Cross-Sectional Studies: Types, Pros, Cons & Uses
By busayo.longe
| Last updated:
Jul 27
|
12 min read
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As a researcher, when you want to study the relationship between two variables to determine if there’s a cause and effect factor what do you do?
Although there are diverse ways to measure the prevailing characteristics in a sample group, a cross-sectional study is the most efficient. Read on to understand the concept of a cross-sectional study, and how you can apply it to your research study.
A cross-sectional study is a type of observational study where participants selected are chosen solely on the addition and subtraction yardstick initially designed for the study. In this study, the exposure of the participants and results are measured consecutively by the researcher.
Cross-sectional studies are used in population surveys and can be performed quickly with less cost. They can be conducted before a population study and also be used as a basis when studying a population with similar traits.
A researcher should note that a cross-sectional study is a one-time measurement of results and it is difficult to determine a cause-and-effect relationship from the outcome. The prevalence of a particular factor can, however, be analyzed in a cross-sectional study.
1. As a researcher, you can conduct cross-sectional research using the same set of values of variables at a time.
2. Closely related studies may consider the same variable as the desired interest, however, each study analyzes new data.
3. There is always a starting point and a stopping point in a cross-sectional study. This study analyzes subjects in a single stage.
4. A researcher can only focus on one independent variable and one dependent variable in a cross-sectional study.
Let’s briefly look into this example of a cross-sectional study
A family at a wedding took a snapshot.
We can determine what is happening in real-time using the family in that photo. This is because all the people in the picture in the photo share one common variable and other variables that are not common to the entire group.
The common variable is their relationship as a family while the uncommon variables are individual traits.
A researcher can derive some observations and make some analysis starting from the point of the snapshot which is why a cross-sectional study is said to have a starting point and a stopping point.
The researcher can also use a cross-sectional study to determine a common variable or prevailing variable at a specific point in time. This can be in the past or in the present. For example, If we look back at the family in the photo, the researcher can decide to use a cross-sectional study to analyze whether there is a similar trait or same sense of humor.
There are two types of cross-sectional studies and they are:
Descriptive research analyzes how frequently or wide the variables of interest appear in a particular population. In descriptive research, the researcher tries to identify the trends and use the outcome to develop products or services that can be useful for the population. Descriptive research is not necessarily looking for why the trends or patterns in the study were present.
Analytical research on the other hand studies the relationship between two common variables and two uncommon variables.
It is noteworthy that original data and results are simultaneously studied together.
For example, to determine whether a Tobacco factory worker could develop lung problems, the study will focus on the variables in the tobacco factory. It will not look at the probability of other factors causing the lung problem or even the possibility that the lung problem started before the worker gained employment in the tobacco factory.
It should be noted here that the researchers use both descriptive research and analytical research methods when conducting a cross-sectional study.
Read: What is Applied Research? + [Types, Examples & Method]
There are some issues in the design of cross-sectional surveys and we are going to examine them.
1. Selecting a sample group to represent the entire population: We have noted that a cross-sectional study serves as a representative of a population.
For a cross-sectional study to be valid, characteristics from the population have to be present in the sample group. For example, a researcher studies the prevalence of diabetes in women between the age of 45 to 60 in a city. To carry out this study, the researcher has to select women to represent the population of age group 45 to 60, using randomization.
2. Sample Size: the sample size selected by the researcher should be large enough to properly analyze the prevalence of the condition.
3. The possibility of research bias in a cross-sectional study: There can be an issue in a cross-sectional study if the characteristics of the participants are different from the characteristics of the nonparticipants. This can lead to bias in the outcome of the study.
Read: Experimental Research Designs: Types, Examples & Methods
It is difficult to assess cause and effect relationships in a cross-sectional study. This is because the cross-sectional study is a single-time analysis of the exposure and the result.
For example, if a researcher wishes to find the association between diet and obesity, they will conduct a cross-sectional study. Sample size will be selected to represent the population and the sample size selected can be 150 participants.
Their BMI (Body Mass Index), dietary and exercise habits at a specific time will also be examined in the study. If obese participants changed their diet, started to eat more vegetables, and began to exercise, it implies that in the cross-sectional study, there may be more results showing that obese participants are more likely to exercise and eat veggies.
So a researcher must be careful when interpreting the sample direction and relationships in a cross-sectional study.
Another limitation of a cross-sectional study is that the prevailing result is dependent on the incident and the time it took to recover after the result.
If a cross-sectional study is used alone to study diseases in the medical field, it might be difficult to fully understand the trends of the disease.
Read: Correlational Research Designs: Types, Examples & Methods
Read: 11 Retrospective vs Prospective Cohort Study Differences
You can create just about any form you can think of or imagine on Formplus. The templates available are over 1000 and they are super easy to use.
Here’s a detailed guideline on how you can create a cross-sectional survey.
1. Sign in to your Formplus account. If you have not yet signed up on Formplus, it’s absolutely free.
2. On the form builder, develop the information that will be on your survey. Outline your research goals and then choose your population sample.
3. You can begin to develop your survey questions after which you will choose the method you want to use to apply your survey.
4. Customize your surveys using any of the available themes or using a custom CSS.
5. Formplus allows you to share your survey on social media platforms and through emails
6 Gather your survey responses and analyze them on Formplus. Set your survey to segregate answers based on age, socioeconomic status, and gender.
You can use a cross-sectional study when;
Example 1
You want to know the number of students relocating to a new area so you can find out how much money you’d gain from renting your apartment in that area over the next five months. Since all you need to know is the current number of new students, all you need is a cross-sectional study.
Example 2
Let’s say you want to know the demographic of Londoners who like Almond milk. A cross-sectional design will help you identify the patterns for this research.
Data about a particular subject is mostly gathered at a point in time in a cross-sectional study.
For example, you can send the questionnaire to an industry where forestry is prevailing. The questionnaire may ask questions such as, is there a presence of osteoarthritis in the industry?
Asking this question helps a researcher to determine if there are cases of this condition.
The questionnaire can also help researchers to gather information about the condition, find out if there has been any exposure, and analyze the relationship that may exist.
Although it can be a little bit difficult to trust the results of the relationship, the information gathered from cross-sectional research can be the beginning of a study that will lead to more substantial designs. Here, the researcher can merge the initial outcome and find the most accurate results.
If you go back to the definition of a cross-sectional study, It says cross-sectional study research is a particular subject at a particular point in time.
This means that research of longer years, mostly later than five years, would blur the line of cross-sectional study although this depends on the data being studied. In real life, research can be conducted for five years. There is no reason why a five-year period shouldn’t be enough to conduct a cross-sectional study.
A rule of thumb is that if the data you want to collect cannot be gathered in a short amount of time, then perhaps other research methods cannot do it even for a longer time.
What this means is that you can collect survey data in five years. However, what you want to do with the collected data will determine whether the time is enough or not.
Your research questions will determine if the time you are using for the study is sufficient to get the desired outcome.
You’re going to look at this from the angle of characteristics of a cross-sectional study and that of a longitudinal study.
A Cross-sectional study is done faster and easier than a longitudinal study. That is why most researchers begin their studies with a cross-sectional study so that they can establish if there’s a relationship between the variables of their research.
After the initial cross-sectional study the researcher would then conduct a longitudinal study. This is because a longitudinal study is also observational in nature, just as a cross-sectional study.
Unlike cross-sectional studies where the researcher conducts a single finding on the subject, in longitudinal studies, the researcher conducts a series of observations on one subject over a long period of time. This study is to analyze if there is a cause and effect in the variables.
In a longitudinal study, the researcher can detect changes in the characters of the sample group and the participants. We may not be able to conclude on which of the studies is better. However, if you need to conduct a quick and less expensive study your best bet is a cross-sectional study.
If you want to conduct longer research and you want to measure developments in your population then make use of longitudinal study. We can then say that the right method of study to apply in research depends on what is to be researched, the objectives and goals of the research, along with the cost and time available for the research.
Most cross-sectional studies are quantitative. They gather data through interviews, questionnaires, and focus groups over a certain period in time which may be in the past or the present, and then analyze the results.
However, there are some qualitative cross-sectional study and they can also be a mixture of both quantitative and qualitative. For example, most research in the medical industry is cross-sectional qualitative studies.
Cross-sectional studies are cheap and quick to conduct.
Questionnaires are usually used to conduct cross-sectional studies so information is gathered quickly.
A cross-sectional study focuses on the predominant incidents in the population and determines the outcome so it doesn’t study the relationship between variables.
However, in cohort study both the treatment group and controlled group results are analyzed. This is because the researcher tries to find the relationship between the cause and effect in a cohort study. A cohort study requires a large population and it is very expensive. Inadequate data in a cohort study would lead to errors in the result.
A researcher can use self-developed questionnaires to gather data in a cross-sectional study. This is because the aim of a cross-sectional study is mostly to analyze the characteristics of a sample group. Therefore a researcher can put up survey questions for the respondents so as to measure the variables of interest.
If your interest as a researcher is to study the causal relationship that exists between two variables in a population, the most accurate method to achieve that is a cross-sectional study. Start by creating online surveys with Formplus.
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