In studies with an experimental design, the independent element is the key feature of our group. The dependent variable is the outcome that we measure in the control group and the experimental group(s). We might also refer to an independent variable as a predictor variable, explanatory variable, control variable, manipulated variable, or regressor. Then we might also refer to a dependent variable as a predicted variable, response variable, responding variable, or outcome variable. No, a variable cannot be both independent and dependent at the same time. You can think of the independent variable as the cause and the dependent variable as the effect.
Dependent Variable – Definition, Types and…
- The independent variable is usually applied at different levels to see how the outcomes differ.
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- The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).
- Changing (independent variable) affects the value of (dependent variable).
- They’re the constants, the elements that researchers keep the same to ensure the integrity of the experiment.
Known values for the target variable are provided for the training data set and test data set, but should be predicted for other data. The target variable is used in supervised learning algorithms but not in unsupervised learning. A variable is a characteristic, attribute, or value that can change or vary across participants, objects, or conditions within a research study. Variables allow researchers to quantify or categorize aspects of the subject under investigation, serving as the foundation for data collection and analysis.
Experimental variables
The independent variable is a critical element in research, especially in experimental design, where it helps establish causal relationships. By carefully defining, manipulating, and controlling the independent variable, researchers can gain insights into how different factors influence outcomes. Understanding the types and roles of independent variables is essential for designing valid, reliable studies that yield meaningful results. Independent variables in research can be altered or manipulated to offer insight into their influence on dependent variables. They are “independent,” as they are not affected or influenced by other variables in an experimental study.
- For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value.
- An independent variable is the variable you manipulate or vary in an experimental study to explore its effects.
- The independent and dependent variables are the two main types of variables in a science experiment.
- Since we think the type of clothing will affect how many waves are given, we can determine that the type of clothing is the independent variable.
- Up next, we’ll look at tons of examples to see how independent variables work their magic in different areas.
These theories are like ancient scrolls, providing guidelines and blueprints that help scientists use independent variables to uncover the secrets of the universe. Now that we’re acquainted with the basic concepts and have the tools to identify independent variables, let’s dive into the fascinating ocean of theories and frameworks. In our adventure through the realm of independent variables, we’ll delve deeper into some fundamental concepts and definitions to help us navigate this exciting world. A variable is considered dependent if it depends on (or is hypothesized to depend on) an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables.
Categorical or discrete dependent variables
These are quantitative variables and can represent an infinite number of values within a given range. They are measurable quantities and can be further divided into smaller parts. Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “control variable,” which is variable that is held constant so it won’t influence the outcome of the experiment. The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them. By exploring different possibilities and wondering how changing one thing could affect another, you’re on your way to identifying independent variables.
A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression. The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals.
Difference Between Independent and Dependent Variables
Further, we discuss the circumstances under which we can use them in our research. Thus, we know that we must have the independent and dependent variables switched around. It is possible for a function to have multiple independent and dependent variables, though this is more common in higher mathematics, not algebra. Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data.
Dependent and independent variables
Have you ever wondered how scientists make discoveries and how researchers come to understand the world around us? A crucial tool in their kit is the concept of the independent variable, which helps them delve into the mysteries of science and everyday life. In the investigation of covariation between variables, they are usually labeled as either “independent” or “dependent” (variables). In this context, variation in the independent variable(s) is produced – optimally in a systematic manner – while the effect of this variation on the dependent variable(s) is observed. Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.
While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment. A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. The researchers at the lab have their usual test volunteers sleep in individual rooms every independent variable definition night for one week. In doing so, the researchers can better determine the specific effect of the instructional method. For instance, they can determine whether the flipped classroom teaching model leads to higher test scores and engagement compared with the lecture-based method.
In a cause-and-effect relationship, they represent the effect or outcome of a study. The following example of dependent and independent variables gives a better insight. Let’s examine non-experimental research and how variable are used.11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable.