This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables. The independent variable is often manipulated by the researcher in order to create different experimental conditions. By varying the independent babyquest foundation variable, the researcher can observe how the dependent variable changes in response. For example, in a study of the effects of caffeine on memory, the independent variable would be the amount of caffeine consumed, while the dependent variable would be memory performance.
Levels of Independent Variable
The efficacy of a treatment may depend on the age and the weight of the patient taking the treatment. And so when the age and weight are kept the same for both groups, then, the experimenters can make valid conclusions that otherwise would lead to bias and false claims. If the experimenter cannot control an extraneous variable, then, this variable is referred to as a confounding variable.
Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them. That is because this variable helps to “predict” and explain changes in response. For example, the amount of fertilizers, an independent variable, can help predict the extent of plant growth (a dependent variable). In this case, the amount of fertilizers serves as a predictor variable whereas plant growth is the outcome variable.
The levels of independent variables pertain to the different categories or groupings of that variable. For instance, in a study about social media use and the hours of sleep per night, the independent variable is social media use and the hours of sleep per night is the dependent variable. Then, social media use is categorized into low, medium, and high, which are a total of three levels. It should be noted that in some experiments there are other variables present apart from the independent and the dependent variables.
Impact
At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV. In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.
Operationalizing Variables
The treatment variable may be further altered by varying the dosages, the route of administration, the timing, or the duration. The results are monitored and recorded by identifying or measuring physiological, morphological, or behavioral modifications following the treatment. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won’t make sense. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating. Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV).
This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables. These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker. The independent and dependent variables are key to any scientific experiment, but how do you tell them apart?
Independent and Dependent Variable Examples
- This method is used to determine the strength and direction of the relationship between two continuous variables.
- Simply put, the independent variable is the “cause” in the relationship between two (or more) variables.
- These variables are dichotomous or binary in nature, meaning they can take on only two values.
- Going back to the given example above, factors such as age, gender, ethnicity, and medical history (e.g. allergies), may have an effect on the results.
- In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not.
In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest. These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity. An independent variable is defined as a variable that is changed or controlled in a scientific experiment.
Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research. In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.
In this example, the independent variable is the light exposure and the dependent variable is the plant growth. Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables, and what makes them rather tricky is that they can’t be directly observed or measured. Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments. Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously.
In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).
These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure. For example, you want to know if taking your indoor plants outside will make them grow faster than making them stay inside near the window. So, you take a group of indoor plants outside and leave them there for about three hours daily. If you notice a significant change in plant growth that means you may need to give them a daily dose of sunshine for at least three hours each day for better growth.
This enables another psychologist to replicate your research and what is cost allocation is essential in establishing reliability (achieving consistency in the results). For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered. Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.
(Ref. 2) As the name implies, the presence of a confounding variable will confound the results. It may be due to the independent variable or to a confounding variable, and therefore the result will likely be inconclusive. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep. Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult.