Control of Corruption, Foreign Direct Investment Political Stability, Government Effectiveness,Regulatory Quality, Rule of Law, Exchange Rate, Population, Population Growth, Trade Openness, Infrastructure, Inflation, Voice and Accountability, Market. Please I need help classifying the level of measurement for these variables.
SCALE is quantitative obtained through score or scale score, i.e. (1,2,3,4,5). They can be subjected to basic mathematical operations. With multiple variables, i.e. independent and dependent variables, they can be used to run regression modeling to verify linear relationship, etc.
ORDINAL is ranking data type, i.e. 1st, 2nd, 3rd, etc. this data is used for finding order of rank. the category of interest (rank that you desired) is called success (s). the probability of success is: P = (s = 1) / (n + 2) and the probability of failure is Q = 1 - P. The test statics may be accomplished by the DeMoivre-Laplace theorem:
Z = (s - np) / sqrt(npq)
NOMINAL data is used for identification. This type of data cannot be subjected to basic mathematical operations, such as addition, subtraction, multiplication or division. However, it can be analyzed using discrete distribution like that used in ordinal data. (Yes | No) is an example of nominal data.
Nominal: nominal is from the Latin nomalis, which means “pertaining to names”. It’s another name for a category.
Examples:
- Gender: Male, Female, Other.
- Hair Color: Brown, Black, Blonde, Red, Other.
- Type of living accommodation: House, Apartment, Trailer, Other.
- Genotype: Bb, bb, BB, bB.
- Religious preference: Buddhist, Mormon, Muslim, Jewish, Christian, Other.
Ordinal: means in order. Includes “First,” “second” and “ninety ninth.”
Examples:
- High school class ranking: 1st, 9th, 87th…
- Socioeconomic status: poor, middle class, rich.
- The Likert Scale: strongly disagree, disagree, neutral, agree, strongly agree.
- Level of Agreement: yes, maybe, no.
- Time of Day: dawn, morning, noon, afternoon, evening, night.
- Political Orientation: left, center, right.
Interval: has values of equal intervals that mean something. For example, a thermometer might have intervals of ten degrees.
Examples:
- Celsius Temperature.
- Fahrenheit Temperature.
- IQ (intelligence scale).
- SAT scores.
- Time on a clock with hand
Ratio: exactly the same as the interval scale except that the zero on the scale means: does not exist. For example, a weight of zero doesn’t exist; an age of zero doesn’t exist. On the other hand, temperature is not a ratio scale, because zero exists (i.e. zero on the Celsius scale is just the freezing point; it doesn’t mean that water ceases to exist).
Examples:
- Age.*
- Weight.
- Height.
- Sales Figures.
- Ruler measurements.
- Income earned in a week.
- Years of education.
- Number of children.
Nominal variables are categorical variables without a specific ranking, for instance, gender composition in a questionnaire for a survey study while ordinal scale takes into account the ranking or wondering of the variables or measurements
Variables are classified as follows:
- Nominal: Categories without a specific order (e.g., gender, nationality).
- Ordinal: Categories with a meaningful order but no consistent difference between them (e.g., education level, customer satisfaction ratings).
- Interval: Ordered categories with equal intervals between values, but no true zero (e.g., temperature in Celsius).
- Ratio: Ordered categories with equal intervals and a meaningful zero point (e.g., height, weight, income).
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