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Correlational Research

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❶Are the two words interchangeable? Now there are good and bad things about surveys in research.

Correlational Research

Correlation Coefficients
Correlational Study Example
Let's clear something up, correlation isn't causation, but it's important!

Correlation means association - more precisely it is a measure of the extent to which two variables are related. If an increase in one variable tends to be associated with an increase in the other then this is known as a positive correlation. An example would be height and weight. Taller people tend to be heavier. If an increase in one variable tends to be associated with a decrease in the other then this is known as a negative correlation.

An example would be height above sea level and temperature. As you climb the mountain increase in height it gets colder decrease in temperature. When there is no relationship between two variables this is known as a zero correlation. For example their is no relationship between the amount of tea drunk and level of intelligence. A correlation can be expressed visually. This is done by drawing a scattergram - that is one can plot the figures for one variable against the figures for the other on a graph.

When you draw a scattergram it doesn't matter which variable goes on the x-axis and which goes on the y-axis. Remember, in correlations we are always dealing with paired scores, so the values of the 2 variables taken together will be used to make the diagram.

Decide which variable goes on each axis and then simply put a cross at the point where the 2 values coincide. Strictly speaking correlation is not a research method but a way of analysing data gathered by other means. This might be useful, for example, if we wanted to know if there were an association between watching violence on T. Another area where correlation is widely used is in the study of intelligence where research has been carried out to test the strength of the association between the I.

The correlation coefficient r indicates the extent to which the pairs of numbers for these two variables lie on a straight line. Values over zero indicate a positive correlation, while values under zero indicate a negative correlation. 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. Obviously other possibilities exist, but as more researchers assess this relationship in different ways, we'll learn more about its true nature.

It's also how we found out that smoking causes cancer. Through endlessly repeated findings showing an association. That turned out pretty well, I think As a science teacher I really appreciate this post. My only quibble is that once a correlation is established you failed to point out that, in many cases, with current technology, it is possible to identify a biological mechanism to establish causation.

For example we can now identify the chemicals in cigarette smoke which cause the DNA damage leading to lung cancer. I tried to stick to the statistics area when writing that post, but you're certainly correct to note that we have other ways to verify relationships between things once an initial association has been revealed.

I feel another post coming on I never understood how causation could ever be proved at all. How do you study photons if light is required but disrupts their activity?

Do smokers in clinical studies have more stress which leads them go seek a way to make extra money? Does the combustion of the paper influence the results? What about the foam filters being heated? Or the tobacco blend recipe or strain of plant predominantly used that decade versus today? Or whole herb tobacco as opposed to isolated nicotine in vapor liquid? Pipe tobacco still has moisture in it, so does that mean it's more of a vaporization? What about hookah shisha that is completely vaporized and not technically smoked?

And speaking of, meth is never really smoked. Besides, I thought the Scientific Method resulted in theories and not laws. But I'm not an expert epidemiologist.

Just a tweaked out meth addict with OCD and degrees in art and cake with mood swings from withdrawal that keep getting diagnosed as bipolar. But maybe I am? That's another interesting example of causation vs association. Thank you for the article though. I never thought about the fact that studies on meth addiction were designed by people who have probably never used or experienced addiction or anhedonia.

Nor have the prescribing doctors or regulators. That could influence a lot of things too. I'm much friendlier in person--like Snow White. But my tone often seems so harsh in writing because I'm hiding behind a screen and can avoid ever reading responses for fear of conflict. Also an interesting and confusing factor in everyone's assumptions of online communication.

But I saw your reference to meth smoking and instantly categorized you as the enemy who doesn't understand. I have been trying to figure out answers to those questions and pads and pads of sticky notes all over the place with others for a while. I'm sure the studies factored in most of them anyway.

And as an artist its hard to limit things to black and white, but as a graphic design and science nerd its hard to handle grayscale. Or how the foundation stones of the New Jerusalem in Revelation were color combinations found in precious stones or nature and not a single universally reproducible color.

I have such a hard time deciding when there are so many beautiful references in the natural world. But like limits in calculus, eventually you reach near certainty even if you never actually touch it. That's what I see as causality. I started out thinking you were going to require an answer but you got it there in the end. Pure science doesn't prove anything but rather continues to produce evidence in support, or opposition, of a specific view.

If you manipulate some variable in an experiment dose of meth vapor you inhale, for instance - 0,. But it's only one piece of the puzzle. Replication is where it's at. I presume there is none, although somebody said there was. Are the two words interchangeable? It can be observed that if anyone studied horses and carts with current statistical methods in an environment where a horse and cart alone could not be studied or known about , there would be a very high correlation that whenever one moves, so does the other.

Causation, however, could not be determined in such a way. The unlearned might even suggest there is a high probability that the cart is pushing the horse. In drug studies, there is also various reasons for correlation besides causation. Marijuana studies pick people who have mental or other cognitive problems. Are they using the drug as a crutch for existing problems and thus causing the correlation?

It is just as likely as the alternative explanation. From recent work that I have researched, I have come to the conclusion that "from a preponderance of correlation, causation may be established".

That being a direct quote that I recently wrote. Unfortunately, a few were quick to shoot it down claiming that a single uncontrolled untested example can not prove causation. Aside from the fact that the definitions of "preponderance" and "may" escapes them, when does it establish causation?

I was under the impression it was when all other possible controllable variables were taken into consideration and managed, preventing "A causes B" from being flipped to "B can just as easily cause A" and other rational explanations have been quashed in the process.

The work I am doing right now is with child maltreatment. We are determining whether or not spanking is harmful to children. A conclusion was drawn from a plethora of studies that corporal punishment, spanking being the isolated form, is associated with anxiety disorders.

Someone tried to flip A and B and say that children who were prone to being more anxious might act out and behave clumsy more frequently, thus, warranting more spankings. Neurology bridged this gap showing that the portions of the brain responsible for threat detection flared when children discussed spankings and when looking at grumpy faces. The same portion is responsible for anxiety disorders later in life. What then, when the control for behavior prior to spankings began is accounted for and there is a scientific field bridging the gap?

Technology such as MRI's are certainly not perfect, but I'd like to believe they are an advancement we should not be ignoring. Another example, in terms of worsened behavior with more frequent spankings, we discussed the reactive neurological pathways formed when struck by a parent.

The more of these developed, the more likely a child is going to be impulsive and aggressive later in life.

Differences between Experiments and Correlations

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While we focus on correlation in research, we must also note that the correlation can be positive or negative. Positive correlations mean that as variable A increases, so does variable B. A negative correlation is defined as when .

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Correlation means association - more precisely it is a measure of the extent to which two variables are related. If an increase in one variable tends to be associated with an increase in the other then this is known as a positive correlation. An example would be height and weight. Taller people tend to be resrebal.tk: Saul Mcleod.

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Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. Correlation. A correlation is a statistical index used to represent the strength of a relationship between two factors, how much and in what way those factors vary, and how well one factor can predict the other.

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A correlational study is a type of research design where a researcher seeks to understand what kind of relationships naturally occurring variables have with one another. In simple terms. Correlational Research. There are many types of correlational research. The commonality among all types of correlational research is that they explore relationships between variables. Where descriptive research only described what was going on, correlational research talks about the link between different things.