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Research Variables: Dependent, Independent, Control, Extraneous & Moderator

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❶For instance, you might be tempted to represent the variable "Employment Status" with the two attributes "employed" and "unemployed. Please use a different card.

Intervening and Moderator Variables

Independent and Dependent Variables
This article is a part of the guide:
Dependent and Independent Variables

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. 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.

Because the salt affected the experiment's results, it's both a lurking variable and a confounding variable. Mariecor Agravante earned a Bachelor of Science in biology from Gonzaga University and has completed graduate work in Organizational Leadership. By Mariecor Agravante; Updated April 13, References Arizona State University: Designing an Experiment Iowa State University: There are many types of variable but the most important, for the vast majority of research methods , are the independent and dependent variables.

The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design. For many physical experiments , isolating the independent variable and measuring the dependent is generally easy.

If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature. In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable. In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this.

For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim.

The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage.

Randomization and controls are generally used to ensure that only one independent variable is manipulated. To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them. For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender. The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups.

This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity.

In our unusually competent group example, the confounding variable could be that this group is made up of players from the baseball team. In our original example of hungry people throwing the ball, there are several confounding variables we need to make sure we account for.

Some examples would be:. Confounding variables are a specific type of extraneous variable. Extraneous variables are defined as any variable other than the independent and dependent variable. So, a confounding variable is a variable that could strongly influence your study, while extraneous variables are weaker and typically influence your experiment in a lesser way. Some examples from our ball throwing study include:.

Get access risk-free for 30 days, just create an account. Our scientific sentence is now, 'You're going to manipulate the independent variable to see what happens to the dependent variable, controlling for confounding or extraneous variables. In an experiment, if you have multiple trials, you want to reduce the number of changes between each trial.

If you tell the ball throwers on the first day to toss a ping-pong ball into a little red cup, and on the second day you tell ball throwers to hurl a bowling ball into a barrel, your results are going to be different. Each experiment has control variables , which are variables that are kept the same in each trial. These would be things like:. Moderator variables are variables that can increase or decrease the relationship between the independent and dependent variables. These are often identified when repeating an experiment or coming at it from a different angle.

In our ball throwing example, if you secretly placed a research assistant - someone who works for you - among the participants and made them complain loudly about how bad they're failing, this would likely make everyone else's ball throwing worse. These are not necessarily bad things, but they will change your final result. So now, the scientific version of our first sentence will read like this, 'A researcher manipulates the independent variable with the goal of measuring changes in the dependent variable, while accounting for and controlling confounding, extraneous and moderator variables by using control variables.

Confounding variables are defined as interference caused by another variable. Control variables are variables that are kept the same in each trial. Lastly, the moderator variables are variables that increase or decrease the relationship between the independent and dependent variable. To unlock this lesson you must be a Study. Did you know… We have over college courses that prepare you to earn credit by exam that is accepted by over 1, colleges and universities.

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Devin Kowalczyk Devin has taught psychology and has a master's degree in clinical forensic psychology. This lesson explores the terminology of experimental design. Research As a researcher, you're going to perform an experiment.

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The dependent variable is the variable a researcher is interested in. The changes to the dependent variable are what the researcher is trying to measure with all their fancy techniques. In our example, your dependent variable is the person's ability to throw a ball.

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The dependent variable is what is affected by the independent variable-- your effects or outcomes. For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones.

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Unlike extraneous variables, moderator variables are measured and taken into consideration. Typical moderator variables in TESL and language acquisition research (when they are not the major focus of the study) include the sex, age, culture, or language proficiency of the subjects. The key to designing any experiment is to look at what research variables could affect the outcome. There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.

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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 . Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.