RESEARCH PAPER SERIES, 2016–17                   30 September 2016

Understanding statistics in social policy development and evaluation: a quick guide

Carol Ey

Social Policy Section

Introduction

Statistics are widely used in the development and evaluation of social policy. Closing the Gap indicators, studies on the effectiveness of drugs being considered for inclusion in the Pharmaceutical Benefits Scheme (PBS), analyses of National Assessment Program—Literacy and Numeracy (NAPLAN) results and the evaluation of welfare interventions such as income management all include statistics that guide decisions about where government resources are directed.

However, not all statistics have the same level of robustness, and their interpretation can be questionable. This paper attempts to provide some guidance for non-statisticians about the questions they might ask when presented with statistical information in order to assess how much reliance they can put on it. This is not intended to be a comprehensive coverage of the factors to be considered (more detailed references are provided in links and in the further reading), but rather to provide a checklist of some of the more common issues.

Given the nature of most of the data used in social policy, the paper focusses on data about, or collected from, people.

What is being measured?

It is essential to check the definitions used in social policy statistics. For example, different researchers may use different definitions of terms such as homelessness or poverty, potentially leading to very different results. Even generally agreed definitions such as unemployment may be measured differently in some contexts. The definition of such terms can also vary over time or between jurisdictions, so it is important to be cautious about comparing statistics from different sources.

How was the data collected?

Types of data

Social policy development relies on data from two different types of collection:

•        censuses where data is collected from all those in a particular population. For example, the five-yearly Census of Population and Housing  (the Census) conducted by the Australian Bureau of Statistics (ABS) attempts to collect a range of information on everyone in Australia (except foreign diplomats and their families) on a particular night and

•        samples which collect data on selected members of a population.

The type of information collected can include:

•        questionnaires, where respondents are asked questions about the topic

•        tests, which can assess abilities or measure attributes

•        observation, where the researcher collects information from observing respondents and

•        administrative data, which is collected as a by-product of running a program or providing a service, for example, Centrelink data, hospital admissions data, crime statistics or school enrolment information.

Information can also be collected through unstructured interviews or focus groups. Information obtained through these techniques is not generally suitable for quantitative estimates, but can be useful in providing greater understanding of an issue, such as causal linkages.

Longitudinal data is where information is collected from the same people multiple times over a period of time. This data can be collected by any of the means described above. Major longitudinal studies include the Household, Income and Labour Dynamics in Australia (HILDA) survey and the Australian Census Longitudinal Dataset (ACLD).

Who collected it?

Major data collection agencies (for example the ABS) have extensive processes in place to ensure the data they collect is of as high a quality as possible. This includes pilot-testing survey questions, having subject matter experts oversee the questionnaire design, and having extensive training programs for interviewers. They may also have data quality statements to provide users with information on possible issues with the data.

University and publicly funded research is typically subject to review from experienced researchers. Publishing copies of questionnaires and details of sampling methodology, or making the data itself available for others to analyse allows for independent assessment of the data quality.

Be cautious about using data when you cannot be sure it was collected in a rigorous way.

Who did they ask?

For samples, a critical aspect of the wider applicability of their results is how representative those selected are of the population as a whole.

Random samples are designed to ensure that anyone in the population has an equal likelihood of being selected. Many surveys modify this random selection to ensure they collect information from a range of people from different gender/age groups and locations (for example state, city/country), and then weight their results based on the proportion each group represents of the total population (stratified samples).

Other forms of sampling include quota and convenience samples, which are non-probability samples. In quota sampling, interviewers select respondents until a pre-determined number of respondents in particular categories are surveyed. This technique is often used by political pollsters and can produce reasonably reliable estimates if done properly.

Convenience sampling includes online ‘opt-in’ polls. This means that those who have a particular interest in the subject matter are more likely to respond, which may not represent the views of the community as a whole. Where the survey was advertised, for example, via a particular website or Facebook page, will influence who responds.

The way the information is collected may also influence who is asked. Online surveys exclude those without access to the internet, phone surveys that don’t include mobile numbers are likely to underrepresent younger people, while most surveys using face-to-face interviews exclude from possible selection anyone living in a remote community because of the cost of collecting the data.

People in institutions such as gaols, mental health facilities and nursing homes and the homeless are usually not included in general sample surveys, but may be included in specific surveys such as the Survey of Disability, Ageing and Carers which specifically collects information on people in cared accommodation.

Who answered?

The ABS technically has the power to compel those selected in some of its surveys to respond (although penalties are generally not enforced), but even in the Census it is acknowledged that some people are not included.

For most other researchers, even when they have provided incentives such as payments for participants, non-response is a major concern. The key issue is whether there is any difference between those who did and didn’t respond. For example, are non-respondents likely to have lower levels of education than respondents, are particular minority groups underrepresented, and if so, are those from such groups who did respond representative of the group as a whole?

What questions were asked?

The precise wording and order of questions is important. This is particularly true in regard to sensitive or contentious issues. Respondents are also more inclined to agree with a question than disagree.

Scales are often used to assess the depth of feeling on an issue, for example, rating on a scale of one to five how strongly you agree with a proposition. However there is no standard on what number of points are offered, whether the points are all labelled, or whether a neutral option is offered, and these factors may influence responses.

How reliable are the answers?

In some cases, data may be verified by checking with documented evidence. For example, date of birth can be checked against a birth certificate, or expenditure validated with receipts. In face-to-face interviews, tests can be administered or measurements taken. Much administrative data is verified, which is one of its strengths.

Diaries are more reliable than asking respondents to recall events, and there is increasing use of technology to collect data, such as wearable devices that record activity levels and sleep patterns.

Where data is not, or cannot be, verified, there are some particular issues to be aware of. For example, people often self-report themselves as taller and weighing less than actual measurements show, while social desirability bias means that aspects such as racist opinions, drug use or unusual sexual practices are typically underreported.

It is also important to be aware of who actually answered the questions. For example, the Census form is usually completed by one member of the household who answers on behalf of the other members. This means answers on questions such as religious beliefs may be filtered by the respondent.

Data collected through observation can be distorted if the observers are looking for a specific effect.

Caution must also be used when looking at data on minority groups such as Indigenous Australians. Minority group identification is usually self-reported, and hence prevalence may depend on factors such as how comfortable the person feels about identifying or whether they consider it relevant to the particular circumstances. This is a particular issue in relation to reporting minority group status in administrative data.

The way the data is collected can also influence how people respond, particularly to sensitive questions. There is evidence to suggest that respondents answer questions about sensitive issues more honestly when completing online surveys than when replying directly to a person.

In sample data, the size of the sample makes a difference in the reliability of the estimates produced. The National Statistical Service (NSS) has a sample size calculator that can be used to assess the accuracy of estimates if the sample size is given. For example, to measure a response around the 50 per cent mark in a random sample poll the accuracy of the result from a survey of 1,000 people will be plus or minus three per cent, while if 10,000 people are sampled the accuracy is plus or minus one per cent.

How is the data described?

Descriptive statistics summarise the responses and describe aspects such as the shape (is the data distributed symmetrically around a central value?), mid-point and spread of the data (what is the range of values, is it tightly grouped around the middle values or evenly spread across most of them?). Examples of descriptive statistics include the proportion of respondents who replied yes to a question, or what the average income of respondents is. While these are not statistically complex measures, they can be misrepresented.

The use of percentages can be misleading if the number involved is small, so if only percentages are quoted it is important to know how many people actually responded to that question. On the other hand, numbers can sometimes be misleading if the underlying population is not taken into consideration, so for much social policy data, the rate per head of population is often more relevant than the absolute number. Studies that quote the percentage increase in percentages or rates can also be difficult to interpret or misleading. If possible, using the underlying numbers will put these in context.

What does ‘average’ mean?

While in common parlance ‘average’ may convey ‘typical’, in statistics it is the mathematical mean (that is, the sum of all responses divided by the number of responses). Much of the data in social policy is distributed symmetrically (see (b) in Figure 1 below), and hence the mean is the same as the mid-point of the population. However, some data, such as income, is skewed with a long tail, that is, most responses are concentrated at the low end of the distribution, but there are a small number of responses stretching out to high values (see (c) in Figure 1). In such cases the average does not reflect the ‘typical’ value, and a more relevant measure would usually be the median, or 50th percentile, which is the point where half of the responses are above it and half below. Similarly, the mode, or most frequent response will also differ. The mode is generally only used in data where a limited number of responses are possible, in which case the mode is the most common response.

Figure 1: Normal distribution and skewed distributions

Source: IB Geography

Charts and graphs

Data is often displayed in chart or graphic form. This can be a useful aid in showing some aspect of the data or making patterns clear. However charts and graphs can also be used to distort data. In considering any graphic information it is important to check the axes and the scale, as well as being clear about exactly what data is being presented.

Time series

In many cases changes over time are more significant for policy purposes than absolute values. For example, whether a particular indicator is increasing or decreasing, or whether there has been any change following the implementation of new policy. In order to emphasise effects, researchers will sometimes selectively use timeframes in the presentation of their data. If possible, it is useful to consider the following:

•        does the outcome change if a slightly different time period is used (for example, using a ten year span rather than five, or shifting the time period by a year or two)?

•        do the results change if the data is accumulated in financial years rather than calendar years?

•        is there seasonal variation that needs to be considered (for many of its economic indicators the ABS produces ’seasonally adjusted’ series to overcome this issue)?

•        if the data includes money, is it reported on a consistent dollar basis over time (for example, all rebased to 2016 dollars)? This is sometimes referred to as being in ‘real’ dollar terms or using ‘current prices’.

Trend series are smoothed versions of time series, where small irregularities are removed to make the underlying trend easier to observe, and are recommended by the ABS for their key economic indicators.

Indexes

Researchers will sometimes develop an index to reduce a complex range of information to a single figure, generally for comparison purposes. Examples include the Gini Index, which is a measure of income inequality, and the Human Development Index, which itself is the average of three other indexes. While indexes are often valuable summary measures, they can obscure underlying differences.

Social policy studies frequently use an index to indicate socio-economic status (SES) in their analysis. Such measures are derived from information about a person’s economic (income, wealth, occupational status) and social (education, communication skills, contacts) background. However, there is no standard measure of SES, and the data available to a researcher often dictates which measure they will use.

Sometimes this level of detail is not available in the data, in which case researchers will use a proxy measure such as location of residence. The ABS has produced rankings of socio-economic advantage and disadvantage for a range of geographic areas such as postal areas and Local Government Areas (LGAs). However these measures may be a poor indicator of individual SES, particularly (but not only) in rural locations such as mining towns which might include both people with high incomes in professional occupations and a long term resident population with low levels of education and high unemployment, or working in labouring jobs. It is therefore important to consider the context in which the SES measure is being used to assess whether applying a geographic average to individuals is appropriate.

Interpretation of results

Correlation and causation

Much statistical analysis looks at the relationship between variables to attempt to identify patterns. The extent to which two aspects appear to be linked is described as their correlation. Statistical methods are good at identifying whether two factors are correlated, however correlation alone cannot show whether one of them causes the other.

The role of chance

Results being ‘statistically significant’ mean they are unlikely to have happened by chance. Most analysis uses a significance level of five per cent, that is, the chance of the result occurring if there is no relationship is less than one in 20, although one and ten per cent levels are also sometimes used. But it is important to note that such a finding could have happened by chance. In particular, where a complex study is undertaken and many different factors are analysed to determine which ones may be related, it is likely that at least some of the relationships will prove to be ‘statistically significant’ just through chance.

A 95 per cent ‘confidence interval’ (sometimes called the ‘margin of error’) means that there is a 95 per cent chance that the ‘real’ result is within the range quoted. Again, 99 and 90 per cent confidence intervals are sometimes used. Projections often use a ‘confidence interval’ to reflect the uncertainty of projecting past trends into the future, or the effect of assumptions made. Knowing the confidence interval is very important in assessing the validity of the result or projection, in particular if comparing results from two different time periods or studies. Different outcomes may not actually be different if their confidence intervals overlap, that is, both results are within an overlapping range.

Clustering of rare events, such as particular medical conditions, is also an area where chance plays a significant role. Counterintuitively, clustering of rare events is actually more likely than an even spread. This means that reports of a small cluster of rare cancers in a particular town, for example, may well be the outcome of chance rather than a particular feature of the location.

Comparing like with like

For some, the ‘gold standard’ method for social policy research is the use of Randomised Control Trials (RCTs). In these trials, people are randomly allocated to two groups which are designed to be as equivalent as possible. Then a program is administered to one group only and the outcomes compared between this group and the ‘control’ group to determine the effectiveness of the intervention. RCTs are commonly used in drug trails, where placebos are usually supplied to the control group to overcome the influence that receiving any treatment often has. Ideally such trials require that those conducting the trial do not know which group will receive which treatment, and those administering the drug do not know whether they are giving the test drug or a placebo. Even in medical research this purity is hard to achieve, while in most other areas of social research it is impossible. However, RCTs are still more powerful than most other methods in assessing what works.

Natural experiments are where aspects such as differing laws, policy or practices occur across different states or regions, making it possible to observe the impact of the differences on similar populations. These have similar advantages to RCTs in that they are comparing like with like, but as researchers are observing the effect of differences rather than administering different treatments, they are less subject to researcher influence or placebo effects. However they are obviously limited in what aspects can be considered because they can only be used where different practices happen to be implemented in similar populations.

Comparing administrative data across jurisdictions should be treated with caution, as differing definitions and environments may apply. For instance, crime statistics may reflect differences in legislation, public awareness, reporting, enforcement and sentencing arrangements even for the same criminal act. International comparisons in many areas of social policy can be extremely difficult, as different definitions, social conditions, administration and data collection methods will all impact on the data.

Validation

It is often not possible to consider all the elements described above to determine the validity of the results of a particular study. Therefore some judgement will need to be made based on external factors such as:

•        what expertise does the person conducting the study have? For assistance in evaluating expertise, see the Parliamentary Library publication Expertise and public policy: a conceptual guide

•        where is it published? Publication in a refereed journal or at an academic conference does not guarantee quality but does at least mean the study is subject to scrutiny by other qualified researchers

•        what do other studies or data show? If the study is high profile, academics or sites such as The Conversation may have identified related studies or data which may confirm or contradict the findings. Parliamentary Library staff can also assist clients by identifying related information.

Possible biases

Most research funding relies on the researcher attempting to find something new. This means that researchers will often keep working with their data to find a result they can report on, to justify their funding. Also, journals are more likely to publish interesting or controversial findings. As noted above, if enough tests are conducted it is likely that something will appear to be statistically significant, just on the basis of chance. For various reasons, including lack of funding to reproduce someone else’s results, much research is never fully validated.

Where research is conducted or funded by an interested group there is a possibility that conflict of interest produces biased findings, however this should be identifiable through considering aspects previously identified, such as who was surveyed and what questions were asked.

Personal views can also influence how valid research is perceived to be. People are more likely to accept a research finding that confirms their existing opinion than one that disputes it, without necessarily considering the validity of either study.

Also, statistics is the study of populations rather than individuals. People often have difficulty accepting the results of a study if it contradicts their personal experience.

Conclusion

Unless it is deliberately falsified, all data has some validity. Conversely, in social policy research no data will ever be perfect due to the constraints in dealing with people—it is not possible to collect all aspects that might be relevant in a particular circumstance, and as noted above, the accuracy of the data collected can be difficult to determine. However, considering factors such as sampling methodology, questionnaire design and the expertise of the researcher provides some guidance to the reliability of findings and their application.

Social policy decision makers will often be confronted with a situation where high quality data and analysis is not available, but decisions have to be made on the basis of what information there is. Awareness of the potential limitations of this information provides them with a better basis for such decisions.

Further reading

The National Statistical Service (NSS) Learning Hub has a wide range of information available to assist non-statisticians in understanding statistical concepts and terminology, including:

•        A guide to using statistics in evidence-based policy

•        Statistical language, which provides definitions of many statistical terms and

•        An Introduction to Sample Surveys: A User's Guide, which details a range of aspects of survey design and analysis.

Several publications provide non-technical discussions of some of the issues of the use of statistics in public discourse including:

•        The House of Commons Statistical Literacy Guide

•        Sense about Science and Straight Statistics: Making Sense of Statistics

•        Stat-spotting : a field guide to identifying dubious data by Joel Best, which is available to clients from the Parliamentary Library collection, and

•        Spurious correlations, which has many examples of high level correlations between obviously unrelated factors.

Parliamentary Library publications on statistics in specific social policy areas include:

•        What counts as welfare spending?

•        Migration to Australia: a quick guide to the statistics

•        Australia’s Humanitarian Program: a quick guide to the statistics since 1947

•        Measures of student achievement: a quick guide

•        How many abortions are there in Australia? A discussion of abortion statistics, their limitations, and options for improved statistical collection

•        Unemployment statistics: a quick guide

•        Long-term unemployment statistics: a quick guide

•        Youth unemployment statistics: a quick guide

•        Statistics on wages and gender: a quick guide.

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