Personal Fraud methodology

Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest.

Weighting is the process of adjusting results from a sample survey to infer results for the total 'in-scope' population. To do this, a 'weight' is allocated to each enumerated person. The weight is a value which indicates the number of persons in the population represented by the sample person.

The first step in calculating weights for each unit is to assign an initial weight, which is the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 people).

The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks'. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the population rather than the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular categories of persons/households which may occur due to either the random nature of sampling or non-response.

The MPHS estimates do not (and are not intended to) match estimates for the total Australian person/household populations obtained from other sources.

The survey was benchmarked to the Estimated Resident Population (ERP) living in private dwellings in each state and territory at December 2022. People living in Indigenous communities were excluded. These benchmarks are based on the 2021 Census.

While LFS benchmarks are revised every 5 years, to take into account the outcome of the 5-yearly rebasing of the ERP following the latest Census, the supplementary surveys and MPHS (from which the statistics in this publication are taken) are not. Small differences will therefore exist between the civilian population aged 15 years and over reflected in the LFS and other labour household surveys estimates, as well as over time. If comparisons are being made over time then proportions should be used rather than estimates of persons.

The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey. This is known as sampling error.

Non-sampling error is caused by factors other than those related to sample selection. It is any factor that results in the data values not accurately reflecting the true value of the population.

It can occur at any stage throughout the survey process. Examples include:

• selected people that do not respond (e.g. refusals, non-contact) 
• questions being misunderstood
• responses being incorrectly recorded
• errors in coding or processing the survey data.

Sampling error is the expected difference that can occur between the published estimates and the value that would have been produced if the whole population had been surveyed. Sampling error is the result of random variation and can be estimated using measures of variance in the data.

One measure of sampling error is the standard error (SE). There are about two chances in three that an estimate will differ by less than one SE from the figure that would have been obtained if the whole population had been included. There are about 19 chances in 20 that an estimate will differ by less than two SEs.

Victimisation surveys are best suited to measuring crimes against specific individuals. However, respondents need to be aware of and recall what happened to them and how it happened, as well as be willing to relate what they know to interviewers.

Crimes which rely on deception, such as fraud, can be more challenging to recognise, and therefore may not be fully represented in the data collected.

Information collected in this survey is essentially 'as reported' by respondents, and hence may differ from that which might be obtained from other surveys or administrative data sources. This factor should be considered when interpreting the estimates and when making comparisons with other data sources.

The level of victimisation can be measured and expressed in more than one way. The most common measure derived from the Personal Fraud Survey is prevalence, that is, the number of the relevant population who have experienced a given type of fraud at least once in the reference period. Victimisation rates used in this publication represent the prevalence of selected types of fraud in Australia, and are expressed as a percentage of the total relevant population. Reporting rates used in this publication are expressed as the percentage of persons whose most recent/serious incident of each type of fraud had been reported to (or detected by) an authority.

Country of birth data are classified according to the Standard Australian Classification of Countries (SACC), 2016. 

Education data are coded to the Australian Standard Classification of Education (ASCED), 2001. The ASCED is a national standard classification which can be applied to all sectors of the Australian education system including schools, vocational education and training and higher education. The ASCED comprises two classifications: Level of Education and Field of Education.

Equivalised weekly household income is household income adjusted by the application of an equivalence scale to facilitate comparison of income levels between households of differing size and composition, reflecting that a larger household would normally need more income than a smaller household to achieve the same standard of living. Using an equivalising factor for household income enables the direct comparison of the relative economic well-being of households of different size and composition (for example, lone person households, families and group households of unrelated individuals).

For more information about equivalised weekly household income see Household Income and Wealth, Australia and Survey of Income and Housing, User Guide, Australia.

Australian geographic data are classified according to the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016. Remoteness areas are classified according to the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016.

This survey uses the 2016 Socio-Economic Indexes for Areas (SEIFA).  

SEIFA is a suite of four summary measures that have been created from 2016 Census information. Each index summarises a different aspect of the socio-economic conditions of people living in an area. The indexes provide more general measures of socio-economic status than is given by measures such as income or unemployment alone.

For each index, every geographic area in Australia is given a SEIFA number which shows how disadvantaged that area is compared with other areas in Australia.

Two indexes are used in this publication – the Index of Relative Socio-Economic Advantage and Disadvantage; and the Index of Relative Socio-Economic Disadvantage. These measures are derived from Census variables related to income, educational attainment, unemployment, occupational skill level and whether a dwelling has a motor vehicle.

SEIFA uses a broad definition of relative socio-economic disadvantage in terms of people's access to material and social resources, and their ability to participate in society. While SEIFA represents an average of all people living in an area, it does not represent the individual situation of each person. Larger areas are more likely to have greater diversity of people and households.

For more detail, and for the SEIFA 2016 Technical paper (under Downloads), go to Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016.

Comprises statistical areas outside the Greater Capital City Statistical Areas of states and territories as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016.

A fake offer to invest in ‘foolproof’ betting systems or software that offer returns on sporting events.

Scams involving requests for payment of fake invoices or products, or the purchase of non-existent, stolen or counterfeit goods. Includes false billing, classifieds, overpayments, and online shopping scams.

Refers to the Greater Capital City Statistical Areas of states and territories as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016.

Card fraud involves the use of credit, debit, or EFTPOS card, or card details, to make purchases or withdraw cash without the account owner's permission.

A fake request to donate to a charity or person in need. Includes fraudulent impersonation of real charities.

A computer support scam is a fake offer over the phone, or through other means, to fix a problem with a computer/laptop (for example to deal with viruses).

All people aged 15 years and over who met one of the following criteria during the reference week:

• Worked for one hour or more for pay, profit, commission or payment in kind, in a job or business or on a farm (employees and owner managers of incorporated or unincorporated enterprises).
• Worked for one hour or more without pay in a family business or on a farm (contributing family workers).
• Were employees who had a job but were not at work and were:
  • away from work for less than four weeks up to the end of the reference week; or
  • away from work for more than four weeks up to the end of the reference week and received pay for some or all of the four week period to the end of the reference week; or
  • away from work as a standard work or shift arrangement; or
  • on strike or locked out; or
  • on workers' compensation and expected to return to their job.
• Were owner managers who had a job, business or farm, but were not at work.

A fake offer of a job, franchise, or other business opportunity, often involving working from home.

A fake offer of an investment or any other financial advice with a guaranteed high return.

Employed people who usually worked 35 hours or more a week (in all jobs) and those who, although usually working less than 35 hours a week, worked 35 hours or more during the reference week (i.e. the week before the interview).

Fraud is the act of intentionally deceiving another for the purpose of gaining an advantage or benefit, whether financial or otherwise.

Greater Capital City Statistical Areas (GCCSA) are geographical areas built from Statistical Areas Level 4 (SA4), as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016. They are designed to represent the functional extent of each of the eight State and Territory capital cities. This includes the population within the urban area of the city, as well as people who regularly socialise, shop or work within the city, and live in small towns and rural areas surrounding the city. Within each State and Territory, the area not defined as being part of the Greater Capital City is represented by a Balance of State/Territory region.

Identity theft is the use of someone's personal details in stolen, fraudulent or forged documents without permission, or otherwise illegally appropriating another's identity.

A single occurrence of a crime event, which may involve one or more crime types.

An information request scam is where a person receives a fake notification or request from a bank, business, or other organisation asking for your personal details.

A classification of the civilian population aged 15 years and over, including employed, unemployed or not in the labour force, as defined in Labour Force, Australia. These definitions conform closely to the international standard definitions adopted by the International Conference of Labour Statisticians.

Non-school qualifications are awarded for education attainments other than those of pre-primary, primary or secondary education. They include qualification at the following levels: Postgraduate degree, Master degree, Graduate Diploma and Graduate Certificate, Bachelor degree, Advanced Diploma and Diploma and Certificates I, II, III and IV. Non-school qualifications may be attained concurrently with school qualifications.

A lottery scam is where a person receives a fraudulent notification of having won a lottery or prize for a draw that they did not enter, and are asked to provide personal details or pay a fee in order to collect their prize or winnings. 

Persons who were neither employed nor unemployed as defined by Labour Force Status.

Where someone’s personal details have been purposefully misused to impersonate them via computer, online or by phone without their permission. This includes incidents where someone pretended to be someone else and either posted content or interacted with others. Online impersonation excludes where someone accessed another person’s accounts or information solely to track or follow them, or to use their streaming service(s) without their permission e.g. Netflix, Stan, Binge and Disney Plus.

Any other scam not separately identified in the survey.

Employed people who usually worked less than 35 hours a week (in all jobs) and either did so during the reference week (i.e. the week before the interview), or were not at work in the reference week.

Pyramid schemes are a multi-level selling technique where the main feature is that earning money and gaining promotion depends on recruiting other people into the operations rather than selling a product or providing a service.

A romance/relationship scam is a request from someone the respondent has built an online relationship with (for example, through social media or an online dating website) for money or the respondent's bank details, where the person is pretending to be romantically interested for the purposes of financial gain.

Reporting rate refers to the number of persons who reported the most recent/serious incident to an authority (or were notified of the incident by them), expressed as a proportion of all persons who experienced the fraud type.

A measure of the extent to which an estimate might have varied by chance because only a sample of dwellings was surveyed, and not the entire in-scope population. Relative standard error (RSE) is obtained by expressing the standard error as a percentage of the estimate.

A person is considered to have responded to a scam if, after being exposed to the scam, they sought further information, provided money or personal information, or accessed links associated with the scam. In this survey, only those who responded to a scam were considered to have been a victim of (experienced) a scam.

A scam is a fraudulent invitation, request, notification or offer, designed to obtain personal information or money or otherwise obtain a financial benefit by deceptive means.

Scams involving threats of legal action, arrest, death or other harm if demands are not met.

People aged 15 years and over who were not employed during the reference week (i.e. the week before the interview), and:

• had actively looked for full-time or part-time work at any time in the four weeks up to the end of the reference week and were available for work in the reference week
• were waiting to start a new job within four weeks from the end of the reference week and could have started in the reference week if the job had been available then.

A request to send money or banking details in return for the false promise of a monetary payment or entitlement. Includes offers of unexpected money from inheritance, rebates, and international money transfer scams.

A victim is a person who has experienced card fraud, identity theft, online impersonation, or responded to one or more selected scams.

Victimisation rate is the number of persons in a population who experienced a personal fraud type, expressed as a proportion of that population. This is a measure of how prevalent a fraud type is in a given population, and is used to measure changes in rates over time.