Explanatory Hypothesis – Concept, Components, Examples, Challenges | Business Research Methodology
Explanatory Hypothesis
An explanatory hypothesis is a research hypothesis or cause-and-effect hypothesis that provides insight into the relationship between variables in scientific research.
An explanatory hypothesis proposes that changes in one variable lead to changes in another variable. Its primary objective is to determine causal relationships between variables and predict the effects of one variable on another.
A researcher can use an explanatory hypothesis to understand the mechanism or factors underlying a particular outcome. It is possible for researchers to gain insight into the relationships between variables and make predictions about the impact of changes in one variable on another by establishing cause-and-effect relationships.
Components of an Explanatory Hypothesis
Explanatory hypotheses have several main components that shape their structure and content. These include variables, causal relationships, predictions, and testability.
i. Variables:
Explanatory hypotheses focus on relationships between two or more variables. A variable is a property or characteristic that can change.
An explanatory hypothesis typically has independent variables and dependent variables.
a. Independent Variable:
An independent variable is a variable that is controlled or manipulated by the researcher. A researcher changes the independent variable to observe how it affects the dependent variable.
The independent variable is hypothesized to influence the dependent variable.
b. Dependent Variable:
In a research study, the dependent variable is the variable to which the independent variable is hypothesized to influence the outcome or effect.
In order to achieve clarity and precision in their hypothesis, researchers identify and define variables according to the research question and phenomenon under investigation.
ii. Cause and Effect Relationship:
Explanatory hypotheses are based on the concept of cause-and-effect relationships between independent and dependent variables.
They suggest that an increase in the independent variable leads to an increase in the dependent variable. Independent variables are considered causes, whereas dependent variables are considered effects.
Researchers can understand how variables relate to one another when they establish a cause-and-effect relationship, which is essential for scientific research.
With this understanding, various fields of study can be advanced theoretically and practically.
It is common for researchers to use experimental or quasi-experimental designs to demonstrate the causal relationship between the independent and dependent variables.
A researcher can use this process to determine the causal influence of an independent variable on a dependent variable.
iii. Prediction:
Researchers can make predictions about how the independent variable affects the dependent variable by proposing a cause-and-effect relationship.
This is one of the significant advantages of explanatory hypotheses. The predictions form testable hypotheses that guide research.
In order to predict a relationship between variables, researchers use explanatory hypotheses. The explanatory hypothesis, for example, might predict that increased exercise leads to a greater loss of weight if it is studied in the context of weight loss.
Using predictions, researchers can design studies, collect data, and analyze results to determine if their hypotheses are valid.
Researchers can refine existing theories or develop new ones as a result of this process, which contributes to the understanding of the relationships between variables.
iv. Testability:
Explanatory hypotheses need to be testable in order to be scientifically valid. To be valid, hypotheses must be tested empirically using rigorous research methods.
Hypotheses that can be tested based on empirical evidence must be testable.
A researcher can use a variety of research designs and methodologies to test an explanatory hypothesis. Experimental designs manipulate the independent variable, while quasi-experimental designs compare groups with different levels of the independent variable.
Variables are observed and measured without manipulation in observational studies.
Using experiments, data collection, and statistical analysis, researchers can determine how variables are related to the explanatory hypothesis.
As a result, the hypothesis is supported or revised based on the results of these analyses.
Examples of Explanatory Hypotheses
Some of the examples of explanatory hypotheses are as follows:
i. Example from Psychology:
It is possible to propose the following explanation hypothesis, “Sleep deprivation impairs cognitive performance,” if a researcher is interested in understanding how sleep deprivation impacts cognitive performance.
It is hypothesized that sleep deprivation is the causal factor that influences cognitive performance. Sleep deprivation is the independent variable, and cognitive performance is the dependent variable.
An experiment might be designed where participants were randomly assigned to either a sleep-deprived or a well-rested condition to test this hypothesis.
As a result, sleep deprivation will lead to a decline in cognitive function, if the hypothesis is supported by standardized tests or tasks.
ii. Example from Economics:
Suppose an economist wished to investigate the relationship between government spending and economic growth.
An explanatory hypothesis might be: “Increased government spending results in increased economic growth.”
An economist hypothesizes that increased government spending leads to economic growth. Government spending is the independent variable, while economic growth is the dependent variable.
Using historical data, the economist could examine the relationship between government spending levels and economic growth rates across different countries and times to test this hypothesis.
Economic growth and government spending should be positively correlated, if the hypothesis is correct.
iii. Example from Biology:
The study of biology often examines the relationship between genes and phenotypes or traits. Here’s one hypothesis explaining how genes affect disease:
“A homozygous mutation of a gene increases the risk of developing a disease.”
Research suggests that individuals with specific gene mutations are more likely to develop the disease. The independent variable is the gene mutation, while the dependent variable is the risk of developing the disease.
It may be possible to test this hypothesis by collecting genetic data from a sample of individuals and determining whether the presence of the gene mutation increases the disease risk.
Challenges of Explanatory Hypothesis
It is important for researchers to consider several challenges associated with explanatory hypotheses, just as they do with any other scientific hypotheses. These challenges can affect the formulation, testing, and interpretation of explanatory hypotheses.
Here are some of the common challenges associated with them:
i. Relationship Complexity:
Explanatory hypotheses usually attempt to explain a relationship between variables. It is difficult to capture the full complexity of these relationships in a single explanatory hypothesis since real-world relationships are complex and involve multiple factors.
In order to determine the relationship between the independent and dependent variables, researchers must consider additional variables, potential interactions, and contextual factors.
ii. Causal Inference:
Establishing causality is the fundamental goal of an explanatory hypothesis. There are a number of factors that make it challenging to establish a cause-and-effect relationship. Researchers need to consider alternative explanations as well as possible confounding variables that may influence the observed relationship.
For causality to be established, researchers must use appropriate research designs. In order to make valid causal inferences, randomization, control groups, and statistical analysis are crucial.
iii. Third-Variable Problem:
A common challenge in explanatory research is the third-variable problem, sometimes called confounding. This is caused when a third variable is linked to both the independent and dependent variables, creating a false relationship between them.
In order to ensure that the observed relationship between independent and dependent variables is not caused by the influence of a third variable, researchers must identify and control for potential confounding variables.
It can be achieved through research design, statistical techniques (e.g., regression analysis), or matching procedures.
iv. Causality Reversed:
Reverse causality occurs when the hypothesized causes and effects are reversed. In other words, the independent variable may be the outcome or result of the dependent variable. This is a problem that can arise when studying dynamic systems or processes.
Ensure that the proposed causal relationship does not reverse by carefully considering the temporal sequence of events and potential feedback loops.
v. Sample Selection Bias:
When the study sample does not reflect the target population, sample selection bias occurs. The results may not be generalizable if certain characteristics of the sample are biased.
The external validity of a research study depends on obtaining a diverse and representative sample. Techniques such as stratified sampling and random sampling can be used to mitigate selection bias.
vi. Generalizability:
The generalizability of explanatory hypotheses is important when formulating and testing them within a specific context, population, or time frame.
An observed relationship between variables may not be universal. It is important to consider the generalizability of findings when analyzing them.
A researcher should use diverse samples, replicate their findings, and cross-validate their findings to ensure their findings are generalizable.
vii. Ethical Considerations:
Research aimed at testing explanatory hypotheses may involve manipulating variables or exposing participants to certain conditions.
It is important for researchers to ensure that ethical concerns, including informed consent, privacy, and participant well-being, are addressed at every stage of their research.
Conducting research ethically requires following ethical guidelines and obtaining the approval of institutional review boards.
viii. Unobserved Variables and Mediation:
There are unobserved variables and mediating variables that can influence explanatory hypotheses but may be overlooked.
In an experiment, unobserved variables are factors that are not measured or taken into account in the hypothesis, but which may affect the relationship between the independent and dependent variables.
The relationship between independent and dependent variables can be explained by mediating variables. In order to determine the impact of these possibilities, researchers must be aware of them and take them into account.
Explanatory hypotheses are fundamental components of scientific research, enabling researchers to examine causal relationships between variables.
Researchers can investigate underlying mechanisms and factors that contribute to an outcome by proposing cause-and-effect relationships.
In studies, researchers can test the validity of explanatory hypotheses by designing, collecting, and analyzing data. This leads to advancements in scientific knowledge, refinements in existing theories, and applications in real-world situations.
Although explanatory hypotheses offer valuable insights, researchers must also consider the complexity of relationships, causal inference challenges, generalizability considerations, and ethical issues.
The research community can improve its quality and impact by addressing these challenges, resulting in a deeper understanding of the relationships between variables.
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I am Bijisha, an enthusiast with a profound eagerness for learning. I hold a Bachelor’s degree in Business Administration(BBA) from Apex College. I am constantly driven by a relentless curiosity and a genuine desire to expand my knowledge horizons.
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