# quasi-experimental design

“There is more likely to be a causal relationship between the variables investigated in a
quasi-experimental design than a correlational design”. Yes? No? Maybe? Demonstrate
your knowledge and understanding of these designs in answering this question. Examples
of your own devising will help.

Based on the statement, there is a possibility of there being a causal relationship that does
exist between variables that are being investigated in a quasi- experiment more than in a
correlation research design. The causal relationship is also referred to as a cause and effect
relationship and deals with establishing a change between variables all the things being held
constant (McMillan and Schumacher, 2014, p. 22). For a cause-effect relationship to be
determined five aspects have to be present; causal mechanism, empirical causation, temporal
priority, nonspuriousness, and the specification of the context of occurrence (Best and Kahn,
2014, p.11).
A quasi- experiment lacks some attributes of an actual experiment an aspect that increases its
effectiveness in establishing a cause and effect relationship between the variables. The attributes
of a true experiment are random allocation, strict control and the variation in the independent
variables to determine time order. The most prominent characteristics that lack in a quasi-
experimental is the random allocation this makes it decreases the confidence of the findings
established from this research design (Campbell and Stanley, 2015).

STATISTICS 2
According to Cohen et al., (2013), correlation refers to the existence of a definite relationship
between two or more variables. It does not tell anything about the cause-effect relationship. Even
in situations of a high degree of correlation does not mean that there exists a causal relationship
between the variables. In other words, correlation does not necessarily imply causation
relationship, though the existence of causation always implies correlation. On its correlation does
establish the co-variation (Creswell, 2013).
Correlation does arise out of pure chance in a small sample for example in the world; there may
be a high degree of correlation between two variables in the universe, there may not be any
relationship between the variables at all hence minimal chance of establishing cause and effect
relationship (Leary, 2016).
Also, the correlated variables may be influenced by one or more other variables. For example, in
the agricultural sector, there exists a high correlation between the yields that a farmer makes per
the hectares they have concerning corn and wheat. The correlation may arise as a result of the
two variables being attributed to the amount of rainfall. When evaluating the cause-and-effect
relationship, none of the variables are related to each other.
Moreover, the variables may be mutually exclusive in that they influence each other in their
occurrence so that neither of them can be designated as the cause and the other as the effect
(Leary, 2016). For example, in economics, according to the law of supply and demand. An
increase in prices results in a decrease in demand and vice versa, but in some cases, an increase
in demand due to increase in population or other reasons may force its price up. In this case, we
can state that the correlation does not necessarily infer the presence of a cause and effect
relationship existing between the variables.

STATISTICS 3
In conclusion, correlation does not necessarily mean the existence of a causal relationship as
discussed in the essay. The causal relationship is less prominent in the application of correlation
in the analysis of data. Conversely, the cause and effect relationship does prominently manifest
itself in the quasi- experiments due them lacking the attributes of a true experiment making them
easy to manipulate.

REFERENCES

Best, J. W., & Kahn, J.V. (2014). Research in education. Pearson Higher Ed.
Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi- experimental designs for
research. Ravenio Books.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation
analysis behavioral sciences. Routledge.
Creswell, J.W. (2013). Research design: Qualitative, quantitative, and mixed methods
approaches. Sage Publications.
Leary, M.R. (2016). Introduction to behavioral research methods. Pearson.

STATISTICS 4
McMillan, J.H., & Schumacher, S. (2014). Research in education: Evidence-based inquiry.
Pearson Higher Ed.