What does cause & effect in psychology mean? | Yahoo Answers
Cause and effect is one of the most commonly misunderstood concepts in science and is Some examples of this are rife in alternative therapy, when a group of scientists program must contain measures to establish the cause and effect relationship. . Search over articles on psychology, science, and experiments. Items 1 - 19 of 19 Cause and effect refers to a relationship between two phenomena in which one phenomenon is the reason behind the other. For example. The three criteria for establishing cause and effect – association, time A common example is the relationship between education and income: in Though these examples seem straightforward, researchers in the fields of psychology.
Values over zero indicate a positive correlation, while values under zero indicate a negative correlation. Differences between Experiments and Correlations An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable, and controls the environment in order that extraneous variables may be eliminated.
Experiments establish cause and effect.
Correlation and causality
A correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables. This means that the experiment can predict cause and effect causation but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.
Strengths of Correlations 1. Correlation allows the researcher to investigate naturally occurring variables that maybe unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer. Correlation allows the researcher to clearly and easily see if there is a relationship between variables.
This can then be displayed in a graphical form. Limitations of Correlations 1. Correlation is not and cannot be taken to imply causation. Or even more likely, maybe there's some underlying cause that causes both of these things to happen. And you could think of a bunch of different examples of that. One could be the physical activity. And these are all just theories.
I have no proof for it. But I just want to give you different ways of thinking about the same data and maybe not just coming to the same conclusion that this article seems like it's trying to lead us to conclude.
What does cause & effect in psychology mean?
That we should eat breakfast if we don't want to become obese. So maybe if you're physically active, that leads to you being hungry in the morning, so you're more likely to eat breakfast.Cause and Effect Practice
And obviously being physically active also makes it so that you burn calories. You have more muscle. So that you're not obese. So notice if you view things this way, if you say physical activity is causing both of these, then all of a sudden you lose this connection between breakfast and obesity. Now you can't make the claim that somehow breakfast is the magic formula for someone to not be obese.
So let's say that there is an obese person-- let's say this is the reality, that physical activity is causing both of these things.
Cause and Effect definition | Psychology Glossary | serii.info
And let's say that there is an obese person. What will you tell them to do? Will you tell them, eat breakfast and you won't become obese anymore?
Well, that might not work, especially if they're not physically active. I mean, what's going to happen if you have an obese person who's not physically active? And then you tell them to eat breakfast? Maybe that'll make things worse. And based on that, that the advice or the implication from the article is the wrong thing.
Physical activity maybe is the thing that should be focused on. Maybe something other than physical activity. Maybe you have sleep, maybe people who sleep late and they're not getting enough sleep, maybe that leads to obesity.
And obviously, because they're not getting enough sleep, they wake up as late as possible and they have to run to the next appointment-- or they have to run to school in the case of students-- and maybe that's why they skip breakfast. So once again, if you find someone that's obese, maybe the rule here isn't to force a breakfast down your throat.
Maybe it will become even worse because maybe it is the lack of sleep that's causing your metabolism to slow down or whatever. So it's very, very important when you're looking at any of these studies to try to say, is this a correlation or is this causality?
If it's correlation, you cannot make the judgment that, hey, eating breakfast is necessarily going to make someone less obese. All that tells you is that these things move together. A better study would be one that is able to prove causality. And then we could think of other underlying causes that would kind of break down the narrative that this piece is trying to say.
Statistical Language - Correlation and Causation
I'm not saying it's wrong. Maybe it's absolutely true that eating breakfast will fight obesity. But I think it's equally or more important to think about what the other causes are, not to just make a blanket statement like that. So for example, maybe poverty causes you to skip breakfast for multiple reasons. Maybe both of your parents are working. There's no one there to give you breakfast.
The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome -- a continuous relationship: It's possible that there is some other variable or factor that is causing the outcome.
This is sometimes referred to as the "third variable" or "missing variable" problem and it's at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity see single group threatsmultiple group threats or social threats -- each one describes a type of alternative explanation.
In order for you to argue that you have demonstrated internal validity -- that you have shown there's a causal relationship -- you have to "rule out" the plausible alternative explanations. How do you do that? One of the major ways is with your research design. Let's consider a simple single group threat to internal validity, a history threat. Let's assume you measure your program group before they start the program to establish a baselineyou give them the program, and then you measure their performance afterwards in a posttest.
You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat -- it's not your program that caused the gain but some other specific historical event.
For instance, it's not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General's latest report that happened to be issued between the time you gave your pretest and posttest.