The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants). 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. Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.
Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights. In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured). In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable. For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable). In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.
Yes, both quantitative and qualitative data can have independent and dependent variables. Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied. In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.
To preclude the “placebo” effect — wherein the patient apparently feels better after taking the placebo pill, the patients were not informed if the pill they were taking was real or the placebo. Then, the recovery rates of both groups (i.e. the patients taking the placebo and those taking the real pill) were monitored. This doesn’t really make sense (unless you can’t sleep because you are worried you failed a test, but that would be a different experiment).
Overview: Variables In Research
By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable. For another example, say you are measuring whether amount of sleep affects test scores. The hours of sleep would be the independent variable while the test scores would be dependent variable. This type of hypothesis is constructed to state the independent variable followed by the predicted impact on the dependent variable. In this scenario, the variables are the treatments (i.e. the pill or the placebo) and the recovery rates of the patients.
- For example, the amount of fertilizers, an independent variable, can help predict the extent of plant growth (a dependent variable).
- Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.
- This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).
- In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).
How Do You Tell Independent and Dependent Variables Apart?
In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized. It is not intended to provide medical, legal, or any other professional advice.
Extraneous variables, for example, are the variables that also have an impact on the relationship between the independent and the dependent variables. 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. Also, controlling the extraneous variables in an experiment is important to come up with more precise conclusions based on the empirical data. It is called independent because its value does not depend on and is not affected by the state of any other variable in the experiment. Sometimes you may hear this variable called the “controlled variable” because it is the one that is changed. Do not confuse it with a control variable, which is a variable that is purposely held constant so that it can’t affect the outcome of the experiment.
What is a confounding variable?
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. Try to answer the quiz below to check what you have learned so far about the independent variables. When variables are kept constant, we refer to them as the controlled variables. Continuing with the given example, we may want to keep the age and weight ranges of the subjects from both groups (those taking the real pill and those taking the placebo) the same.
For example, students who use effective coping strategies might be less stressed but also perform better academically business news headlines due to their improved mental state. In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.
Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions. So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can. The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable. This method is used to compare the means of two groups for a continuous dependent variable.
In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list. As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of.
As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant.
If the dependent and independent variables are plotted on a graph, the x-axis would be the independent variable and the y-axis would be the dependent variable. You can remember this using the DRY MIX acronym, where DRY means dependent or responsive variable is on the y-axis, while MIX means the manipulated or independent variable is on the x-axis. Students are often asked to identify the independent and dependent variable in an experiment. It is even possible for the dependent variable to remain unchanged in response to controlling the independent variable. As already cited above, the type of treatment (pill vs. placebo) is the independent variable.
That is in contrast to a dependent variable that is influenced by other variables. The independent variable meaning in an experiment is the variable that is to be manipulated and observed. In an independent variable psychology experiment, for instance, it refers to the factor that influences the value of the variable that depends on it. Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. For example, accounts payable vs notes payable air pollution could confound the impact of the variables of interest in a study investigating health outcomes.
