Most often, in experimental research, when a researcher wants to compare groups in a more natural way, the approach used is causal design. On the other hand, in a non-experimental setting, if a researcher wants to identify consequences or causes of differences between groups of individuals, then typically causal-comparative design is deployed.
Causal-comparative, also known as ex post facto (after the fact) research design, is an approach that attempts to figure out a causative relationship between an independent variable & a dependent variable. It must be noted that the relationship between the independent variable and dependent variable is a suggested relationship and not proven as the researcher do not have complete control over the independent variable.
This method seeks to build causal relationships between events and circumstances. Simply said, it determines to find out the reasons/causes of specific occurrences or non-occurrences. Based on Mill’s canon of agreement and disagreement, causal-comparative research involves comparison in contrast to correlation studies which looks at relationships.
For example, you may wish to compare the body composition of individuals who are trained with exercise machines versus individuals trained only free weights. Here you will not be manipulating any variables, but only investigating the impact of exercise machines and free weights on body composition. However, since factors such as training programs, diet, aerobic conditioning affects the body composition, causal-comparative research will be assessed scrupulously to determine how the other factors were controlled.
This research design is further segregated into:
- Retrospective causal-comparative research – In this method, a research question after the effects have occurred is investigated. The researcher aims to determine how one variable may have impacted another variable.
- Prospective causal-comparative research – This method begins with studying the causes and is progressed by investigating the possible effects of a condition.
How to conduct causal-comparative research?
The basic outline for performing this type of research is similar to other researches. The steps involved in this process are:
- Topic selection – Identify & define a specific phenomenon of interest and consider the possible consequences for the phenomenon. This method involves the selection of two groups that differ on a certain variable of interest.
- Review the literature – Assess the literature in order to identify the independent and dependent variables for the study. This process lets you figure out external variables that contribute to a cause-effect relationship.
- Develop a hypothesis – The hypothesis developed must define the effect of the independent variable on the dependent variable.
- Selection of comparison groups – Choose groups that differ in regards to the independent variable. This enables you to control external variables and reduce their impact. Here, you can use the matching technique to find groups that differ mainly by the presence of the independent variable.
- Choosing a tool for variable measurement variables and data collection – In this type of research, the researcher need not incorporate a treatment protocol. It is a matter of gathering data from surveys, interviews, etc. that allows comparisons to be made between the groups.
- Data analysis – Here, data is reported as a frequency or mean for each group using descriptive statistics. This is followed by determining the significant mean difference between the groups using inferential statistics (T-test, Chi-square test).
- Interpretation of results – In this step carefully state that the independent variable causes the dependent variable. However, due to the presence of external variables and lack of randomisation in participant selection, it is probably ideal to state that the results showcase a possible effect or cause.
Flow chart
So, when should one consider using this research design?
Typically, causal-comparative research design can be considered as an alternative to experimental design due to its feasibility, cost-affordability and easy to perform the research.
However, in causal-comparative design, the independent variables cannot be manipulated, unlike experimental research. For example, if you want to investigate if ethnicity affects self-esteem, you cannot manipulate the self-esteem of the participants’. The independent variable here is already selected, and hence, some other method needs to be utilised to determine the cause.
Threats to the internal validity of the research
In this type of research, since the participants are not randomly selected and placed in the groups, there is a threat to internal validity. Another threat to internal validity is its inability to manipulate the independent variable.
In order to counter the threats and strengthen the research, impose selection strategies of matching utilising ANCOVA or homogeneous subgroups.
Causal-comparative design includes basic features such as:
- Involves selection of two comparison groups (experimental & control group) to be studied
- Includes making comparisons between pre-existing groups in regards to interested variables
- Studies variables which cannot be manipulated for practical or ethical reasons
- Consumes reduced amount of time and cost
Although this approach gives an opportunity to analyse data on the basis of your personal opinion and come out with the best conclusion, while predicting the relationship, you might fall to post hoc fallacy. Therefore, pay extra attention while predicting the relationship and then arrive at a conclusion.
