For example, gender is a categorical data because it can be categorized into male and female according to some unique qualities possessed by each gender. There are 2 main types of categorical data, namely; nominal data and ordinal data.

Gender and race are the two other categorical variables in our medical records example. Quantitative variables take numerical values and represent some kind of measurement. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values.

Quantitative information is often called data, but can also be things other than numbers. Qualitative Information – Involves a descriptive judgment using concept words instead of numbers. Gender, country name, animal species, and emotional state are examples of qualitative information.

For example, gender is a commonly used categorical variable. Categorical variables can be either ordinal (the categories can be ranked from high to low) or nominal (the categories cannot be ranked from high to low).

For example, gender is a categorical data because it can be categorized into male and female according to some unique qualities possessed by each gender. There are 2 main types of categorical data, namely; nominal data and ordinal data.

A nominal variable has no intrinsic ordering to its categories. For example, gender is a categorical variable having two categories (male and female) with no intrinsic ordering to the categories.

Gender (male/female) is not a quantitative variable. Can you think of any ways you could study gender in quantitative research? If we wanted to study gender, we would have to give the categories of the variable a number rather than a name. For example, by giving men the code 1 and women the code 2.

For example, gender and ethnicity are always nominal level data because they cannot be ranked. However, for other variables, you can choose the level of measurement.

In the previous example, "Gender" was a qualitative/categorical variable. Gender was categorized as either male or female. A continuous variable is a quantitative variable with an infinite number of values.

dependent variable. Independent variables included in the first step are demographic variables such as age, gender, marital status, and education, and in the next steps are eco- nomic related variables including employment status and self-rated economic condition.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age). Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

Gender can be a continuous variable, not just a categorical one: Comment on Hyde, Bigler, Joel, Tate, and van Anders (2019) Am Psychol. 2019 Oct;74(7):840-841. doi: 10.1037/amp0000505.

Gender is an example of a nominal measurement in which a number (e.g., 1) is used to label one gender, such as males, and a different number (e.g., 2) is used for the other gender, females.

Quantitative data consist of numerical measurements or counts. There are four different levels of measurement which determines which statistical calcula- tions are meaningful. They are nominal, ordinal, interval, and ratio.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age). Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

Quantitative Variables. As discussed in the section on variables in Chapter 1, quantitative variables are variables measured on a numeric scale. Height, weight, response time, subjective rating of pain, temperature, and score on an exam are all examples of quantitative variables.

How does quantitative research help gender studies?

Applying the diversity continuum shows that quantitative techniques offer gender scholars a means to increase our knowledge of differences, make scientific progress and simultaneously enable scholars to relate to existing knowledge outside gender studies in a way that makes communication between the different fields ...

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

Categorical data is a type of data that can be stored into groups or categories with the aid of names or labels. This grouping is usually made according to the data characteristics and similarities of these characteristics through a method known as matching.

Gender is an example of a nominal variable. Discrete variables are quantitative variables whose possible values can be listed. The list may be infinite — for example, the list of all whole numbers.

In the social sciences, many quantitative research findings as well as presentations of demographics are related to participants' gender. Most often, gender is represented by a dichotomous variable with the possible responses of woman/man or female/male, although gender is not a binary variable.