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.

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

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 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

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

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.

Percentiles

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

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

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

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.

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 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

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.

Learning Hub

Children of immigrants’ cognitive achievement

Teenage Bullying

Do children of immigrants perform better or worse than their peers on cognitive assessments? Longitudinal data is a valuable evidence source for investigating children's development trajectories.

Key finding

In the 1970 cohort, children of immigrants showed significant gaps at ages 5 and 10 in reading and maths assessments. However, no gaps were found in the 2000-01 cohort at ages 11 and 14.

About the research

The research examines the cognitive development of children born in the UK to two immigrant parents by modelling assessment scores in reading and maths. The author compares the findings from assessments undertaken during the 1970 British Cohort Study (BCS70) with those which formed part of the more recent Millennium Cohort Study (MCS) to examine whether children of immigrants achieve along systematically different trajectories when compared to children of non-immigrants and whether children of immigrants today are performing better academically than their counterparts 30 years ago.

Research questions

  • Do children of immigrants perform similarly, worse or better than other children in the cohort on cognitive assessments?
  • Do children of immigrants in the MCS perform better in their cognitive assessment scores than their counterparts in the BCS70?

Studies used

1970 British Cohort Study

The 1970 British Cohort Study (BCS70) follows the lives of 17,198 people born in England, Scotland and Wales in a single week of 1970.

Millennium Cohort Study

The Millennium Cohort Study (MCS) is following the lives of 19,517 children born across England, Scotland, Wales and Northern Ireland in 2000-01.

Data and definition

Reading assessment

In the BCS70, reading was assessed at ages 5 and 10 using the Schonell Reading model and scores were standardized to give them a mean of 50 and a standard deviation of 10; for the MCS cohort, reading (or verbal skills) was assessed at ages 3, 5, 7, 11 and 14 using age-appropriate British Ability Scales models. These scores also underwent transformation to give them a mean of 50 and a standard deviation of 10.

Maths assessment

In the BCS70, maths skills were assessed at ages 5 and 10 using the Copying Designs (age 5) and Friendly Maths (age 10) models; for the MCS cohort, maths (or quantitative) skills were assessed at ages 3, 5, 7 and 11, again using age-appropriate models. As with reading assessment scores, maths scores were standardized to give them a mean of 50 and a standard deviation of 10.

Key findings

In the BCS70 cohort, children of immigrants show significant gaps at ages 5 and 10 in both reading and maths scores. Even after controlling for gender, socioeconomic status, residential location, birth order and home language environment, children of Caribbean immigrants appear to perform worse in both assessments.

On the other hand, no gaps remain in age-11 and age-14 assessments for the MCS cohort. Indeed in the final model, all children of immigrants follow positive trajectories and Indian children of immigrants even out-perform their peers.

Advantages and challenges of using longitudinal data to study immigrant children's cognitive achievement

Detailed cross-cohort analysis can allow us to examine how cognitive achievement has changed amongst children of immigrants at similar developmental time points in the last 30 years. The cognitive assessment scores of children born to immigrants in 1970 (relative to their peers) are compared to those of immigrant children born in 2000-01 (again, relative to their peers) and this can provide important insights as to the relative attainment of minorities in these two birth cohorts. Using longitudinal data from each of the cohort studies also allows the author to plot development trajectories over time using consecutive waves of assessment data to give a clearer understanding of when any differences emerge or disappear.

Nonetheless, there are some key challenges involved in adopting this approach. In particular, the comparability between measures used in the two cohort studies will necessarily be approximate as they employ different instruments and are not always administered at the same age. Indeed, analysis of the older cohort is restricted to assessments at two time points, whereas for the young cohort data are available relating to four or five assessment waves.

The author focuses more attention on the more recent cohort, for which a much greater amount of information is available and can undertake more complex multilevel modelling to assess the relative importance of several individual- and family-level characteristics for this cohort of young people. However, this analysis is limited to the MCS cohort and therefore no comparison with the older BCS70 cohort.

Implications for policy and practice

The issue of minority ethnic children’s educational underachievement was first highlighted by the Swann Report (1985) and, since then, has received much academic and policy attention. The findings of this study show positive trends in the educational development of immigrant children in the present day, compared to the cohort of 30 years ago. However, the author cautions against undue optimism and highlights that continued policy initiatives promoting ethnic minorities’ achievement are required to ensure that these trends do not experience any back-sliding. A more fine-grained examination of individual ethnic groups’ educational experiences and attainment would also allow for more targeted policies towards supporting the more vulnerable groups.

Access the paper

Hoffmann, N. I. (2018). Cognitive achievement of children of immigrants: Evidence from the Millennium Cohort Study and the 1970 British Cohort StudyBritish Educational Research Journal44(6), 1005-1028.

Discussion topics

  • What challenges might children of immigrants face in relation to their cognitive achievement?
  • How might these challenges be different today from 30 years ago?
  • To what extent do the issues facing children of immigrants overlap with issues facing ethnic minority groups? What about the grandchildren of immigrants? What about the children of White immigrants?