An extraneous variable is a variable that is not the primary focus of the study but may affect the outcome(s) of the study. Extraneous variables can be sources of research error and can potentially confound the relationships between the variables under study.
For example, imagine a study that aims to investigate the relationship between exercise and weight loss. External variables in this study could be, among others, age, gender, diet, sleep habits, and genetics. If these variables are not adequately controlled, they can potentially affect the results of the study.
Types of foreign variables
Types of foreign variables are as follows:
These are variables that are related to both the independent and dependent variables and may cause a spurious association between them. For example, if a study found that people who ate more fruit lived longer, but did not control for confounding variables such as exercise and healthy lifestyle habits, the results may not accurately reflect the effect of fruit consumption on longevity.
These are the individual characteristics of the participants that may affect the outcome of the study. This may include age, gender, personality traits, health status and other demographic factors. For example, if a study on the effects of caffeine on alertness includes only young, healthy participants, the results may not be generalizable to older or unhealthy people.
These are the characteristics of the study environment that can affect the results. For example, if a memory study is conducted in a noisy or distracting environment, the results may be affected by situational variables.
These are the characteristics of the experimenter that can influence the results. This may include prejudices, expectations and behavior towards participants. For example, if the experimenter expects a certain result from the study, he may unintentionally influence the behavior of the participants or interpret the results in a biased way.
These are factors related to the timing of the test that may affect the results. For example, if the study is conducted during the holiday season, factors such as increased stress or changes in eating habits may affect the results.
These are factors related to the physical environment in which the study is conducted and which can affect the results. For example, if a sleep quality study is conducted in a room with bright lights, the results may be affected by the environmental variable of lighting.
These are factors related to the instruments or methods used to measure the variables of interest. For example, if a study uses a faulty or unreliable measurement tool, the results may not accurately reflect the variable being measured.
These are factors related to the sampling procedure used to select participants. For example, if a study only includes participants from a particular geographic region, the results may not be generalizable to other populations.
These are factors related to the statistical methods used to analyze the data. For example, if a study uses inappropriate statistical tests or does not control for confounding variables, the results may be inaccurate.
How to deal with foreign variables
Here are some ways to control external variables:
Randomization is a technique that helps control extraneous variables by distributing them evenly among groups. This involves randomly assigning participants to different groups or conditions to ensure that all extraneous variables are evenly distributed across groups.
Standardization involves the use of standardized procedures and instructions to reduce variation in data. This helps control for situational, environmental, and measurement variables. For example, standardizing the data collection procedure, measurement tools, and experimental conditions can help reduce the variation caused by these factors.
Merging involves selecting participants who are similar in certain characteristics that may affect the outcome. This helps control participant variables. For example, if age is a potential extraneous variable, participants can be matched by age to ensure that the groups are balanced.
Statistical control involves the use of statistical methods to control external variables. This can be done by including an extraneous variable as a covariate in the analysis, which helps adjust for the effect of the independent variable on the dependent variable.
Manipulation involves manipulating an extraneous variable to see its effects on an outcome. This helps to understand the role of the external variable and to control its effects.
Elimination involves completely removing the extraneous variable from the study. This is often not possible or desirable, but may be necessary in certain situations.
Examples of foreign variables
Here are some examples of foreign variables:
- Dob: Age is a common extraneous variable that can affect many different types of studies. For example, if a study is examining the effects of a new drug on blood pressure, age may be a redundant variable to control for. Older people tend to have higher blood pressure, so not controlling for age could lead to inaccurate results.
- Company: Gender is another strange variable that can affect many different types of studies. For example, if a study is examining the effects of a new drug on depression, gender may be a redundant variable to control for. Women are more likely to experience depression than men, so not controlling for gender could lead to inaccurate results.
- Time of day: Time of day is a strange variable that can affect many different types of studies. For example, if a study is examining the effects of caffeine on alertness, time of day may be a redundant variable to control for. Alertness tends to vary throughout the day, so not controlling for time of day can lead to inaccurate results.
- Inclusion: Lighting is a strange variable that can affect studies involving visual perception. For example, if a study is examining the effects of a new drug on visual acuity, illumination may be an extraneous variable to control. Bright lighting can improve visual acuity, so not controlling the lighting could lead to inaccurate results.
- Experimental setting: Experimental setting is a strange variable that can affect many different types of research. For example, if a study is examining the effects of social support on recovery from surgery, the study setting may be an extraneous variable to control for. A sterile hospital environment may not provide the same level of social support as a home environment, so not controlling for the experimental environment may lead to inaccurate results.
Applications of strange variables
Here are some applications of extraneous variables in research:
- Experimental design:When designing experiments, researchers must identify potential extraneous variables that may affect the outcome of the study. By controlling for these variables, researchers can ensure that the results accurately reflect the relationship between the independent and dependent variables.
- Statistical analysis:In statistical analysis, extraneous variables can be controlled by including them as covariates in the analysis. This helps in adjusting the effect of the independent variable on the dependent variable and increases the precision of the results.
- causal inference:Extraneous variables can affect causal inference by creating spurious relationships between variables. By controlling extraneous variables, researchers can ensure that the relationship between variables is not caused by other factors.
- Possibility of generalization:External variables can affect the generalizability of research results. By controlling extraneous variables, researchers can increase the external validity of a study and ensure that the results are applicable to a wider range of people and situations.
- Effectiveness of treatment:External variables can affect the effectiveness of treatment in clinical settings. By controlling external variables, doctors can ensure that treatment is effective and that improvements are not due to other factors.
The purpose of the foreign variable
The purpose of controlling external variables in research is to increase the internal validity of the study. Internal validity refers to the degree to which a study accurately measures the relationship between independent and dependent variables without the interference of extraneous variables. By controlling extraneous variables, researchers can ensure that any observed effect is due to the independent variable and not to other factors.
Extraneous variables can affect a study in various ways, such as creating spurious relationships between variables or confounding results. For example, if a study is examining the relationship between a new drug and depression, gender may be a redundant variable to control for. Women are more likely to experience depression than men, so if gender is not controlled for, study results may be confounded by gender differences and may not accurately reflect the relationship between medication and depression.
Controlling external variables is important in research because it increases the reliability and precision of the results. If extraneous variables are not controlled, the results may be skewed or invalid, leading to incorrect conclusions and negative consequences in real-world applications.
Advantages of controlling external variables
Controlling extraneous variables can have several advantages in research, including:
- Greater internal validity: Controlling extraneous variables can increase the internal validity of a study by reducing the risk of confounding and spurious relationships between variables. This means that the results accurately reflect the relationship between the independent and dependent variables.
- More accurate results:By controlling extraneous variables, researchers can ensure that any observed effect is due to the independent variable and not to other factors. This increases the precision of the results and improves the reliability of the study.
- Greater generalization: Controlling extraneous variables can increase the generalizability of a study by reducing the influence of variables that may only apply to certain populations or situations. This means that the results are more applicable to a wider range of people and situations.
- Better treatment efficiency: In clinical research, controlling for extraneous variables can ensure that the effectiveness of a treatment is accurately measured and not confounded by other factors. This can lead to better treatment outcomes and better patient care.
- andimproved statistical analysis:By controlling extraneous variables, the accuracy of statistical analyzes can be improved by reducing the influence of unwanted variables that can affect the results. This can help researchers draw more accurate conclusions and make better decisions based on the data.
Disadvantages of a foreign variable
Extraneous variables can have several disadvantages in research, so it is important to control for them. Here are some potential disadvantages of foreign variables:
- Reduced internal validity:If extraneous variables are not controlled, they can reduce the internal validity of a study by introducing confounding or spurious relationships between variables. This means that the results may not accurately reflect the relationship between the independent and dependent variables.
- Biased Results:External variables that are not controlled can distort the results of the study and lead to incorrect conclusions. This can have negative consequences in real applications.
- limited generalization: If extraneous variables are not controlled, the results may only apply to a specific population or situation and may not be generalizable to other groups or settings.
- Increased complexity:Controlling extraneous variables can further complicate the research process, including increasing time, effort, and cost. This can make the investigation more challenging and less feasible.
- Ethical issues:In some cases, controlling extraneous variables may not be feasible or ethical, particularly if it requires manipulation or withholding treatment from participants. This can make it difficult to control for all potential confounders.
What are the 4 types of variables? ›
You can see that one way to look at variables is to divide them into four different categories ( nominal, ordinal, interval and ratio).What are the 3 types of variables and their meaning? ›
These changing quantities are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled. The independent variable is the one that is changed by the scientist.What are the different types of variables explain with the help of a suitable example? ›
A variable is a characteristic that can be measured and that can assume different values. Height, age, income, province or country of birth, grades obtained at school and type of housing are all examples of variables. Variables may be classified into two main categories: categorical and numeric.What are some examples of independent and dependent variables in research? ›
You want to see the effect of studying or sleeping on a test score. In the example, “test score” is the dependent variable. “Studying” or “sleeping” is the independent variable because these factors impact how much a student scores on the test.What are the four types of variables and give examples? ›
Such variables in statistics are broadly divided into four categories such as independent variables, dependent variables, categorical and continuous variables. Apart from these, quantitative and qualitative variables hold data as nominal, ordinal, interval and ratio. Each type of data has unique attributes.What are the 3 types of variables examples? ›
There are three main variables: independent variable, dependent variable and controlled variables. Example: a car going down different surfaces. Independent variable: the surface of the slope rug, bubble wrap and wood. Dependent variable: the time it takes for the car to go down the slope.What is an example of a control variable? ›
In an experiment to observe the growth of a plant, the temperature can be classified as a control variable if it is controlled during an experiment. Other examples of control variables could be the amount of light, duration of the experiment, amount of water, and pot of the plant.What are the 5 major variables? ›
There are different types of variables and having their influence differently in a study viz. Independent & dependent variables, Active and attribute variables, Continuous, discrete and categorical variable, Extraneous variables and Demographic variables.What are the 3 identifying variables? ›
An experimental inquiry typically has three main types of variables: an independent variable, a dependent variable and controlled variables.What are three examples of continuous variables? ›
Examples of continuous variables are body mass, height, blood pressure and cholesterol.
What is an example of a categorical variable? ›
Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.What is an example of a quantitative variable? ›
Quantitative variables are also called numerical variables.. Height, weight, age, speed, diameter, and the number of marbles in a bag are all examples of quantitative variables.What are 2 common examples of independent variable? ›
Two examples of common independent variables are age and time. There's nothing you or anything else can do to speed up or slow down time or increase or decrease age. They're independent of everything else.What is an example of a control group? ›
Example. Assume you want to test a new medication for ADHD. One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.How do you identify independent dependent and controlled variables? ›
Independent variable – the variable that is altered during a scientific experiment. Dependent variable – the variable being tested or measured during a scientific experiment. Controlled variable – a variable that is kept the same during a scientific experiment.What are 4 examples of independent variables? ›
For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.What is an example of an independent variable in research? ›
An independent variable has no dependence on the other variables in the study. It stands alone and is independent of the other variables you are measuring. For example, a person's age stays the same regardless of other variables within a study.What is controlled variable in research? ›
A control variable is anything that is held constant or limited in a research study. It's a variable that is not of interest to the study's objectives, but is controlled because it could influence the outcomes.What's an example of a control? ›
Controls are typically used in science experiments, business research, cosmetic testing and medication testing. For example, when a new type of medicine is tested, the group that receives the medication is called the “experimented” group. The control group, however, receives no medicine or a placebo.What is an example of control and independent variable? ›
For example: Question Independent Variable (What I change) Dependent Variables (What I observe) Controlled Variables (What I keep the same) Who listens to music the most: teenagers or their parents? Sometimes a variable simply represents an either/or (binary) condition.
What is a control vs controlled variable example? ›
A control variable is any factor you control or hold constant during an experiment. A control variable is also called a controlled variable or constant variable. If you are studying the effect of the amount of water on seed germination, control variables might include temperature, light, and type of seed.What are the 10 variables? ›
- Independent variables.
- Dependent variables.
- Quantitative variables.
- Qualitative variables.
- Intervening variables.
- Moderating variables.
- Extraneous variables.
- Confounding variables.
byte , short , int , long , float , double , char , boolean .What are 10 examples of variables in math? ›
Some examples of variables in Math are a,b,x,y,z,m, a , b , x , y , z , m , etc. A symbol that has a fixed numerical value is called a constant.What are the six common types of variables? ›
In all there are six basic variable types: dependent, independent, intervening, moderator, controlled and extraneous variables.What describes a control variable? ›
A control variable is any variable that's held constant in a research study. It's not a variable of interest in the study, but it's controlled because it could influence the outcomes.What is the control group and dependent variable? ›
Dependent Variable = What is measured or observed; the "data" collected in the experiment. Experimental Group = Those participants exposed to the independent variable. Control Group = Those participants treated just like the experimental group EXCEPT they are not.What are the 5 examples of discrete variables? ›
- The number of books you check out from the library.
- The number of heads in a sequence of coin tosses.
- The result of rolling a die.
- The number of patients in a hospital.
- The population of a country.
Examples of discrete random variables include the number of children in a family, the Friday night attendance at a cinema, the number of patients in a doctor's surgery, the number of defective light bulbs in a box of ten.What are 4 examples of continuous data? ›
Height, weight, temperature and length are all examples of continuous data.
What is an example of a binary variable? ›
Some examples of binary variables, i.e. attributes, are: Smoking is a binary variable with only two possible values: yes or no. A medical test has two possible outcomes: positive or negative. Gender is traditionally described as male or female.What is an example of binary categorical variable? ›
A binary variable is a variable that has two possible outcomes. For example, sex (male/female) or having a tattoo (yes/no) are both examples of a binary categorical variable.What is an example of categorical and numerical variables? ›
|Features||Categorical data||Numerical data|
|Examples||What is your gender? Male Female Other||What is your test score out of 20? Below 5 5-10 10-15 15-20 20|
|Types||Nominal data and Ordinal data.||Discrete data and Continuous data.|
Qualitative variables are those that express a qualitative attribute such as hair color, eye color, religion, favorite movie, gender, and so on. The values of a qualitative variable do not imply a numerical ordering.Which of the following is an example of qualitative variable? ›
Qualitative Variables. Also known as categorical variables, qualitative variables are variables with no natural sense of ordering. They are therefore measured on a nominal scale. For instance, hair color (Black, Brown, Gray, Red, Yellow) is a qualitative variable, as is name (Adam, Becky, Christina, Dave . . .).What is an example of dependent variable in quantitative? ›
For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it.What is an example of a single independent variable? ›
The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable.What is a constant variable? ›
A constant variable, sometimes known as a control variable, is something you keep the same during an experiment.What are examples of independent variables in functions? ›
What is an independent variable? An independent variable is a variable that does not depend on any other variable for its value. For example, in an expression, 2y = 9x + 1, x is an independent variable. So, for each value of x, there will be a different value of y.What is an example of experimental and control group? ›
For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines, the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect).
What is an example of a negative control in an experiment? ›
As a negative control, you might just wipe a sterile swab on the growth plate. You would not expect to see any bacterial growth on this plate, and if you do, it is an indication that your swabs, plates, or incubator are contaminated with bacteria that could interfere with the results of the experiment.What is an example of a negative control group? ›
Negative control group
This type of control group allows researchers to compare variables against a group they know won't produce different results. For example, if a company produces a new drug to treat stomach pain, researchers may only give the drug to one set of study participants.
An example of a control in science would be cells that get no treatment in an experiment. Say there is a scientist testing how a new drug causes cells to grow. One group, the experimental group would receive the drug and the other would receive a placebo. The group that received the placebo is the control group.Is time A controlled variable? ›
Time is a common independent variable, as it will not be affeced by any dependent environemental inputs. Time can be treated as a controllable constant against which changes in a system can be measured.What variables are controlled independent variables? ›
An independent variable is the variable the experimenter controls. Basically, it is the component you choose to change in an experiment. This variable is not dependent on any other variables. For example, in the plant growth experiment, the independent variable is the light color.What are 4 independent variables? ›
In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable).What are the 4 variables in an experiment? ›
Variables are the factors, traits, and conditions you can modify and measure. You'll find different variables in all types of subjects. But, the most common variables found in a science experiment include dependent, independent, and controlled.What is an example of a dependent variable? ›
It is something that depends on other factors. For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it.What are the 3 levels of independent variables? ›
A dependent variable is what you measure in the experiment and what is affected during the experiment. The dependent variable responds to the independent variable. It is called dependent because it "depends" on the independent variable.
How do you identify variables? ›
An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.What type of variable is values? ›
Quantitative. A quantitative variable is a variable that reflects a notion of magnitude, that is, if the values it can take are numbers. A quantitative variable represents thus a measure and is numerical.What are built in primitive data types in Java? ›
Built-in Data types are those data types that can be directly used by the programmer to declare and store different variables in a program. They are also called Primary or Primitive Data Types.What are the 3 system variables? ›
In order to solve systems of equations in three variables, known as three-by-three systems, the primary goal is to eliminate one variable at a time to achieve back-substitution. A solution to a system of three equations in three variables (x,y,z), ( x , y , z ) , is called an ordered triple.