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.

Table 1: Periods of recruitment and data collection

Wave	Task	Period
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

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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.

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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

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Wave Indicator

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.

Table 2: Research domains and abbreviations

Abbreviation	Research Domain
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
dc	Data Collection
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
ld	Linked Data
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.

Table 3: List of questionnaire indicators and descriptions

Indicator	Description
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)

Derived Indicator

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.

Table 4: Standard classifications used

Variable	Classification used
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.

Table 5: Standard missing value code frame used in the data

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.

Table 6: Reference of derived scale scores

Measure	Reference
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:10.1037/1524-9220.4.1.3
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.

Table 7: Details of calculated variables

Variable Label	Formula
Body Mass Index (BMI)	Weight (kg) / (Height (cm) x Height (cm))

3.9 Confidentialisation

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.

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.

General Release

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.

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
• Wave
• Variable Position in the dataset
• Research Domain
• Questionnaire number
• Questionnaire number in each of the four questionnaires
• Question
• 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.

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 ttmdatamanager@aifs.gov.au

7.3 Data access approval process

All users must submit a Data Access Request Form to the Ten to Men Data Manager at ttmdatamanager@aifs.gov.au. 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.

References

• 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.

Glossary

Glossary of terms

Term	Description
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
MB	Mesh Block
MMM	Modified Monash Model
NHMRC	National Health and Medical Research Council
RA	Remoteness Area
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