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What is the difference between moderating and extraneous variables?
Extraneous variable a.k.a. Confounding vaiable is a variable that affects an independent variable n also afects a dependent variable at d same time confounding relatnship btn the independent and dependent variable. Mediating variable a.k.a. Intervening variable, it is a variable forming a link btn two variables that are causualy conected.
What are Confounding Variables?
In any experiment there are many kinds of variables that will effect the experiment. The independent variable is the manipulation for the experiment and the dependent variable is the measure you take from that experiment. Confounding variables are things in which have an effect on the dependent variable, but were taken into account in the experimental design. For example, you want to know if Drug X has an effect on causing sleep. The experimenter must take care to design the experiment so that he can be very sure that the subjects in the study fell asleep because of the influence of his Drug X, and that the sleepiness was not caused by other factors. Those other factors would be confounding variables.
What is meant by confounding in math?
In statistics a confounding variable is one which can give rise to spurious correlations. For example, my age is fairly well correlated with the number of television sets in the UK. This is not because my getting older sells more TV sets, nor is it because the sale of TV sets makes me grow older. The real reason is that both these are correlated with time and, as the years pass, both increase. So, time is the confounding variable which gives rise to an apparent relationship between TV sets and my age. Confounding variables can have serious effects when statistical methods are being used to develop a cause-and-effect model. In truth, there may be no direct causal relationship, only two independent relationships with a third variable - the confounding factor.
What third variables might there be that would cause a spurious correlation in the survey results?
The third variable could be one which is correlated to both variables. These are called confounding variable. For example, in the UK you could find a correlation between coastal air pollution and ice cream sales. This is not because eating ice cream causes air pollution nor because air pollution causes people to eat ice cream. The confounding variable is the temperature. Warm weather gets people to drive to the sea!
What is the importance of unexpected outcomes?
depends on what subject: for psychology, stress is elevated more when people are suprised with new unpredicted challenges rather than handling a known stress. In an experiment, it would mean the variables are not related and another confounding varialbe is affecting the change, statistical indifference?
How can you eliminate confounding variables in your experiment?
To eliminate confounding variables, or variables that were not controlled and damaged the validity of the experiment by affecting the dependent and independent variable, the experimenter should plan ahead. They should run many checks before actually running an experiment.
Will a 60 cycle appliances interfere with a ECG tracing?
yes
What confounding variables can interfere with study on color of recovery room walls on patients recovery time?
Confounding variables in this case are unrelated variables that can simultaneously affect color and recovery time. One made-up example of such a variable could be, let's see... height of the floor in the building. That way, perhaps all rooms in the highest floor of the hospital are painted a certain color. The nurses stay in the office at the bottom so patients in those higher rooms get the least attention, therefore recovering slower. One could then falsely conclude that the room color caused lengthened recovery times when it was really the confounding variable (position of the rooms in their floor). This example requires a bit of creativity but I'm sure you can find other variables.
What are confounding variables in Stanford prison experiment?
one example would be if the participants had mental issues, or behavior disorders.
What is the difference between moderating and extraneous variables?
Extraneous variable a.k.a. Confounding vaiable is a variable that affects an independent variable n also afects a dependent variable at d same time confounding relatnship btn the independent and dependent variable. Mediating variable a.k.a. Intervening variable, it is a variable forming a link btn two variables that are causualy conected.
What is mean by compounding variable?
I think there is confusion between the terms "compounding variable" and "confounding variable". My way of looking at it is that compounding variables describe elements of mathematical functions, only. Confounding variables apply to any research in any domain and are external variables to the research design which might impact on the dependent variable to a lesser or greater extent than the independent variable, which are part of the research design. I am Peter Davies at classmeasures@aol.com
What are Confounding Variables?
In any experiment there are many kinds of variables that will effect the experiment. The independent variable is the manipulation for the experiment and the dependent variable is the measure you take from that experiment. Confounding variables are things in which have an effect on the dependent variable, but were taken into account in the experimental design. For example, you want to know if Drug X has an effect on causing sleep. The experimenter must take care to design the experiment so that he can be very sure that the subjects in the study fell asleep because of the influence of his Drug X, and that the sleepiness was not caused by other factors. Those other factors would be confounding variables.
Define internal validity?
Internal validity is higher when you stop confounding variables interfering with the experiment (things that effect the results). Internal validity occurs when a researcher controls all confounding variables and the only variable influencing the results of a study is the one being manipulated by the researcher. This means that the variable the researcher intended to study is indeed the one affecting the results and not something else.
How do you use the word confounding in a sentence?
Confounding means perplexing and amazing. Two similar words to confounding are dumbfounding and astounding. "The man walking down the street wearing a giant chicken-suit was a confounding sight."
What are Sources of internal invalidity?
Sources of internal invalidity in research studies include confounding variables, selection bias, measurement bias, and researcher bias. These factors can affect the internal validity of the study results and make it difficult to draw accurate conclusions about the relationship between variables.
Which experiment method is important for controlling bias?
Randomizing the unwanted variables is one method of building a stronger causal argument. Controlling or a strong attempt at controlling the unwanted variables would be recommended. One variable, and only one should remain independent; this would ensure the dependent variable could be assessed in the proper light. Eliminating unwanted confounding variables my be necessary for a stronger causal argument; the confounding variables distort the conclusion in the causal argument. Eliminating unwanted variables could mean categorising data; it could mean separating data; it could mean some guess work, such as adding/subtracting figures like a statistician.
What is meant by confounding in math?
In statistics a confounding variable is one which can give rise to spurious correlations. For example, my age is fairly well correlated with the number of television sets in the UK. This is not because my getting older sells more TV sets, nor is it because the sale of TV sets makes me grow older. The real reason is that both these are correlated with time and, as the years pass, both increase. So, time is the confounding variable which gives rise to an apparent relationship between TV sets and my age. Confounding variables can have serious effects when statistical methods are being used to develop a cause-and-effect model. In truth, there may be no direct causal relationship, only two independent relationships with a third variable - the confounding factor.
