Answer: An independent variable is exactly what it sounds like. It is a variable that stands alone and isn’t changed by the other variables you are trying to measure. For example, someone’s age might be an independent variable.

Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause, while a dependent variable is the effect. In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable.

Manipulated Variables in Process Control In process control there are two types of input variables: manipulated variables and disturbance variables. In this context, the manipulated variable is the input that is controlled by the process operator or control system.

An independent variable is used in statistics to predict or explain a dependent variable. For example, Age and Gender might be used as independent variables to predict the age of death or life expectancy (dependent variables).

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).

Four Types of Variables You can see there are four different types of measurement scales (nominal, ordinal, interval and ratio). Each of the four scales, respectively, typically provides more information about the variables being measured than those preceding it.

A ratio variable, has all the properties of an interval variable, and also has a clear definition of 0.0. Examples of ratio variables include: enzyme activity, dose amount, reaction rate, flow rate, concentration, pulse, weight, length, temperature in Kelvin (0.0 Kelvin really does mean “no heat”), survival time.

INTERVAL VARIABLES –values that lie along an evenly dispersed range of numbers. It is a variable whose data values are ranged in a real interval and can be as large as from negative infinity to positive infinity.

As with nominal level variables, ordinal level variables are typically described with frequencies and percentages. Interval and ratio level variables (also called continuous level variables) have the most detail associated with them.

integer numbers

With a discrete variable it is possible to list all possible values. Other examples of discrete quantitative variables are the number of legs on an animal, number of siblings, ACT scores, IQ scores, and shoe size.

McGrew says, ‘Yes’. Individuals can change their IQ scores. The person’s scores may change, not because of any real change in his/her general intelligence but that different tests may be used, which measure different mixtures of activities possessed by any individual.

Ordinal (ordered) variables, e.g., grade levels, income levels, school grades. Discrete interval variables with only a few values, e.g., number of times married. Continuous variables grouped into small number of categories, e.g., income grouped into subsets, blood pressure levels (normal, high-normal etc)

Predictor variable is the name given to an independent variable used in regression analyses. The predictor variable provides information on an associated dependent variable regarding a particular outcome. At the most fundamental level, predictor variables are variables that are linked with particular outcomes.

The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted “Y” and the independent variables are denoted by “X”.

In statistical modeling, the predictor variable is analogous to an independent variable and is used to predict an outcome (the criterion variable). One of the main differences between independent/dependent and criterion/predictor variables is the concept of causation.

A predictor variable explains changes in the response. Typically, you want to determine how changes in one or more predictors are associated with changes in the response. For example, in a plant growth study, the predictors might be the amount of fertilizer applied, the soil moisture, and the amount of sunlight.

What is Outcome variables? Outcome variables are usually the dependent variables which are observed and measured by changing independent variables. These variables determine the effect of the cause (independent) variables when changed for different values.

You can think of a mediator as a go-between for two variables. For example, sleep quality (an independent variable) can affect academic achievement (a dependent variable) through the mediator of alertness.

Categorical variables (also known as factor or qualitative variables) are variables that classify observations into groups. They have a limited number of different values, called levels. For example the gender of individuals are a categorical variable that can take two levels: Male or Female.