Glossary

Glossary

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

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.

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.

Conditioning

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

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

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 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.

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.

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.

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.

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

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.

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.

Sweep

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 from econometrics that describes the existence of variables about an individual that have not been measured (unobserved) but 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

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).

Wave

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.

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Longitudinal vs cross-sectional studies

Longitudinal studies differ from one-off, or cross-sectional, studies. The main difference is that cross-sectional studies interview a fresh sample of people each time they are carried out, whereas longitudinal studies follow the same sample of people over time.

Features of longitudinal vs cross-sectional studies

Download this table as a PDF handout

Some cross-sectional studies take place regularly, each time including a large number of repeat questions. For example, the British Social Attitudes Survey is a repeat cross-sectional study that has been carried out nearly every year since 1983. It provides excellent data about how Britain’s attitudes and values have changed (or not changed) over time.

Repeating the same questions in each round allows researchers to look at how society as a whole has changed over time. But because the questions are asked of a new sample every time, these studies can only reveal change at an aggregate level – they can shed little light on who has changed, or how or why.

For example, the 2015 British Social Attitudes (PDF) survey found that 66 per cent of people thought that “it’s everybody’s duty to vote” in a general election, down from 76 per cent in 1987.

What do these findings tell us? The data clearly show us that, overall, fewer people now than in the late-1980s think that citizens have a duty to vote. We can look at the characteristics of those who do or don’t agree with this view, and how the profile of these groups had changed over time. We can also examine how the likelihood of thinking that voting is a duty has changed among different population groups (for example, different age groups or ethnicities).

These sorts of calculations would provide some very helpful insights. But there are many things that this kind of cross-sectional data cannot tell us, but which longitudinal data would help us to address. For example:

  • Which individuals changed their views about voting over the period? What are their characteristics? Do some people switch, and then switch back again?
  • The British Social Attitudes findings show us the net or aggregate change over time – the difference between the proportion who thought voting was a duty in 1987 and the equivalent proportion now. But this tells us nothing about change at an individual level. For example, what proportion became more inclined to think voting is a duty and what proportion became less inclined? It is important to bear in mind that if these two proportions are similar, they could cancel one another out at a net level and make it appear that very little has changed over all, even though substantial numbers of people have changed their views.
  • What factors best explain the transition away from thinking voting is a duty?

Sometimes data from longitudinal studies is analysed cross-sectionally. This means that the researcher is just focusing on the information collected at one round of the study, and not linking that information to data from earlier or later rounds.