Median) to 2022, with 51 observations. The data reached an all-time high of 4.706 % in 2000 and a record low of 0.008 % in 2016. Australia Children Out of School: % of Primary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

This longitudinal study investigates the effect of children's social, economic and cultural environments on their wellbeing over the life course. Biennial interviews ask questions about parenting, …Show full descriptionThis longitudinal study investigates the effect of children's social, economic and cultural environments on their wellbeing over the life course. Biennial interviews ask questions about parenting, family relationships, education, child care and health. For further information about the dataset visit [this page on the Department of Social Services website https://www.dss.gov.au/about-the-department/longitudinal-studies/growing-up-in-australia-lsac-longitudinal-study-of-australian-children-overview

Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) is a major study following the development of approximately 10,000 young people and their families from all parts of Australia. It is conducted in partnership between the Department of Social Services, the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice provided by a consortium of leading researchers. The study began in 2003 with a representative sample of children (who are now teens and young adults) from urban and rural areas of all states and territories in Australia. The study has a multi-disciplinary base, and examines a broad range of research questions about development and wellbeing over the life course in relation to topics such as parenting, family, peers, education, child care and health. It will continue to follow participants into adulthood. The study informs social policy and is used to identify opportunities for early intervention and prevention strategies. Participating families have been interviewed every two years from 2004, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). The B cohort (“Baby” cohort) of around 5,000 children was aged 0–1 years in 2003–04, and the K cohort (“Kinder” cohort) of around 5,000 children was aged 4–5 years in 2003–04. Study informants include the young person, their parents (both resident and non-resident), carers and teachers. The study links to administrative databases including Medicare (Immunisation, MBS and PBS), NAPLAN, AEDC and Centrelink – with participant consent – thereby adding valuable information to supplement the data collected during fieldwork. In 2014-15, a special one-off physical health and biomarkers assessment of parent-child pairs was undertaken in the younger cohort. The cross-generational datasets from this ‘Child Health CheckPoint’ are available in the Additional Release files.

Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) is a major study following the development of approximately 10,000 young people and their families from all parts of Australia. It is conducted in partnership between the Department of Social Services, the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice provided by a consortium of leading researchers. The study began in 2003 with a representative sample of children (who are now teens and young adults) from urban and rural areas of all states and territories in Australia. The study has a multi-disciplinary base, and examines a broad range of research questions about development and wellbeing over the life course in relation to topics such as parenting, family, peers, education, child care and health. It will continue to follow participants into adulthood. The study informs social policy and is used to identify opportunities for early intervention and prevention strategies. Participating families have been interviewed every two years from 2004, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). The B cohort (“Baby” cohort) of around 5,000 children was aged 0–1 years in 2003–04, and the K cohort (“Kinder” cohort) of around 5,000 children was aged 4–5 years in 2003–04. Study informants include the young person, their parents (both resident and non-resident), carers and teachers. The study links to administrative databases including Medicare (Immunisation, MBS and PBS), NAPLAN, AEDC and Centrelink – with participant consent – thereby adding valuable information to supplement the data collected during fieldwork. In 2014-15, a special one-off physical health and biomarkers assessment of parent-child pairs was undertaken in the younger cohort. The cross-generational datasets from this ‘Child Health CheckPoint’ are available in the Additional Release files.

Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) is a major study following the development of approximately 10,000 young people and their families from all parts of Australia. It is conducted in partnership between the Department of Social Services, the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice provided by a consortium of leading researchers. The study began in 2003 with a representative sample of children (who are now teens and young adults) from urban and rural areas of all states and territories in Australia. The study has a multi-disciplinary base, and examines a broad range of research questions about development and wellbeing over the life course in relation to topics such as parenting, family, peers, education, child care and health. It will continue to follow participants into adulthood. The study informs social policy and is used to identify opportunities for early intervention and prevention strategies. Participating families have been interviewed every two years from 2004, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). The B cohort (“Baby” cohort) of around 5,000 children was aged 0–1 years in 2003–04, and the K cohort (“Kinder” cohort) of around 5,000 children was aged 4–5 years in 2003–04. Study informants include the young person, their parents (both resident and non-resident), carers and teachers. Please note that this release of LSAC is now superseded, and is available by request for approved training courses only. For the current release, please visit https://ada.edu.au/lsac_current

English(Australia) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) is a major study following the development of approximately 10,000 young people and their families from all parts of Australia. It is conducted in partnership between the Department of Social Services, the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice provided by a consortium of leading researchers. The study began in 2003 with a representative sample of children (who are now teens and young adults) from urban and rural areas of all states and territories in Australia. The study has a multi-disciplinary base, and examines a broad range of research questions about development and wellbeing over the life course in relation to topics such as parenting, family, peers, education, child care and health. It will continue to follow participants into adulthood. The study informs social policy and is used to identify opportunities for early intervention and prevention strategies. Participating families have been interviewed every two years from 2004, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). The B cohort (“Baby” cohort) of around 5,000 children was aged 0–1 years in 2003–04, and the K cohort (“Kinder” cohort) of around 5,000 children was aged 4–5 years in 2003–04. Study informants include the young person, their parents (both resident and non-resident), carers and teachers. The study links to administrative databases including Medicare (Immunisation, MBS and PBS), NAPLAN, and Centrelink – with participant consent – thereby adding valuable information to supplement the data collected during fieldwork. In 2014-15, a special one-off physical health and biomarkers assessment of parent-child pairs was undertaken in the younger cohort. The cross-generational datasets from this ‘Child Health CheckPoint’ are available in the Additional Release files. LSAC Wave 9 (aka 9C) covered the impact of the COVID-19 pandemic on young persons, their families and communities. Wave 9C was unlike any other wave undertaken by LSAC. Instead of the traditional face-to-face methodology, the data collection was split into two shorter online surveys (9C1 and 9C2), with Survey 9C2 also offering a telephone interview option. Two short survey in Wave 9C allows measurement of similarities and differences in responses as COVID-19 restrictions changed over time. Survey 9C1 was in field October–December 2020 and Survey 9C2 was in-field June–September 2021.

This dataset presents the footprint of the percentage of deaths of infants and young children per 1,000 live births. The data spans every two years between 2010-2016 and is aggregated to Statistical Area Level 3 (SA3) geographic boundaries from the 2011 Australian Statistical Geography Standard (ASGS).

The Child and Maternal Health Indicators have been calculated from the Australian Institute of Health and Welfare (AIHW) National Mortality Database and Register of Births and National Perinatal Data Collection. This measure has been calculated with the numerator as the number of deaths from birth to less than 5 years, and the denominator as the total number of live births.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Child and Maternal Health Data Tables.

Please note:

• Deaths are attributed to the area in which the infant or child usually resided, irrespective of where they died.

• Births are attributed to the area of usual residence of the mother, not location of birth.

• Deaths are reported by year of registration of death.

• Data for 2010 have been adjusted for the additional deaths arising from outstanding registrations of deaths in Queensland in 2010.

• Mortality rates for an area are suppressed for publication and marked as 'NP' if the total number of live births for the area is less than 100.

The Australian Early Development Index (AEDI) is a measure of how young children are developing in Australian communities. It involves collecting information to help create a snapshot of early childhood development in communities across Australia.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

This dataset, released December 2017, contains family statistics relating to Single parent families with children aged less than 15 years, 2016; Jobless families with children aged less than 15 years, 2016; Children aged less than 15 years in jobless families, 2016; Children in families where the mother has low educational attainment, 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 …Show full descriptionThis dataset, released December 2017, contains family statistics relating to Single parent families with children aged less than 15 years, 2016; Jobless families with children aged less than 15 years, 2016; Children aged less than 15 years in jobless families, 2016; Children in families where the mother has low educational attainment, 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016; the ABS Census of Population and Housing, August 2016 (unpublished) data. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

NATSEM indicators of child well-being variables of SLAs, excluding SLAs in Brisbane and Canberra, in Australia (2006). These data were provided by NATSEM, University of Canberra, and are based on data from the 2006 Census of Population and Housing supplied by the Australian Bureau of Statistics. The data were developed as part of a project funded by a Discovery Grant from the Australian Research Council (DP664429: Opportunity and Disadvantage: Differences in Wellbeing among Australia's Adults and Children at a Small Area Level.

A school zone or catchment area is a defined area from which the school or preschool accepts its core intake of students. The school or preschool gives priority enrolment to children who live inside that zone or catchment area.

This layer shows the location of regulatory signs & Main Roads Western Australia is the responsible authority. The term regulatory sign describes a range of signs that are used to indicate or reinforce traffic laws, regulations or requirements which apply either at all times or at specified times or places upon a street or highway, the disregard of which may constitute a violation. One type of regulatory signs are stop signs or give way signs, intended to instruct road users on what they must or should do (or not do) under a given set of circumstances. Signs can be on the State Road Network or other public access roads and is provided for information only.

The Australian Child Wellbeing Project (ACWP) is a child-centred study in which young people’s perspectives have been used to design and conduct Australia’s first major nationally representative and internationally comparable survey of wellbeing among young people aged 9-14 years. Particular attention was given to understanding the perspectives of seven groups of young people with specific experiences and needs that may have a bearing on their wellbeing: Aboriginal and Torres Strait Islander young people, culturally and linguistically diverse young people, young people with disability, young carers, young people in regional and remote Australia, materially disadvantaged young people, and young people in out-of-home care. The survey aimed to benchmark young people's wellbeing in Australia, provide the basis for international comparison of their wellbeing, and provide information to contribute to the development of effective services for young people’s healthy development. The survey instrument includes questions that are comparable with the international Health Behaviour in School Aged Children study (www.hbsc.org) and the international Children's World's study (www.isciweb.org). The ACWP survey was dstributed to a national probability sample of students in Years 4, 6 and 8 and was successfully completed during Term 3 2014 in 180 schools across Australia, with final sample of 5440 students. Please refer to the project website for more details: http://www.australianchildwellbeing.com.auMethodologySampling Procedure: Multi-stage stratified random sampleData Kind: Survey dataResponse Rate: 12.3 percent (39.4 percent of schools, and 31.1 percent of students within those schools). See Technical Report Section 4 for further details.Date coverage: 2014Location: Australia

Children that are fully immunised at 1, 2 and 5 years of age, 2014 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: Compiled by PHIDU based on data provided by the Australian Childhood Immunisation Register, Medicare Australia, 2014.

SA1 based data for Family Composition and Country of Birth of Parents by Age of Dependent Children, in General Community Profile (GCP), 2016 Census. Count of dependent children. Excludes overseas …Show full descriptionSA1 based data for Family Composition and Country of Birth of Parents by Age of Dependent Children, in General Community Profile (GCP), 2016 Census. Count of dependent children. Excludes overseas visitors. Includes same-sex couple families. The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

Surveys in other countries suggest that children and adolescents experience high rates of mental health problems, however in Australia there has been no information at a national level about the prevalence of child and adolescent mental health problems. The Child and Adolescent Component of the National Survey of Mental Health and Well-Being is the first survey to investigate the mental health and well-being of children and adolescents at a national level in Australia. It provides an accurate estimate of the prevalence of mental health problems among children and adolescents in Australia. It also provides information about the degree of disability associated with mental health problems and the extent to which children and adolescents are receiving help for their problems. Information was collected from children aged 4-17 and their parents. Children and parents completed questionnaires assessing mental health problems (assessed using the Youth Self-Report and Child Behaviour Checklist) health related quality of life, health-risk behaviour and service utilisation. In addition, parents completed a face-to-face interview (3 modules from the Diagnostic Interview Schedule for Children) designed to identify Depressive Disorder, Attention Deficit/Hyperactivity Disorder and Conduct Disorder. Background variables include age, sex, metro/rural, parents employment/ education/income.