This is in reference to the ways in which numbers, as well as variables, are categorized and defined. Every scale of measurement essentially has several unique properties that determine its appropriateness in regard to statistical analysis. There are four scales of measurement, namely interval, nominal, ratio and ordinal (Harrell, 2015).
Statistical significance and the null hypothesis
Statistical significance can be described as the probability of acquiring a specific deviation from the null hypothesis. The term null hypothesis can be said to be a default position or general statement acknowledging that there exists no relationship or association between two phenomena that are measured (Benjamin et al, 2018).
Correlation
Correlation can be described as a mutual connection or relationship that exists between two things. Essentially, it is the process associated with establishing a connection or relationship between two things.
Regression
Regression can be described as the measure of the existing relation between variables mean value and the corresponding values of various other variables such as cost and time.
Self-esteem
For one to be able to define this construct, you must first assess the meaning of both words. “Self” as well as “esteem”. The reason for this is that both have unique meanings that must be understood for one to define this construct. Without a thorough understanding of the specific words, it becomes hard to define the construct and any definition that attempts to be made is inaccurate. The difficulties in defining this construct lie in misinterpreting the two words.
Notably, self-esteem can be described as the reflection of an individual’s subjective emotional evaluation in regard to what he thinks he is worth. Essentially, this is how the individual views himself.
References
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., " Cesarini, D. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6.
Harrell, F. E. (2015). Ordinal logistic regression. Regression modeling strategies
(pp. 311 325). Springer, Cham.
