In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable. Show
A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust,[1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking. When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter.[2] Although there are many types of bias, two common types are selection bias and information bias. Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study.[2] For example, an online website that rates the quality of primary care physicians based on patients’ input may produce ratings that suffer from selection bias. This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating. Information bias refers to a “systematic error due to inaccurate measurement or classification of disease, exposure, or other variables.”[3] Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely. The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation. Although two variables may be correlated, this does not imply that there is a causal relationship between them. One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect; covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation; and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation.[4]. These alternative explanations are closely related to the concept of internal validity. [1]Trochim, W.M.K. “Establishing Cause and Effect.” Research Methods Knowledge Base, 10/20/2006. Web 1/24/2017. Previous Section Next Section
Compare the independent variable and dependent variable in research. See other types of variables in research, including confounding and extraneous variables. Updated: 07/25/2021 The definition of a variable in the context of a research study is some feature with the potential to change, typically one that may influence or reflect a relationship or outcome. For example, potential variables might be time it takes for something to occur, whether or not an object is used within a study, or the presence of a feature among members of the sample. Within research, independent and dependent variables are key, forming the basis on which a study is performed. However, other types of variables may come into play within a study, such as confounding variables, controlled variables, extraneous, and moderator variables. ResearchAs a researcher, you're going to perform an experiment. I'm kind of hungry right now, so let's say your experiment will examine four people's ability to throw a ball when they haven't eaten for a specific period of time - 6, 12, 18 and 24 hours. We can say that in your experiment, you are going to do something and then see what happens to other things. But, that sentence isn't very scientific. So, we're going to learn some new words to replace the unscientific ones, so we can provide a scientific explanation for what you're going to do in your experiment. The starting point here is to identify what a variable is. A variable is defined as anything that has a quantity or quality that varies. Your experiment's variables are not eating and throwing a ball. Now, let's science up that earlier statement. 'You are going to manipulate a variable to see what happens to another variable.' It still isn't quite right because we're using the blandest term for variable, and we didn't differentiate between the variables. Let's take a look at some other terms that will help us make this statement more scientific and specific.
Dependent Variables in ResearchA dependent variable is one being measured in an experiment, reflecting an outcome. Researchers do not directly control this variable. Instead, they hope to learn something about the relationship between different variables by observing how the dependent variable reacts under different circumstances. Although "dependent variable" is the most commonly used term, they may also be referred to as response variables, outcome variable, or left-hand-side variable. These alternate names help to further illustrate their purpose: a dependent variable shows a response to changes in other variables, displaying the outcome. The meaning of "left-hand-side" is less immediately transparent, but becomes more obvious when considering the format of a basic algebraic equation. Typically, the dependent variable in these is referred to as "Y" and placed on the left-hand-side of the equation. Because of this standard, dependent variables may also be called the Y variable as well, and the dependent variable is usually seen on the y-axis in graphs. One example of a dependent variable would be a student's test scores. Several factors would influence these scores, such as the amount of time spent studying, amount of sleep, or the stress levels of the student. Ultimately, the dependent variable is not static or controlled directly, but is subject to change depending on the independent variables involved. Independent Variables in ResearchAn independent variable is one that the researcher controls or otherwise manipulates within a study. In order to determine the relationship between dependent and independent variables, a researcher will purposefully change an independent variable, watching to see if and how the dependent variable changes in response. The independent variable can alternately be called the explanatory, predicator, right-hand-side, or X variable. Similarly to dependent variables, these reflect the uses of independent variables, as they are intended to explain or predict changes in the dependent variables. Likewise, independent variables are often referred to as "X" in basic algebraic equations and plotted using the x-axis. In research, the experimenters will generally control independent variables as much as possible, so that they can understand their true relationship with the dependent variables. For example, a research study might use age as an independent variable, since it influences some potential dependent variables. Obviously, a researcher cannot randomly assign ages to participants, but they could only allow participants of certain ages into a study or sort a sample into desired age groups. Comparing Dependent and Independent Variables
Examples of Independent and Dependent Variables in Research StudiesMany research studies have independent and dependent variables, since understanding cause-and-effect between them is a key end goal. Some examples of research questions involving these variables include:
Dependent and Independent VariablesA moment ago, we discussed the two variables in our experiment - hunger and throwing a ball. But, they are both better defined by the terms 'dependent' or 'independent' variable. 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. We're trying to measure the change in ball throwing as influenced by hunger. An independent variable is a variable believed to affect the dependent variable. This is the variable that you, the researcher, will manipulate to see if it makes the dependent variable change. In our example of hungry people throwing a ball, our independent variable is how long it's been since they've eaten. To reiterate, the independent variable is the thing over which the researcher has control and is manipulating. In this experiment, the researcher is controlling the food intake of the participant. The dependent variable is believed to be dependent on the independent variable. Your experiment's dependent variable is the ball throwing, which will hopefully change due to the independent variable. So now, our scientific sentence is, 'You are going to manipulate an independent variable to see what happens to the dependent variable.' Unwanted InfluenceSometimes, when you're studying a dependent variable, your results don't make any sense. For instance, what if people in one group are doing amazingly well while the other groups are doing about the same. This could be caused by a confounding variable, defined as an interference caused by another variable. 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: ResearchAs a researcher, you're going to perform an experiment. I'm kind of hungry right now, so let's say your experiment will examine four people's ability to throw a ball when they haven't eaten for a specific period of time - 6, 12, 18 and 24 hours. We can say that in your experiment, you are going to do something and then see what happens to other things. But, that sentence isn't very scientific. So, we're going to learn some new words to replace the unscientific ones, so we can provide a scientific explanation for what you're going to do in your experiment. The starting point here is to identify what a variable is. A variable is defined as anything that has a quantity or quality that varies. Your experiment's variables are not eating and throwing a ball. Now, let's science up that earlier statement. 'You are going to manipulate a variable to see what happens to another variable.' It still isn't quite right because we're using the blandest term for variable, and we didn't differentiate between the variables. Let's take a look at some other terms that will help us make this statement more scientific and specific. Dependent and Independent VariablesA moment ago, we discussed the two variables in our experiment - hunger and throwing a ball. But, they are both better defined by the terms 'dependent' or 'independent' variable. 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. We're trying to measure the change in ball throwing as influenced by hunger. An independent variable is a variable believed to affect the dependent variable. This is the variable that you, the researcher, will manipulate to see if it makes the dependent variable change. In our example of hungry people throwing a ball, our independent variable is how long it's been since they've eaten. To reiterate, the independent variable is the thing over which the researcher has control and is manipulating. In this experiment, the researcher is controlling the food intake of the participant. The dependent variable is believed to be dependent on the independent variable. Your experiment's dependent variable is the ball throwing, which will hopefully change due to the independent variable. So now, our scientific sentence is, 'You are going to manipulate an independent variable to see what happens to the dependent variable.' Unwanted InfluenceSometimes, when you're studying a dependent variable, your results don't make any sense. For instance, what if people in one group are doing amazingly well while the other groups are doing about the same. This could be caused by a confounding variable, defined as an interference caused by another variable. 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: What is dependent and independent variable in research?The dependent variable in a research study or experiment is what is being measured in the study or experiment. The independent variable in a research study or experiment is what the researcher is changing in the study or experiment. It is the variable that is being manipulated. The independent variable is responsible for changing the dependent variable. What are variables in research examples?A variable is something that can be measured in a study or experiment. The independent and dependent variable are vital to the understanding and development of research. Create an account to start this course today Used by over 30 million students worldwide Create an account What are some examples of independent variables?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. Other factors (such as what they eat, how much they go to school, how much television they watch) aren't going to change a person's age.
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 are the 5 types of dependent variables?Dependent Variables in Psychology.. An experimental variable.. An explained variable.. A measured variable.. An outcome variable.. An output variable.. A responding variable.. A regressand (in regression analysis.). A response variable.. |
What are some examples of independent and dependent variables
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