Administrative data is the term used to describe everyday data about individuals collected by government departments and agencies. Examples include exam results, benefit receipt and National Insurance payments.
Age effects relates to changes in an outcome as a result of getting older.
Anonymisation refers to the removal of study participants’ identifying information (e.g., name, address) in order to preserve their privacy.
Attrition is the discontinued participation of study participants in a longitudinal study. Attrition can reflect a range of factors, from the study participant not being traceable to them choosing not to take part when contacted. Attrition is problematic both because it can lead to bias in the study findings (if the attrition is higher among some groups than others) and because it reduces the size of the sample.
Baseline refers to the start of a study when initial information is collected on participation (however, in longitudinal studies, researchers may adopt an alternative ‘baseline’ for the purposes of analysis).
Biological samples is the term used for specimens collected from human subjects from which biological information, such as genetic markers, can be extracted for analysis. Common examples include blood, saliva or hair.
Body mass index is a measure used to assess if an individual is a healthy weight for their height. It is calculated by dividing the individual’s weight by the square of their height, and it is typically represented in units of kg/m2.
Boosted samples are used to overcome sample bias due to attrition or to supplement the representation of smaller sub-groups within the sample. Inclusion of boosted samples must be accompanied by appropriate survey weights.
Computer-assisted personal interviewing (CAPI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a face-to-face interview.
Computer-assisted self-interviewing (CASI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a self-completion questionnaire.
A categorical variable is a variable that can take one of a limited number of discrete values. They can be either nominal – they contain no inherent order of categories (e.g. sex; marital status) – or ordinal – they can be ranked in some meaningful order (e.g. level of satisfaction with a service).
Computer-assisted telephone interviewing (CATI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a telephone interview.
For some study participants the exact time of an event will not be known because either: the study ends (or the analysis is carried out) before they have had the event, or the participant drops out of the study before experiencing the event. It is therefore, only known that the event has not occurred up to the time that they were last observed in the study.
Census refers to a universal and systematic collection of data from all individuals within a population. In the UK, the government conducts a census every ten years with the next one due in 2021.
A codebook is a document (online or hard-copy) that contains all the information about how a dataset has been coded, such that it can be deciphered by a researcher not familiar with the original coding frame.
Coding is the process of converting survey responses into numerical codes to facilitate data analysis. All potential responses (as well as possible reasons for non-response) for each variable are assigned numerical values according to a coding frame.
Cognitive assessments are exercises used to measure thinking abilities, such as memory, reasoning and language. Longitudinal studies collecting data in this way can track the extent to which someone’s cognitive abilities change (develop or decline) over time.
Cohort studies are concerned with charting the lives of groups of individuals who experience the same life events within a given time period. The best known examples are birth cohort studies, which follow a group of people born in a particular period.
Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we have no missing data on the variables of interest. Participants with any missing data are excluded.
Conditioning refers to the process whereby participants’ answers to some questions may be influenced by their participation in the study – in other words, their responses are ‘conditioned’ by their being members of a longitudinal study. Examples would include study respondents answering questions differently or even behaving differently as a result of their participation in the study.
Confounding occurs where the relationship between independent and dependent variables is distorted by one or more additional, and sometimes unmeasured, variables. A confounding variable must be associated with both the independent and dependent variables but must not be an intermediate step in the relationship between the two (i.e. not on the causal pathway).
For example, we know that physical exercise (an independent variable) can reduce a person’s risk of cardiovascular disease (a dependent variable). We can say that age is a confounder of that relationship as it is associated with, but not caused by, physical activity and is also associated with coronary health. See also ‘unobserved heterogeneity’, below.
A continuous variable is a variable that has an infinite number of uncountable values e.g. time, temperature. They are also known as quantitative variables or scale variables.
Cohort effects relates to changes in an outcome associated with being a member of a specific cohort of people (e.g. born in the same year; or starting school at the same time).
In metadata management, coverage refers to the temporal, spatial and topical aspects of the data collection to describe the comprehensiveness of a dataset. For longitudinal studies, this can relate to the topics that are covered across waves, the population to which one can generalise or the geographic extent of the dataset.
Cross-sectional surveys involve interviewing a fresh sample of people each time they are carried out. Some cross-sectional studies are repeated regularly and can include a large number of repeat questions (questions asked on each survey round).
Within the context of data protection, a data access agreement specifies the terms under which users are provided access to specified datasets. This usually forms part of the application process to the data controller to ensure that researchers adhere to a set of terms regarding data confidentiality, sensitivity and dissemination before accessing the data. See also: research ethics
Data cleaning is an important preliminary step in the data analysis process and involves preparing a dataset so that it can be correctly analysed. ‘Cleaning’ the data usually involves identifying data input errors, assessing the completeness of the dataset and verifying any anomalies (e.g. outliers).
Within the context of data protection, data confidentiality is the process of protecting participants’ data from being accessed or disclosed by those unauthorised to do so. Key methods employed in data confidentiality include anonymisation of responses (removal of personal identifying information) and data encryption (protecting the data using codes and/or passwords).
Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected. This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.
Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches.
Data linkage simply means connecting two or more sources of administrative, educational, geographic, health or survey data relating to the same individual for research and statistical purposes. For example, linking housing or income data to exam results data could be used to investigate the impact of socioeconomic factors on educational outcomes.
Data protection refers to the broad suite of rules governing the handling and access of information about people. Data protection principles include confidentiality of responses, informed consent of participants and security of data access. These principles are legally protected by the Data Protection Act (DPA) and the General Data Protection Regulation (GDPR).
Data structure refers to the way in which data are organised and formatting in advance of data analysis.
In analysis, the dependent variable is the variable you expect to change in response to different values of your independent (or predictor) variables. For example, a students’ test results may be (partially) explained by the number of hours spent on revision. In this case, the dependent variable is students’ test score, which you expect to be different according to the amount of time spent revising.
A derived variable is a variable that is calculated from the values of other variables and not asked directly of the participants. It can involve a mathematical calculation (e.g. deriving monthly income from annual income by dividing by 12) or a recategorisation of one or more existing variables (e.g. categorising monthly income into £500 bands – £0 to £500, £501 to £1,000, etc.)
Diaries are a data collection instrument that is particularly useful in recording information about time use or other regular activity, such as food intake. They have the benefit of collecting data from participants as and when an activity occurs. As such, they can minimise recall bias and provide a more accurate record of activities than a retrospective interview.
Dissemination is the process of sharing information – particularly research findings – to other researchers, stakeholders, policy makers, and practitioners through various avenues and channels, including online, written publications and events. Dissemination is a planned process that involves consideration of target audiences in ways that will facilitate research uptake in decision-making processes and practice.
Dummy variables, also called indicator variables, are sets of dichotomous (two-category) variables we create to enable subgroup comparisons when we are analysing a categorical variable with three or more categories.
Empirical data refers to data collected through observation or experimentation. Analysis of empirical data can provide evidence for how a theory or assumption works in practice.
In metadata management, fields are the elements of a database which describes the attributes of items of data.
General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient (IQ) scores.
Growth curve modelling is used to analyse trajectories of longitudinal change over time allowing us to model the way participants change over time, and then to explore what characteristics or circumstances influence these patterns of longitudinal change.
Hazard rate refers to the probability that an event of interest occurs at a given time point, given that it has not occurred before.
Health assessments refers to the assessments carried out on research participants in relation to their physical characteristics or function. These can include measurements of height and weight, blood pressure or lung function.
Heterogeneity is a term that refers to differences, most commonly differences in characteristics between study participants or samples. It is the opposite of homogeneity, which is the term used when participants share the same characteristics. Where there are differences between study designs, this is sometimes referred to as methodological heterogeneity. Both participant or methodological differences can cause divergences between the findings of individual studies and if these are greater than chance alone, we call this statistical heterogeneity. See also: unobserved heterogeneity.
Household panel surveys collect information about the whole household at each wave of data collection, to allow individuals to be viewed in the context of their overall household. To remain representative of the population of households as a whole, studies will typically have rules governing how new entrants to the household are added to the study.
As a way of encouraging participants to take part in research, they may be offered an incentive or a reward. These may be monetary or, more commonly, non-monetary vouchers or tokens. Incentives are advertised beforehand and can act as an aid to recruitment; rewards are a token of gratitude to the participants for giving their time.
In analysis, an independent variable is any factor that may be associated with an outcome or dependent variable. For example, the number of hours a student spends on revision may influence their test result. In this case, the independent variable, revision time (at least partially) ‘explains’ the outcome of the test.
A key principle of research ethics, informed consent refers to the process of providing full details of the research to participants so that they are sufficiently able to choose whether or not to consent to taking part.
Kurtosis is sometimes described as a measure of ‘tailedness’. It is a characteristic of the distribution of observations on a variable and denotes the heaviness of the distribution’s tails. To put it another way, it is a measure of how thin or fat the lower and upper ends of a distribution are.
A person’s life course refers to the experiences and stages an individual passes through during their life. It centres on the individual and emphasises the changing social and contextual processes that influence their life over time.
Longitudinal studies gather data about the same individuals (‘study participants’) repeatedly over a period of time, in some cases from birth until old age. Many longitudinal studies focus upon individuals, but some look at whole households or organisations.
Metadata refers to data about data, which provides the contextual information that allows you to interpret what data mean.
Missing data refers to values that are missing and do not appear in a dataset. This may be due to item non-response, participant drop-out (or attrition) or, in longitudinal studies, some data (e.g. date of birth) may be collected only in some waves. Large amounts of missing data can be a problem and lead researchers to make erroneous inferences from their analysis. There are several ways to deal with the issue of missing data, from casewise deletion to complex multiple imputation models.
Multi-level modelling refers to statistical techniques used to analyse data that is structured in a hierarchical or nested way. For example. study participants who belong to the same household, or students who attend the same school may be expected to be more similar to each other than to participants in other households or schools (such as sharing similar contextual influences). This similarity means that the data from participants within these households/schools are not independent. Multi-level models can account for variability at both the individual level and the group (e.g. household or school) level.
Non-response bias is a type of bias introduced when those who participate in a study differ to those who do not in a way that is not random (for example, if attrition rates are particularly high among certain sub-groups). Non-random attrition over time can mean that the sample no longer remains representative of the original population being studied. Two approaches are typically adopted to deal with this type of missing data: weighting survey responses to re-balance the sample, and imputing values for the missing information.
Observational studies focus on observing the characteristics of a particular sample without attempting to influence any aspects of the participants’ lives. They can be contrasted with experimental studies, which apply a specific ‘treatment’ to some participants in order to understand its effect.
Panel studies follow the same individuals over time. They vary considerably in scope and scale. Examples include online opinion panels and short-term studies whereby people are followed up once or twice after an initial interview.
Peer review is a method of quality control in the process of academic publishing, whereby research is appraised (usually anonymously) by one or more independent academic with expertise in the subject.
Period effects relate to changes in an outcome associated with living during a particular time, regardless of age or cohort membership (e.g. living through times of war, economic recession or global pandemic).
Piloting is the process of testing your research instruments and procedures to identify potential problems or issues before implementing them in the full study. A pilot study is usually conducted on a small subset of eligible participants who are encouraged to provide feedback on the length, comprehensibility and format of the process and to highlight any other potential issues.
Population refers to all the people of interest to the study and to whom the findings will be able to be generalized (e.g. a study looking into rates of recidivism may have a [target] population encompassing everyone with a criminal conviction). Owing to the size of the population, a study will usually select a sample from which to make inferences. See also: sample, representiveness.
A percentile is a measure that allows us to explore the distribution of data on a variable. It denotes the percentage of individuals or observations that fall below a specified value on a variable. The value that splits the number of observations evenly, i.e. 50% of the observations on a variable fall below this value and 50% above, is called the 50th percentile or more commonly, the median.
Primary research refers to original research undertaken by researchers collecting new data. It has the benefit that researchers can design the study to answer specific questions and hypotheses rather than relying on data collected for similar but not necessarily identical purposes. See also: secondary research
In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.
Qualitative data are non-numeric – typically textual, audio or visual. Qualitative data are collected through interviews, focus groups or participant observation. Qualitative data are often analysed thematically to identify patterns of behaviour and attitudes that may be highly context-specific.
Quantitative data can be counted, measured and expressed numerically. They are collected through measurement or by administering structured questionnaires. Quantitative data can be analysed using statistical techniques to test hypotheses and make inferences to a population.
Questionnaires are research instruments used to elicit information from participants in a structured way. They might be administered by an interviewer (either face-to-face or over the phone), or completed by the participants on their own (either online or using a paper questionnaire. Questions can cover a wide range of topics and often include previously-validated instruments and scales (e.g. the Rosenberg Self-Esteem Scale).
Recall error or bias describes the errors that can occur when study participants are asked to recall events or experiences from the past. It can take a number of forms – participants might completely forget something happened, or misremember aspects of it, such as when it happened, how long it lasted, or other details. Certain questions are more susceptible to recall bias than others. For example, it is usually easy for a person to accurately recall the date they got married, but it is much harder to accurately recall how much they earned in a particular job, or how their mood at a particular time.
Record linkage studies involve linking together administrative records (for example, benefit receipts or census records) for the same individuals over time.
A reference group is a category on a categorical variable to which we compare other values. It is a term that is commonly used in the context of regression analyses in which categorical variables are being modelled.
Regression analysis refers to analytical techniques that use a mathematical ‘model’ to predict values of a dependent variable from values of one or many independent variable(s).
Repeated measures are measurements of the same variable at multiple time points on the same participants, allowing researchers to study change over time.
Representativeness is the extent to which a sample is representative of the population from which it is selected. Representative samples can be achieved through, for example, random sampling, systematic sampling, stratified sampling or cluster sampling.
Research ethics relates to the fundamental codes of practice associated with conducting research. Ethical issues that need to be considered include providing informed consent to participants, non-disclosure of sensitive information, confidentiality and anonymity safeguarding of vulnerable groups, and respect for participants’ well-being. Academic research proposals need be approved by an ethics committee before any actual research (either primary or secondary) can begin.
Research impact is the demonstrable contribution that research makes to society and the economy that can be realised through engagement with other researchers and academics, policy makers, stakeholders and members of the general public. It includes influencing policy development, improving practice or service provision, or advancing skills and techniques.
Residuals are the difference between your observed values (the constant and predictors in the model) and expected values (the error), i.e. the distance of the actual value from the estimated value on the regression line.
Respondent burden is a catch all phrase that describes the perceived burden faced by participants as a result of their being involved in a study. It could include time spent taking part in the interview and inconvenience this may cause, as well as any difficulties faced as a result of the content of the interview.
Response rate refers to the proportion of participants in the target sample who completed the survey. Longitudinal surveys are designed with the expectation that response rates will decline over time so will typically seek to recruit a large initial sample in order to compensate for likely attrition of participants.
In retrospective studies, individuals are sampled and information is collected about their past. This might be through interviews in which participants are asked to recall important events, or by identifying relevant administrative data to fill in information on past events and circumstances.
Sample is a subset of a population that is used to represent the population as a whole. This reflects the fact that it is often not practical or necessary to survey every member of a particular population. In the case of birth cohort studies, the larger ‘population’ from which the sample is drawn comprises those born in a particular period. In the case of a household panel study like Understanding Society, the larger population from which the sample was drawn comprised all residential addresses in the UK.
Sample size refers to the number of data units contained within a dataset. It most frequently refers to the number of respondents who took part in your study and for whom there is usable data. However, it could also relate to households, countries or other institutions. The size of a sample, relative to the size of the population, will have consequences for analysis: the larger a sample is, the smaller the margin of error of its estimates, the more reliable the results of the analysis and the greater statistical power of the study.
A sampling frame is a list of the target population from which potential study participants can be selected.
Scales are frequently used as part of a research instrument seeking to measure specific concepts in a uniform and replicable way. Typically, they are composed of multiple items that are aggregated into one or more composite scores. Examples of standardised scales include the British Ability Scale (BAS); the Malaise Inventory; and the Rosenberg Self-Esteem Scale.
A scatterplot is a way of visualising the relationship between two continuous variables by plotting the value of each associated with a single case on a set of X-Y coordinates. For example, students’ test scores in English and maths can be represented as point on a graph, with each point representing a single student’s English (x-axis) and maths (y-axis) score. Looking at data for many students allows us to build up a visualisation of the relationship between students’ scores in maths and English.
Secondary research refers to new research undertaken using data previously collected by others. It has the benefit of being more cost-effective than primary research whilst still providing important insights into research questions under investigation.
Skewness is the measure of how assymetrical the distribution of observations are on a variable. If the distribution has a more pronounced/longer tail at the upper end of the distribution (right-hand side), we say that the distribution is negatively skewed. If it is more pronounced/longer at the lower end (left-hand side), we say that it is positively skewed.
A statistical model is a mathematical representation of the relationship between variables.
Statistical software packages are specifically designed to carry out statistical analysis; these can either be open-source (e.g. R) or available through institutional or individual subscription (e.g. SPSS; Stata).
Structured metadata define the relationship between data items/objects to enable computer systems to understand the contextual meaning of the data. It uses standardised content to facilitate the use of metadata for data discovery and sharing, and the relationship between metadata elements.
Study participants are the individuals who are interviewed as part of a longitudinal study.
Also called conditional routing (sometimes called ‘filters’), survey logic refers to the flow that takes respondents through a survey. Respondents may be required to answer some questions only if they had provided a relevant response to a previous question. E.g. Only respondents who are currently at university may be asked to answer a question relating to their degree subject. This is important when considering missing data.
Survey weights can be used to adjust a survey sample so it is representative of the survey population as a whole. They may be used to reduce the impact of attrition on the sample, or to correct for certain groups being over-sampled.
Survival analysis is an analytical technique that uses time-to-event data to statistically model the probability of experiencing an event by a given time point. For example, time to retirement, disease onset or length of periods of unemployment.
The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 sweep of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term wave often has the same meaning.
The population of people that the study team wants to research, and from which a sample will be drawn.
Time to event refers to the duration of time (e.g. in hours, days, months, etc.) from a defined baseline to the time of occurrence of an event of interest (e.g. diagnosis of an illness, first re-offence following release from prison). Survival analysis can be used to analyse such data.
Tracing (or tracking) describes the process by which study teams attempt to locate participants who have moved from the address at which they were last interviewed.
Unobserved heterogeneity is a term that describes the existence of unmeasured (unobserved) differences between study participants or samples that are associated with the (observed) variables of interest. The existence of unobserved variables means that statistical findings based on the observed data may be incorrect.
Part of the documentation that is usually provided with statistical datasets, user guides are an invaluable resource for researchers. The guides contain information about the study, including the sample, data collection procedures, and data processing. Use guides may also provide information about how to analyse the data, whether there are missing data due to survey logic, and advice on how to analyse the data such the application of survey weights.
Variables is the term that tends to be used to describe data items within a dataset. So, for example, a questionnaire might collect information about a participant’s job (its title, whether it involves any supervision, the type of organisation they work for and so on). This information would then be coded using a code-frame and the results made available in the dataset in the form of a variable about occupation. In data analysis variables can be described as ‘dependent’ and ‘independent’, with the dependent variable being a particular outcome of interest (for example, high attainment at school) and the independent variables being the variables that might have a bearing on this outcome (for example, parental education, gender and so on).
Vulnerable groups refers to research participants who may be particularly susceptible to risk or harm as a result of the research process. Different groups might be considered vulnerable in different settings. The term can encompass children and minors, adults with learning difficulties, refugees, the elderly and infirm, economically disadvantaged people, or those in institutional care. Additional consideration and mitigation of potential risk is usually required before research is carried out with vulnerable groups.
The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 wave of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term sweep often has the same meaning.
Those who identified as heterosexual reported better health outcomes than other groups while those who identified as bisexual reported the worst health outcomes.
Researchers from the Institute for Social and Economic Research at the University of Essex analysed the responses of over 40,000 individuals from the UK Household Longitudinal Study (Understanding Society) to investigate health inequalities between members of sexual minority groups and heterosexuals.
Specifically, the research analysed data from the second and third waves (data collected between 2010 and 2012) of the longitudinal panel study, focusing on different aspects of health including physical and mental functioning, psychological distress, self-rated health, health behaviours and substance use.
Overall, heterosexuals reported better health outcomes than other groups under study, whilst bisexuals reported the worst health outcomes. Gays/lesbians reported poorer health than heterosexuals as well, but specifically with regards to mental functioning, distress and illness status.
Following 40,689 individuals from UK households from across the UK. In this study, researchers included data from two waves of the survey (2010-12), and included subjects aged 16 and above.
The measure of sexual orientation was derived from an item in the survey questionnaire - “Which of the following options best describes how you think of yourself?” Responses were “heterosexual or straight”, “gay or lesbian” (GL), “bisexual”, “other” and “prefer not to say” (PNS). Therefore, the analysis was undertaken in reference to five groups: heterosexuals, gay/lesbians, bisexuals, other, and PNS (the last four groups being referred to as sexual minorities).
The vast majority of individuals identified as heterosexual (94%, n = 38,073), with a total of 1% gay/lesbians (n = 482), 1% bisexuals (n = 407), 1% other (n = 426) and 3% PNS (n = 1,301).
Physical health comprised three different measures – (i) self-rated health (SRH), measured using a question with responses ranging from “excellent” to “poor” on a 5-point Likert scale; (ii) illness status, self-reported and categorised into three groups: “no illness”, “non-limiting long-standing illness” (NLLSI) and “limiting long-standing illness” (LLSI); (iii) physical functioning, as measured by the physical component score (PCS) in the SF-12, a 12-Item Short Form Health Survey that assesses the impact of health on an individual's everyday life (e.g. “Are you accomplishing less than you would like due to your health?”). This was scored to have a range of 0-100 with a mean of 50 and a standard deviation of 10.
This was measured using four indicators: (i) mental functioning, using the SF-12 mental component score (MCS), which includes items, such as “Have you felt downhearted and blue in the past week?” As with the PCS, this was scored to range from 0 to 100 with a mean of 50 and standard deviation of 10; (ii) Psychological distress, measured using the General Health Questionnaire (GHQ-12), a measure of generalized psychiatric morbidity (e.g. “Have you recently been thinking of yourself as a worthless person?”). This employs a 4-point Linkert scale to produce a total score with a range of 0–36. In addition, (iii) health satisfaction and (iv) overall life satisfaction were assessed with two additional self-rated questions using a 7-point Likert scale.
For the main survey respondents, (i) smoking status (“Never a smoker”, “Former smoker” and “Current smoker”) and (ii) alcohol consumption group (5 groups, ranging from “Once a week or more” to “Never had a drink”) were derived from questions in the self-report survey.
Additional indicators on substance use were collected from participants aged 16 to 21 and included questions on smoking history, alcohol consumption and drug use (i.e. cannabis, solvent and other).
The findings of the analysis confirm that although there were differences among groups depending on the measure of health used, heterosexual respondents reported relatively better health outcomes compared to the other groups under study, whilst bisexual respondents reported the worst outcomes.
More than half of the heterosexual and gay/lesbian participants reported being in good physical health (i.e. “very good” or “good”), whereas only 44% of bisexuals, other and PNS rated their health as such. At the same time, only 19% of heterosexuals reported having poor physical health compared to 21-28% for sexual minorities.
Bisexual and PNS participants had lower physical functioning compared to heterosexuals, whereas for mental functioning, all sexual minorities suffered more than heterosexuals. Bisexuals and others also reported less psychological distress than heterosexuals, however, these groups also reported lower health satisfaction and overall life satisfaction.
Compared to heterosexuals, gay/lesbians and bisexuals were between 1.6 and 2.2 times more likely to report having a limiting long-standing illness. Gay/lesbian respondents were also 1.6 times more likely to report a non-limiting long-standing illness.
In terms of smoking status, while gay/lesbian and bisexual respondents were twice as likely to be current smokers compared to heterosexuals, PNS were more likely to have never smoked. With regards to alcohol consumption, PNS participants were most likely of all groups to never have had an alcoholic drink or to not have drunk alcohol in the past 12 months, whereas heterosexuals and gay/lesbians were the most likely to have a drink regularly (once a week or more).
The substance-use rates for younger responders showed that over 20% of gay/lesbians, bisexuals and others reported having smoked cannabis in the past year compared to 16% of heterosexual and 14% of PNS young people. Furthermore, sexual minorities were more likely to report using other drugs compared to heterosexual and PNS young people, and over 10% of bisexual and other participants also reported using drugs five or more times in the past year.
This study uses a nationally representative dataset and was one of the first to include respondents who identified as “other” or who preferred not to disclose their sexual identity. Considering its findings, this research highlights the need for sexual inclusivity in health research, especially when considering implications for potential healthcare costs. Another strength of the study lies its design. For instance, sexual orientation questions were introduced in the self-completion portion of the UKHLS rather than face-to-face interviews, which likely led to more accurate reporting of being part of a sexual minority.
Nonetheless, there are some key methodological and logistical challenges to bear in mind with this research. First, the data come from only two waves of the Understanding Society survey, which is analysed cross-sectionally, rather than longitudinally (i.e. following changes over time). Given the novelty of sexual orientation identification in research, this should be considered more as a stepping-stone to future research rather than a limitation. Second, there is an issue with disclosure of sexual orientation – not only might this be more difficult for some ethnic or religious groups, but sexual orientation can be fluid and change over time. This again points to the salience of longitudinal analysis of data on sexual minorities’ health outcomes. Moreover, the study did not include all types of sexual orientation (e.g. asexual), with some groups being excluded or subsumed into a diverse “other” category. Finally, self-rated health as reported in surveys may not be comparable across different population subgroups. For example, research has shown that women tend to report lower health compared to men (Boerma et al., 2016) and that self-rated health is more strongly associated with mortality in men compared to women (Spiers et al., 2003).
The findings of this research indicate that sexual minority groups tend to report worse mental and/or physical health outcomes compared to heterosexuals. By identifying which sexual minorities are most at risk, the study allows for health providers to deliver better and more specialised treatments to those most in need. Therefore, the study provides strong grounds for sexual orientation-specific interventions to be investigated further.
Given that it is still relatively unexplored, the research raises awareness on the matter of sexual orientation and health inequality. Furthermore, this study can act as a stepping-stone for future research, as not only does it provide effective methods for further studies to follow, but it also highlights challenges that should be addressed in the future. For instance, researchers might consider using more flexible measures of sexual orientation and ask participants about their sexual identity across several waves of the study, which would allow for a longitudinal exploration of the relationship between sexuality and indicators of mental and physical health.
Booker, C. L., Rieger, G., & Unger, J. B. (2017). Sexual orientation health inequality: evidence from Understanding Society, the UK Longitudinal Household Study. Preventive Medicine, 101, 126-132. https://www.sciencedirect.com/science/article/pii/S0091743517302141
Cohen, M.-E., Kaye, N. & Fitzsimons, E. (2022). Research Case Studies: Sexual orientation & health inequality. CLOSER Learning Hub, London, UK: CLOSER.