Regression analysis has become a staple tool among researchers. Indeed, regression analysis serves as a familiar associative test, aiming to discern the impact of one variable on another.
In the realm of regression analysis, we encounter both linear and non-linear regression types. As of today, linear regression remains the most favored choice among researchers.
Within linear regression, variables are classified into two: dependent (or the influenced variable) and independent (or the influencing variable).
Linear regression equations comprising two or more independent variables are termed as multiple linear regressions. Conversely, an equation consisting of just one independent and one dependent variable is known as simple linear regression.
A commonly used approach for linear regression among researchers is the ordinary least squares (OLS) method. Certain assumptions need to be met to ensure consistent and unbiased estimates.
To fulfill these assumption tests, it’s advisable for researchers to employ parametric measurement scales (interval and ratio) for both dependent and independent variables.
But what if the dependent variable’s scale isn’t interval or ratio? For instance, what if I have a nominally scaled (non-parametric) dependent variable? Can I then apply multiple linear regression as usual?
I’d recommend exploring alternative regression analysis options. In this article, I’ll delve into regression tests suitable for nominally scaled dependent variables.
Defining Nominal Scale in Variable Measurement
Statistically, variable measurement scales can be categorized into four: nominal, ordinal, interval, and ratio. Each scale possesses distinct characteristics.
Broadly, nominal and ordinal scales fall under non-parametric statistics, while interval and ratio scales belong to parametric statistics.
Nominal scale measurement categorizes variables, distinguishing them based on their attributes. Examples include gender and type of employment.
For gender, the categories are male and female. Categorizing gender into these two distinct classes aims to differentiate between the two.
Such categorization lacks any ranking or hierarchy. If any ranking exists, then it enters the realm of ordinal scales, moving beyond nominal scales.
Regression Analysis for Nominally Scaled Dependent Variables
For studies using nominally scaled dependent variables, it’s recommended to avoid the OLS linear regression. Doing otherwise might adversely affect the classical assumption tests when forcing the use of nominally scaled dependent variables.
As an alternative, consider regression analysis for nominally scaled variables. For binary nominally scaled dependent variables, e.g., yes or no, male or female, rural or urban, pre-policy or post-policy, etc., binary logistic regression analysis is appropriate.
Also termed as logit regression, what is binary logistic regression?
Binary logistic regression predicts the probability of a binary (two-category) dependent variable based on one or more independent variables.
Unlike OLS linear regression, logistic regression doesn’t necessitate several assumptions. The crucial factor is ensuring the dependent variable measurement comprises two binary categories.
Scoring Technique for Dependent Variables in Logistic Regression
As earlier highlighted, logistic regression’s dependent variable employs a nominal scale, thus falling under non-parametric statistics.
For non-parametric variables, a scoring conversion method is essential. For instance, ordinal scales commonly use the Likert scale approach. But with nominal scales, how should we score?
For nominally scaled dependent variables, a score of 0 or 1 is applicable. Let’s take technology adoption as an example, categorized into adopting and not adopting technology.
Here, not adopting can score 0, and adopting can score 1. These scores can then be input for all observations, enabling numeric measurements.
The resulting analysis reveals the influence of independent variables on technology adoption. This insight proves invaluable for discerning the impact of independent variables on non-parametric dependent variables.
That concludes my article for now. I hope it proves enlightening and augments your knowledge base. Stay tuned for more insights in the upcoming weeks. Thank you.
