Administrative data

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.


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.

Body mass index

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.

Cohort studies

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

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.


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).

Data harmonisation

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

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

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.

Dummy variables

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.

General ability

General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient (IQ) scores.


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

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.


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.

Longitudinal studies

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.

Non-response bias

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

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

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.


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.

Prospective study

In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.

Recall error or bias

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

Record linkage studies involve linking together administrative records (for example, benefit receipts or census records) for the same individuals over time.

Reference group

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.


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

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.

Retrospective study

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.

Sampling frame

A sampling frame is a list of the target population from which potential study participants can be selected.


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.

Study participants

Study participants are the individuals who are interviewed as part of a longitudinal study.

Survey weights

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.


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.

Target population

The population of people that the study team wants to research, and from which a sample will be drawn.

Tracing (or tracking)

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

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.


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).


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.

Learning Hub

Scarring effects of childhood bullying

Teenage Bullying

Is bullying just a part of growing up? Researchers have used longitudinal data to track the effects of childhood bullying well into adult life.

Key finding

Being bullied as a child is associated with a range of negative social, physical and mental health outcomes later in life, including depression, unemployment and lower life satisfaction.

About the research

Researchers from the Institute of Psychiatry at King’s College London analysed information on 7,771 people born across England, Scotland and Wales in 1958, who are being followed as part of the National Child Development Study.

Just over a quarter of the participants had been bullied occasionally at ages 7 and 11, and 15 per cent were bullied frequently – similar to rates in the UK today. The researchers then looked at a range of factors in adulthood, and found that the negative effects of bullying can be as damaging as other forms of childhood adversity, such as being put in care.

This study was funded by the British Academy and the Royal Society.

Research questions

  • Is being bullied in childhood associated with psychological distress, psychiatric diagnoses, general health, socioeconomic outcomes, social relationships, and wellbeing in adult life?
  • How do the effects of bullying on adult outcomes compare with the effects of other forms of childhood adversity?

Study used

National Child Development Study (1958 British birth cohort)

The 1958 British birth cohort study is following 17,000 people born across England, Scotland and Wales in a single week in 1958. The participants have been surveyed at birth and ages 7, 11, 16, 23, 33, 42, 45, 50 and 55. The next survey will take place in 2018, when the participants are 60 years old.

Data and definitions

Defining bullying

When the participants were aged 7 and 11, their parents were asked if their child was bullied by other children never, sometimes, or frequently.

The researchers combined the parents’ responses for both ages, and classified each child as having been ‘never bullied’ (not bullied at ages 7 or 11), ‘occasionally bullied’ (sometimes bullied at either age 7 or 11), ‘frequently bullied’ (frequently bullied at age 7 and/or 11, or sometimes bullied at both ages).

Outcomes of interest in adulthood

The researchers used ordinal logistic regression analyses to test associations between childhood bullying and adult outcomes, and to compare the effects of bullying to those of other childhood adversities. The adult outcomes they considered were:

• psychological distress (measured by the Malaise Inventory)
• depression and anxiety disorders (measured by the Revised Clinical Interview Schedule )
• suicidal thoughts or plans
• alcohol dependence (as measured by the World Health Organisation’s AUDIT)
• general adult health
• cognitive function
• highest educational qualifications
• partnership status
• employment status
• net pay
• how often they saw friends
• whether they felt they had friends, family or other social support they could call on if they were in need
• wellbeing and life satisfaction.

Childhood adversities

To separate the effect of bullying from other adversities, all the analyses controlled for childhood factors that may have been related to childhood and adult outcomes. These included:

• emotional and conduct problems at ages 7 and 11 (measured by the Bristol Social Adjustment Guides)
• childhood social class (determined by father’s occupation when the child was age 7)
• whether the person had experienced a range of childhood adversities, such as poverty, family conflict, drug and alcohol problems, abuse, low parental involvement, or whether the child has ever been in care by age 11.

The researchers only included participants in their analysis if there was complete information on their experiences of bullying at ages 7 and 11, and on their psychological distress at ages 23 and 50. They used STATA software to conduct their analyses.

Key findings

The findings showed a scarring effect of childhood bullying 40 years later. The impact of bullying was pervasive, touching all areas of adult life. The effect of bullying on adversity in adulthood were similar to the effect of being in care, or facing multiple family adversities.

Mental health

Victims of childhood bullying had higher rates of depression and psychological distress at ages 23 and 50 than those who were never bullied. Those who were bullied frequently while they were growing up had higher risks of anxiety and were more likely to have thought about suicide by age 45, compared with those who were never bullied.

The effects of childhood bullying on adults’ mental health remained even after taking into consideration related factors such as family social class, parenting and behavioural problems.

Effects of bullying on mental health

Education and employment

People who had been bullied tended to have less education and fewer qualifications by age 50 than those who were never bullied. Men who had been bullied were at greater risk of being unemployed at age 50, and those who were in work were more likely to earn less than their peers.


By age 50, victims of childhood bullying were less likely to be living with a partner or have good social support from friends and family than those who had never been bullied. They were also less likely to report being satisfied with their lives.

Alcohol use

Interestingly, being bullied as a child was not related to alcohol problems in adulthood. The researchers speculated that this could be because alcohol problems often start in the teenage years and are often influenced by peer groups. Victims of bullying may not be as exposed to this given the difficulties they have faced with other young people of the same age.

Advantages and challenges of using longitudinal data to study bullying

Understanding the long-term effects of bullying can be tricky as few studies with measures of bullying in childhood have traced participants to adult life. To understand the long-term effects of bullying, researchers need information on the same people from a very young age, all the way through to adulthood. Only longitudinal data can provide this information on the same people over time.

Longitudinal studies have the added advantage of covering a wide range of different areas of life. Victims of bullying often suffer from other problems, and it can be very difficult to unpick the impact of bullying alone. In this example, the 1958 cohort data allowed the researchers to control for a huge range of related factors throughout the life course. Of course, it is always possible that there are other influences that have not been captured, but longitudinal data capture significantly more than most other data sources.

However, there are several limitations of longitudinal data that should be considered when studying bullying.

First, the parents of the 1958 cohort were not given a definition of what bullying was, so one parent’s idea of what constitutes bullying will have differed to another’s. More recent longitudinal studies have improved how bullying is captured, by asking parents and children about specific behaviours, for example ‘how often do other children at school call you names?’ More recent longitudinal studies have also included questions about whether the study members bully other children, and whether they are involved in bullying among their siblings.

In this example, the researchers compared the prevalence of bullying among the 1958 cohort to children today, and found little difference. This suggests that there has not been a significant shift in how people think about bullying.

Second, the 1958 cohort participants were asked at age 50 if they had experienced psychiatric problems in the past week, so those who have had problems in the past, but not in the previous week, would not be included. As a result, it is likely that the researchers’ findings are a conservative estimate of the effects of bullying on this aspect of mental health in mid-life.

It is often helpful to know the exact wording of questions in a longitudinal survey, which you can review using CLOSER Discovery.

Implications for policy and practice

“We need to move away from any perception that bullying is just an inevitable part of growing up. Teachers, parents and policymakers should be aware that what happens in the school playground can have long-term repercussions for children. Programmes to stop bullying are extremely important, but we also need to focus our efforts on early intervention to prevent potential problems persisting into adolescence and adulthood.”

— Prof Louise Arseneault, King’s College London

It is clear from these findings that bullying is not just a part of growing up, but a significant disadvantage in childhood that can have life-long consequences. Policies and interventions should not only aim to prevent bullying, but should also aim to improve poor mental health among victims of bullying. This may help prevent problems persisting into adulthood.

Access the paper

Takizawa, R., Maughan, B. and Arseneault, L. (2014) Adult health outcomes of childhood bullying victimization: Evidence from a 5-decade longitudinal British birth cohort. American Journal of Psychiatry 171(7), pp. 777-784.

Discussion topics

  • What are the potential mechanisms through which childhood bullying could influence adult mental health?
    How would you structure a model to test this?
  • What are the limitations of the questions asked in the 1958 cohort about bullying? [Prompts: limitations of quantitative vs qualitative measures of bullying, wording of the question, position of the question within the questionnaire, issues with asking the parent vs the child about bullying]
  • How could policymakers and people working with children act on these findings? What other evidence would they need?