Assignment Prompt & Instructions
Listed below is my response to this week’s discussion question. I look forward to your comments and questions. A quick review of the Statistical concepts from other classes, the Scales of Measurement are used to categorize and quantify data (Loewenthal & Lewis, 2020). Some ways to do this are (a) nominal, (b) ordinal, (c) interval, and (d) ratio. Nominal does not require a specific order (Bock et al., 2020). Thus, categories could be listed as age, race, creed, and gender. Whereas, Ordinal data requires that the order is “meaningful” (Wyrwich et al. 2020, para.2). Thus age could be specific ranges, or generations, such as X and Z. Intervals argue that “the distance between points is meaningful” (Frost, 2024, para.24) however 0 is not defined (Frost, 2024). Whereas Ratio’s while also using numeric has to have a defined zero (Frost, 2024). For example, temperature or height.
Statistical difference is testing to determine if the difference, will or will not occur. The not occurring is the null hypothesis (H0), which states that there is no statistical relationship between what is being studied. While the hypothesis (H1) states that what is being studied will occur.
Correlation measures the relationships between two variables by their strength and direction (BMJ, 2025). The Pearson correlation coefficient ( **r**) ranges from “-1 to +1. A high score indicates high similarity, while a score near zero indicates no correlation” (Berman, 2016, para.1).
Regression, predicts the relationship between the independent and dependent variables (BMJ, 2025). There are two forms of regression, linear and multiple. Linear regression “predicts” (Statistics Solutions, 2025b, para.1) how one variable influences the other. Whereas in multiple regression, there are multiple “predictors” (Statistics Solutions, 2025b, para.8).
To create the self-esteem measure, self-esteem would have to be defined. This is known as the “construct” (Statistical Solutions, 2025a, para.1). The construct is something that is not “observable” (Statistical Solutions, 2025a, para.1), but has certain behaviors or characteristics that can be measured (Statistical Solutions, 2025a). For example, anxiety and caffeine intake.
Additionally, because self-esteem is associated with a person’s self worth (Harris & Orth, 2020), it would have to be determined how people value their self worth and why. For example, grades, looks, and money.
This is why it would be difficult to create a self esteem measure because of the subjectivity involved with a person’s estimation of their self worth. Also, it would have to be taken into consideration where a person learned to determine their self-worth through the measure. For example, cultural influence, versus material influence, versus peer pressure, versus intimate relationship influences.
**References**
Berman, J., J. (2016). Pearson Correlation. _Science Direct,_ 135-187.
[BMJ.com](http://bmj.com/). (2025). Correlation and regression. Retrieved March 12, 2025 from [https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-](https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression)
[one/11-correlation-and-regression](https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression)
Bock, C. H., Barbedo, J. G., Del Ponte, E. M., Bohnenkamp, D., & Mahlein, A. K. (2020). From visual estimates to fully automated sensor-based measurements of plant
disease severity: status and challenges for improving accuracy. _Phytopathology Research_, _2_, 1-30.
Frost, J. (2024). Statistics By Jim. Retrieved March 11, 2025 from [https://statisticsbyjim.com/basics/nominal-ordinal-interval-ratio-scales/](https://statisticsbyjim.com/basics/nominal-ordinal-interval-ratio-scales/)
– 2 years ago