Fundamentals of Longitudinal Analyses

1. Definition and Role of Time

Longitudinal analyses form a group of analyses focusing on the study of pheno­mena over the course of time. Longitudinal analyses are often contrasted with cross-sectional studies, by arguing that the data collected for longitudinal analyses relate to at least two distinct points in time, while for cross-sectional studies data are collected in relation to a distinct moment. This distinction, while ostensibly correct, does however raise some problems, and therefore needs to be refined.

First, it should be noted that this dichotomy is only a simplification of a more complex phenomenon. Thus, a pure cross-sectional analysis would require the data to be collected at one particular moment, or at least over a period suffi­ciently short to be considered as such. However, in management, data collection often extends over a relatively long period, frequently lasting for several months. We then must question whether this is still a cross-sectional collection. Similarly, this makes it necessary to explain how the time that has elapsed might have an impact on the study. Take, for example, a research study that seeks to investigate the perception two different managers in the same company have of a past investment. If a week passes between interviewing one manager and interviewing the second, we would wonder whether this time lapse had had an impact on the study or not – that is, whether the responses of the second manager would have been the same a week earlier. It is quite possible that during the week something might have happened to change her perception.

Drawing from Menard’s (1991) definition, we can recognize the following three characteristics:

  • the data relate to at least two distinct periods;
  • the subjects are identical or at least comparable from one period to the next;
  • the analysis consists basically in comparing data between (or over the course of) two distinct time periods, or in retracing the observed evolution.

Depending on the research, time may be attributed an important role or it may be relegated to a secondary level. At the extremes of this continuum we would find studies strongly influenced by the time factor, and studies of the development of a phenomenon without particular reference to time. It is there­fore essential that researchers consider the significance they wish to attribute to time in their project, to ensure that the research design will enable the research question to be answered.

When time is an important factor in the research, it might be considered in terms of either duration or chronology. Duration corresponds to an interval between two points in time, and is measured in terms of the subject at hand. According to the phenomenon being studied, duration may be expressed in seconds, hours, days, years, etc. For example, it could concern the duration of the development of an innovation, or the time lapse between a takeover bid and a restructuring. Chronology, however, is external to the subject of the study, existing outside the research. Chronology is used to determine the order of occurrence of events, and in management research it is generally expressed by dates.

Finally, another possible use of time is in terms of cohorts. The concept of cohorts is drawn from demographics, where a cohort refers to a group of indi­viduals born at the same period (birth cohort). Generalizing from this we can define a cohort as a group of observations having experienced the same event on a particular date. Determining a cohort in a research project allows researchers to make multiple comparisons. We can measure the differences between cohorts, or the evolution of a cohort. Table 15.1 presents a summary of these different possibilities.

2. Preliminary Questions

A researcher who decides to undertake longitudinal research is confronted with two major questions: what period of time should be covered by the study, and how many points in time should data be collected on over this time period? While the answer to the first question depends on the research question, the second question is linked to the importance the research places on time.

2.1. Analysis period

Determining the analysis period requires fixing the limits of the time interval in which data are to be gathered. In setting these limits several elements must be considered.

The first element is the research question. This provides us with the infor­mation needed to determine the phenomenon that is to be studied, and thus to determine the study period. It is always up to researchers themselves to set the time limits of their studies, while ensuring that the data they collect will enable them to answer their research question.

The researcher can also question the continuity of the phenomenon being studied: is it regarded as a permanent element in the life of an organization or is it only temporary? To fully understand this distinction let us look at the topic of change. In some studies, change is considered as forming an integral part of the life of an organization (a permanent phenomenon). Conversely, when change is seen as a particular event (a temporary phenomenon) it is studied over a limited period in the evolution of an organization. This period could be extended, depending on the research question, from the realization of the necessity for change to the stabilization of an organization after that change has been implemented.

2.2. Data collection points

Data collection points refer to moments in the life of the phenomenon for which data are to be collected. They do not necessarily coincide with the moments the researcher collects data. For example, the data may be collected on a single occa­sion, some time after the event, when the phenomenon has passed completely.

Given that a study qualifies as longitudinal on the basis of having two or more data collection points, the problem arises as to whether it must be limited to those two points, or whether this number should be increased. In the latter case, then the time interval separating them needs to be determined.

Limiting data collection to two points in time comes down to carrying out a study using a pre-test, post-test design. The choice of collection points can therefore have a strong impact on the results (Rogosa, 1988). Furthermore, this type of research does not enable the process of the phenomenon’s evolution to be studied, that is, what happens between what we observe ‘before’ and what we observe ‘after’? If one wants to focus on analyzing a process, it is therefore appropriate to increase the number of data collection points.

When researchers increase the number of data collection points, however, they are still confronted with the problem of how much time should separate these various points. To determine this time interval they once again need to consider the place of time in longitudinal research. There are three possible scenarios here:

  • When time is not important, collection intervals depend on the evolution of the phenomenon being studied. In this case these intervals are irregular and vary according to the successive states of the phenomenon or the occurrence of events affecting this phenomenon.
  • When time (in terms of duration) is a key element in the research, the period that elapses between two events is measured by predefined units: hours, days, years, etc. In this case data must be collected regularly, respecting this period.
  • When time (in terms of chronology) is important, it has a starting point that is common to all the observations (usually a date). In theory, continuous data collection is necessary so as to be able to note the dates each event occurs (other dates correspond to non-occurrences). These occurrences can then be accurately positioned in the context of time. In practice this type of collection can be difficult to implement, in which case the researcher can gather information within the context of regular periods, and reconstitute the chronology of the events afterwards.

There is a fourth case, however, although this is transversal to the three pre­ceding case scenarios. Time can be used to class individuals into cohorts – groups of individuals (or, more generally, of observations) that experienced a common event on a particular date. Once a cohort has been identified, the posi­tion of time can vary according to the research question, as before.

Table 15.2 summarizes longitudinal research designs according to the above case scenarios.

3. Problems Related to Data Collection

Researchers deciding to carry out longitudinal research can choose between:

  • collecting data retrospectively, and therefore studying a past phenomenon
  • collecting data in real time, on a phenomenon that may occur or on a pheno­menon as it occurs.

3.1. Problems related to retrospective data collection

Retrospective studies (concerning a past phenomenon) draw upon archived secondary data and/or primary data retracing the evolution of a phenomenon after the events (mainly retrospective interviews).

The secondary data that is necessary for retrospective research raise two types of problems: accessibility and validity. In its most acute form, the prob­lem of accessibility can make it absolutely impossible for the researcher to obtain the information required for the research. This information might not exist, not have been preserved, be impossible to find, or be refused (explicitly or implicitly). The question of the validity of documents, when they can be obtained, also arises. The original purpose of the document is one important initial consideration, as biases, intentional or not, might have been introduced into it by its author. Documents should also be considered in the context in which they were written. The organization of a company may have been dif­ferent, the way in which certain indices were calculated could have changed – factors such as these make comparisons precarious.

Primary data is generally in the form of retrospective interviews, which can be influenced by two important biases: faulty memory and rationalization after the fact. By faulty memory we mean that the person questioned may not remember certain events, either intentionally (he or she does not want to remember) or unintentionally (the phenomenon relates to an unremarkable event which he or she has forgotten). Rationalization too may be either inten­tional (a desire to present things in a positive light) or not (an unconscious ‘tidying up’). To add to the problem, these two biases are not mutually exclu­sive. Memory lapses and rationalization have created doubt about using retro­spective interviews (Golden, 1992), although their supposed limitations have been hotly debated. Miller et al. (1997) argue that the validity of retrospective interviews relies above all on the instrument used to gather the data.

There are several ways researchers can limit the effects of these biases. To limit errors resulting from memory lapses the following methods are recommended: [1]

  • If the research question permits, interviews can focus on events that are relatively memorable for the people being questioned, or interviewees can be selected according to their degree of involvement in the phenomenon being studied (Glick et al., 1990).
  • Information given in different interviews can be compared, or secondary data can be used to verify it (Yin, 1990).
  • A non-directive interview method can be used, in which interviewees are not pushed to answer if they don’t remember (Miller et al., 1997).
  • Each interview can be transcribed or recorded, so that interviewees can add to their initial statements.

Several strategies can be employed to limit rationalization after the fact:

  • Interviewees can be asked to list events chronologically before they are asked to establish causal connections between them.
  • Information given in different interviews can be compared.
  • Dates of events can be verified through secondary sources.

3.2. Problems related to collecting longitudinal data in real time

The collection of data in real time consists in studying a phenomenon at the same time as it is happening.

As the collection of data in real time extends over a given period, the problem arises of how to interpret any changes or developments that may be observed. Should they be attributed to the phenomenon itself or to the measuring instru­ment used (in collecting and analyzing the data)? When a measuring instrument is used on successive occasions there is a risk of it falsifying the observations. To avoid such a bias administration conditions must not vary from one period to another. It is incumbent on the researcher to control external variables that might influence responses to the questions. These variables include, for example, the person who administers the survey, external events that may occur between two collection points, and the context in which the survey is administered.

A second source of bias emerges when the first wave of data collection leads to the introduction of new hypotheses, or to the modification of existing ones. In extreme cases the original hypotheses may be brought into question between two successive rounds of data collection. In this situation the researcher has no choice but to take these new hypotheses into account, while ensuring that the data collected before the initial hypotheses were modified as appropriate to the new questions that have been raised. If this is not the case, the information will not be included in the study.

3.3. General problems related to collecting longitudinal data

Longitudinal research also presents the more general problem of the evolution of the variables used to explain the successive modifications of a phenomenon (Menard, 1991). Explanatory variables are prone to variation between the time they are collected and the time the phenomenon occurs. If the impact of such an evolution might falsify the results of the study, the researcher will have to change the data collection strategies used or use an analysis taking this into account. For example, data collection could instead be spaced out over the period of the study.

Longitudinal studies that involve several organizations face the problem of having to take the life cycles of these organizations into consideration (Kimberly, 1976). Comparative studies, for instance, would be best carried out using data from comparable periods in the organizations’ life cycles.

Source: Thietart Raymond-Alain et al. (2001), Doing Management Research: A Comprehensive Guide, SAGE Publications Ltd; 1 edition.

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