Social Research update logo
Issue 20 Spring 2000

Social Research Update is published quarterly by the Department of Sociology, University of Surrey, Guildford GU2 7XH, England. Subscriptions for the hardcopy version are free to researchers with addresses in the UK. Apply by email to

Longitudinal Research in the Social Sciences

Elisabetta Ruspini

Dr Elisabetta Ruspini has a PhD in Sociology and Social Research. She is a post-doctoral research fellow at the Department of Sociology, University of Padova. In 1999 she was an International Visiting Fellow in Social Research Methods at the University of Surrey.

Her current research focuses on the feminisation of poverty and women’s poverty dynamics in Italy in a comparative perspective.

Her research interests include gender issues, comparative welfare research, social and family policies, poverty, and the study of living conditions. Within the methodological field, her main interests are longitudinal data analysis and the design and collection of complex data sets such as household panel surveys.

She has published a number of articles and contributed papers to national and international conferences in the fields of longitudinal research and research on poverty.

‘Longitudinal’ is a broad term. It can be defined as research in which:

There are a number of different designs for the construction of longitudinal evidence: repeated cross-sectional studies; prospective studies, such as household panel surveys or cohort panels; and retrospective studies, such as oral histories and life and work histories.

Repeated cross-sectional studies

In the social sciences, cross-sectional observations are the form of data most commonly used for assessing the determinants of behaviour (Coleman 1981; Davies 1994; Blossfeld and Rohwer 1995). However, the cross-sectional survey, because it is conducted at just one point in time, is not suited for the study of social change. It is therefore common for cross-sectional data to be recorded in a succession of surveys at two or more points in time, with a new sample on each occasion. These samples either contain entirely different sets of cases for each period, or the overlap is so small as to be considered negligible. Where cross-sectional data are repeated over time with a high level of consistency between questions, it is possible to incorporate a time trend into the analysis. Examples of repeated cross-sectional social surveys are: the UK’s General Household Survey and Family Expenditure Survey, and the EU’s Eurobarometer Surveys.

Prospective designs

The temporal data most often available to social researchers are panel data, in which the same individuals are interviewed repeatedly across time. Variations of this design (Buck et al. 1994: 21-22) include:

Representative Panels with a random sample of respondents and repeated data collections at fixed intervals (typically from 2-3 months to a year). Thus panel surveys trace individuals at regular discrete points in time. The fundamental feature they offer is that they make it possible to detect and establish the nature of individual change. For this reason, they are well-suited to the statistical analysis of both social change and dynamic behaviour. Among the best known prospective panel studies are the US Panel Study of Income Dynamics (PSID), the British Household Panel Study (BHPS) and the German Socio-Economic Panel (SOEP).

Cohort Panels can be considered as a specific form of panel study that takes the process of generation replacement explicitly into account. A cohort is defined as those people within a geographically or otherwise delineated population who experienced the same significant life event within a given period of time. Researchers select an age group, or some subset of an age group, and then administer a questionnaire to a sample or to the whole group. Thus, one or more generations are followed over their life course. The interest is usually in the study of long term change and in individual development processes: such studies typically re-interview every five years. If, in each particular generation the same people are investigated, a cohort study amounts to a series of panel studies; if, in each generation, at each period of observation, a new sample is drawn, a cohort study consists of a series of trend studies (Hagenaars 1990). Examples are the UK National Child Development Study and the German Life History Study.

Linked Panels In these cases data items which are not collected primarily for panel purposes (Census or administrative data) are linked together using unique personal identifiers. This is the least intrusive method of collecting longitudinal data (Buck et al. 1994).

Retrospective studies (event oriented observation design) All the data types discussed so far have been recorded with reference to fixed and predetermined time points. But, for many processes within the social sciences, continuous measurement of qualitative variables seems to be the most suitable method of empirically assessing social change. When data are recorded in a continuous time, the number and sequence of events and the duration between them can all be calculated. Data recorded in continuous time are often collected retrospectively via life history studies that cover the whole life course of individuals. The main advantage of this approach lies in the greater detail and precision of information (Blossfeld and Rohwer 1995). A good example is the UK 1980 Women and Employment Survey, which obtained very detailed work histories from a nationally representative sample of women of working age in Britain.

Strictly speaking, longitudinal studies are limited to prospective studies, while retrospective studies have been defined as a quasi-longitudinal design, since they do not offer the same strengths for research on causal processes (Hakim 1987:97).

Because several types of data may be regarded as longitudinal, methods for the analysis of social change may also vary substantially: from time-series techniques for repeated cross-section data to logistic and log-linear models; from structural equation models to longitudinal multilevel methods; from regression analysis to event history analysis (Davies and Dale 1994).

Advantages and limitations of longitudinal data

Longitudinal data allow the analysis of duration; permit the measurement of differences or change in a variable from one period to another, that is, the description of patterns of change over time; and can be used to locate the causes of social phenomena (Menard 1991:5) and sleeper effects, that is, connections between events that are widely separated in time (Hakim 1987).

Insights into processes of social change can thus be greatly enhanced by making more extensive use of longitudinal data. Dynamic data are the necessary empirical basis for a new type of dynamic thinking about the processes of social change (Gershuny 1998). The possibility of developing research based on longitudinal data also builds a bridge between ‘quantitative’ and ‘qualitative’ research traditions and enables re-shaping of the concepts of qualitative and quantitative (Ruspini 1999). Longitudinal surveys usually combine both extensive and intensive approaches (Davies and Dale 1994). Life history surveys facilitate the construction of individual trajectories since they collect continuous information throughout the life course. Panel data trace individuals and households through historical time: information is gathered about them at regular intervals. Moreover, they often include relevant retrospective information, so that the respondents have continuous records in key fields from the beginning of their lives. As an example, the British Household Panel Study has taken the opportunity (over the first three waves) to get a very good picture of respondents’ lives by asking for life-time retrospective work-histories, and marital and fertility histories, hence investigating both illuminating and vital areas of the lives of those who make up a representative sample of the households of Britain.

Taking the German Socio-Economic Panel as another example, two calendars are included in the core questionnaires: an activity calendar that, on a monthly basis, records participation in schooling, vocational education, military service, full-time and part-time employment, unemployment, homemaking and retirement for the previous year; and an income calendar where respondents indicate, also on a monthly basis, whether they have received income from various sources in the past year and the average monthly amount received from each source (Burkhauser 1991).

Thus, longitudinal analysis presupposes the development of a methodological mix where neither of the two aspects alone is sufficient to produce an accurate picture of social dynamics (Mingione 1999).

However, although dynamic data have the potential to provide richer information about individual behaviour, their use poses theoretical and methodological problems. In addition, longitudinal research typically costs more and can be very time-consuming. The principal limitations of the repeated cross-sectional design are its inappropriateness for studying developmental patterns within cohorts and its inability to resolve issues of causal order. Both of these limitations result directly from the fact that in a repeated cross-sectional design, the same cases are neither measured repeatedly nor for multiple periods (Menard 1991). Thus, more data are required to characterise empirically the dynamic process that lies behind the cross-sectional snapshot (Davies 1994).

Concerning panel data, the main operational problems with prospective studies (other than linked panels) (Magnusson and Bergmann 1990; Menard 1991; Duncan 1992, Rose 1993; Blossfeld and Rohwer 1995) are:

Panel attrition

If the same set of cases is used in each period, there may be some variation from one period to another as a result of missing data (due to refusals, changes of residence or death of the respondent). Such systematic differences between waves cause biased estimates. For example, a major problem in most surveys on poverty is the under-sampling of poor people: they are hard to contact (and therefore usually undersampled in the first wave of data) and hard to retain for successive annual interviews. Even though weight variables could be used to mitigate under-representation, it is difficult to assess the real efficiency of such weights.

Course of events

Since there is only information on the states of the units at predetermined survey points (discrete time points), the course of the events between the discrete points in time remains unknown;

Panel conditioning

Precisely because in a panel survey the same subjects are repeatedly interviewed, it is possible that responses given in one wave will be influenced by those given in the previous waves (Trivellato 1999). Unwillingness to participate in the study may also result from continued study and may result in attrition. Yet another possibility is that respondents will change as a result of participation in the survey (Menard 1991).

Consequently, the potential of panel data can only be fully realised if such data meet high quality standards (Duncan 1992; Ghellini and Trivellato 1996). In particular, Trivellato (1999) stated that for a panel survey to be successful, the key ingredients are a good initial sample and appropriate following rules, that is, a set of rules that permit mimicing the population that almost always changes in composition over time. Taking the BHPS as an example, because the BHPS is a household panel study which tracks household formation and dissolution, individuals may join and leave the sample. Thus, the study has a number of following rules determining who is eligible to be interviewed at each wave. New eligibility for sample inclusion could occur between waves in the following ways: 1) A baby is born to an Original Sample Member (OSM); 2) An OSM moves into a household with one or more new people; 3) One or more new people move in with an OSM (Freed Taylor et al. 1995).

The drawback of linked panels is that they can only provide a very limited range of information and often on a highly discontinuous temporal basis (as in the case of a Census). Moreover, such panels suffer from problems of confidentiality and of data protection legislation, so there is often only very limited access (Buck et al. 1994). Even if retrospective studies have the advantage of usually being cheaper to collect than panel data, they suffer from several limitations that are increasingly being acknowledged (Davies and Dale 1994; Blossfeld and Rohwer 1995):

  1. recall bias: retrospective questions concerning motivational, attitudinal, cognitive or affective states are particularly problematic because respondents find it hard to accurately recall the timing of changes in these states;
  2. there is a limit to respondents’ tolerance for the amount of data that can be collected on one occasion;
  3. retrospective studies must be based on survivors. Those subjects who have died or migrated will, necessarily, be omitted and biases may arise; retrospective studies can also misrepresent specific populations.


The use of longitudinal data (both prospective and retrospective) can ensure a more complete approach to empirical research. Longitudinal data are collected in a time sequence that clarifies the direction as well as the magnitude of change among variables. However, the world of longitudinal research is quite heterogeneous. Some important general suggestions are (Menard 1991):

  1. If the measurement of change is not a concern, if causal and temporal order are known, or if there is no concern with causal relationships, then cross-sectional data and analysis may be sufficient. Repeated cross-sectional designs may be appropriate if a problem of panel conditioning as a result of repeated interviewing or observation in a prospective panel is anticipated.
  2. If change is to be measured over a long span of time, then a prospective panel design is the most appropriate, because independent samples may differ from one another unless both formal and informal procedures for sampling and data collection are rigidly replicated for each wave of data. Within this context, it is important to remember that a period of time needs to occur before it is feasible to do an analysis of social change: a consistent number of waves is necessary to permit in-depth long term analyses to be carried out.
  3. If change is to be measured over a relatively short time (weeks or months), then a retrospective design may be appropriate for data on events or behaviour, but probably not for attitudes or beliefs.
  4. In order to combine the strengths of panel designs and the virtues of retrospective studies, a mixed design employing a follow-up and a follow-back strategy seems appropriate (Blossfeld and Rohwer 1995).

Finally, due to the complexity of longitudinal data sets, the user documentation is crucial for the researcher. It should contain essential information required for the analysis of the data (including details of fieldwork, sampling, weighting and imputation procedures) and information to assist users in the linking and aggregating data across waves. The documentation should both make the analysis easier and more straightforward and help evaluate data quality.


Blossfeld, H.P. and Rohwer, G. (1995) Techniques of Event History Modeling. New Approaches to Causal Analysis, Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Buck, N., Gershuny, J., Rose, D. and Scott, J. (Eds.) (1994) Changing Households: the BHPS 1990 to 1992, ESRC Research Centre on Micro-Social Change, Colchester: University of Essex.

Burkhauser, R.V. (1991) An Introduction to The German Socio-Economic Panel for English Speaking Researchers, Project Paper n. 1, Syracuse University.

Coleman (1981) Longitudinal Data Analysis, New York: Basic Books.

Davies, R.B. (1994) ‘From Cross-Sectional to Longitudinal Analysis’, in Dale, A. and Davies, R.B. (Eds.) Analysing Social and Political Change. A Casebook of Methods, London: Sage Publications.

Davies, R.B. and Dale, A. (1994) ‘Introduction’, in Dale, A. and Davies, R.B. (Eds.) Analysing Social and Political Change. A Casebook of Methods, London: Sage Publications.

Duncan, G.J. (1992) Household Panel Studies: Prospects and Problems, Working Papers of the European Scientific Network on Household Panel Studies, Paper n. 54, Colchester: University of Essex.

Freed Taylor M., Brice J. and Buck N. (Eds.) (1995), BHPS User Manual. Introduction, Technical Report and Appendices, vol. A, University of Essex, Colchester.

Gershuny, J. (1998) ‘Thinking Dynamically: Sociology and Narrative Data’, in Leisering, L. and Walker, R. (Eds.) The Dynamics of Modern Society, Bristol: The Policy Press.

Ghellini, G. and Trivellato, U. (1996) ‘Indagini panel sul comportamento socio-economico di individui e famiglie: una selezionata rassegna di problemi ed esperienze’, in Quintano, C. (Ed.), Scritti di Statistica Economica 2, Napoli: Rocco Curto Editore.

Hagenaars, J.A. (1990) Categorical Longitudinal Data. Log-Linear Panel, Trend and Cohort Analysis, London: Sage Publications.

Hakim, C. (1987) Research design. Strategies and Choices in the Design of Social Research, London: Allen and Unwin.

Magnusson, D. and Bergmann, L.R. (Eds.) (1990) Data Quality in Longitudinal Research, Cambridge: Cambridge University Press.

Menard, S. (1991) Longitudinal Research, Newbury Park: Sage Publications.

Mingione, E. (1999) ‘Foreword. Longitudinal Research: a Bridge between Quantitative and Qualitative Social Research?’, in Ruspini, E. (Ed.) ‘Longitudinal Analysis: A Bridge between Quantitative and Qualitative Social Research’, Special Issue of Quality and Quantity, vol. 33, n. 3, July-August.

Rose, D. (1993) European Household Panel Studies, Working Papers of the ESF Network on Household Panel Studies, Paper n. 45, Colchester: University of Essex.

Ruspini, E. (1999) ‘Longitudinal Research and the Analysis of Social Change’, in Ruspini, E. (Ed.) ‘Longitudinal Analysis: A Bridge between Quantitative and Qualitative Social Research’, Special Issue of Quality and Quantity vol. 33, n. 3, July-August.

Trivellato, U. (1999) Issues in the Design and Analysis of Panel Studies: A Cursory Review, in Ruspini, E. (Ed.) ‘Longitudinal Analysis: A Bridge between Quantitative and Qualitative Social Research’, Special Issue of Quality and Quantity, vol. 33, n. 3, July-August.

Social Research Update is published by:

Department of Sociology
University of Surrey
Guildford GU2 7XH
United Kingdom.

Telephone: +44 (0) 1 483 300800
Fax: +44 (0) 1 483 689551

Edited by Nigel Gilbert.

Spring 2000 © University of Surrey

Permission is granted to reproduce this issue of Social Research Update provided that no charge is made other than for the cost of reproduction and this panel acknowledging copyright is included with all copies.