A cross-sectional study is a research study that collects data at one specific point in time. It enables researchers to compare and study sub-cohorts within the cohort of people studied, but cannot identify changes in variables over time.
The biggest advantage of a cross-sectional study is that it can develop descriptive statistics about sampling groups, which may be generalizable if the sample cohort is sufficiently representative of a broader population.
However, its main weakness is that it is only taking a single snapshot, giving a rather two-dimensional descriptive picture of the cohort. To address this, longitudinal research may be undertaken, where another cross-sectional study takes place with the same sample group after a period of time. This allows for testing of changes in variables over time.
Cross-Sectional Study: Definition and Overview
Cross-sectional research is a type of observational study that involves the analysis of data collected from a population, or a representative subset, at one specific point in time.
This type of study is often used in psychology, economics, public health, and other fields to examine variables in different groups that are equivalent in all respects except the variable being studied.
For example, a researcher might use cross-sectional research to determine whether income levels are associated with educational attainment. In this study, the researcher would collect data on income and education level for a large sample of individuals at a single point in time.
A cross-sectional study collects data at a single point in time to provide descriptive data about the groups within the study, as below:
Cross-sectional research is contrasted with longitudinal research, which studies variables over time. In a longitudinal study, the researcher would collect data from the same group of individuals at multiple time points:
Cross-Sectional Study Advantages and Disadvantages
|1. Efficient and Inexpensive: Utilizes existing databases for efficient and inexpensive data collection.||1. Cannot Infer Causality: It’s impossible to determine causality due to observational nature and single-point data collection.|
|2. Easily Identifies Risk Factors: Helps identify risk factors related to certain outcomes in fields like medicine or psychology.||2. Reliance on Self-Report Measures: Relies on self-reported data, which may not always be accurate due to factors like social desirability bias or lack of self-awareness.|
|3. Can Compare Subgroups: Allows comparison of different segments within a large sample.||3. Sampling Issues: There may be issues with sample’s characteristics, such as lack of heterogeneity or small size.|
|4. Lots of Data: Offers large datasets with numerous variables that can be analyzed for insights using advanced statistical procedures.||4. Response Rates: There can be poor response rates, limiting the dataset and potentially affecting the results of the study.|
|5. Generalizability: Cross-sectional studies that are representative of larger populations can establish plausible generalizability, unlike qualitative observational methods.||5. Cannot Establish Causation: While a longitudinal study can establish clear correlations (e.g. “the men in the cohort had higher income than the women in the cohort”), they cannot explain what caused those correlations.|
1. Advantage: Efficient and Inexpensive
Cross-sectional studies often utilize existing databases. The datasets may have been constructed as part of routine data collection procedures that occur in hospitals, law enforcement agencies, insurance companies, or government offices.
For example, the Centers for Disease Control in most countries collects large amounts of data, as do certain law enforcement agencies such as the FBI in the US.
Sometimes researchers have access to those datasets. The availability of these large datasets makes collecting information extremely efficient and relatively inexpensive.
2. Advantage: Can Easily Identify Risk Factors
In medical research, cross-sectional studies are sometimes conducted to examine potential risk factors associated with certain health outcomes. Risk factors might include health habits such as exercise or alcohol consumption, which are then correlated with health measures such as obesity or cirrhosis.
In psychological research, risk factors might include personality characteristics such as Type A personality or social support factors such as number of friends or family cohesion. Psychologists can link these factors to other variables such as hypertension, eating disorders, or depression.
Although this type of information cannot be used to treat physical or psychological ailments, it does provide a snapshot of the sample’s characteristics and produce insights regarding where to direct intervention efforts.
3. Advantage: Can Compare Subgroups of the Sample
Within one large sample, there can exist various subgroups. For example, collecting data on college students at a large university can allow comparisons of different segments of that population.
One can compare freshmen with seniors, males with females, or students across different majors. This can lead to numerous insights that may be informative in and of themselves, or lead to additional hypotheses which could be explored in the future utilizing other methodologies.
4. Advantage: Lots of Data
One of the biggest strengths of a cross-sectional study is that it can involve a very large data set that contains tons of variables. This gives researchers an opportunity to examine how numerous variables are related to each other and related to the primary outcome variable.
Having data on so many variables can be analyzed through advanced statistical procedures such as multiple regression or structural equation modelling.
These types of analyses allow researchers to determine which variables have a unique and direct impact on the outcome variable. They will also reveal how two or more variables work together to impact the outcome variable.
For understanding complex phenomenon, a cross-sectional study can provide valuable insights that cannot be obtained through other methodologies.
5. Disdvantage: Cannot Infer Causality
The biggest weakness of a cross-sectional study is that researchers cannot determine causality. There is no way to identify if specific variables of interest are causing outcome variables or not.
In order for causality to be inferred, temporal data must exist or researchers need to manipulate the level of an independent variable.
Since cross-sectional research is purely observational and data collection only occurs at a single point in time, no inferences regarding causality are possible.
6. Disdvantage: Reliance on Self-Report Measures
Many cross-sectional studies involve administering questionnaires. This means that participants in the sample must respond to various questions regarding aspects or their typical behaviors or attitudes.
All self-report measures are inherently flawed for several reasons. One, people may not be accurately aware of their habits. They may overestimate or underestimate the frequency with which they engage in certain behaviors.
For example, people can often overestimate how often they exercise or may not be the best evaluators of their nutritional intake.
In addition, social desirability is a significant concern when asking individuals to report on their own behavior. Some participants may try to respond to questions in a way that they believe will make them look more favorable. This can occur despite the researchers’ assurances that responses will be anonymous or confidential.
For these reasons, and several others, self-report data can lack accuracy.
7. Disdvantage: Sampling Issues
Although utilizing existing large datasets is an option that limits many issues involving sampling, researchers do not always have access to these datasets or they may not be suitable to the subject under study.
In this case, the researchers must identify a sample themselves to implement the study. This can lead to issues regarding the sample’s characteristics.
For instance, the sample may not be heterogeneous. A sample that is not heterogeneous is not representative of the general population. Therefore, the results of the research have limited generalizability.
Secondly, even a heterogeneous sample may have limited value if it is too small. In general, the larger the sample size the better. However, sometimes researchers may be unable to obtain a large sample for practical reasons. This can also limit the accuracy and generalizability of the results.
8. Disdvantage: Response Rates
Another weakness of cross-sectional studies involving the sample has to do with the response rate. The response rate is how many of the study participants actually fill-out the questionnaires.
For instance, if the research team sends out 1,000 questionnaires, but only receive 455 of them back, that is a response rate of less than 50%.
In a literature review, Manfreda et al. (2008) found that responses rates for online surveys ranged from 15 to 60%.
Unfortunately, even if the statistical analyses reveal a very strong correlation between two variables, it is only based on half of the dataset. If all of the participants had returned the questionnaires, the results of the statistical analyses may be completely different.
Poor response rates are a serious issue and a lot more common than you might think.
Cross-Sectional vs Longitudinal Pros and Cons
There are certainly times when cross-sectional studies are more useful to use than longitudinal studies.
For example, compared to longitudinal studies, cross-sectional studies are more efficient and cost-effective, and are perfectly useful when simply attempting to capture data about a snapshot in time.
Indeed, for many of my research students, a longitudinal study is infeasible within the timeframe of their projects. Cross-sectional projects are far more manageable for research students.
|Cross-Sectional Studies||Longitudinal Studies|
|Efficiency||More efficient as data is collected at a single point in time.||Less efficient and more time-consuming as data is collected at multiple points in time.|
|Attrition||Cross-sectional studies suffer from less attrition because there is less demand on participants to return for a follow-up study.||Suffers from high attrition because participants may go missing (for a range of reasons) between each data collection session.|
|Cost||Typically less expensive due to single-time data collection.||Usually more expensive due to repeated data collection over time.|
|Data Complexity||Less complex data due to a single time point.||More complex data due to multiple time points, requiring advanced statistical methods.|
However, there are unarguably large benefits to longitudinal research. Key here is the focus on changes over time. Longitudinal studies can achieve this; cross-sectional studies cannot.
|Cross-Sectional Studies||Longitudinal Studies|
|Changes in Variables||Cannot establish changes in variables or sub-groups over time.||Can establish changes in variables or sub-groups over time.|
|Accuracy of Self-Report Measures||Often relies on self-reported measures, which can have accuracy issues.||Also often uses self-reported measures, but repeated measures can sometimes provide more accurate data.|
The key to whether you should choose a cross-sectional study is whether it will help you to sufficiently address your research question. The study design should always follow from the research question. If a cross-sectional study is ideal for your research project, then you should be able to argue in your methodology chapter why you chose this approach, despite its many disadvantages.
Note that I haven’t fully explored cross-sectional research in this article. I would recommend reading my more detailed piece on cross-sectional study examples here.