In qualitative research, variables are typically not classified as independent or dependent as in quantitative research. Instead, qualitative research focuses on exploring complex phenomena through in-depth analysis of non-numerical data such as interviews, observations, and textual analysis. Researchers in qualitative studies aim to understand the relationships, meanings, and contexts within the data rather than test specific hypotheses with independent and dependent variables.
Dependent and Independent variables
The three types of variables commonly used in research and statistics are independent variables, dependent variables, and controlled variables. Independent variables are manipulated or changed to observe their effect, while dependent variables are the outcomes measured in response to the independent variables. Controlled variables are kept constant to ensure that the results are due to the independent variable alone. This framework helps clarify cause-and-effect relationships in experiments.
Variables in the scientific method are elements that can be changed or controlled in an experiment to test their effects on other variables. They are typically classified into three types: independent variables, which are manipulated by the researcher; dependent variables, which are measured in response to changes in the independent variable; and controlled variables, which are kept constant to ensure that the results are due to the manipulation of the independent variable. Properly identifying and managing these variables is crucial for obtaining valid and reliable results in scientific research.
Extraneous variables are factors other than the independent variable that can influence the dependent variable, potentially skewing results. The four common types of extraneous variables include: Participant variables (individual differences between subjects, such as age or intelligence) Situational variables (environmental factors like temperature or time of day) Measurement variables (inconsistencies in how data is collected or measured) Confounding variables (factors that are related to both the independent and dependent variables, leading to false conclusions). Controlling these variables is crucial for ensuring the validity of research findings.
temperature, pressure , volume, are independent density, viscosity, etc are dependent Properties of mater are always dependent of independents. as (dependent) density , viscosity , mass density , phase conduction , etc always vary when we change independents .(temperature, pressure , volume) so you can understand dependent & in dependent
Dependent and Independent variables
Factorial designs
Some times. At other times it uses mutually dependent variables (changes in each variable affect the other).
When you do an experiment the variable you control is the independent variable, and the variable you measure is the dependent variable. The independent variable is controlled by the experimenter; the dependent variable is measured. In this case, corporate social responsibility is the independent variable, and the others are dependent variables.
In qualitative research, researchers do not typically control variables in the same way as in quantitative research. Instead, they aim to explore and understand the complexities and nuances of a phenomenon without manipulating variables. The focus is on gaining in-depth insights and understanding the context in which the research is conducted.
I want to know the role of variables in the qualitative research design Independent Variable: It is the variable presumed to affect the dependent variable. It is the variable manipulated by the researcher to create an effect on the dependent variable. It is also known as "the treatment." Dependent Variable: The presumed effect that changes with a change in the independent variable. The "effect," "outcome," "response," or where one looks to see the influence of the independent variable. Extraneous Variable: Variable other than the independent variable that may bear any effect on the behavior of the subject being studied: http://en.wikipedia.org/wiki/Extraneous_variable Research Variable: May be used when the study is observing or measuring variables without looking at cause-effect relationships. May be used when there is no specific expectation of one variable influencing the other. The variables' definitions do not change, only the design. And where you cannot QUANITFY the data, you QUALIFY it, describe it, find common major themes, and classify it.
The dependent variable is influenced by changes in the independent variable. The dependent variable's values depend on the values of the independent variable. This relationship is often explored through statistical analysis in research studies.
Ex-post facto research measures the cause and effect relationship without manipulating the independent variable. While the experimental research starts from manipulating and controlling the independent variables and proceeds to observing the effect on the dependent variables.
Ex-post facto research measures the cause and effect relationship without manipulating the independent variable. While the experimental research starts from manipulating and controlling the independent variables and proceeds to observing the effect on the dependent variables.
History Effect is an event that intervenes in the course of one's research and makes it difficult if not impossible to interpret the relations among independent and dependent variables.
Variables to study in a thesis depend on the research question, but common ones include independent variables that impact the dependent variable. Examples include demographics, behavior, attitudes, and environmental factors. It's essential to specify these variables clearly to align with the research objectives.
There should be one dependent variables. Depending on the type of research you are doing, the amount of independent variables will change. If you are doing research on a large scale, you will use more independent variables. If it's on a small scale, you will use very little. If you are not able to run your regression it means your sample size is too small or you have too many independent variables.