Cross-sectional research- definition, methodology,methods,characteristics,types, uses,examples and advantages

1.1 Definition

Cross-sectional research is a type of systematic investigation which is classified under time data point dimension. The study is observational for the researcher describes characteristics of the unit of observation from the population by doing physical watching of the behavior thereof. The researcher undertakes physical observation based on the variable of interest that he/she wants to observe.

Cross-sectional research entails examining of the population so as to make a conclusion on the group being investigated. Such that, he/she can analyze a particular group of participants and study different variables of interest at the same time. The variables of the population which can be considered are such as sex, age, income or level of education. So, in this case, the researcher will have a concept or variable to study such as mortality rate amongst women for the last five years from 2016 to 2020 (that is sex status which is descriptive) or malaria disease infection level amongst children for the last three years from 2019 to 2021(that is age status which is descriptive) or HIV/Aids infection level amongst females who are divorced for the last ten years from 2011 to 2021 (that is marriage status which is descriptive).


Cross-sectional studies examine a population and draw conclusions from that group. This means that researchers can analyze that group and study different variables at the same time. Variables of a population could include gender, age, income or level of education. Cross-sectional studies typically have a subject or concept that researchers wish to address, such as blood sugar levels within a community, and they can analyze the different variables of a group to gather information about that subject.

Cross-sectional research aims at describing the characteristics of the study variable but it does not determine the cause-effect association between or amongst variables. The study mostly investigates inferences about possible relationships for further research.      

1.2 Classification of Cross-Sectional Research

Cross-sectional research is classified into three categories, that is: -

1.2.1 Descriptive Cross-Sectional Research

Descriptive cross-sectional research is a time data point dimension-based investigation that explains in detail how a certain variable affects a certain population. For instance, this study/investigation describes how drug abuse affect the rate of school completion amongst the youth in Minnesota State in USA. To achieve this objective the researchers in this type of investigation will concentrate on giving a point of detail may be on the age or sex which is affected by the variable of interest such as drug abuse. They will not consider the causes of drug abuse amongst the youth.

1.2.2 Analytical Cross-Sectional Research

Analytical cross-sectional research is a time data point dimension-based investigation that involves comparison of two dissimilar criteria used on a particular study subject. The aim being to establish new information based on the criteria of interest.

For instance, the researcher may wish to know whether tuition/remedial classes improve candidates’ academic performance. So, in one case, a class is subjected to tuition class in addition to normal class work.

You should note that, although there may be other factors which can make students perform better such as being genius, in such a case you will realize that whether one attends the tuition/remedial classes or not will still perform exemplary.

1.2.3 Repeated Cross-Sectional Research

Repeated cross-sectional research is a time data point dimension-based investigation whereby the researcher adopts follow-up test procedure after the initial test has been done so as to gather more data/information pertaining their original test. So, the researcher uses the same population but new participants in five or even 12 months to see how the results change or vary.  The aim of the researcher is to complete that process severally, then they can collect cross-sectional data about the population.

Cross-sectional research methodology

2.1 Definition

Cross-sectional Research Methodology is the logical process or step by step blueprint or design on how to solve a research problem that calls for measurement of both the outcome and the exposures of the study participants at the same time. Cross-sectional Research methodology entails selection of logical procedure on the topic to be studied. The aforementioned description /definition on Cross-sectional Research methodology is in line with the argument of (Kothari, 1984) who was of the idea that research methodology is the justification behind the methods we use in the context of our research study. This provides a logic as to why one is using a particular method or technique at a particular stage in the research process and not others so that research output is accomplished as supposedly.

2.1.1 Questions Cross-sectional Research Methodology tries to Answer

The aim of Cross-sectional research is to answer questions related to prevalence or occurrence of a certain condition, conviction or a situation on the ground. Therefore, cross-sectional research tries to answer what the norm is for a certain demographic at some point in time such as expected age, expected sex etc.

In short, cross-sectional research tries to answer the “What” and “How” questions.

For instance,

What is the expected age for women to get married in the African context?

What effect has Covid-19 vaccine caused amongst the old age group?

What is the life expectancy level of males in Europe?

How does traditions affect learning in school in an African context?

The following matrix portrays the link between Cross-sectional type of research and the type of research methodology adopted and then an explanation of the logical approach associated with this category and then in the last column, the research method(s) used in formulating the research problem.

2.2 Research Methodology-Diagrammatic Approach

The following diagram represents a summary of logical roadmap to be adhered to in Cross-sectional research methodology

2.3 Logical Steps; Cross-sectional Research Methodology

The following logical steps describe the Cross-sectional Research methodology. From step one to eight, it represents a logical way of how systematically the subject matter need to be dealt with. Remember that in this approach, the researcher is concerned with whether there is any relationship between the two variables so as to conclude whether further research is necessary or not.

Step 1: Identification of the Research Problem

Under research problem, the researcher is concerned of whether there exists any relationship or correlation between two variables. So, he/she wants to infer if there is an association although it may not be sufficient enough to guarantee a direct cause. In case of existence of any element of a link between the two variables, then this may prompt the researcher to do further research or somebody else can take up the mantle.

Step 2: Recruitment of Participants on the Basis of Inclusion &Exclusion Criteria

With the research problem in mind, the researcher identifies the population in question so as to undertake his/her investigation. At this stage, the researcher follows the study to assess the exposure and the outcomes as per the proclamation.

Step 3: Data Collection

In this stage, the researcher assesses the participant on exposure and the outcomes at the same time. This mission is achieved through collection of the relevant data using a questionnaire using survey approach. Remember that this is a cross-sectional research where by the data is collected at one data point in time. It is at this stage that the researcher will study the exposure and the outcome pertaining the matter under investigation. Yes, the data collected is representing exposure and outcome at the same time for a particular variable of interest by the researcher.

Step 4: Data Analysis

Once data is collected, the next step is data analysis based on inclusion and exclusion criteria. In this step, the researcher measures the participant for outcome and exposure at the same time.

Step 5: Estimation of Prevalence

The researcher engages in estimation of the occurrence of outcome and exposure at the same time as pertains the participants to know the in-depth of the impact due to exposure. At this step, the researcher has empirical evidence of the end results of the exposure and can compute the odd ratio.

Step 6: Computation of Odd Ratio

Computation of odd ratio is a comparison quotient where by the coefficient or the number of participants in the sub-group of inclusion criteria is compared with the coefficient or the number of participants in the sub-group of exclusion criteria. The odd ratio is the indicator which portrays inference of a relationship/correlation between the two variables.

Step 7: Inference of a Correlation

In this stage, the researcher does his or her judgement based on the outcome in step six above as to whether there exists any relationship/correlation. Remember at this point the researcher cannot tell any cause-effect link.

Step 8: Report Writing/Further Research

The last step is that of reporting the research findings and it is very simple. Either to provide evidence that there exists correlation or not and may be show the necessity of carrying further study or someone else moving on to test for cause-effect link.

Cross-sectional research methods

Research methods are all the techniques that are utilized in all the stages of research processes. They are tools used to ensure the end results of research task are accomplished. These techniques vary from one stage of research process to another. These methods are further classified in to two categories, namely;

a) Pre-Data analysis methods

b) Data Analysis related methods

As per Table 1.1 in this article, the cross-sectional research method indicated in that table (refer), namely Surveys, method is used for the purposes of formulating the research problem and are some of the methods which fall under pre-data analysis category.  However, in this discussion of cross-sectional research, there is only one method of collecting data, that is observation.


In cross-sectional research, the researcher uses observation approach to collect data pertaining the behavior he/she is studying. Observation entails collecting data from many different individuals at a single data point. During the observation of the characteristic, the researcher cannot manipulate the variable being studied. 

Characteristics of cross-sectional studies

  1. Data is collected from one data point in time. That is a researcher can conduct this type of research using the same set of values of variables at a time.
  2. Similar studies make use of a common variable of interest although the data generated in each case is not alike.
  3. Independent-dependent variable analysis-this type of research only focuses on the predictor and response variables only.
  4. Observational in nature-cross-sectional research or study does not manipulate variables.
  5. Allows for both exposure and outcome prevalence to be recorded at the same time. For instance, characteristics pertaining the participant such as age, income, sex, etc. can be recorded at the same time.
  6. Use of population-cross-sectional research focus on the characteristics of the population and not the sample.
  7. It furnishes the researcher with the situation or prevailing circumstance currently taking place with the population.

Advantages of a cross-sectional research

  1. Time saving-it is conducted in a faster manner than the rest of the studies. Hence, saves time. This can be justified by the fact that the data on all the variables are collected only once. For example, if the researcher wants to know about sale performance of a firm, he/she will ask one respondent at that moment on how the aspect of sales was over the last five years. The answer will be either increasing, constant or declining. This saves time for it is a quick answer
  2. Easy to be carried out-in other words, it is a procedure which is not cumbersome for data collection is only done once
  3. Ability to measure prevalence or occurrence for all variables of interest by the investigator.
  4. Cost effective-using cross-sectional research, the researcher saves on costs, in other words, it is cheap.
  5. The researcher can simultaneously establish multiple outcome and exposure being studied.
  6. Appropriate in describing an analysis when the researcher want to develop a hypothesis.
  7. Foundation for further areas of research-cross-sectional research ushers other researches for it provides a hint as to whether there exists any relationship/correlation between the two variables

Disadvantages of a cross-sectional research

  1. Difficulty to establish whether it is exposure which caused the outcome or the other way round. In other words, this study may not determine what comes first between an outcome or an exposure.
  2. Difficulties in interpreting the relationships thereof.
  3. Subjective outcome which are bias-since the identification of the participants is on the basis of inclusion or exclusion criteria, the results gotten may be bias. Hence loss of data validity.
  4. Does not measure causal-effect relationship-this study only establishes the dominant variable and not the causal event for the study’s focus is on prevalent case.

About the Author - Dr Geoffrey Mbuva(PhD-Finance) is a lecturer of Finance and Accountancy at Kenyatta University, Kenya. He is an enthusiast of teaching and making accounting & research tutorials for his readers.