Causal Research (Explanatory research)
Causal research, also known as explanatory research is a type of research that aims to identify the extent and nature of cause-and-effect relationships. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc.
On this page:
- Meaning of causal research and its components
- Causal research methods
- Examples of causal research methods
- Advantages and disadvantages of causal research methods
The table below compares the main characteristics of causal research to exploratory and descriptive research designs:[1]
| Causal research | Exploratory research | Descriptive research | |
| Amount of uncertainty characterising decision situation | Clearly defined | Highly ambiguous | Partially defined |
| Key research statement | Research hypotheses | Research question | Research question |
| When conducted? | Later stages of decision making | Early stage of decision making | Later stages of decision making |
| Usual research approach | Highly structured | Unstructured | Structured |
| Examples | ‘Will consumers buy more products in a blue package?’
‘Which of two advertising campaigns will be more effective?’ |
‘Our sales are declining for no apparent reason’
‘What kinds of new products are fast-food consumers interested in?’ |
‘What kind of people patronize our stores compared to our primary competitor?’
‘What product features are the most important to our customers?’ |
Main characteristics of research designs
Meaning of Causal Research and Its Components
Causal studies focus on an analysis of a situation or a specific problem to explain the patterns of relationships between variables. Experiments are the most popular primary data collection methods in studies with causal research design.
The presence of cause cause-and-effect relationships can be confirmed only if specific causal evidence exists. Causal evidence has three important components:
1. Temporal sequence. The cause must occur before the effect. For example, it would not be appropriate to credit the increase in sales to rebranding efforts if the increase had started before the rebranding.
2. Concomitant variation. The variation must be systematic between the two variables. For example, if a company doesn’t change its employee training and development practices, then changes in customer satisfaction cannot be caused by employee training and development.
3. Nonspurious association. Any covarioaton between a cause and an effect must be true and not simply due to other variable. In other words, there should be no a ‘third’ factor that relates to both, cause, as well as, effect.
Causal Research Methods
The following are main methods for causal research:
1. Experiments. Experiments refer to manipulating an independent variable and observing its effects on a dependent variable while controlling for other factors. Experiments are considered the gold standard of causal research due to their ability to provide strong evidence for cause-and-effect relationships. Unlike other methods that observe correlations, experiments actively manipulate independent variables to observe their impact on dependent variables, allowing researchers to isolate and measure the causal effect.
2. Quasi-Experiments. These is utilizing existing natural variations in independent variables and comparing outcomes between groups. While experiments offer the gold standard for causal research, they are not always feasible or ethical in certain situations. This is where quasi-experiments come in. Though not as rigorous as experiments, they provide valuable insights into causal relationships when randomization is not possible.
3. Propensity Score Matching (PSM). It is a causal research technique used to evaluate the effectiveness of interventions, policies, or marketing campaigns. It aims to minimize the impact of selection bias, which occurs when the groups being compared differ in ways that can influence the outcome variable.
4. Instrumental Variables. Using an instrumental variable that is correlated with the independent variable but not directly with the outcome to estimate the causal effect. Instrumental variables are a powerful tool in causal research, allowing researchers to estimate the effect of an independent variable on an outcome variable even in the presence of confounding factors.
5. Regression Discontinuity Design. This method refers to Exploiting discontinuities in a variable used to assign individuals to groups to identify causal effects. Regression discontinuity design is another effective causal research method gaining traction in business studies. This design leverages naturally occurring discontinuities or cutoffs in a variable to estimate the causal effect of an intervention or treatment.
Examples of Causal Research (Explanatory Research)
The following are examples of research objectives for causal research design:
- To assess the impacts of foreign direct investment on the levels of economic growth in Taiwan
- To analyse the effects of re-branding initiatives on the levels of customer loyalty
- To identify the nature of impact of work process re-engineering on the levels of employee motivation
Advantages of Causal Research (Explanatory Research)
- Causal studies may play an instrumental role in terms of identifying reasons behind a wide range of processes, as well as, assessing the impacts of changes on existing norms, processes etc.
- Causal studies usually offer the advantages of replication if necessity arises
- This type of studies are associated with greater levels of internal validity due to systematic selection of subjects
Disadvantages of Causal Research (Explanatory Research)
- Coincidences in events may be perceived as cause-and-effect relationships. For example, Punxatawney Phil was able to forecast the duration of winter for five consecutive years, nevertheless, it is just a rodent without intellect and forecasting powers, i.e. it was a coincidence.
- It can be difficult to reach appropriate conclusions on the basis of causal research findings. This is due to the impact of a wide range of factors and variables in social environment. In other words, while casualty can be inferred, it cannot be proved with a high level of certainty.
- It certain cases, while correlation between two variables can be effectively established; identifying which variable is a cause and which one is the impact can be a difficult task to accomplish.
When to Use Causal Research
You can use causal research when you reserch objective is to test cause-and-effect relationships between variables and provide evidence for management decision-making. In business studies, causal research is commonly used in areas such as marketing effectiveness, organizational performance, policy evaluation, and impact assessment of innovations such as artificial intelligence or digital transformation.
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[1] Source: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning