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Let’s walk through a few of them so you can better understand what is a longitudinal survey. Additional data points can be collected to study unexpected findings, allowing changes to be made to the survey based on the approach that is detected. One of the longest longitudinal studies, the Harvard Study of Adult Development, has been collecting data on the physical and mental health of a group of men in Boston, in the US, for over 80 years. Numerous predisposing factors were postulated to align together to produce cardiovascular disease, with increasing age being considered a central determinant. Lewis Terman aimed to investigate how highly intelligent children develop into adulthood with his "Genetic Studies of Genius." Results from this study were still being compiled into the 2000s.
Longitudinal Study Definition, Approaches & Examples
Presumably, this process helps us to impart meaning to our experiences and predict what might happen next, but it also influences the type of information we take with us from the episode, thereby affecting how we might report on this period of time. For example, in research on sense-making processes in newcomer adaptation, the total period of study often ranged from 6 months to 1 year, with 6 to 12 time points, equally spaced at time intervals of 1 or 2 months between adjacent time points. On the other extreme, a much shorter time interval and total time period, ranging from several hours to several days, will be appropriate for a change process that should take a short time to manifest itself such as activation or inhibition of mood states primed by experimentally manipulated events. Also, when the theoretical process at hand has a very short time frame (e.g., minutes or seconds), then cross-sectional designs can be entirely appropriate (e.g., for factor analysis/measurement modeling, because it might only take a moment for a latent construct to be reflected in a survey response). Also, first-stage descriptive models of group differences (e.g., sex differences in pay; cross-cultural differences in attitudes; and other “black box” models that do not specify a psychological process) can be suggestive even with cross-sectional designs. Cross-sectional research can also be condoned in the case of a 2-study design wherein cross-sectional data are supplemented with lagged/longitudinal data.
Longitudinal vs cross-sectional studies
Random walk variables are dynamic variables that I mentioned earlier when describing the computational modeling approach. The random walk expression comes from the image of a highly inebriated individual, who is in some position, but who staggers and sways from the position to neighboring positions because the alcohol has disrupted the nerve system’s stabilizers. This inebriated individual might have an intended direction (called “the trend” if the individual can make any real progress), but there may be a lot of noise in that path. In the aging and retirement literature, one’s retirement savings can be viewed as a random walk variable. The random walks (i.e., dynamic variables) have a nonindependence among observations over time.
Longitudinal study
They're particularly useful when studying developmental and lifespan issues because they allow glimpses into changes and possible reasons for them. Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations. Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.
Then due to a critical organizational event (e.g., the downsizing of the company, a newly introduced policy to weed out poor performers in the newcomer cohort), newcomers’ coworker satisfaction may start to drop. A spline model can be used to capture the dramatic change in the trend of newcomer attitude as a response to the event (see Figure 4 for an illustration of this example). The time points at which the variable changes its trajectory are called spline knots.
Qualitative longitudinal research in health research: a method study - BMC Medical Research Methodology - BMC Medical Research Methodology
Qualitative longitudinal research in health research: a method study - BMC Medical Research Methodology.
Posted: Sat, 01 Oct 2022 07:00:00 GMT [source]
Longitudinal Data: Definition and Uses in Finance and Economics - Investopedia
Longitudinal Data: Definition and Uses in Finance and Economics.
Posted: Sat, 25 Mar 2017 21:40:31 GMT [source]
This tendency, known as selective attrition, shrinks the sample size and decreases the amount of data collected. To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation. When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.
Allows researchers to look at changes over time
Further, consider the case of the predictive validity design, where a selection instrument is measured from a sample of job applicants and performance is assessed some time later. Again, however, internal validity is not much improved, which is fine given that prediction, not cause, is the primary concern in the selection context. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a ‘cross-section’) of the population at one point in time. In contrast, cross-sectional analysis is another study type that may analyse multiple variables at a given instance, but provides no information with regards to the influence of time on the variables measured—being static by its very nature. Nonetheless, cross-sectional studies require less time to be set up, and may be considered for preliminary evaluations of association prior to embarking on cumbersome longitudinal-type studies. There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data.
As with all research, the design needs to allow the researcher to address the research question. For example, if one is seeking to assess a change rate, one needs to ask if it is safe to assume that the form of change is linear. One might also use a computational model to assess whether violations of the linearity assumption are important. The researcher needs to also have an understanding of the likely time frame across which the processes being examined occur. Alternatively, if the time frame is unclear, the researcher should sample continuously or use short intervals. If knowing the form of the change is desired, then one will need enough waves of data collection in which to comprehensively capture the changes.
For example, Bollen and Curran (2006, p. 109) address the issue of cycles (recurring ups and downs but that follow a general upward or downward trend.) Once more the values of the change variable would be coded to reflect those cycles. Similarly, Singer and Willett (2003, p. 208) address recoding when one wants to remove through transformations the nonlinearity in the change function to make it more linear. They provide an excellent heuristic on page 211 to guide one’s thinking on this issue. As noted earlier, practical hurdles are perhaps one of the main reasons why researchers choose cross-sectional rather than longitudinal designs.
Another useful set of cross-sectional studies would be those developed for the purpose of verifying within field settings the findings from a series of well-designed laboratory experiments. Again, knowing issues such as thresholds, minimal/maximal values, and intervals or timing of the x-variable onset would be very useful for informing a theory of change. A design context that would be of little use for developing a theory of change is the case where a single cross-sectional study was completed to evaluate the conceptual premises of interest. The theory underlying the study may be useful, but the findings themselves would be of little use. It is important to point out that true experimental designs are also a type of longitudinal research design by nature. This is because in experimental design, an independent variable is manipulated before the measure of the dependent variable occurs.
The substantive processes occur or unfold through time but they did not cause time to exist. The way that growth modeling techniques analyze longitudinal data is consistent with the above conceptualization of time. For example, in latent growth modeling, time per se is not represented as a substantive variable in the analysis. Instead, a specific time point is coded as a temporal marker of the substantive variable (e.g., as basis coefficients in a latent growth model to indicate the time points in the sequence of repeated measurement at which the substantive variable was measured). The time-varying nature of the substantive variable is represented either at the individual level as the individual slopes or at the group level as the variance of the slope factor. It is the slopes and variance of slopes of the substantive variable that are being analyzed, and not time per se.
As a result, items will reflect more or less stable estimates of the phenomenon of interest. Consider the hypothetical temporal break-down of helping behavior depicted in Figure 3. No matter how unstable the most disaggregated level of helping behavior may appear, aggregations of these behaviors will always produce greater stability. So, asking about helping behavior over the last hour will produce greater observed variability (i.e., over the entire scale) than averages of helping behavior over the last day, week, month, or one’s overall general level.
This means it is dependent on the nature of the substantive construct, its underlying process of change over time, and the context in which the change process is occurring which includes the presence of variables that influence the nature and rate of the change. In theory, the time interval for data collection is optimal when the time points are appropriately spaced in such a way that it allows the true pattern of change over time to be observed during the period of study. When the observed time interval is too short or too long as compared to the optimal time interval, true patterns of change will get masked or false patterns of change will get observed. One exception is a definition by Taris (2000), who explained that longitudinal “data are collected for the same set of research units (which might differ from the sampling units/respondents) for (but not necessarily at) two or more occasions, in principle allowing for intra-individual comparison across time” (pp. 1–2). Compared to Taris (2000), Ployhart and Vandenberg’s (2010) definition explicitly emphasizes change and encourages the collection of many waves of repeated measures. For example, it precludes designs often classified as longitudinal such as the prospective design.
Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.
The issue of mental representations of events at particular points in time should always be discussed and evaluated within the research context of the conceptual questions on the underlying substantive constructs and change processes that may account for patterns of responses over time. Each of the two measurement perspectives (i.e., momentary and global retrospective) has both strengths and limitations. For example, momentary measures are less prone to recall biases compared to global retrospective measures (Kahneman, 1999).
