How to Analyze Nominal Data

When we talk about data, the first thing that comes to our mind is the collection of raw facts and figures. With the advent of modern technology and advancement in computer systems, the term data got redefined. Now it is mainly referred to as the computer information stored, transmitted, or retrieved from a computer system or over the internet. In addition to that, the data is collected, analyzed and results are drawn from the analysis to meet a satisfactory outcome.

The definition of data is not limited to data processing in applications for computing purposes. It has gained different meanings in different fields, as in Data Science. The sense of data expands beyond the processing of data in computing applications. Similarly, other areas such as statistics, finance, wealth, and demographics have their meanings of data and can vary based on their applications.

Before we delve deeper into the meaning of data in statistics, let us recall the two main types of data.

  1. Quantitative Data
  2. Qualitative Data

Quantitative Data

Qualitative data refers to the type of data that contains numerical values associated with the data sets. These data sets contain the quantitative information that can be used to draw any conclusion of real-life problems from mathematical derivations, including mathematical calculations, and also help in statistical analysis. Mathematical procedures and formulas used in this type of data make it easy to control various parameters. Quantitative data is described through parameters such as kilograms, pounds, or dollars, etc.

Quantitative data helps in statistical data analysis by utilizing questionnaires, surveys, and polls containing different questions. The outcomes drawn from the results can be associated with a specific population. This type of data includes descriptive, relationship, and comparative data. It helps to expand the results of any particular group to a larger population if the results are generalizable.

Qualitative Data

Qualitative data is the data that defines the information about anything descriptively. This type of data includes primarily open-ended questions, and the method of data collection is investigative, providing the respondents to describe their choice and defend their answers fully. As the name suggests, qualitative data is characterized by the labels, attributes, and multiple other identifiers unique for every individual rather than numbers or other quantitative parameters.

Qualitative data is analyzed using measures of central tendency such as Mode, median, frequency, etc. Moreover, this data type is also essential for data visualization techniques as the qualitative data is mapped onto charts and graphs. The patterns in the data can be observed through the use of proper coding practices and software. This approach helps the researchers to identify the trends that correspond correctly to research questions and helps in data analysis.

When we talk about statistical data analysis, the data is regarded as the building block of research and analysis upon which the outcome is based. Analyzing a specific form of data helps draw a conclusion and predict the results. As stated earlier, the data is of different types, and various methods are used to analyze such diverse forms of data. The most prominent qualitative data types are

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Discrete Manufacturing: What It Is and How It Works
  1. Nominal Data
  2. Ordinal Data
  3. Ratio Data
  4. Interval Data

Are you struggling with the analysis of your data and wonder what kind of data it is? If you don’t know about different types of statistical data and want to know the methods, you can analyze the data. This article aims to answer all your questions regarding the nominal data and how you can quickly diagnose the nominal data type and hit suitable outcomes. Read the article till the end, and you’ll have all your queries answered.

What is Nominal Data?

Nominal data can be described as labeled data and can be divided into non-overlapping groups. The data is categorized and assigned to multiple unique groups having unique properties from each other. This type of data is not dependent on the order of collected data. Any alterations in the order of data do not affect the implication of data or outcomes.

How to Analyze Nominal Data
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As the data is divided into categories based on the properties; therefore, nominal data is also regarded as Categorical data. This particular type of data makes the researchers’ research and data analysis process more manageable. Nominal data belongs to the qualitative variety of data.

For Example

Nominal data can be of any category, such as gender, grades, age, currency, blood groups, etc. They are unique for every individual and are easily classified without any specific order. The nominal category includes discrete data, and you cannot perform arithmetic operations on that data.

The table below best describes how nominal or categorical data looks like. Each variable corresponds to multiple unique categories.

AttributeCategory
GradesA+B+C+F
Blood GroupsA+B+AB+O+
CurrencyUS DollarPoundsRupeeLira
Movie GenreHorrorActionRomanceComedy

 

Analyzing Nominal Data

Nominal data can be analyzed by dividing data into categories based on their attributes and presenting the results in captivating visuals. Following are some ways by which one can quickly analyze nominal data. In addition to these methods, multiple statistical procedures help in categorical data analysis, which will be discussed later in this blog.

1.     Identify the category and its variables.

The first step to getting started with the analysis of nominal data is to differentiate between attributes and categories. This helps to understand the data better and list all the types.

For Example:

In an office, how many employees have the same blood group? We have to list the employees with the same blood groups separately. In this way, nominal data is divided into categories.

2.     Data Observation

As nominal data is collected using open-ended questionnaires and surveys. Data observation is a crucial step because you cannot divide it into categories without analyzing the information.

For Example:

A manager conducts an open-ended survey and receives answers from his employees in any industry. If he did not read and observe the responses, he would not understand how his employees feel under his leadership. For this purpose, data observation after collection is significant.

3.     Computation of the data:

The data can be computed using different statistical procedures to identify how many times each variable appeared in any category. Mode, frequency distribution, percentages, and proportions help compute the collected data and infer the results.

4.     Decide what you want to do with the data:

Decide on the goal of collecting your data. What is the purpose of analyzing data, and where will the results lead? Understanding the purpose of data will help create more relevant questionnaires and assist in predicting the outcomes.

5.     Presenting the results:

After analyzing the data, use proper data visualization tools to present your nominal data categories and conclude properly. Pie chart or bar graph can be used to visualize patterns and see distributions of variables for more straightforward analysis.

How to Analyze Nominal Data
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Statistical Tools to analyze Nominal Data

Below are some of the statistical tools to analyze nominal data:

Measures of Central tendency

These statistical measures help identify the categories and all the variables in them. Measures of central tendency include Mean, Median, and Mode. Mean is calculated by performing arithmetic operations on the data, and median refers to the middlemost value in any quantitative data. However, these operations cannot be performed on qualitative data. Therefore, in carrying out analysis for nominal data, Mode is mainly used. Mode helps you separate the categories and all the variables under each category from discrete data.

Chi-square Test:

This test belongs to the non-parametric statistical tests meant for nominal data. Note that chi-square test of independence is used for nominal data in two variables, and the goodness of fit chi-square test is used for the category with one variable.

The chi-square test of independence helps researchers analyze any association between two nominal variables. Since two variables are utilized in this test, the frequency of one categorical variable is compared across the categories of other variables of the data sets. The data thus obtained is displayed in the contingency table. In this table, each column represents the category of one variable, and each row presents the types of other variables.

Moreover, the Chi-square goodness of fit test helps analyze the data collected using random sampling and from a single source. This test helps you know whether you achieved your goals or not as it assesses the frequency distribution of your data with the expected ones over a broader population.

Analyzing Nominal Data in SPSS

Statistical Package for the Social Sciences, abbreaviated as SPSS, is a widely used statistical analysis software package. Not only does it help users perform analysis on descriptive data, but it also assists in both paramteric and non-parametric analysis over a range of data. Moreover, it carries a great GUI with modern charts and graphs as you’d expect. Lastly, there’s support for advanced processing for complex research analysis applications.

SPSS uses the term “scale” to show the intervals for measurements and is mainly for quantitative data. Descriptive analysis is meant for nominal data. 

How to Analyze Nominal Data
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Defining Nominal Variables in SPSS:

To define nominal variables and obtain their descriptive analysis, follow the following steps:

● Click Analyze

● Click Descriptive Statistics

● Click frequencies

● place the categorical variables you want to inspect in the box named “variables.”

● Click on the statistics button.

● Check the “Mode” box in the central tendency section and “minimum, maximum” in the dispersion section of the statistics dialogue box.

● Click the “Continue” button and “OK.”

 

Binomial Test using SPSS:

This test proves to be very valuable in determining if the proportion of people in any category differs from a specific amount. This can be explained by the example that if several people are asked to choose any of the two types of cars. We can check how many people selected which kind of car from a specific amount. The software assumes that the variable is set numerically when conducting this test in Statistical Package for Social Sciences (SPSS)

How to Analyze Nominal Data
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Merits of analyzing Nominal Data over other Data Type

There are different data types, and each of them is equally important for their use. Following are some of the advantages that categorical or nominal data type offers.

● The respondents are free to express their opinions in their answers.

● The research is conducted with a lot of ease because of the presence of closed-ended questions.

● A large number of questions can be answered quickly and in less amount of time.

● Nominal data also dominates other data types over the reliability factor. 

● Answering the questions included in this data type is easy because they do not require expertise.

● Analysis of nominal data is also practical in terms of cost as we only need a few questions and the questionnaires.

Conclusion:

In this article, we talked about different data types, nominal data, and different statistical and advanced ways to analyze nominal data. To conclude this discussion, we can say that categorical data analysis is a fruitful procedure because of the flexibility it offers to the researchers both in terms of questions and the surveys.

It involves categories and other discrete data, which is easy to analyze. To add further credibility to the results, one can use the chi-square test and binomial test in the software named SPSS. SPSS provides several options to the researchers to efficiently carry out the analysis process and draw adequate results from them. Those results are further displayed using attractive visualizations containing charts, graphs, tables, etc.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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