Data User Guide
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|May 2016||1.0||Original release of Data User Guide for Wave 1.||Currier, D., Koelmeyer, R., Spittal, M. J., Gordon, I., English, D., Gurrin, L., Carlin, J., Sahabandu, S., & Pirkis, J. (2015) Ten to Men - Data User Manual (Version 1). Melbourne: The University of Melbourne.|
|December 2017||2.0||Original release of Data User Guide for Wave 1 and Wave 2.||Currier, D., Koelmeyer, R., Spittal, M. J., Gordon, I., English, D., Gurrin, L., Carlin, J., Sahabandu, S., & Pirkis, J. (2015) Ten to Men - Data User Manual (Version 2). Melbourne: The University of Melbourne.|
|September 2019||3.0||Updated Data User Guide for Release 2.1, Wave 1 and Wave 2.||Bandara, D., Howell, L., Silbert, M., Mohal, J., Garrard, B., & Daraganova, G. (2019). Ten to Men: The Australian Longitudinal Study on Male Health - Data User Guide, Version 3.0, September 2019. Melbourne: Australian Institute of Family Studies.|
|September 2021||4.0||Original release of Data User Guide for Wave 3.||Bandara, D., Howell, L., Silbert, M., & Daraganova, G. (2021). Ten to Men: The Australian Longitudinal Study on Male Health - Data User Guide, Version 4.0, September 2021. Melbourne: Australian Institute of Family Studies.|
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About this guide
This Data User Guide is a reference tool for users of the Ten to Men: The Australian Longitudinal Study on Male Health datasets.
It is intended to provide the necessary information to be able to use the Ten to Men data. This includes information on the study design, questionnaires, data files, data linkage, user resources, statistical considerations and access to the Ten to Men data.
Additional resources available for users of the Ten to Men data include:
- Study Questionnaires
- Data Dictionary
- Data Books
- Data Issues Paper.
These resources are available on Data documentation.
If you have any feedback, or would like us to include additional information, please do not hesitate to email us at: email@example.com
1. Introduction to Ten to Men
1.1 Background and objectives
The Commonwealth Department of Health commissioned the Australian Longitudinal Study on Male Health (Ten to Men) in 2011 in response to the 2010 National Male Health Policy, which recognised significant gaps in the knowledge on male health. Addressing those gaps and building the evidence base on male health was identified as a priority to reduce disparities in male health, both between males and females and between different sub-populations of males. Reflecting that focus, the objectives of Ten to Men are:
- to examine male health and its key determinants, including social, economic, environmental and behavioural factors that affect the length and quality of life of Australian males
- to address a range of key research questions about the physical and mental health of Australian males, including their health behaviours and risk factors, key life transition points, social and economic environments in which they work and live together with their use of health and other services
- to identify policy opportunities for improving the health and wellbeing of males and providing support for males at key life stages, particularly those at risk of poor health.
The content of Ten to Men was designed around six key research domains relevant to male health:
- wellbeing and mental health
- use of health services
- health-related behaviours
- health status
- health knowledge
- social determinants.
1.2 Ten to Men study design and sampling
Ten to Men is Australia's first national longitudinal study that focuses exclusively on male health and wellbeing. A number of key decisions informed the development of the final sampling plan for Ten to Men:
- The decision to conduct face-to-face interviews with boys aged 10-14 years. The cost of conducting face-to-face interviews ruled out a simple random sample and introduced the need for clustering.
- The decision to oversample regional males. Oversampling regional males introduced the need for some mechanism to stratify the sample by region.
- The decision to adopt a household recruitment methodology. Household recruitment requires defined geographic units and also required clustering.
- The decision that a population sample would be drawn at the national level with no stratification by state.
The final Ten to Men cohort was recruited using a stratified, multi-stage, cluster random sampling design to select males aged 10-55 years from the target population (Currier et al., 2016).
1.3 Ten to Men recruitment
To date, three waves of data collection have been completed for the Ten to Men study. Table 1 shows the periods of recruitment and data collection for each wave.
|1||Recruitment of eligible participants and data collection||October 2013 - July 2014|
|2||Data collection||November 2015 - May 2016|
|3||Data collection||July 2020 - February 2021|
Key elements of the sample design were the use of stratification, multi-stage and cluster sampling to select Statistical Areas (SA1) for the study. More details about this process are provided in Section 6.
Fieldworkers approached 104,884 households in each of the selected SA1s. By talking to the residents of the household, they determined if there were any eligible males for inclusion in the Ten to Men study. The eligibility criteria included males aged 10-55 years, Australian permanent residents or citizens and sufficient skills in English to complete the survey. Up to four eligible males were invited to join the study from each household.
There were 33,724 households, with 45,510 males, that were eligible for inclusion in Ten to Men at Wave 1. Initially 15,988 males aged 10-55 years were recruited into the Ten to Men study. All participants involved in Wave 1 of the study were invited to participate in Wave 2 of the study.
During Wave 2 of Ten to Men, 33 additional participants were identified for Wave 1. They were not included in the original Wave 1 dataset as their eligibility and consent status had not been determined at that stage, but this issue was resolved during Wave 2. These participants were subsequently included in Wave 1, taking the reconciled sample size for Wave 1 to 16,021.
1.4 Ten to Men retention rate
Although the original Ten to Men cohort for Wave 1 included 16,021 participants, not all participants were available in subsequent waves. As with any longitudinal study, participants may be lost between subsequent waves due to withdrawal, death or other reasons.
Figure 1 shows the eligible sample for each wave. The overall retention rate of participants from Wave 1 to Wave 2 was 98%, and from Wave 1 to Wave 3 was 93%.
Figure 1: Number of eligible participants across waves
1.5 Ten to Men cohort
The reconciled cohort sample size for Wave 1 is 16,021 males, with three age-based cohorts: boys aged 10-14 years, young men aged 15-17 years and adults aged 18-55 years.
Figure 2 shows the distribution of the males between the three age-based cohorts for Wave 1 and the movement of the participants for each subsequent wave.
In Wave 2, the type of questionnaire completed was based on the participants' age at the time of the Wave 2 survey.
There was only one questionnaire used at Wave 3. Some participants were under 18 years but branching was used to allow these participants to skip questions that were not applicable
Figure 2: Number of participants across waves by each cohort
Note: *There was only one survey form in Wave 3. Branching was used to exclude some questions for respondents aged under 18 years. Only 148 of respondents were aged under 18 years.
2. Ten to Men questionnaires and data collection
Four questionnaires were developed for the Wave 1 data collection:
- Adult Males (18-55 years)
- Young Men (15-17 years)
- Boys (10-14 years)
- Parents of the Boys (10-14 years). This questionnaire was separated into two parts:
- Part A - information about the study boy
- Part B - information about the household and parents.
Excluding the Boys (10-14 years), all were self-completed hard copy questionnaires. A computer-assisted personal interview (CAPI) was used to collect the data for the Boys (10-14 years).
The same method was used for the Wave 2 data collection, except that the questionnaire for the Parents of the Boys was not separated into Part A and Part B.
For Wave 3, only one questionnaire was used as most of the respondents were over 18 years of age. This wave was conducted online.
The questionnaires for each wave are available at Waves 1–2 Questionnaire sources and permissions.
3. Ten to Men data
One of the key outcomes of the study is to ensure that the Ten to Men datasets are widely available for researchers to undertake analyses on male health. The datasets are large and complex, consisting of over 800 variables collected across each wave. As well as the raw data, derived variables and indicator variables (questionnaire, survey date, etc.) are included.
This section provides information about the Ten to Men dataset releases, the structure of the datasets, variable naming conventions, coding frameworks and confidentialisation. It is important that researchers read and understand this section.
3.1 Dataset releases
Periodically a new release of the Ten to Men datasets will be generated as additional information becomes available after each data collection wave. The releases will be numbered in sequential order and a new Digital Object Identifier (DOI) will be minted. A history of the dataset releases and suggested citations can be found in Appendix A.
Release 2.1 comprised of updated Wave 1 and Wave 2 datasets. These datasets have undergone significant changes to previous releases, including the renaming of variables, confidentialisation and other modifications as listed in detail in the Ten to Men Data Issues Paper. This paper is available at Data documentation.
Release 3.0 was the most recent release following the conclusion of Wave 3. This release includes Wave 1, Wave 2 and Wave 3 datasets.
3.2 Datasets available
The University of Melbourne issued releases 1.0 and 2.0. Release 1.0 contains data from Wave 1 only, while Release 2.0 contains data from both Wave 1 and Wave 2. An additional dataset, Respondent, was also available. The Respondent dataset contained key indicator data, such as the unique study identifier, age, household identifier and geographical information.
AIFS issued Release 2.1 and it comprised of updated Wave 1 and Wave 2 datasets. Relevant data from the respondent data were included in the dataset within the wave, and this is no longer available as a separate dataset.
In 2021, AIFS issued Release 3.0, which includes the Wave 1, Wave 2 and Wave 3 datasets.
A Ten to Men dataset naming convention was developed to ensure that each dataset name included information about the type of release and wave. The datasets available for Release 3.0 are:
- TTMGRW1 - Ten to Men, General Release, Wave 1
- TTMRRW1 - Ten to Men, Restricted Release, Wave 1
- TTMGRW2 - Ten to Men, General Release, Wave 2
- TTMRRW2 - Ten to Men, Restricted Release, Wave 2
- TTMGRW3 - Ten to Men, General Release, Wave 3
- TTMRRW3 - Ten to Men, Restricted Release, Wave 3.
3.3 Merging waves of data
A one-to-one merge can be used to combine the Ten to Men datasets across waves. The common variable to all the datasets that should be used for the merge is the unique study identifier (zdcid0001md).
3.4 Variable naming conventions
The Ten to Men variable naming convention was developed so that the variables have predictable names and characteristics across the waves.
This structure is presented in Figure 3. The standard format is W RD xxxxxx Q D, where:
W = Wave
RD = Research Domain
xxxxxx = Variable descriptor
Q = Questionnaire
D = Derived (optional)
Using the variable 'abpactlevud' as an example:
Wave = a (Wave 1)
Research Domain = bp (Behaviours - Physical Activity)
Variable descriptor = actlev (Activity level)
Questionnaire = u (Males under 18 years)
Derived = d (derived variable)
Figure 3: Example of variable naming convention
The wave indicator is the first character in the variable name and indicates the wave. For example:
- a indicates Wave 1
- b indicates Wave 2
- c indicates Wave 3
- z indicates any variable that is common across all waves (i.e. time invariant variables).
Research Domain Indicator
The second and third characters in the variable name represent the research domain of the corresponding question. For example, 'xbaxxxxxx' has 'ba' as the second and third characters, which represent the research domain of 'Behaviours - Alcohol'.
A list of research domains and abbreviations is provided in Table 2.
|ba||Behaviours - Alcohol|
|bd||Behaviours - Drugs|
|bg||Behaviours - Gambling|
|bh||Behaviours - Help Seeking|
|bn||Behaviours - Nutrition|
|be||Behaviours - Peers|
|bp||Behaviours - Physical Activity|
|br||Behaviours - Risky and Antisocial Behaviour|
|bx||Behaviours - Sexual Behaviour|
|bs||Behaviours - Sun Exposure|
|bt||Behaviours - Tobacco|
|bw||Behaviours - Weight|
|hb||Health Status - Pubertal Development|
|hd||Health Status - Diagnoses|
|hi||Health Status - Injury and Disability|
|hp||Health Status - Prostate Function|
|hs||Health Status - Health Status|
|hx||Health Status - Sexual Function|
|hz||Health Status - Sleep|
|kl||Knowledge - Health Literacy|
|se||Social Determinants - Socioeconomic Status|
|sf||Social Determinants - Family and Household Structure|
|sg||Social Determinants - Gender Roles and Sexuality|
|sh||Social Determinants - Housing and Tenure|
|si||Social Determinants - Interpersonal Relations|
|sl||Social Determinants - Life Events|
|st||Social Determinants - Transport|
|sw||Social Determinants - Work Satisfaction, Control and Security|
|ua||Use of Health Services - Access to Health Services|
|up||Use of Health Services - Satisfaction and Preferences|
|us||Use of Health Services - Service Use|
|wd||Wellbeing - Mental Health Diagnoses|
|wl||Wellbeing - Life Satisfaction|
|wp||Wellbeing - Positive Mental Health|
|ws||Wellbeing - Self-Injury|
Variable Descriptor Indicator
The variable descriptor is represented by the fourth to the ninth characters in the variable name. A combination of the first letter, acronyms and abbreviations are used in order to provide an abbreviation of the full variable description. Zeroes have also been used where less than six characters are required to describe the attribute. Other numerical values have been used to describe variables with similar attributes.
Questionnaire Indicator (Waves 1 and 2 only)
The 10th character of the variable name represents the questionnaire indicator. There are 10 possible values, with each value representing the questionnaire/s that the variable is available for. For example, ' class="small"xg' has 'g' as the 10th character, which indicates that this variable was associated with both the Young Men and Adult questionnaires.
A list of questionnaire indicators and descriptions is provided in Table 3.
From Wave 3, this indicator has been dropped from the variable name as there was only one questionnaire.
|a||Adults (18-55 years)|
|b||Boys (10-14 years)|
|e||Everyone, including the Parents of boys (10-14 years)|
|f||Young Men (15-17 years)|
|g||Young Men (15-17 years), Adults (18-55 years)|
|i||Field completed by interviewer|
|m||Everyone, excluding the Parents of boys (10-14 years)|
|p||Parents of boys (10-14 years)|
|u||Boys (10-14 years), Young Men (15-17 years)|
|x||Young Men (15-17 years), Adults (18-55 years), and the Parents of boys (10-14 years)|
The last character of the variable name is optional. In Waves 1 and 2, this is the 11th character of the variable name, and the 10th character of the variable name in Wave 3. If present, it is 'd' and indicates that the variable was derived. Some of these derived values are variables consolidated into a common measurement unit, others are a score calculated from one or more other variables.
For example, a participant may answer their height in any measurement unit and these answers have all been converted into centimetres.
3.5 Standard classifications
Several variables in the Ten to Men datasets are coded using three standard classifications. These include details around the country of birth, parent's country of birth, main language spoken and occupation.
The information for these variables was collected using free text fields, with the responses coded using the standard classifications, as shown in Table 4. More information about these codes can be found from the Australian Bureau of Statistics (ABS) website.
|Country of Birth||Standard Australian Classification of Countries (SACC)|
|Main Language Spoken||Australian Standard Classification of Languages (ASCL)|
|Occupation||Australian and New Zealand Standard Classification of Occupations (ANZSCO)|
3.6 Missing value code frame
Missing values occur when the data value is not stored for a variable and this may happen for a number of reasons. It is important to understand the reasons for the missing values as they can have a significant effect on any conclusions drawn from data.
As missing values are present in the Ten to Men data, a standard missing value code frame has been implemented. This is shown in Table 5.
|Numeric Value||Formatted Value||Description|
|-1||Not asked||Used when the question is not asked in the questionnaire|
|-2||Not applicable||Used when the question is not required due to response to preceding questions.|
|-3||Don't know||Only used if 'Don't know' is an option listed for that question.|
|-4||Refused or not answered||Used when an answer to a question has not been provided or suppressed due to confidentiality reasons|
|-5||Invalid multiple response||Multiple responses have been provided for a single response question, and the intention is unclear|
|-6||Value implausible||Value is implausible (as determined after data checking)|
|-7||Unable to determine value||Where the response provided is unclear|
|-8||No questionnaire or interview completed||Where a respondent does not return their questionnaire, or participate in an interview|
3.7 Types of variables
The Ten to Men dataset contains the following types of variables:
- Raw data: These are the data collected directly from the questionnaire.
- Key indicators: Each respondent has a number of assigned identification variables, such as Unique ID and Household ID.
- Categorised: The categorised variables refer to both numerical and qualitative data. Some of the numerical data collected has been sorted into ranges, whereas the qualitative data in response to any 'Other' category has been categorised.
- Reclassified: A small number of variables were reclassified using the relevant ABS classifications. These variables include the country of birth, languages spoken and occupations.
- Derived: The questionnaires include sets of questions that have been designed to measure specific attributes. The results of these measures are included as derived variables.
- Derived & categorised: These variables have been derived and then categorised into groups. This is common when the result of the derived variable is a score, and then the scores are categorised into ranges.
- Linked data: Additional data, such as geographic and socio-economic data from the ABS, have been linked to the Ten to Men data and provided in the dataset. Further information on linked data is provided in Section 4 of this Data User Guide.
3.8 Derived variables
Derived variables simplify and enhance the analytical utility of the Ten to Men dataset. At AIFS, the derived variables have been developed to maintain uniform standards and consistent application of scales or complex calculations. Many of the derived variables are denoted by the character 'd' as the last character in the variable name.
In the Ten to Men dataset, there are three different types of derived variables:
- variables where the responses have been converted into a comparable measurement unit
- variables relating to branching and questionnaire-wide frequencies
- variables created or calculated based on the responses to a set of questions.
Within the dataset, all derived variables have been positioned with the associated variables that have been used to derive them. For example, the calculation of Body Mass Index (BMI) uses the height and weight variables, and thus the derived variable of BMI appears in the dataset directly after the height and weight variables.
Comparable measurement units
The Ten to Men dataset contains some measurement variables such as height, weight and time. Although these variables may be recorded in a range of different measurement units, they have all been converted to the corresponding standard unit of measurement. For example, height is in centimetres, weight is in kilograms and time is in minutes.
Branching and questionnaire-wide frequencies
The Ten to Men study asked questions about lifetime prevalence, such as 'Have you ever smoked a cigarette?' For any respondent that answered 'No' to these questions, branching logic was used to allow further questions on this topic to be skipped.
Additional questionnaire-wide variables were derived for each question where branching logic was used. This enables the questionnaire-wide current or recent use numbers to be easily obtained for those questions where only a proportion of the respondents answered due to branching logic. For example, based on responses to previous questions about smoking, only a proportion of the respondents would be asked the question 'Have you smoked a cigarette in the last 12 months?' If this question was skipped due to branching, this would be reported as 'No' in the additional questionnaire-wide variable that would be derived.
It should be noted that questionnaire-wide does not necessarily refer to the entire Ten to Men population - it includes only respondents that received that particular questionnaire/s (Boys, Young Men and/or Adults).
These derived variables contain the text 'All respondents included' in the variable label, as well as the optional 'd' as the last character in the variable name.
Calculated variables and scale scores
The Ten to Men dataset also contains numerous scale scores to assist users in interpreting the data. Each scale score was calculated as directed by the source provided in Table 6. These variables have the optional 'd' as the last character in the variable name.
One new variable has been calculated using standard formulae and responses to questions. Details of this variable are listed below in Table 7.
|Active Australia Survey||Australian Institute of Health and Welfare. (2003). The Active Australia Survey: A guide and manual for implementation, analysis and reporting. Canberra, Australia: Australian Institute of Health and Welfare.|
|Alcohol Use Disorders Identification Test (AUDIT)||Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monterio, M. G. (2001). AUDIT - The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care (2nd ed.). Geneva, Switzerland: Department of Mental Health and Substance Dependence, World Health Organization.|
|Child Special Health Care Needs Screener||The Child and Adolescent Health Measurement Initiative. (1998). The Children with Special Health Care Needs (CSHCN) Screener. Portland, OR: Department of Paediatrics, School of Medicine, Oregon Health & Science University.|
|Conformity to Masculine Norms Inventory (22-Item Version)||Mahalik, J. R., Locke, B. D., Ludlow, L. H., Diemer, M. A., Scott, R. P. J., Gottfried, M., & Freitas, G. (2003). Development of the Conformity of Masculine Norms Inventory. Psychology of Men & Masculinity, 4(1), 3-25. doi:10.1037/1524-9184.108.40.206|
|Connor-Davidson Resilience Scale||Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18(2), 76-82. doi:10.1002/da.10113
Campbell-Sills, L., & Stein, M. B. (2007). Psychometric analysis and refinement of the Connor-Davidson resilience scale (CD_RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies, 20(6), 1019-1028. doi:10.1002/jts.20271
|Father figure - Paternal Affection Score||Brim, O. G., Baltes, P., Bumpass, L., Cleary, P., Featherman, D., Hazzard, W. et al. National Survey of Midlife Development in the United States (MIDUS), 1995-1996. Ann Arbor, Michigan: Inter-university Consortium for Political and Social Research.|
|Feneralized Anxiety Disorder (GAD-7) Scale||Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092-1097. doi:10.1001/archinte.166.10.1092|
|General Help Seeking Questionnaire||Wilson, C. J., Deane, F. P., Ciarrochi, J., & Rickwood, D. (2005). Measuring help-seeking intentions: Properties of the General Help-seeking Questionnaire. Canadian Journal of Counselling, 39(1), 15-28.|
|Health Literacy Questionnaire (HLQ)||Osborne, R. H., Batterham, R., Elsworth, G. R., Hawkins, M., & Buchbinder, R. (2013). The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health, 13(1), 1-17. doi:10.1186/1471-2458-13-658|
|Job Quality Measure||Leach, L., Butterworth, P., Rodgers, B., & Strazdins, L. (2010). Deriving an evidence-based measure of job quality from the HILDA survey. Australian Social Policy Journal, 9, 67-86.|
|Medical Outcomes Study (MOS) Social Support Survey||Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science & Medicine, 32(6), 705-714. doi:10.1016/0277-9536(91)90150-b|
|National Survey of Sexual Attitudes and Lifestyle Sexual Functioning||Mitchell, K. R., Ploubidis, G. B., Datta J., & Wellings, K. (2012). The Natsal-SF: A validated measure of sexual function for use in community surveys. European Journal of Epidemiology, 27(6), 409-418. doi:10.1007/s10654-012-9697-3|
|Patient Health Questionnaire (PHQ-9)||Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606-613. doi:10.1046/j.1525-1497.2001.016009606.x
Richardson, L. P., McCauley, E., Grossman, D. C., McCarty, C. A., Richards, J., Russo, J. E. et al. (2010).Evaluation of the Patient Health Questionnaire-9 Item for detecting major depression among adolescents. Paediatrics, 126(6), 1117-1123. doi:10.1542/peds.2010-0852
|PedsQL General Wellbeing Scale||Varni, J. W. (2013). Scaling and scoring of the Paediatric Quality of Life Inventory (PedsQL) Condition- Specific Modules (Version 12). Lyon, France: MAPI Research Trust.|
|Personal Wellbeing Index||The International Wellbeing Group (2013). Personal Wellbeing Index: 5th Edition. Melbourne, Australia: Australian Centre on Quality of Life, Deakin University.|
|Problem Gambling Severity Index||Ferris, J. & Wynne, H. (2001). The Canadian Problem Gambling Index: Final report. Ottawa, ON: Canadian Centre on Substance Abuse.|
|Pubertal Development Scale||Carskadon, M. A., & Acebo, C. (1993). A self-administered rating scale for pubertal development. Journal of Adolescent Health, 14(3), 190-195. doi:10.1016/1054-139x(93)90004-9|
|Self-determination Scale||Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self- determination of behaviour. Psychological Inquiry, 11(4), 227-268.|
|SF12 v2.0 Health Survey||Ware, J. E., Kosinski, M., Turner-Bowker, D. M., & Gandek, B. (2002). How to Score Version 2 of the SF-12 Health Survey. Lincoln, RI: QualityMetric Incorporated.
Ware, J. E., Kosinski, M., Turner-Bowker, D. M., & Gandek, B. (2002). User's manual for the SF-12v2 Health Survey. Lincoln, RI: QualityMetric Incorporated.
|Spence Children's Anxiety Scale||Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566.|
|Washington Group Short Set of Questions on Disability||Washington Group on Disability Statistics. (2006). Washington Group Short Set of Questions on Disability. Hyattsville, MD: National Center for Health Statistics, Centers for Disease Control and Prevention.|
|Body Mass Index (BMI)||Weight (kg) / (Height (cm) x Height (cm))|
Confidentialisation was undertaken at different levels for all Ten to Men datasets. To increase the availability of information while minimising disclosure risks, a data sharing framework to differentiate the user's access level was implemented. This resulted in two datasets for each wave being generated with different levels of confidentialisation - General Release and Restricted Release.
A lower level of confidentialisation is applied to the Ten to Men Restricted Release dataset, with all initial information preserved. The only information not included in this dataset is the name, address and other contact details.
The following geospatial items have been confidentialised:
- SA1 - identifiers were replaced with non-identifiable codes
- SA2 - identifiers were replaced with non-identifiable codes.
Access to the Restricted Release datasets may only be granted where data users are able to demonstrate a genuine need for the additional data and meet the necessary additional security requirements.
The General Release dataset has undergone additional data confidentialisation in order to:
- reduce the risk of re-identification of participants
- ensure that AIFS meets its obligations in accordance with the National Health and Medical Research Council (NHMRC) Guidelines (Australian Code for the Responsible Conduct of Research) and the Australian Privacy Principles
- permit the transmission of data to overseas users.
In addition to the information removed or confidentialised for the Restricted Release dataset, further confidentialisation for the General Release dataset includes:
- suppressing some information
- aggregating some response categories
- top and/or bottom coding some variables (recoding outlying values to a less extreme value).
Confidentialisation was generally undertaken on variables with low cell counts on identifying or sensitive information. These variables include height and weight, number of children and some health conditions such as schizophrenia, conduct disorder and drug/alcohol problems.
For a complete list of confidentialised variables, users should consult the Ten to Men Data Dictionary.
4. Linked variables
Additional variables have been linked to the Ten to Men data and are provided in both the General and Restricted Release datasets. These include geographical variables linked by participant's residential location at the time of the survey.
The State of residence was added to each unit record based on the participant's residential location at the time of the survey.
4.2 Statistical Area Levels 1 (SA1) and 2 (SA2)
The SA1 and SA2 are geographical areas in the Australian Statistical Geography Standard (ASGS) Main Structure. SA1s are the second smallest unit in the ASGS and have a population of between 200 and 800 people. SA1s aggregate to form SA2s, which have a population between 3,000 to 25,000 people. This structure is based on the census data and maintained by the ABS. Detailed information about the ASGS may be sourced from the ABS website.
Information on SA1 and SA2 were linked to each unit record based on the participant's residential location at the time of the survey.
For Wave 1, confidentialised SA1s and SA2s from the 2011 Census were included in the Ten to Men datasets. For Wave 2 and Wave 3, confidentialised SA1s and SA2s from both 2011 and 2016 Censuses were included in the Ten to Men datasets.
4.3 Socio-Economic Indexes for Areas (SEIFA)
The SEIFA is a product developed by the ABS that ranks the geographical areas of Australia according to relative socio-economic advantage and disadvantage. It is based on information from the census, and currently consists of the following four indexes:
- Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD)
- Index of Economic Resources (IER)
- Index of Education and Occupation (IEO)
- Index of Relative Socio-Economic Disadvantage (IRSD).
Detailed information about the SEIFA may be sourced from the ABS website.
SEIFA information was added to each unit record based on the geographical area of the participant at the time of the survey. Rank, decile and percentage were provided for each index.
For Wave 1, only SEIFA 2011 was included in the dataset. For Wave 2 and Wave 3, both SEIFA 2011 and SEIFA 2016 were included in the datasets.
4.4 Australian Statistical Geography Standard (ASGS) Remoteness Area
The Remoteness Area is a geographical classification that defines locations in terms of remoteness. It is based on information from the census and is developed by the ABS. Detailed information about the Remoteness structure may be sourced from the ABS website.
For Wave 1, only Remoteness Area 2011 was included in the Ten to Men datasets. For Wave 2 and Wave 3, both Remoteness Area 2011 and 2016 were included in the Ten to Men datasets.
4.5 Modified Monash Model
The Modified Monash Model (MMM) is a geographical classification developed in 2015 by the Department of Health (DoH) to address the disparities in health service access that exist across Australia. It is similar to the ASGS Remoteness classification, but further subdivides regional Australia into categories based on the size of the local town or city. The MMM uses seven categories to define locations. Detailed information about the MMM may be sourced from the DoH website.
This variable has been included in the datasets for all waves.
4.6 Medicare Australia data
In Wave 1, participants (or their parent/guardian) were asked to consent to have their data linked with Medicare Australia data on an ongoing basis. This includes data from the Medicare Benefits Scheme (MBS) and the Pharmaceutical Benefit Scheme (PBS).
The linked MBS and PBS datasets are updated for currency at different time points. To date, there have been two data linkage updates.
- Data linkage in 2017. The consent rate at this time point was 68%. For MBS data, the linkage was successful for 94% of consented records. For PBS data, the linkage was successful for 85% of consented records.
- Data linkage in 2021. After excluding participants who had withdrawn from the study, the consent rate at this time point was 63%. For MBS data, the linkage was successful for 92% of consented records. For PBS data, the linkage was successful for 83% of consented records
These datasets are available on request, subject to approval from the Ten to Men Data Access Review Committee.
Records are currently available for services between March 2012 and February 2021.
5. Ten to Men data user resources
There are a number of products available to assist the user in navigating the Ten to Men dataset. These include the questionnaires, Data Dictionary, Data Books, Data Issues Paper and Technical Reports.
5.1 Data Dictionary
The Ten to Men Data Dictionary contains a detailed listing of all variables, including those that have been derived or calculated. The variables are listed in the order that they appear in the dataset, starting with Wave 1.
The Data Dictionary is available as an Excel spreadsheet and therefore the data can be easily sorted, filtered using the drop-down menus or searched according to the user's requirements.
Each row in the Excel spreadsheet describes a single variable and includes the following fields:
- File name
- Variable Position in the dataset
- Research Domain
- Questionnaire number
- Questionnaire number in each of the four questionnaires
- Variable Label
- Variable Name
- Characters 2-9 of variable name (research domain and activity description only)
- Format Name
- Formatted Data values
- Confidentialised field listing brief details of any confidential steps undertaken for the variable
- Variable Type as to whether the variable is raw data, derived, calculated, etc.
- General/Restricted field indicating the availability of the variable.
5.2 Data Books
Data Books have been produced for each wave of Ten to Men using the data collected from the questionnaires. Each Data Book contains a frequency table or descriptive statistics for every variable and are useful for simple queries to particular questions.
The frequencies in the Data Books have not been weighted and, except for confidentialisation reasons, there is no editing of any data. Caution needs to be taken when interpreting results from the Data Books, especially any table with extreme values.
The Data Books are available as PDF files and have been further separated by questionnaire. For example, Wave 1 has three Data Books available - Boys, Young Men and Adults.
5.3 Data Issues Paper
This paper provides a summary of data-related issues that have been identified over the course of Ten to Men. It has been designed to assist users of the data as they undertake research and analysis, and should be read in conjunction with the Data User Guide.
Some data-related issues that have been discussed in the Data Issues Paper include:
- differences in sample sizes between various data releases
- changes to the variable names
- differences in the wording of questions across questionnaires.
Further sections will be added to the Data Issues Paper as any data-related issues emerge.
5.4 Technical Reports
The Ten to Men Technical Reports aim to assist and inform users about specific types of data analysis, study methodology, survey development and any key research findings.
Six Technical Reports for Ten to Men were released by the University of Melbourne between January 2012 and January 2017.
6. Statistical considerations with Ten to Men data
In Ten to Men, the key elements of the sample design were the use of stratification, multi-stage and cluster sampling to select prospective participants and invite them to take part in the study (Currier et al., 2016). This section describes the stratification, multi-stage design, clustering and weights used in Ten to Men. It also provides recommendations for the analysis of data that acknowledges these aspects of the design.
Stratified sampling the remoteness of the residential location was used for the Ten to Men survey. The reference for all geographic units was the ASGS (ABS, 2011).
The ASGS classifies all locations in Australia into one of five levels of remoteness:
- Major Cities
- Inner Regional
- Outer Regional
- Very Remote.
The areas of Remote and Very Remote were excluded from the sampling, as it was considered too difficult to recruit and conduct interviews in these areas.
The Inner Regional and Outer Regional areas were both over-sampled. They represented 23% and 20% of the sample, whereas the population proportions were 18% and 9% (ABS, 2011).
6.2 Multi-stage design
The main structure of the ASGS comprises five levels of hierarchical spatial units, all of which aggregate progressively upwards. Mesh blocks (MB) are the smallest unit that aggregates into Statistical Area 1s (SA1), which aggregate into SA2s, then SA3s, and then SA4s.
SA1s are the smallest unit for the release of census data and generally have a population of 200-800 persons with an average of 400. Whole SA1s aggregate directly to SA2s in the ASGS structure, and do not cross state or territory borders.
SA2s are medium-sized areas, comprised of the whole SA1s and represent a social/economic community unit. They usually have a population range of 3,000-25,000 persons with an average of 10,000 (ABS, 2011).
In Major Cities, a random sample of SA1s was obtained proportional to the number of boys in the SA1. Boys were chosen based on the earlier but subsequently superseded plan to oversample young males. It was assumed that the population size of the SA1s in the Major Cities is unlikely to be strongly associated with any particular covariate profiles that might lead to lack of representation of some of the covariate distributions.
Inner and Outer Regional Areas
In the Inner and Outer Regional areas, it was decided to sample SA2s and then SA1s within the sampled SA2s. SA2s vary a lot more in size than SA1s - for example, there were 577 SA2s in the sample frame for Inner Regional, each consisting of between 1 and 63 SA1s. A random sample of SA2s proportional to size was chosen, where 'size' was the ABS number of boys in the SA2.
In both the Inner and Outer Regional areas, particularly because of the large variation in SA2 size, it was decided to make the sample approximately equally weighted (for individuals). This was done by choosing a fixed number of SA1s (four) in each SA2, thereby compensating for the higher probability of selection of a larger SA2. This is a standard way of producing an equally weighted sample. It means that the chance of any given SA1 in the Inner and Outer Regional areas being selected was approximately the same.
6.3 Cluster sampling
For the three strata, all eligible males in the selected household were invited to participate in Ten to Men. Thus, within a stratum, the Ten to Men study can be described as a cluster sample of eligible households, with SA1s defining the cluster.
Given that SA1s vary in population, SA1s were adjusted to distribute 65%, 20%, and 15% of that initial estimated target sample across the Major Cities, Inner Regional and Outer Regional strata, respectively. Having identified a target sample for each stratum, the number of SA1s required to meet those targets was determined as follows:
- Using 2011 Census data and response fractions of 27.1% for 10-17-year-olds and 34.1% for adult males, an estimate of participants per SA1 was produced.
- In each Regional stratum, the participant estimate was summed down the sequentially sampled SA1 list to produce a cumulative total.
- Based on that total, the number of SA1s required to produce the designated regional proportion of participants per stratum was identified.
For a total of 621 SA1s, this resulted in 362 SA1s in Major Cities, 144 in Inner Regional areas and 115 in Outer Regional areas. While the design of the sample did not aim to guarantee state/territory representation, the final sample did include SA1s from every state and (mainland) territory. Table 8 provides the details of the SA1 distribution by state and territory.
The Ten to Men study weighting can improve the relation between the inferences about males in the study and inferences about males in the population. Weights can be used to adjust for potential bias due to non-response and unequal sampling fraction, and to allow inferences about population estimates to be made.
At each wave, the following population and sample weights are provided in the Ten to Men datasets:
- cross-sectional weights for each wave
- trimmed and raked cross-sectional weights for each wave
- longitudinal weights between the waves
- trimmed and raked longitudinal weights between the waves.
Cross-sectional weights adjust the sample attained at the current wave to be representative of the population at the time of selection. These weights are most suitable for analysis that makes use only of the one wave of data.
Longitudinal weights adjust the sample that has responded to all waves of Ten to Men to be representative of the population. These weights are most suitable for analysis that makes use of data from many waves.
Raking is an iterative method used to adjust the weights based on socio-demographic characteristics that are available for both the sample and the population. It reduces the non-response bias, and also allows inferences from the sample to population to be made.
A complete list of the Ten to Men weighting variables is provided in Table 9. It is the responsibility of data users to determine when and how weighting needs to be accounted for when developing their analyses.
|adcicpwtad||Initial cross-sectional population weight for Wave 1|
|adcrcpwtad||Raked cross-sectional population weight for Wave 1|
|adcicswtad||Initial cross-sectional sample weight for Wave 1|
|adcrcswtad||Raked cross-sectional sample weight for Wave 1|
|bdcicpwtbd||Initial cross-sectional population weight for Wave 2|
|bdcrcpwtbd||Raked cross-sectional population weight for Wave 2|
|bdcilpwabd||Initial longitudinal population weight between Wave 1 and Wave 2|
|bdcrlpwabd||Raked longitudinal population weight between Wave 1 and Wave 2|
|bdcicswtbd||Initial cross-sectional sample weight for Wave 2|
|bdcrcswtbd||Raked cross-sectional sample weight for Wave 2|
|bdcilswabd||Initial longitudinal sample weight between Wave 1 and Wave 2|
|bdcrlswabd||Raked longitudinal sample weight between Wave 1 and Wave 2|
|cdcicpwtcd||Initial cross-sectional population weight for Wave 3|
|cdcrcpwtcd||Raked cross-sectional population weight for Wave 3|
|cdcilpwacd||Initial longitudinal population weight between Wave 1 and Wave 3|
|cdcrlpwacd||Raked longitudinal population weight between Wave 1 and Wave 3|
|cdcilpwbcd||Initial longitudinal population weight between Wave 2 and Wave 3|
|cdcrlpwbcd||Raked longitudinal population weight between Wave 2 and Wave 3|
|cdcicswtcd||Initial cross-sectional sample weight for Wave 3|
|cdcrcswtcd||Raked cross-sectional sample weight for Wave 3|
|cdcilswacd||Initial longitudinal sample weight between Wave 1 and Wave 3|
|cdcrlswacd||Raked longitudinal sample weight between Wave 1 and Wave 3|
|cdcilswbcd||Initial longitudinal sample weight between Wave 2 and Wave 3|
|cdcrlswbcd||Raked longitudinal sample weight between Wave 2 and Wave 3|
7. Accessing Ten to Men Data
The Ten to Men study has provided invaluable longitudinal data about the health of males in Australia. AIFS support the sharing of the Ten to Men data to increase research opportunities on male health.
The data request process is managed by AIFS in a consistent and appropriate way to ensure the security of the data.
Researchers affiliated with an Australian university or recognised Australian research institute or research organisation, Australian government department or enrolled research students in Australia are eligible to access Ten to Men data. International researchers who collaborate with an eligible Australian university, organisation, institution or department can also apply for access to General Release data.
The following documents (available in Data documentation) will aid researchers in determining whether Ten to Men data are suitable for their purposes: Ten to Men questionnaires, Data Dictionary, Data Books and the Data Issues Paper.
7.1 Security requirements for accessing Ten to Men data
The privacy of study participants is to be protected at all times. The Ten to Men Terms and Conditions of Data Access and Use detail the security requirements agreed to by users of the Ten to Men data. In summary:
- Access is limited to those researchers named in the Data User Agreement and Deed of Confidentiality.
- Upon receipt, the data must be stored on a password-protected computer or electronic device that is only accessible by the individual named in the Data User Agreement.
- All electronic files, the data and copies of the data must be stored in accordance with the NHMRC Guidelines and destroyed after completion of the project.
- Data cannot be accessed or stored outside of Australia. This also applies to copies of the data.
7.2 Data access documentation and forms
A number of data access documents are available for prospective users of the Ten to Men data. Users should familiarise themselves with the access requirements and security protocols outlined in the following documents prior to applying for access.
- Data Access Request Form. Used by data users to apply for access to the Ten to Men data.
- Data Access Amendment Form. For currently approved users who require extension to project timelines, additional personnel, or access to additional datasets.
- Data Access Policy. Outlines the procedural and technical aspects of the data access application, review and approval process.
- Terms and Conditions of Data Access and Use. Details the legal terms and conditions relating to access, storage and use of the data.
These documents are available at Data documentation.
All data access queries should be sent to firstname.lastname@example.org
7.3 Data access approval process
All users must submit a Data Access Request Form to the Ten to Men Data Manager at email@example.com. Requests are reviewed by the Ten to Men Data Access Review Committee. Projects must meet the public good, be scientifically and ethically sound and contribute to the knowledge base on male health as described in the study aims and objectives.
Access is limited to those users named in an approved request. Approved users will be notified by the AIFS Ten to Men Data Manager and required to sign a Data User Agreement and Deed of Confidentiality. Upon receipt of the signed Agreement, the Ten to Men Data Access Coordinator will notify the Australian Data Archive (ADA) to release the requested data to the approved applicant. Access to the approved dataset(s) will be available for users to download via a secure link provided by the ADA.
Further information about the data access approval process is available in the Ten to Men Data Access Policy.
7.4 Release of Ten to Men data
AIFS, in partnership with the ADA, is using Dataverse to facilitate access to the Ten to Men datasets. Dataverse is an online platform that enables the user to:
- access Ten to Men datasets, once approved
- access Ten to Men data documentation, such as the Data User Guide, Data Dictionary, Study Questionnaires and Data Issues Paper.
The Ten to Men datasets are available free of charge for download by approved data users from the ADA in SAS, STATA and SPSS formats.
A single dataset is provided combining data from all questionnaires for each wave of data. Data from the Parent questionnaire has been included with the relevant Boy participant unit record. Other confidentialised information is available in the dataset at the unit record level, including basic demographic information, recruitment area and household identifiers, area-level variables, and meta-data related to participation in each wave of data collection. Personal or identifying information of Ten to Men participants is not available.
7.5 Follow up requirements
Users must provide a written update on the progress of their project every year and on completion of the project. Progress updates will include the stage of the project, relevant data outputs and dissemination.
Where applicable, users will provide the Ten to Men Data Manager with a copy of valuable or important syntax files (relating to derived variables) to enable replication or verification of outputs for other researchers.
Users will receive notifications from the Ten to Men Data Manager via email regarding updates/corrections to the Ten to Men data.
7.6 Destroying data
Users must notify the Ten to Men Data Manager in writing to confirm when they are finished using the Ten to Men data. This includes:
- no longer conducting analyses with the data or producing outputs
- no longer producing publications or reports using the data
- no longer presenting findings using the data.
Users are then required to delete all data and any datasets, including new variables created by manipulating the data, by reformatting or rewriting the data. An End-of Project form must be completed to verify this.
8. User support and training
User training sessions, such as data webinars or data workshops, will be offered by AIFS to provide more detailed information than in this Data User Guide. This training will allow users to interact with AIFS staff and benefit from their in-depth knowledge and experience with the Ten to Men data.
These sessions will consist of an introduction to Ten to Men data, and any newly released datasets, including:
- study methodology
- introduction to the datasets
- issues for data analysts (e.g. weighting, clustering, confidentialisation)
- variable naming
- user resources.
The Ten to Men website will provide further details as to when the user training sessions are being offered.
In 2019, AIFS produced a two-part series of webinars on Ten to Men. These provided an opportunity to learn more about the data, covering topics such as sample design, cohorts and data linkage. Recordings of these webinars, including a copy of the presentation slides, are available for free by contacting AIFS: firstname.lastname@example.org
8.2 Online assistance
An email alert list is used to convey key information and updates to users. Important information distributed via the email alert list is also stored in the data access area of the Ten to Men website. This area contains:
- all reference material made available to users
- critical updates and alerts, as distributed through the email alert list
- updates on data-user workshops.
8.3 Getting more information
More information about the Ten to Men survey data is available from:
- Currier, D., Pirkis, J., Carlin, J., Degenhardt, L., Dharmage, S. C., Giles-Corti, B. et al. (2016). The Australian longitudinal study on male health-methods. BMC Public Health, 16(Suppl 3), 1030. doi: 10.1186/s12889-016-3698-1
- Kalton, G. (1983). Introduction to Survey Sampling. Newburry Park, CA: Sage Publications.
- Spittal, M. J., Carlin J. B., Currier, D., Downes, M., English, D. R., Gordon, I. et al. (2016). The Australian longitudinal study on male health sampling design and weighting: Implications for analysis and interpretation of clustered data. BMC Public Health, 16(Suppl 3), 1062. doi: 10.1186/s12889-016-3699-0
- Australian Bureau of Statistics. (2011). Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure. Canberra: Australian Bureau of Statistics.
|Date||Release||Dataset||Suggested citation and DOI|
|July 2016||Release 1.0||Wave 1||Pirkis, J., English, D., & Currier, D. (2016).The Australian Longitudinal Study on Male Health (Ten to Men), 2013 [computer file]. Canberra: Australian Data Archive, The Australian National University, 2016.
|August 2017||Release 2.0||Respondent
|Pirkis, J., English, D., & Currier, D. (2017). The Australian Longitudinal Study on Male Health (Ten to Men), 2013 [computer file]. Canberra: Australian Data Archive, The Australian National University, 2017.
|September 2019||Release 2.1||Wave 1
|Bandara, D; Howell, L; Daraganova, G, 2019, "Ten to Men: The Australian Longitudinal Study on Male Health, Release 2.1 (Waves 1-2)", doi:10.26193/V2IVIG, ADA Dataverse.|
|September 2021||Release 3.0||Wave 1
|Bandara, D; Howell, L; Silbert, M; Daraganova, G, 2021, "Ten to Men: The Australian Longitudinal Study on Male Health, Release 3 (Waves 1-3)", doi:10.26193/JDE1TD, ADA Dataverse.|
|ABS||Australian Bureau of Statistics|
|ADA||Australian Data Archive|
|AIFS||Australian Institute of Family Studies|
|ANZSCO||Australian and New Zealand Standard Classification of Occupations|
|ASCL||Australian Standard Classification of Languages|
|ASGS||Australian Statistical Geographic Standards|
|CAPI||Computer-Assisted Personal Interview|
|Data Books||Documents providing the frequency distribution of variables by questionnaire|
|Data Dictionary||Spreadsheet listing all variables and their attributes|
|DLIA||Data Linkage and Integrating Authority|
|DoH||Department of Health|
|DOI||Digital Object Identifier|
|MMM||Modified Monash Model|
|NHMRC||National Health and Medical Research Council|
|RMR||Roy Morgan Research (fieldwork agency commissioned to carry out Waves 1 and 2 fieldwork)|
|SA1||Statistical Area 1|
|SA2||Statistical Area 2|
|SACC||Standard Australian Classification of Countries|
|SEIFA||Socio-Economic Indexes for Areas|
|SRC||Social Research Centre|
|TTM||Ten to Men study|
|UoM||University of Melbourne|
Ten to Men: The Australian Longitudinal Study on Male Health is the first large-scale, nationally representative, longitudinal study to focus exclusively on investigating and improving the health and wellbeing of males in Australia. It is also the largest longitudinal study of male health in the world.
Ten to Men was commissioned and is funded by the Australian Government Department of Health to inform the National Male Health Policy. The study was initially conducted by the University of Melbourne who released datasets, including data documentation, for Wave 1 and Wave 2. Roy Morgan Research undertook the data collection and initial data processing for these two waves.
After a competitive tender process in 2017, the Australian Institute of Family Studies (AIFS) was awarded with the responsibility to conduct Wave 3. Since then, AIFS has updated the Wave 1 and Wave 2 datasets, including data documentation.
In 2020, the study team re-evaluated and revised the survey content and methodology to enable contactless interviewing for Wave 3. New items designed to collect information on the impacts of COVID-19 and the recent effects of natural disasters were also incorporated into the revised survey. The online survey went live at the end of July 2020, with data collection concluding in February 2021. The Social Research Centre (SRC), in collaboration with Ipsos, was contracted to undertake the fieldwork component for Wave 3 of the study.
Bandara, D., Howell, L., Silbert, M., & Daraganova, G. (2021). Ten to Men: The Australian Longitudinal Study on Male Health - Data User Guide, Version 4.0, September 2021. Melbourne: Australian Institute of Family Studies.