Sunday, August 25, 2019

DIFFERENT TYPES OF VARIABLES


Variables in Research
In scientific research, scientists, technicians and researchers utilize a variety of methods and variables when conducting their experiments. In simple terms, a variable represents a measurable attribute that changes or varies across the experiment whether comparing results between multiple groups, multiple people or even when using a single person in an experiment conducted over time. In all, there are six common variable types.
Variables represent the measurable traits that can change over the course of a scientific experiment. In all there are six basic variable types: dependent, independent, intervening, moderator, controlled and extraneous variables.



Independent and Dependent Variables
In general, experiments purposefully change one variable, which is the independent variable. But a variable that changes in direct response to the independent variable is the dependent variable. Say there’s an experiment to test whether changing the position of an ice cube affects its ability to melt. The change in an ice cube's position represents the independent variable. The result of whether the ice cube melts or not is the dependent variable.

Table 1
 Different names of independent and dependent variables

Independent variable

Dependent variable
Exposure variable

Outcome variable
Control variable

Controlled variable
Explanatory variable

Explained variable
Manipulated variable

Response variables

Intervening and Moderator Variables
Intervening variables link the independent and dependent variables, but as abstract processes, they are not directly observable during the experiment. For example, if studying the use of a specific teaching technique for its effectiveness, the technique represents the independent variable, while the completion of the technique's objectives by the study participants represents the dependent variable, while the actual processes used internally by the students to learn the subject matter represents the intervening variables.
By modifying the effect of the intervening variables -- the unseen processes -- moderator variables influence the relationship between the independent and dependent variables. Researchers measure moderator variables and take them into consideration during the experiment.
Constant or Controllable Variable
Sometimes certain characteristics of the objects under scrutiny are deliberately left unchanged. These are known as constant or controlled variables. In the ice cube experiment, one constant or controllable variable could be the size and shape of the cube. By keeping the ice cubes' sizes and shapes the same, it's easier to measure the differences between the cubes as they melt after shifting their positions, as they all started out as the same size.



Extraneous Variables
A well-designed experiment eliminates as many unmeasured extraneous variables as possible. This makes it easier to observe the relationship between the independent and dependent variables. These extraneous variables, also known as unforeseen factors, can affect the interpretation of experimental results. Lurking variables, as a subset of extraneous variables represent the unforeseen factors in the experiment.
Another type of lurking variable includes the confounding variable, which can render the results of the experiment useless or invalid. Sometimes a confounding variable could be a variable not previously considered. Not being aware of the confounding variable’s influence skews the experimental results. For example, say the surface chosen to conduct the ice-cube experiment was on a salted road, but the experimenters did not realize the salt was there and sprinkled unevenly, causing some ice cubes to melt faster.

Figure1. Extraneous variables
A list of common and uncommon types of variables. A “variable” in algebra really just means one thing—an unknown value. However, in statistics, you’ll come across dozens of types of variables in statistics. In most cases, the word still means that you’re dealing with something that’s unknown, but—unlike in algebra—that unknown isn’t always a number. Some variable types are used more than others. For example, you’ll be much more likely to come across continuous variables than you would dummy variables.
Common Types of Variables
Categorical variable: variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aqua fresh.
Confounding variable: extra variables that have a hidden effect on your experimental results.
Continuous variable: a variable with infinite number of values, like “time” or “weight”.
Control variable: a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water.
Dependent variable: the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable.
Discrete variable: a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars.
Independent variable: A variable that is not affected by anything that the researcher does.
Lurking variable: A “hidden” variable the affects the relationship between the independent and dependent variables..
Nominal variable: another name for categorical variable.
Ordinal variable: similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal.
Qualitative variable: a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term.
Quantitative variable: A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables.
Ratio variables: similar to interval variables, but has a meaningful zero(‘Understanding the different types of variable in statistics’, n.d.).
Less Common Types of Variables
Active Variable: a variable that is manipulated by the researcher.
Antecedent Variable: a variable that comes before the independent variable.
Attribute variable: another name for a categorical variable (in statistical software) or a variable that isn’t manipulated (in design of experiments).
Binary variable: a variable that can only take on two values, usually 0/1. Could also be yes/no, tall/short or some other two-variable combination.
Collider Variable: a variable represented by a node on a causal graph that has paths pointing in as well as out.
Covariate variable: similar to an independent variable, it has an effect on the dependent variable but is usually not the variable of interest..
Criterion variable: another name for a dependent variable, when the variable is used in non-experimental situations.
Dichotomous variable: Another name for a binary variable.
Dummy Variables: used in regression analysis when you want to assign relationships to unconnected categorical variables. For example, if you had the categories “has dogs” and “owns a car” you might assign a 1 to mean “has dogs” and 0 to mean “owns a car.”
Endogenous variable: similar to dependent variables, they are affected by other variables in the system. Used almost exclusively in econometrics.
Exogenous variable: variables that affect others in the system.
Explanatory Variable: a type of independent variable. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.
Identifier Variables: variables used to uniquely identify situations.
Indicator variable: another name for a dummy variable.
Interval variable: a meaningful measurement between two variables. Also sometimes used as another name for a continuous variable.
Intervening variable: a variable that is used to explain the relationship between variables.
Latent Variable: a hidden variable that can’t be measured or observed directly.
Manifest variable: a variable that can be directly observed or measured.
Manipulated variable: another name for independent variable.
Moderating variable: changes the strength of an effect between independent and dependent variables. For example, psychotherapy may reduce stress levels for women more than men, so sex moderates the effect between psychotherapy and stress levels.
Nuisance Variable: an extraneous variable that increases variability overall.
Observed Variable: a measured variable (usually used in SEM).
Outcome variable: similar in meaning to a dependent variable, but used in a non-experimental study.
Polychotomous variables: variables that can have more than two values.
Predictor variable: similar in meaning to the independent variable, but used in regression and in non-experimental studies.
Responding variable: an informal term for dependent variable usually used in science fairs.
Scale Variable: basically, another name for a measurement variable.
Study Variable (Research Variable): can mean any variable used in a study, but does have a more formal definition when used in a clinical trial.
Test Variable: another name for the Dependent Variable.
Treatment variable: another name for independent variable(‘Types of Variables in Statistics and Research’, n.d.).


 Figure 2. Types of variables 

Reference


Types of Variables in Statistics and Research. (n.d.). Retrieved 20 August 2019, from Statistics How To website: https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/types-of-variables/
Understanding the different types of variable in statistics. (n.d.). Retrieved 20 August 2019, from https://statistics.laerd.com/statistical-guides/types-of-variable.php 


Click here for original word document







No comments:

Post a Comment