Correlational and causal research both follow similar basic
scientific research design, where a research question is posed, then followed
with a hypothesis and a null-hypothesis, where quantitative data either supports
the research or null hypothesis

**Example: **

The
relationship between age and driving speed

**Does the
example demonstrate a causal or correlational relationship?**

The above example attempts to demonstrate a correlational
relationship due to several reasons. The first is that it is vaguely phrased

**What’s needed
to demonstrate a causal relationship?**

The main requirement for demonstrating a causal relationship is to
account for extraneous or confounding variables. Such variables are third party
variables that aren’t accounted for and might influence the relationship
between the variables being studied (age and driving speed)

**Evaluate
whether a chi-squared test or a correlation coefficient would be more suitable
to analyze the data in the example. **

In order to properly analyze the data in the example, it is
important identify the variable types, which will aid in identifying the most
suitable test type. Chi-squared test, an association test, is used to
demonstrate that the phenomena co-occur among two or more nominal variables (Dancey & Reidy, 2017). Therefore, if the
example was phrased as such: how is gender (nominal) associated with driving
speed (nominal), then Chi-squared would be most suitable.

Moreover, how the data is collected can sway the researcher to
choose one of two types of correlation coefficients in order to evaluate the
data. If an ordinal scale was used to collect the data, a non-parametric, is in
does not rely of a normal distribution, test called Spearman’s rank order
correlation would be the likely option

**References**

Andrade, C. (2018). Internal,
external, and ecological validity in research design, conduct, and evaluation. *Indian
Journal of Psychological Medicine *, 498-499.

Bollen, K. A., & Barb, K. H. (1981). Pearson's
r and coarsely categorized measures. *American Sociological Review*,
232-239.

Dancey, C., & Reidy, J. (2017). Hypothesis
testing and statistical significance . In *Statistics Without Maths for
Psychology (7th ed.)* (pp. 134-173). Harlow, UK: Pearson.

Dancey, C., & Reidy, J. (2017). Non-parametric
statistics . In *Statistics Without Math for Psychology (7th ed)* (pp.
516-550). Harlow, UK: Pearson.

Gonzalez, K. (2018). *What is a null hypothesis?
- definition & examples*. Retrieved 7 19, 2018, from Study:
https://study.com/academy/lesson/what-is-a-null-hypothesis-definition-examples.html

Huff, D. (1973). *How to lie with statistics.*
London: Penguin.

Schmid, F., & Schmidt, R. (2007). Multivariate
extensions of Spearman's rho and related statistics. *Statistics &
Probability Letters*, 407-416.

SEP. (2016). Correlational research. *Salem
Encyclopedia Press*, Retrieved from
https://liverpool.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ers&AN=119214045&site=eds-live&scope=site.

Srinagesh, K. (2006). Planning the experiments in
statistical terms. In *The Principles of Experimental Research *(pp.
333-372). Amsterdam : Butterworth Heinmann.

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