Statistics: Nominal, Ordinal, Interval and Ration Scales and Testing

Gathering information from various sources plays a crucial role. For a researcher, pieces of information are very important. We collect the data using various tools, one of which is a questionnaire. When we collect the data, we use open-ended and closed-ended questionnaires. When we pose a question with options or choices, such as choosing between two or five options, it clarifies our analysis based on the choices we provide in the questionnaire.

We are all familiar with the terms nominal, ordinal, interval, and ratio; these are all termed scales. We use different statistical tools for the different scales. Have you heard the term parametric or non-parametric? If your answer is yes, then you must know about the analysis tools. If your response is negative, you must have to learn. When we discuss and test the population, we are defining the parametric model. If we don’t know about the population, and this is necessary to test the hypotheses, the test will be non-parametric.

A man doing research
A man doing research

Parameters

It is a fixed measure that describes the target.

It is a fixed and unknown numerical value.

Variables

This characteristic is specific to a small portion of the population. I liked the sample size.

It is a known and variable number.

The parameters of the population distribution, from which the data originate,

Parametric statistics

That leads to assumptions about the population distribution parameters from which the data are drawn.

They assume that the data has an underlying statistical distribution.

A parametric test’s result must meet several validity conditions.

Non-parametric statistics

They make no assumptions about the parameters of a distribution.

Do not rely on any distributions.

They can be used even if the parametric validation condition is not met.

Difference between Parametric and Non Parametric

Sr Parametric Non parametric 
1 We make specific assumptions about the population parameter. We make no assumptions about the population parameter.
2 Ratio and interval scale Nominal and ordinal scales
3 For the calculation, more information is needed. The calculation requires less information.
4 Assume a normal bell-shaped distribution curve. Don’t assume a regular bell-shaped distribution curve.
5 More statistical power Less statistical power
6 Less robust More robust
7 The result can be generalized. Results cannot be generalized.

Table-1 Difference between Parametric and Non Parametric

The Case, Parametric and Non-Parametric Test

Case Parametric Test Non Prametric Test
Central Tendency Mean Median/ Mode
Correlation Pearson Spearman
Independent Sample, 2 Groups Independent Sample Test Mann- Whitney Test
Independent Samples, More Than 2 Groups One Way Analysis Of Variance Kruskal Wallis Test
Dependent Samples, 2 Conditions Paired Sample T Test Wilcoxon Signed Rank Test
Dependent Samples, More Than 2 Conditions One Way Repeated Analysis Of Variance Friedman Anova

Table-2 The Case, Parametric and Non-Parametric Test

The Action and Operation

What Can We Do

Operation/Action Nominal Ordinal Interval Ratio
Categories/Classified Yes Yes Yes Yes
Arrange In Order (Sorting) Yes Yes Yes
Quantify Difference Between Values Yes Yes
Addition & Subtraction Yes Yes
True/Absolute Zero Yes
Multiplication& Division Yes

Table 3- The Action and Operation

 

The above table shows that case (the case is the individual unit of the experiment). These are the individuals from whom we collect the data. We refer to the individuals who took part in our research as participants.

We understand that a category refers to a class or division of people who perform certain tasks. Arranging in order refers to assigning the correct positions in relation to each other. The degree of excellence of something is determined by comparing it to other similar objects. The ration scale features a zero, indicating the origin of the character.

The Measurement Scale

Measurement Scales

Nominal Ordinal Interval Ratio
Categories Categories Categories Categories
Rank Rank Rank
Meaningful Distance Meaningful Distance
True Zero

Table-4 Measurement of the Data and Categories

The measurement scale is how the variables are defined and categorized. There are four common scales of measurement that were developed by the psychologist Stanley Stevens. These scales are nominal, ordinal, interval, and ratio. It is the properties of each scale of measurement that determine how the data should be analyzed in the appropriate manner.

Inferential statistics and Testing

Which Statistics Can Be Used

Non -Parametric Data Parametric Data
Discrete Variable Continuous Variable
Inferential Statistics Nominal Ordinal Interval Ratio
Chi Square Yes Yes
Mann-Whitney, Wilcoxon, Kruskal Wallis and Friedman Yes
Anova and T-Test Yes Yes
Rank Order Correlation Yes
Product Moment Correlation Yes Yes
Factor Analysis Yes Yes

Table- 5 Inferential statistics and Testing

The two primary applications of inferential statistics are the establishment of estimates regarding populations and the testing of hypotheses in order to draw conclusions regarding populations.

Which Statistics Can Be Used

Descriptive Statistics Nominal Ordinal Interval Ratio
Frequency Distribution Yes Yes Yes Yes
Percentage Yes Yes Yes Yes
Mode Yes Yes Yes Yes
Percentile Yes Yes Yes
Median Yes Yes Yes
Range Yes Yes
Mean Yes Yes
Standard Deviation Yes Yes
Geometric Mean Yes
Harmonic Mean ` Yes

Table-6: Descriptive statistics and test

The scale serves as a measurement tool for collecting data for further analysis. When we measure the data, it depends on your research questions, objectives, and hypotheses. You must determine the constructs or variables you want to measure, such as attitudes, opinions, behaviors, or perceptions.

 journey of the research
journey of the research

Dr. Abid Hussain Nawaz

Post Doc and Ph.D in Management sciences

Rumana Gull

Scholar Master of Philosophy Biological Sciences

Shakeela Riaz Malik

Scholar Master of philosophy in Linguistics

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