Yes, think about cause and effect when thinking about this issue. Even to describe or observe a phenomena.
It does not have to. It is simply a study where two variables have a joint probability density function. There is no requirement for both variables to be dependent - one may be dependent on the other (which is independent).
The answer to the question depends on what it is that you are trying to study.
Independent variables are the factors or conditions that are manipulated or changed in an experiment to observe their effect on dependent variables. They are often referred to as predictors or explanatory variables. For example, in a study examining the impact of study time on test scores, the amount of study time would be the independent variable.
Yes. The presumed cause is the independent variable and the presumed effect is the dependent varibale. Variablility in the dependent variable is presumed to depend on variablility in the independent variables. It is used more of a direction of influence rather than a cause and effect scenario. Ex. need for increased assistance is dependent on decrease in health. Health is the independent variable and assistance is the dependent.
Extraneous variables are factors other than the independent variable that can influence the dependent variable, potentially skewing the results of an experiment. Confounding variables are a specific type of extraneous variable that is related to both the independent and dependent variables, making it difficult to determine the true effect of the independent variable on the dependent variable. Both types of variables can threaten the internal validity of a study if not properly controlled.
It does not have to. It is simply a study where two variables have a joint probability density function. There is no requirement for both variables to be dependent - one may be dependent on the other (which is independent).
The answer to the question depends on what it is that you are trying to study.
Independent variables are the factors or conditions that are manipulated or changed in an experiment to observe their effect on dependent variables. They are often referred to as predictors or explanatory variables. For example, in a study examining the impact of study time on test scores, the amount of study time would be the independent variable.
Multicollinearity is when several independent variables are linked in some way. It can happen when attempting to study how individual independent variables contribute to the understanding of a dependent variable
Yes. The presumed cause is the independent variable and the presumed effect is the dependent varibale. Variablility in the dependent variable is presumed to depend on variablility in the independent variables. It is used more of a direction of influence rather than a cause and effect scenario. Ex. need for increased assistance is dependent on decrease in health. Health is the independent variable and assistance is the dependent.
Extraneous variables are factors other than the independent variable that can influence the dependent variable, potentially skewing the results of an experiment. Confounding variables are a specific type of extraneous variable that is related to both the independent and dependent variables, making it difficult to determine the true effect of the independent variable on the dependent variable. Both types of variables can threaten the internal validity of a study if not properly controlled.
The independent variable is the thing you are changing. The dependent variable is the result you are trying to measure. In a caffeine study, the amount of caffeine given to a subject would probably be the independent variable. The dependent variable would be what you are measuring, like moodiness, apparent energy, kidney function, etc.
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.
Independent variables are factors in a study that are manipulated or controlled by the researcher in order to observe their effect on the dependent variable. They are variables that are believed to influence the outcome of an experiment or study.
Yes, the dependent variable is the one that is being measured or tested in an experiment, and its values are expected to change in response to manipulations of the independent variable. The relationship between the independent and dependent variables is the main focus of a scientific study.
A hypothesis is a testable statement or prediction about the relationship between variables in a research study. Variables are the elements that can change or vary, typically classified as independent (manipulated) and dependent (measured). The hypothesis often posits how changes in the independent variable will affect the dependent variable, guiding the research design and experimentation. Thus, the relationship between a hypothesis and variables is foundational for empirical investigation and analysis.
a dependent variable is the thing in your experiment you are testing or the thing that you are influencing. for example temperature. The independent variable changes on its on for example time. If you had a question : How long does it take for the water to reach 25c; the water temp is the dependent as you are measuring it, and the time is independent because you are not affecting it.