What Type of Variable is Gender?

What are the 4 Types of Data? What are the Types of Variables in Research? Is Gender a Variable in Statistics? How to analyze Categorical-Gender Data?

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The entity with varying values is said to be a variable and it is essential for statistical data. It can be either qualitative or quantitative and it helps in data collection. Thus, various values can be assigned to these attributes. Therefore, a variable is a factor that can take different values among the surveyed population. Variables can be divided into different categories depending on their role in the research. So, what type of variable is gender? You will get an answer to this question here. Moreover, you will learn about the types of data in data analytics and data types in statistics.

1. What are the 4 Types of Data? Data Types in Statistics

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The individual pieces of information form data that is analyzed and then, interpreted to be presented to the audience. As a result of this analysis, statistics are obtained. To define data and data integrity levels, tags are used from two important processes: data handling and data classification.

Data types in statistics can be divided into four major categories: ordinal, continuous, nominal, and discrete. Nominal and ordinal data are the types of qualitative or categorical data, whereas discrete and continuous data are the types of quantitative or numerical data. These are discussed, in detail, with examples in the succeeding sections.

A. Categorical Data

This type of data, which is not numerical, is known as qualitative data. This includes information describing features like gender and town and divides the population into categories. This data is not represented by numbers but by language. This can be further divided into:

  • Ordinal data: This data type is in a natural order and does not determine the data value difference. Usually represented by a bar chart and investigated using visualization tools. The application is found in finance, questionnaires, surveys, etc.
  • Nominal data: This data type does not provide numerical value but helps in variable labeling. The nominal scale is the other name for nominal data. Such data is examined by the method of grouping, where categories of data are formed, and then the percentage of frequencies is calculated. It is usually represented by a pie chart. For example, gender, letters, symbols, etc. (See What is a Circle Degree Chart?)

B. Numerical Data

This type of data type is also known as quantitative data, representing the data numerically and giving the information in terms of quantities. For example, weight and height.

Based on the data set, it can be further divided into:

  • Discrete Data: This type of data can be used to count things in whole numbers up to a finite range of values. For example, the number of teachers in school.
  • Continuous Data: This type of data is not finite and allows for calculation within a specific range of possible values. For example, temperature. (See Why is Quantitative Research Important?)

2. What are the Types of Data in Data Analytics?

Keep reading to know what type of variable is gender. The types of data in data analytics are different from that of statistics. They are as follows:

A. Predictive Data Type

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This data type is also known as the forecasting data type. The data is transformed into actionable and valuable information by predicting and analyzing. Probable situations and outcomes of any event are determined using this data type. Learning, game theory, modeling, machines, and data mining are various techniques used. Predictions are made using historical facts. Transaction profiling, predictive modeling, decision analysis, and optimization are the cornerstones of this analysis. Also, check out what kind of information do you need?

B. Descriptive Data Type

Before knowing what type of variable is gender,  let’s read about this type of data, also known as data mining and business intelligence. The data is analyzed using the past events to define the approach to future events. This data type is used in operations, sales, marketing, and finances and is often used for classifying customers into groups, thus quantifying relationships. It does not identify only a single customer like predictive data type but looks at many customers at once. Statistics, data queries, data dashboards, and reports of companies that provide such reviews are some examples of this analysis. (See Why marketing research can give inaccurate results?)

C. Prescriptive Data Type

This is also known as simulation and optimization. This data type is automatically synthesized, and predictions are made to suggest decisions. It not only predicts the future but also suggests the benefits of said actions and forecasts the implications of decisions. It also explains why it will happen instead of only what will happen and when. It benefits healthcare sector by using population, demography, and economic data. Must read what is the correct order of steps in the Scientific Method?

D. Diagnostic Data Type

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Among the types of data in data analytics, historical data analysis is used to solve problems after determining the pattern and dependency of the problem. It is not time-consuming and gives detailed information about an issue. Data mining, data discovery, and correlation are the common techniques used in this data type. (See What are Physical Resources?)

3. Categorical vs Numerical Data

The key differences between categorical data and numerical data are listed below:

Categorical Data Numerical Data
The data type identified by labels or names and can be easily stored is categorical data. The data type that does not store data in language but in the form of numbers is numerical data.
It is a type of qualitative data for classifying data. It is a type of quantitative data since it represents it in quantitative values and arithmetic operations.
The two types of categorical data are ordinal data and nominal data. It is used in interviews, questionnaires, and surveys. The two types of numerical data are continuous data and discrete data. It is used in groups and surveys.
The scale is not ordered. The scale is ordered.
Natural language is used. Natural languages are not used.
No quantitative values are used. Quantitative values are used.
They are represented using pie charts and bar charts. They are not represented using pie charts and bar charts.
It is used to find median and mode. It is used in surveys, interviews, and groups statistics and calculations.
It is used to store personal information in business research. It is used to calculate statistical data using the arithmetic operations’ performance.
Researchers do not use it since it is incompatible with statistical analysis. Researchers use it since it is compatible with the analysis of statistics.
It is unstructured. To structure the data, it uses indexing. The data is structured and organized to make sense.
Example: gender classification as male-female and other. Example: classification of test scores as 10 to 15, 15 to 20, above 20.

4. What are the Types of Variables in Research?

To get the answer to what type of variable is gender, note the different types of variables in research :

  • Dependent Variables: These variables are contrary to independent variables; uncontrollable, and cannot be manipulated. Independent variables affect these variables. If there is a change in the independent variable, the dependent variable also changes.
  • Independence Variables: When the dependent variable is predicted, an independent variable is obtained from it, which can be manipulated. The researcher measures and chooses the variable and figures out the relationship of other variables with it. The independent variable affects the dependent variable either positively or negatively. It is not manipulated for non-experimental research.
  • Control Variables: Effects of these variables can be deleted or neutralized by the researcher occasionally. All the variables cannot be examined simultaneously.
  • Moderator Variables: The independent and dependent variable relationship can be changed by a moderator variable. It is also considered the second independent variable. Must check out what are the types of functions?

5. Is Gender a Variable in Statistics?

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Yes, gender is a type of variable in statistics. It is a categorical variable among the data types in statistics. It is of two categories, Male and Female, and has no order. Recently, Other has been included as a gender identity as well.

6. What Type of Variable is Gender? What Type of Data is Gender?

According to the qualities possessed by each person, gender can be grouped and categorized. Therefore, what type of variable is gender can be seen based on similarities and differences. Gender is said to be categorical data because it can be divided into male, female and others, according to their unique qualities.

Gender is a type of qualitative data, not quantitative because it is classified not as numerical values but just labels. To know more about genders and what type of variable is gender, check out the 6 female vs male sign fun facts.

7. Is Gender Ordinal or Nominal?

Gender is data that is represented by labels or names. Hence, it is nominal data. Since there is no order or rank in gender, it is not ordinal data because it is not something that can be measured. For example, people of different genders can be represented by different numbers. Here, numbers do not rank them but are only used to classify them. (See What is Critical Thinking?)

8. Is Gender Discrete or Continuous?

Gender is a discrete variable type that is further said to be categorical. A discrete variable is a qualitative quantity that explains the observations attributes they are associated with and cannot be expressed using arithmetic operations. They contain distinct groups and categories in a finite number that are countable. This classification is only used for data management and data collection. (See What are the Characteristics of Population?)

9. What Type of Variable is Gender in Quantitative Research?

Scientists view gender in different categories as female-male and transsexual. Therefore, in quantitative research, gender can be seen to be a nominal variable. A nominal variable cannot be measured as more or less as there is no evaluative distinction. Also, check out what is the very Last Number in the World?

10. Is Gender Qualitative or Quantitative?

So, what type of variable is gender? Gender is a qualitative variable because it cannot be measured using numbers but can be categorized into groups. (See 20 Reasons Why Students should not Wear Uniforms)

11. How to Analyze Categorical Gender Data?

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Gender can be categorized into one or more categories. Therefore, if you ask, what type of variable is gender, it is said to be categorical data. For example, gender is categorized as male and female. Procedures for dealing with this data are included in stat graphics using the following procedures:

  • Tabulation: A single column of the data is summarized using the tabulation procedure. The frequency can be represented in either tabulation form or a graph. Must read 9 out of 12 is what percent?
  • Pie Chart or Donut Chart: This chart is used to plot the counts of frequency column.
  • Frequency Table: A single categorical factor is analyzed using the frequency table procedure, which has already been tabulated. A pie chart or bar chart is used to display the frequencies.
  • Cross Tabulation: Two columns of attribute data can be summarized using the procedure of cross-tabulation analysis. A two-way table shows the unique pairs’ occurrence in two columns. It can use either graph such as bar chart, mosaic plot, or table.
  • Contingency Tables: This is also a two-way table that displays data frequency. It uses either graphs or tables to represent. It determines the dependence between row and column classification statistically.
  • Median Polish: A two-way table model is constructed by this procedure. A common value is considered to represent the contents themselves. It uses medians instead of mean.
  • Correspondence Analysis: A two-way contingency table is constructed to create a map of rows and columns. It provides the relationship between the rows and column variables. (Also read How long is 8 inches Compared to an Object?)
About the author
Alex Williams is a PhD student in urban studies and planning. He is broadly interested in the historical geographies of capital, the geopolitical economy of urbanization, environmental and imperial history, critical urban theory, and spatial dialectics.

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