Spearman's Rho and its Suitability for Correlational Studies with Ordinal Data

Spearman's rho is a statistical test used to measure the strength and direction of a relationship between two variables. It is a non-parametric test, meaning it does not require the data to be normally distributed. This essay will explain why Spearman's rho was a suitable test for a study investigating the relationship between the number of hours slept and how well-rested students feel, considering the study's correlational hypothesis and the use of ordinal data.

Correlational Hypothesis and Ordinal Data

The study aimed to determine if there was a relationship between the number of hours slept and how well-rested students felt. This type of research question calls for a correlational hypothesis, which predicts a relationship between two variables without manipulating them. In this case, the hypothesis might be: "There will be a positive correlation between the number of hours slept and how well-rested students feel."

The researchers used a scale of 1-5 to assess how well-rested students felt. This type of data is considered ordinal data. While it allows for ranking (e.g., a score of 5 indicates feeling more rested than a score of 3), the intervals between the points on the scale are not necessarily equal. The difference between a 1 and a 2 might not represent the same difference in feeling rested as the difference between a 4 and a 5. Additionally, the scale is subjective, relying on individual perception of "restedness" rather than a standardized, objective measure.

Why Spearman's Rho is Suitable

Spearman's rho is particularly well-suited for this study for the following reasons:

1. Non-parametric Nature:

As mentioned earlier, Spearman's rho is a non-parametric test. This is important because the scale used to measure how well-rested the students feel is unlikely to be normally distributed. Students' perceptions of feeling rested are likely to vary greatly, with some students clustering at the high end of the scale and others at the lower end. This might result in a skewed distribution. Spearman's rho does not assume a normal distribution, making it a more robust choice compared to parametric tests like Pearson's r, which rely on the assumption of normality.

2. Ordinal Data Handling:

Spearman's rho works by ranking the data for each variable and then calculating the correlation coefficient based on the differences in ranks. This makes it appropriate for ordinal data like the 1-5 scale used in the study. The test focuses on the order of the data points rather than the precise numerical values, allowing for a meaningful analysis even when the intervals between points on the scale are not necessarily equal. This is crucial because it allows researchers to study relationships between variables even when using subjective or non-standardized measurements.

Conclusion

In conclusion, Spearman's rho is a suitable statistical test for the described study due to its non-parametric nature and its ability to handle ordinal data. The study's correlational hypothesis, investigating the relationship between two variables measured on an ordinal scale, aligns perfectly with the strengths of Spearman's rho. This test allows researchers to draw meaningful conclusions about the relationship between hours slept and perceived restfulness in students, even with the limitations of using a subjective and non-normally distributed ordinal scale.