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 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 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.
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 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.
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 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 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 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 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.
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.
In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.
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.
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.
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.
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 are the individuals who are interviewed as part of a longitudinal study.
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.
The population of people that the study team wants to research, and from which a sample will be drawn.
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.
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.
Children born since 1990 are up to three times more likely than older generations to be overweight or obese by age 10.
Researchers from CLOSER analysed information on the height, weight and body mass index (BMI) of 56,632 people born in the UK from 1946 to 2001, who are being followed by the British birth cohort studies.
Children born since 1990 are up to three times more likely than older generations to be overweight or obese by age 10. This research is the first to track weight gain across multiple generations through much of their lives – from age 2 to 64 for the oldest participants.
Following 5,000 people born in England, Scotland and Wales in a single week of March 1946
Following 17,000 people born across England, Scotland and Wales in a single week of March 1958
Following 17,000 people born across England, Scotland and Wales in a single week of April 1970
Following 14,000 people born in the former Avon region of England in 1991-1992
Following 19,000 people born across the UK in 2000-2001
BMI was determined from the measures of participants’ height (in centimetres) and weight (in kilograms). This is called a derived variable.
The researchers used the Lambda-Mu-Sigma method to summarise the distribution of BMI at each age by sex, study, and life stage (childhood [age 2-18] and adulthood [age 20-64]). For more explanation of this method, access the paper.
To ensure that the samples from different studies were comparable, the researchers had to exclude groups that are known to have particularly different BMIs. As a result, the researchers made the decision to exclude: immigrants who were added to some studies at later waves, ethnic minority groups (non-white), twins and triplets, and any participants who died in early childhood.
The researchers then classified the participants as thin, normal weight, overweight or obese. For childhood, the weight classifications were defined according to the International Obesity Task Force cut-offs. In adulthood, the standard cut-offs used were 18.5 (thin), 25 (overweight) and 30 (obese).
Next, the researchers conducted a series of statistical analyses, looking at how the risk of being overweight/obese changed as participants aged. These analyses were performed using a technique known as multilevel logistic regression, and the analyses were performed separately for each sex and study to help account for possible differences between these groups.
The findings showed that children born since 1990 are up to three times more likely than older generations to be overweight or obese by age 10. One in ten children born in 1946 were overweight or obese by age 11, compared to roughly one in four 11-year-olds today.
While childhood obesity is more prevalent among more recent generations, the majority of today’s children are still a normal weight.
Since 1946, every generation has been heavier than the previous one – and it is the most overweight people who are becoming even heavier. The heaviest 2 per cent of people born in 1946 had a BMI of around 20 by the age of 11, compared to 27 for the most obese children born at the turn of the century.
People are becoming overweight or obese at an increasingly younger age. Half the men of the 1946 generation were overweight by the time they were 41, compared to age 30 for men born in 1970. Half the women born in 1946 were overweight by age 48, compared to 41 for the 1970 generation.
Cross-sectional surveys can estimate the scale of the obesity problem at a population level, but only longitudinal data can show how obesity develops over the life course, including the ages at which there is an increase in risk. The UK has an added advantage: its portfolio of longitudinal studies dates all the way back to 1946, allowing researchers to track the rise of the obesity epidemic across generations. No other analysis has been able to use such extensive data covering such a wide range of ages and birth years.
This example demonstrates some of the challenges researchers face when working with multiple longitudinal studies at once. Over 70 years, it is understandable that the methods for measuring and recording height and weight will have changed. Researchers must take this into consideration, and find ways to ‘harmonise’ the data to make them more comparable over time and across studies.
Another challenge is that BMI as a measure has been criticised by some for not distinguishing between fat mass and lean mass. Some longitudinal studies have included separate measures of fat and lean mass, and waist circumference, to allow for more nuanced research into body size.
This example also demonstrates the challenges of using self-reported vs objective measures of health. In some cases, particularly in the older studies, participants will have reported their weight themselves, rather than being physically measured by an interviewer. People with higher BMIs are known to under-report their weight, so it is likely that the findings are a conservative estimate of the prevalence of overweight and obesity in the UK.
The more of their lives people spend overweight or obese, the greater their risk of developing chronic health conditions such as coronary heart disease, type 2 diabetes, high blood pressure and arthritis. As a result, the obesity ‘epidemic’ is projected to cost the UK’s National Health Service £22.9 billion per year by 2050.
While other research has shown that losing weight at any point in adulthood can help reduce the risk of becoming obese, this study indicates that the UK needs to target its public health interventions at increasingly young ages to stem the spread of the obesity epidemic.
The findings also suggest that the rise of the epidemic is linked to the rise in the ‘obesogenic environment’, the term used to describe changes in the behavioural, sociocultural, and economic landscape that encourage weight gain. For example, since the 1980s, fast foods and ready meals have become more widely available, and people are more reliant on cars and are walking less as a result.
Johnson, W., Li, L., Kuh, D. and Hardy, R. (2015) How has the age-related process of overweight or obesity development changed over time? Co-ordinated analyses of individual participant data from five United Kingdom Birth Cohorts. PLOS Medicine 12(5), e1001828.