What kind of variable is gender

So what kind of measurement level is this ranking of measurement levels?? I'd say ordinal. In statistics, it's best to be a little conservative when in doubt.

Ok, remember I stated that this is the first and most important distinction when using statistics? Here's why. For the most part, statisticians or researchers wind up only caring about the difference between nominal and all the others.

There are generally two classes of statistics: those that deal with nominal dependent variables and those that deal with ordinal, interval, or ratio variables. Right now we will focus on the dependent variable and later we will discuss the independent variable.

When I describe these types of two general classes of variables, I and many others usually refer to them as "categorical" and "continuous. Note also, that "continuous" in this sense is not exactly the same as "continuous" used in Chapter 1 of the text when distinguishing between discrete and continuous.

Categorical and dichotomous usually mean that a scale is nominal. Ordinal scales with few categories 2,3, or possibly 4 and nominal measures are often classified as categorical and are analyzed using binomial class of statistical tests, whereas ordinal scales with many categories 5 or more , interval, and ratio, are usually analyzed with the normal theory class of statistical tests.

Although the distinction is a somewhat fuzzy one, it is often a very useful distinction for choosing the correct statistical test.

Well this distinction as fuzzy as it may sound has very important implications for the type of statistical procedure used and we will be making decisions based on this distinction all through the course. There are two general classes of statistics: those based on binomial theory and those based on normal theory. Chi-square and logistic regression deal with binomial theory or binomial distributions, and t -tests, ANOVA, correlation, and regression deal with normal theory.

So here's a table to summarize. Type of Dependent Variable or Scale. Examples of Statistical Procedures. For example: height temperature Categorical qualitative variables have values that describe labels or attributes. There are two major scales for categorical variables: Nominal variables have categories with no distinct or defined order. For example: gender favorite color nationality Ordinal variables have an inherent order. Characteristics of Nominal Variable The responses to a nominal variable can be divided into two or more categories.

A nominal variable is qualitative, which means numbers are used here only to categorize or identify objects. They can also take quantitative values. However, these quantitative values do not have numeric properties. That is, arithmetic operations cannot be performed on them. Examples of Nominal Variable Personal Biodata: The variables included in a personal biodata is a nominal variable. This includes the name, date of birth, gender, etc.

How long have you been using our product? The Matched Category: In this category, all the values of the nominal variable are paired up or grouped so that each member of a group has similar characteristics except for the variable under investigation.

The Unmatched Category: This is an independent sample of unrelated groups of data. Unlike in the matched category, the values in a group do not necessarily have similar characteristics. Ordinal Variable An ordinal variable is a type of measurement variable that takes values with an order or rank. Types of Ordinal Variable Similar to the nominal variable, there is no standard classification of ordinal variables into types. What do we mean by value assignment?

It has no standardized interval scale. It establishes a relative rank. It measures qualitative traits. The median and mode can be analyzed. It has a rank or order. Examples of Ordinal Variable Likert Scale: A Likert scale is a psychometric scale used by researchers to prepare questionnaires and get people's opinions.

Very satisfied Satisfied Indifferent Dissatisfied Very dissatisfied Interval Scale: each response in an interval scale is an interval on its own. How old are you? The Matched Category: In the matched category, each member of a data sample is paired with similar members of every other sample concerning all other variables, aside from the one under consideration. This is done to obtain a better estimation of differences.

The Unmatched Category: Unmatched category, also known as the independent category contains randomly selected samples with variables that do not depend on the values of other ordinal variables. Most researchers base their analysis on the assumption that the samples are independent, except in a few cases. Differences Between Nominal and Ordinal Variable The ordinal variable has an intrinsic order while nominal variables do not have an order. It is only the mode of a nominal variable that can be analyzed while analysis like the median, mode, quantile, percentile, etc.

The tests carried on nominal and ordinal variables are different. They both have an inconclusive mean and a mode. They are both visualized using bar charts and pie charts. Interval Variable The interval variable is a measurement variable that is used to define values measured along a scale, with each point placed at an equal distance from one another.

Characteristics of Interval Variable It is one of the 2 types of quantitative variables. It takes numeric values and may be classified as a continuous variable type. Arithmetic operations can be performed on interval variables.

However, these operations are restricted to only addition and subtraction. The interval variable is an extension of the ordinal variable. In other words, we could say interval variables are built upon ordinary variables.

The intervals on the scale are equal in an interval variable. The scale is equidistant. The variables are measured using an interval scale, which not only shows the order but also shows the exact difference in the value.

It has no zero value. Examples of Interval Variable Temperature: Temperature, when measured in Celsius or Fahrenheit is considered as an interval variable. Mark Grading: When grading test scores like the SAT, for example, we use numbers as a reference point. Time: Time, if measured using a hour clock, or it is measured during the day is an example of interval data.

IQ Test: An individual cannot have a zero IQ, therefore satisfying the no zero property of an interval variable. For example, a researcher may be interested in the effect of illegal, recreational drug use the independent variable s on certain types of behaviour the dependent variable s. However, whilst possible, it would be unethical to ask individuals to take illegal drugs in order to study what effect this had on certain behaviours.

As such, a researcher could ask both drug and non-drug users to complete a questionnaire that had been constructed to indicate the extent to which they exhibited certain behaviours.

Whilst it is not possible to identify the cause and effect between the variables, we can still examine the association or relationship between them. In addition to understanding the difference between dependent and independent variables, and experimental and non-experimental research, it is also important to understand the different characteristics amongst variables. This is discussed next.

Categorical and Continuous Variables Categorical variables are also known as discrete or qualitative variables. Nominal variables are variables that have two or more categories, but which do not have an intrinsic order.

For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows.

So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable 50 in fact.

Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable and also a nominal variable. Another example might be if we asked a person if they owned a mobile phone.

Here, we may categorise mobile phone ownership as either "Yes" or "No".