A composite variable is two or more variables combined to make a more complex variable. Overall health is an example of a composite variable if you use other variables, such as weight, blood pressure and chronic pain, to determine overall health in your experiment.
A confounding variable is one you did not account for that can disguise another variable’s effects. Confounding variables can invalidate your experiment results by making them biased or suggesting a relationship between variables exists when it does not. For example, if you are studying the relationship between exercise level (independent variable) and body mass index (dependent variable) but do not consider age’s effect on these factors, it becomes a confounding variable that changes your results.
Qualitative, or categorical, variables are non-numerical values or groupings. Examples might include eye or hair color. Researchers can further categorize qualitative variables into three types:
Binary: Variables with only two categories, such as male or female, red or blue.
Nominal: Variables you can organize in more than two categories that do not follow a particular order. Take, for example, housing types: Single-family home, condominium, tiny home.
Ordinal: Variables you can organize in more than two categories that follow a particular order. Take, for example, level of satisfaction: Unsatisfied, neutral, satisfied.
Quantitative variables are any data sets that involve numbers or amounts. Examples might include height, distance or number of items. Researchers can further categorize quantitative variables into two types:
Discrete: Any numerical variables you can realistically count, such as the coins in your wallet or the money in your savings account.
Continuous: Numerical variables that you could never finish counting, such as time.
Extraneous variables are factors that affect the dependent variable but that the researcher did not originally consider when designing the experiment. These unwanted variables can unintentionally change a study’s results or how a researcher interprets those results. Take, for example, a study assessing whether private tutoring or online courses are more effective at improving students’ Spanish test scores. Extraneous variables that might unintentionally influence the outcome include parental support, prior knowledge of a foreign language or socioeconomic status.
Control or controlling variables are characteristics that are constant and do not change during a study. They have no effect on other variables. Researchers might intentionally keep a control variable the same throughout an experiment to prevent bias. For example, in an experiment about plant development, control variables might include the amounts of fertilizer and water each plant gets. These amounts are always the same so that they do not affect the plants’ growth.
A moderating or moderator variable changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable’s effect. For example, in a study looking at the relationship between economic status (independent variable) and how frequently people get physical exams from a doctor (dependent variable), age is a moderating variable. That relationship might be weaker in younger individuals and stronger in older individuals.
An intervening variable, sometimes called a mediator variable, is a theoretical variable the researcher uses to explain a cause or connection between other study variables—usually dependent and independent ones. They are associations instead of observations. For example, if wealth is the independent variable, and a long life span is a dependent variable, the researcher might hypothesize that access to quality healthcare is the intervening variable that links wealth and life span.
The variables which measure some count or quantity and don’t have any boundaries are are termed as continuous variables. It can be segregated into ratio or interval, or discrete variables. Interval variables have their centralized attribute, which is calibrated along with a range with some numerical values. The example can be temperature calibrated in Celsius or Fahrenheit doesn’t give any two different meaning; they display the optimum temperature, and it’s strictly not a ratio variable.
It can account for only a certain set of values, such as several bikes in a parking area are discrete as the floor holds only a limited portion to park bikes. Ratio variables occur with intervals; it has an extra condition that zero on any measurement denotes that there is no value of that variable. In simple, the distance of four meters is twice the distance of two meters. It operates on the ratio of measurements. Apart from these mentioned variables, a dummy variable can be applied in regression analysis to establish a relationship to unlinked categorical variables. For instance, if the user had categories ”has pet” and ”owns a home” can assign as 1 to ”’has pet” and 0 to ”’owns a home”.
A factor that remains constant in an experiment is termed as a control variable. In an experiment, if the scientist wants to test the plant’s light for its growth, he should control the value of water and soil quality. The additional variable which has a hidden impact on the obtained experimental values are called confounding variables.
It is a wide category of variable which is infinite and has no numerical data. These variables are called as qualitative variables or attribute variable in terms of statistics software. Such variables are further divided into nominal variables, ordinal and dichotomous variables. Nominal variables don’t have any intrinsic order. For instance, a developer classifies his environment into different types of networks based on their structure, such as P2P, cloud computing, pervasive computing, IoT. So here, the type of network is a nominal variable comprised of four categories. The varied categories present in the nominal variable can be known as the nominal variable levels or groups.Dichotomous variables are also called binary values, which have only two categories.
For example, if we question a person that he owns a car, he would reply only with yes or no. such types of two distinct variables that are nominal are called as dichotomous. It just accounts for only two values, such as 0 or 1. It could be yes or no, short or long, etc.Ordinal variables are nominal variables that include two or multiple categories. If you see any hotel feedback form, it has five ratings such as excellent, good, better, poor and very poor. So we can rank the level with the help of ordinal variables that hold meaning to the research. It is unambiguous, and values can be considered for decision making.