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.
There is increasing concern about mental health issues among a range of different groups, as well as growing recognition of the fact that mental health is as important as physical health. These issues matter across society – including the health service, social care, workplaces, and of course families and individuals. In addition, better tools for diagnosis mean increasing numbers of people are being diagnosed with mental health disorders.
Evidence suggests that most adult mental health disorders start in childhood: Kim-Cohen et al (2003) found that 50% of all adult diagnoses were detectable before the age of 15, and 75% before 18. Therefore, by following people throughout their lives, longitudinal studies are uniquely placed to help us to better understand what factors during a person’s life might be contributing to mental health disorders and low levels of wellbeing later in life. They can also shed light on the impact this is having on other areas of their lives, and what policy interventions might help to reduce the impact on society and individuals.
Mental health vs. wellbeing
This page considers how longitudinal studies can help us to understand both mental health and wellbeing.
Mental health disorders can be medically diagnosed using a limited set of indicators. This contrasts with wellbeing, which is a less specific term. This reflects the fact that there can be much more in someone’s life that can contribute to that person’s wellbeing. For example, a range of factors including physical and mental health, education, work (or lack of), housing and social activities and may all contribute to a person’s low – or high – level of wellbeing.
Patalay and Fitzsimons (2016) considered the different factors contributing to mental health and wellbeing in children and concluded that they were largely distinct, although there were some factors that were indicators of both. This is illustrated in the graphic below.
The factors associated with mental illness and wellbeing
A quarter of girls and one in 10 boys are depressed at age 14, according to research at the UCL Institute of Education and the University of Liverpool, which analysed information on more than 10,000 children taking part in the Millennium Cohort Study. Read more.
By comparing data on more than 18,000 children from three national birth cohort studies, researchers found that childhood disadvantage is strongly associated with poorer adult mental wellbeing for Generation X. In contrast, Baby Boomers’ childhood background was not linked to their wellbeing in adulthood. A CLOSER-funded team from the MRC Unit for Lifelong Health and Ageing at UCL used data harmonisation to compare the 1970 British Cohort Study, 1958 National Child Development Study and 1946 National Survey of Health and Development. Read more.
New research using data from 8,000 parents and children taking part in the Avon Longitudinal Study of Parents and Children study found that the children of women with personality traits associated with emotional and relationship difficulties were at greater risk of depression, anxiety and self-harm in their late teens than their peers. Read more.
Using data from the British Household Panel Survey (the predecessor to Understanding Society) researchers found that the mental health and wellbeing of home purchasers experienced higher levels of stress and anxiety than private renters when housing became unaffordable. Read more.
People bullied frequently or even occasionally as children used more mental health services 39 years later than those who were not bullied, according to findings from the 1958 National Child Development Study. Read more.
A research team from the MRC Unit for Lifelong Health and Ageing at UCL examined whether cognition in childhood and adolescence was associated with psychiatric disease in later life. They found that people aged 53 who reported psychotic experiences such as hallucinations, or affective symptoms such as insomnia or anxiety, had lower verbal and non-verbal cognition at both the ages of 8 and 15.They used a sample of 2,384 people from the 1946 MRC National Survey of Health and Development. Read more.
People with a strong sense of neighbourhood belonging have better mental wellbeing, according to a research team that compared the experiences of adults in three longitudinal birth cohorts: the 1946 MRC National Survey of Health and Development, 1958 National Child Development Study and Hertfordshire Cohort Study. It analysed data from more than 10,000 men and women aged 50–76. It also found that the link between neighbourhood belonging and wellbeing was stronger for adults in the 1946 and Hertfordshire cohorts, who had average ages of 64 and 73 years respectively, compared to younger adults in the 1958 cohort study, with an average age of 51. Read more.
Research by What Works Wellbeing used Understanding Society data to examine gender differences in unemployment and wellbeing, beyond loss of income. It found that, on average, women’s life satisfaction is affected less by becoming unemployed compared to men. But this average gap concealed a range of different experiences: not all women suffer less than men when they lose their job. Read more.
Assessments of mental health and wellbeing typically involve asking study participants a set of questions that have been developed and thoroughly tested.
The Malaise Inventory was used in some of the older longitudinal studies to measure psychological distress. Researchers now use this data as a proxy for measuring mental health in the absence of any other more specific measure taken at the time.
However, other assessments have been developed more recently and are used in newer studies. The Warwick-Edinburgh Mental Wellbeing Scale is often used to measure wellbeing. For example, it was used by the research team considering the links between childhood disadvantage and the poorer wellbeing of Generation X.
Read more about the Malaise Inventory and the Warwick-Edinburgh Mental Wellbeing Scale in the Study Design module.
The Millennium Cohort Study used the short Mood and Feelings Questionnaire to ask participants at age 11 how happy they were with each of six different elements of their lives (school, family, friends, school work, appearance; and life as a whole). The questionnaire consists of a series of descriptive phrases asking how the respondent has been feeling or acting recently and is a screening tool for depression in children and young people aged 6−17 years. The participants were asked to rate their level of happiness with each of these on a 7-point scale ranging from ‘not at all happy’ to ‘very happy’.
Their parents were asked to complete a different assessment, the Strengths and Difficulties Questionnaire (SDQ) . This is a set of questions designed to assess emotional and behavioural difficulties among children and young people. The questions can be asked of parents or teachers about their child, or directly of the child. The responses from the parents and children were used in the research that concluded one in four girls is depressed at the age of 14.
Read more about the SDQ on the Youthinmind website.
Understanding Society uses a self-completion questionnaire to ask participants about their satisfaction in a number of aspects of their life – including their job, school and leisure time. There are two scales (e.g. the Rosenberg Self-Esteem Scale).">questionnaires: one for adults and one for young people aged 10-15 years.
Most information about mental health and wellbeing is collected through scales (e.g. the Rosenberg Self-Esteem Scale).">questionnaires. Study participants might complete the scales (e.g. the Rosenberg Self-Esteem Scale).">questionnaires/">scales (e.g. the Rosenberg Self-Esteem Scale).">questionnaires themselves, using a computer or pen and paper, or they might be asked the questions by an interviewer.
Studies need to take into account ethical issues when collecting this data. The questions must be phrased in a way that minimises impact on the participant of asking what could be very sensitive questions. For example, a medical professional asking similar questions is usually doing so face to face and will be able to act depending on the answers; a data collection team are not able to intervene in the same way.
Studies have confidentiality contracts with participants to help build trust and ensure that they answer the survey questions truthfully. Often people are more confident sharing their answers with the study team because they are one of many thousands of respondents rather than a named individual at their own medical consultation.
Study teams always consider how sensitive the questions are, and whether participants would be more or less open to discussing their experiences with an interviewer than they would be if they answered the questions on a self-completion questionnaire.
It is important to take into account who the respondent is as this may have implications for how the data is interpreted. While older studies asked participants’ parents whether their children were bullied, many newer studies, such as the Millennium Cohort Study, ask the children directly. And of course, some studies ask both parents and children – and sometimes teachers. You might be interested in looking at how children, parent and teachers’ reports differ.
If they are able to, studies link to medical records to complement the information collected from survey participants. For example, the Avon Longitudinal Study of Parents and Children study asked participants aged 16 questions about self-harm. It also looked at hospital self-harm records for those participants who had agreed to have their medical data linked to their study responses. The researchers could then compare what someone says about self-harm with medically recorded incidents of self-harm.
Find out more about how longitudinal studies are designed, including sampling and the value of different methods and modes of collecting information, in the Study design module.
There are a number of strengths of longitudinal studies that make them an ideal resource for studying mental health and wellbeing.
Tracking long-term consequences: 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).">Cross-sectional studies can tell us how many people are experiencing mental health disorders or low or high levels of wellbeing at a given point in time. They may also be able to differentiate between different groups, depending on how much other information they collect.
However, they are less effective at telling us what factors may have influenced an individual’s mental health − or what impact mental health disorders or lower levels of wellbeing experienced as a child has on the rest of that person’s life. Because longitudinal studies follow people over the course of their lives they can assess the early determinants of mental health disorders and wellbeing, and get closest to identifying these associations. Read more about the differences between longitudinal and cross-sectional studies in the Introduction to longitudinal studies module.
The long-term nature of longitudinal studies also enables them to assess changes and stability in mental health disorders and wellbeing, for example highlighting points of relapse and remission.
Prospective data collection: Individuals may find it hard or not want to accurately recall the state of their past mental health and wellbeing: mental health disorders or lower levels of wellbeing may be something people want to forget or put behind them. By asking people about their mental health at regular intervals during their lives longitudinal studies can capture how they are feeling at that point in time. Read more about prospective study vs retrospective study design in the Study design module.
Breadth of data available: Longitudinal studies have the added advantage of covering a wide range of different areas of life. People with mental health disorders or lower levels of wellbeing can often suffer from other problems, and it can be difficult to unpick the interplay between these factors and their mental health and wellbeing.
Although there may be other areas of people’s lives that have not been captured, longitudinal data cover significantly more areas than other data sources. This makes it possible for researchers to consider how different factors impact on an individual’s mental health and wellbeing.
Large sample sizes: Birth cohorts and household panels have the advantage of large enough sample sizes to identify particular groups that are at higher risk of low mental health or wellbeing. However, other studies are smaller more specific samples (such as prisoners, children in care) that can provide equally valuable long-term evidence. Read more about longitudinal samples in the Study design module.
Find out more about the strengths of longitudinal data in the Introduction to longitudinal studies module.
Researchers can face challenges using longitudinal data to study people’s mental health and wellbeing.
There are fewer measures of mental health in older studies, which didn’t ask about mental health disorders and wellbeing in as much detail as more recent ones. However, more recent research suggests it is important to understand how mental health and wellbeing develop as a person gets older – leading to studies such as the Millennium Cohort Study asking about mental health in the very early years.
Mental health disorders or low levels of wellbeing can be hard to admit: Like any survey asking people about their mental health disorders and wellbeing, longitudinal studies will struggle because some people don’t want to report their experiences, or may downplay their severity.
Attrition and 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.">missing data: Some study participants drop out over time, and this isn’t always random. This is known as attrition. It is also the case that some participants may choose not to answer every question at every sweep – which can lead to something called 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.">missing data. People with mental health disorders may also be more likely to drop out or choose not to answer. This leads to attrition bias.
There are analytical methods that researchers can use to deal with attrition bias and 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.">missing data. The teams running particular longitudinal studies can provide useful guidance about how best to deal with 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.">missing data from their study.
Timeliness: Determining the long-term effects of mental health disorders experienced by children requires us to wait until study participants have grown up. For example, we can see the longer-term effects of low levels of wellbeing during childhood for study participants born in the 1950s, but not (yet) for those born in the 2000s. When using data from older studies, it is important for researchers to consider how to relate their findings to generations growing up today.
Find out more about the challenges of longitudinal data in the Introduction to longitudinal studies module.
The Avon Longitudinal Study of Parents and Children study asked respondents (mothers and children) whether they have had any psychiatric problems, depression, and eating disorders such as anorexia and bulimia, as well as indicators of low levels of wellbeing. It also asked about children’s feeling about school, including whether they were happy, frightened or being bullied.
This survey asked pregnant women, about their mental wellbeing, during their pregnancy, such as feeling sad, tired, less self-confident and sleep patterns.
This national birth cohort study asked mothers for their opinion on their child’s personality and temperament. The questions covered elements including whether they thought their child was being bullied, had difficulty concentrating or were worried, miserable, irritable, or preferred to do things alone.
This regional birth cohort study has been following the lives of around 19,000 children born in the UK in 2000-01. Unlike the older studies it has collected information on the mental health and wellbeing of its participants from the start. This has provided valuable data for research into the mental health of the participants who are now teenagers.
Understanding Society is a panel survey of households with yearly interviews. Adult household members (age 16 or older) are interviewed and the same individuals are re-interviewed in successive years to see how things have changed. Children aged 10-15 years are asked to complete a short self-completion youth questionnaire. Adult study participants are asked questions about their general physical and mental health, life satisfaction, physical activity, sleep quality, smoking and alcohol consumption. Participants are also asked about their social networks, family relationships, friendships, and community interaction. Children are asked about exercise, nutrition, risky behaviours, social networks and their caring responsibilities.