Gaining Causal Relationships

Experimental research concentrates on how and why something happens. It is the evaluation of how an independent variable (a manipulated factor) affects a dependent variable (an observed factor). The outcome can be affected by a number of elements; obtaining random and representative samples of the study population, experimenter bias and extraneous variables.

Correlation means a mutual relationship or connection between two or more things, and it's different from causation, because that means the effect after an action. For example, in my life the correlation being a parent and their child and the causation could be a child getting an F for not studying. The Early Childhood Longitudinal Study is an overall study of the parents and students. They test their skills in academics then do a survey to better understand the thought process of each one and where it came from. The purpose was to see their correlation and causation. If they went hand-in-hand or not, another example, "A child whose parents are highly educated typically does well in school; not much surprise there" (199). What the parent does for their child is more helpful than what a parent is. With the support it all fits in and helps the child in its academic performance. I however, think it can go both ways; a parent can help and be it to be beneficial. It all depends on the child at the time and what its capabilities are, not their

This essay will attempt to explain the use of the causal model designed by psychologists John Morton and Uta Frith (1995) to distinguish the effects of two developmental disorders, which are specific language impairment (SLI) and autism spectrum disorder (ASD). The main objective is to identify how the model will characterise the similarities and differences of SLI and ASD; also, to evaluate the studies cited within these disorders. This essay will highlight how the model was produced and its purposes, as well as, including future research that can further understand the distinction within the diagnostic groups.

There seems to be a general tendency to attribute causation with correlation. People often conclude that causation exist when the association between variables is seemingly obvious but this is a common mistakes that people makes. It is important to note that correlation does not prove causation. Correlation just simply implies that a relationship between variables exists and that there is a possibility of a cause and effect relationship; however, it does not prove causation actually exist. Correlation do not indicate in which direction the relationship works such as Variable A could cause Variable B, or vice versa, or may a third (or fourth, or even fifth) variable may be the cause of both of the other related variables.

Correlation is usually when two things tend to happen together at the same time and causation is something happens because of something else. I think it is harder to prove causation because

It helps clarify relationsips between variables that cannot be examined by other methods and allows prediction

Thus, this experiment is a way in which the researcher can demonstrate causal relationships because the experimenter has control over the experiment. And the participants were randomly assigned with other peers in different conditions when answering the discussion questions about their own

1. There are three components required to determine a causal relationship. The three components are temporal precedence, covariation of the cause and effect and no plausible alternative explanations, these are the three things you need to determine a causal relationship. Temporal precedence is showing the cause before the effect happen. Covariation of the cause and effect, prove that they have some type of relationship. Between the two things they share a relationship somehow. The last one is no plausible alternative explanations that mean because they have a relationship it does not mean it is causal relationship, but it could be a factor or something that cause the outcome. Example the ice cream sales go up in the summer, and also been reported

Correlation is defined as the occurrence of two of more things or events at the same time that might be associated with each other but are not necessarily connected by a cause and effect relationship. While on the other hand, causation is defined as the action of causing something to occur. In my opinion both causation and correlation are both difficult things to prove but I believe that causation is harder to prove than correlation. The problem with proving causation between events is that there is a possibility that there are other events that are correlated to the event we are trying to determine the causation. These correlated events could just be a coincidence that they are happening at the same time or it could be that theses events are the actual causation of the

The error in the conclusion in this example would be that the linear correlation found between the number of cigarettes and the pulse rate suggests causality. While we can determine that there is a correlation between the pulse rate and number of cigarettes smoked, we cannot prove that the increases in number of cigarettes smoked cause increases in pulse rates, exclusively. Both variables might be affected by some other existing variable. Simply having a correlation between two variables doesn’t imply a causal relationship. To prove a causal relationship, further analysis is required. While a linear correlation shows the strength of the relationship between the two variables, but it does not show that the one variable is changing at direct

one of the main reasons to know about correlation is for prediction that if two variables are correlated, knowing one allows us to take an educated guess about the other one is likely to do. for example your IQ test results can predict how well you will do at university.

In Psychology 101 we learned that research methods are used in order to understand our mental and behavioral processes by making observations in a systematic way, following strict rules of evidence and thinking critically about that evidence. This scientific research is based on theories (tentative explanations of observations in science), hypotheses (predictions based on a theory) and replication (testing a hypothesis in more than one study). Some of the different research methods are firstly, descriptive studies. Descriptive studies are studies that use survey methods, naturalistic observation and clinical methods. Another research method is correlational studies. Correlational studies are studies that help one to determine if a relationship exists between two or more variables and if so it tells one how strongly those two variables relate to one another. With in correlational studies one can have positive correlation (as one variable increases or decreases so does the other), negative correlation (variables go in opposite directions) or zero correlation (no relationship between the variables). Another research method is formal experiments. Formal experiments are studies that allow us to draw conclusions about how one variable may cause or have an effect on another variable. With in formal experiments there are four elements, which are the independent variable (variable that is manipulated or controlled), the dependent variable (variable that is measured), the experimental

Causation can be hard to prove, an example would be hypertension. Over the years there have been many things associated with the cause of hypertension such as the use of table salt, smoking, sleep apnea, alcohol, etc. To use the term causation in this example it would mean that every single person would get hypertension

Then, I may randomly select a number of subjects, like 100 people, and randomly assign them to experiment group and control group. The dependent variable here is whether people are willing to pick up papers that just dropped in front of them. By testing the both groups, researcher can have a conclusion about whether there is a causation between mood and helping. Compared to correlational study, experiment has some advantages. “Experiment is a research method that tests hypotheses and allows researchers to make conclusion about causality”(Research Method Lecture, 8/27/2015). According to the definition of an experiment, we could find an obvious advantage of experiment. While correlational study only allows researchers to see the correlation between variables, experiment allows researchers to see the causality of variables, which is a more clear and direct relation. Also, experiment uses random assignment in the research; It makes the research more objective and reduces some confounding variables. In addition, Experiment allows researcher to have stricter control of the study. researchers could decide the way to test, and whether let subjects expose to independent

The reason why one variable effects another does not always occur in isolation, there are both mediating and moderating variables that may influence a causal relationship.