Cross-Sectional vs. Longitudinal Research Design Compared

Introduction to Quantitative Research Design

Research designs are used to examine the relationships between independent and dependent variables in any given population. One type of research design is the quantitative research design. In quantitative research, the goal is to determine specific relationships; as such, all research is considered either descriptive — where subjects are measured once — or experimental — where subjects are measured before and after a specific treatment or event (Hoover & Donovan, 2004). In descriptive studies, only observation is used, whereas in experimental designs, actual manipulation of variables occurs.

This paper focuses on two types of quantitative design: the cross-sectional design and the longitudinal design. Both types are considered descriptive in nature, in that no manipulation of variables is performed (Woolf, 1998). However, each type has its own advantages and disadvantages, each of which will be discussed in turn.

Cross-Sectional Design: Overview and Advantages

A cross-sectional design is a type of quantitative descriptive research design in which data is collected a single time from the subject pool on the relevant variable. All data is collected within a short period of time, generally through the use of surveys (Saint-Germain, 2004). While cross-sectional designs are extremely useful in determining variables across populations, there cannot be an analysis of cause and effect, since the variables are not manipulated and data is not collected more than once. Thus, it is impossible to infer causality (Woolf, 1998).

Cross-sectional designs have many advantages over other forms of research designs. First, and perhaps most importantly, cross-sectional designs are more cost effective than other forms, such as the cohort study, which examines data over time (Larkin, 1985). Since data collection occurs only once, the costs of continued data collection and follow-up are avoided.

Furthermore, since data is generally collected through surveys, it can be gathered from a large number of subjects simultaneously, thereby increasing the validity of any conclusions drawn from the study. This effect is further enhanced by the ability to study a large variety of subjects at the same time, enabling researchers to examine numerous variables across a wide range of participants (Saint-Germain, 2004).

Still another advantage of the cross-sectional study is that this method can collect data on attitudes and behaviors, which other observational or descriptive research methods cannot. A correctly modeled cross-sectional research design can answer questions on exploratory subjects without the expense of more in-depth forms of research (Hopkins, 2001). This allows researchers to generate hypotheses for future research in a cost-effective way (Saint-Germain, 2004).

Disadvantages of Cross-Sectional Design

However, there are disadvantages to the cross-sectional design. First, as noted, this method cannot be used to establish cause and effect, since data is collected only once during the study. With only a single data point, any relationship observed could have been caused by any number of untested variables. Furthermore, change cannot be measured with this method; more than one data point would be necessary to do so (Saint-Germain, 2004).

Another disadvantage involves the cost increases associated with adding subjects or locations to the study. For each subject added and each location tested, higher costs are incurred. While cross-sectional methods remain relatively cost effective, any alterations needed in study locations involve increases in funding that can present a problem (Larkin, 1985). For example, if the original study design called for collecting surveys from 200 participants at a specific college, and another location is later added to diversify the subject pool, the costs of the study increase dramatically.

There are also disadvantages relating to the variables and conclusions generated by cross-sectional research designs. Since this method is descriptive only, there is no control over the independent variable; further, since neither causality nor change can be measured, there is no possibility of refuting alternative hypotheses. Finally, since the data is collected only once, the study is static — it is bound in time to the single moment of data collection (Saint-Germain, 2004).

Longitudinal Design: Overview and Advantages

Longitudinal designs, on the other hand, involve the collection of data over a period of time. Rather than collecting a single data set, the longitudinal design allows researchers to study an independent variable over time in order to measure changes within the subject population (Gliner, 2000). Measurements are taken for each variable on more than one occasion in order to observe those changes.

It is important to note that there are two forms of longitudinal design: the time series and the panel. With a time series design, data is collected on a single variable at regular intervals — such as each week — and the measurements are combined to form a collective result. Unemployment rates, for example, are considered a time series longitudinal measure. The second form, the panel design, involves collecting data from a specific group of subjects over time, revealing a more individualized pattern of change (Saint-Germain, 2004).

As with the cross-sectional design, there are clear advantages to using the longitudinal method. In particular, this design can show how relationships emerge over the course of time — something a cross-sectional method cannot do. Furthermore, this method allows the researcher to establish the time order of variables, such as determining which of two variables occurred first. This capacity can help to generate hypotheses about cause and effect (Saint-Germain, 2004).

Additional advantages include the ability to report information at an individualized level. Many other types of research methods allow only for broad generalization, but with longitudinal designs it is possible to predict, at least on a short-term basis, trends for small groups of individuals (Woolf, 1998). Additionally, the results tend to be easy to present in graph formats and easy for a general audience to comprehend (Saint-Germain, 2004).

Conclusion

Clearly, both the cross-sectional and longitudinal designs have their own advantages and disadvantages. For certain subjects — such as the study of attitudes and behaviors — a cross-sectional design is often the best alternative, as it is low cost and can efficiently generate hypotheses for use in future quantitative studies. For other research questions, such as how variables change over time, the longitudinal method is preferred. In both cases, however, a well-structured and carefully planned study can yield meaningful and useful results.

References

Community Foundation, Silicon Valley. (2003). Giving Back: A Practitioner's Toolkit. Palo Alto, CA: Community Foundation of Silicon Valley.

Gliner, J. (2000). Research Methods in Applied Settings: An Integrated Approach to Design and Analysis. Mahwah, NJ: Lawrence Erlbaum Associates.

Hoover, K., & Donovan, T. (2004). The Elements of Social Scientific Thinking (8th ed.). Florence, KY: Wadsworth.

Hopkins, W. G. (2000). Quantitative research design. Sportscience, 4(1), 90–92.

Larkin, T. (1985, June). Evidence vs. nonsense: A guide to the scientific method. FDA Consumer, 19, 23–25.

Saint-Germain, M. A. (2004). Research Methods. Retrieved October 25, 2005, from California State University, Long Beach.

Woolf, L. M. (1998). Theoretical Perspectives Relevant to Developmental Psychology. Retrieved October 25, 2005, from Webster University.