Hi students, welcome to Unit 1, Lesson 7. Here we're going to talk about experiments and correlations. In this lesson, we're really going to focus on experiments and correlations. We'll compare and contrast. In this case, research methods include experiments. Now, experiments are cool because these are the things that we can manipulate.
And the things we are manipulating are called the variables. So we have two different sorts of variables. That is the independent variable, that which the researcher controls. and we are studying the independent variables result. Here's a sample.
Example, when examining the effects of breastfeeding upon intelligence, what is the independent variable? If you said the breastfeeding, then you would have correctly identified the independent variable. The dependent variable in an experiment is a factor that may change in response to the independent variable. It is dependent upon an independent variable. It's usually behavior or a mental process.
Example, when examining the effects of breastfeeding upon intelligence, what is the dependent variable? Now remember the independent variable was the breastfeeding. What happens as a result of the independent variable? That is the results of the baby's IQ.
If the baby's IQ measurement is higher or lower, then we would say, hey, breastfeed your child. So here are four scenarios where you are asked to identify the independent variable and the dependent variable. Situation number one, children's reading skill is measured after taking either a special reading class or a standard reading class. Identify the independent variable and the dependent variable. We'll check your work in class.
Number two, a college student's memory for German vocab words is tested after a normal night's sleep or a night of no sleep. Number three. Experiment title is the effect of a daily walking program on elderly people's lung capacity.
What is the independent variable? What is the dependent variable? And number four, people's ability to avoid accidents in a driving simulator is tested before, during. and after talking on a cell phone.
What is the independent variable and what is the dependent variable? Write those down in your notes and then we'll check them out when we get to class. Experiments unfortunately do have some flaws and one of those is because if we know that we are a part of an experiment we may have participants expectations and that leads us to the placebo effect. So if I know that I'm taking part in an experiment my behavior might be altered because I know that I'm part of an experiment.
Maybe I am prescribed medication to improve my mood, and because I'm actually taking an action, my mood improved, even if it is not actually a drug. More on that in class also. We also have, whoop, we have to have random assignments when it comes to an experiment. And so let's say in your class, we're gonna do an experiment. I have to randomly choose the people who are part of the independent and dependent variables, experimental and control group, people who are assigned to the different variables.
We'll watch that short video in class. When I select participants, the experimenter can unintentionally alter patients'behavior by assigning them to different groups or the single blind experiment and double blind experiment. Now in a single blind experiment, we use that so that the participants don't know that they're a part of the research, the treatment, the control group, or the experimental group. So single blind, we want to keep it private from the people who are participating in the study.
And a double blind is when the researcher also does not know who is part of the experimental group and who is part of the control group. Furthermore, more research methods have to do with, here we get into some math details. So interpreting interpreting experimental data or survey data, lots of different data using correlations.
Correlation is just mathy, sciency lingo, where we find positive correlations and negative correlations. A positive correlation is exactly what this says. As one goes up, so does the other. In this picture, you can see that maybe student grades are positively correlated to their participation in sports. Participating in sports leads to higher grades, It's a positive correlation.
A negative correlation would be as one goes up, the other goes down. So if we flip this over, participation in sports, more participation in sports leads to lower grades, that would be a negative correlation. And that's kind of how I do it when I'm trying to figure out if it's positive or negative correlation. So the more TV is in the homes, the less time they spend reading. So that's going to be a negative correlation.
Or sexual. content teens see on TV, the more likely they are to have sex. So positive related.
The longer children are breastfed, the greater their later academic achievement. Positive correlation, and the more income rows, the fewer psychiatric symptoms. So higher income, fewer psychiatric symptoms.
Negative correlation. This leads us to, it will equal a science-y, math-y number, and the relationship between the number one, correlational coefficient is a statistical measure of the relationship between two variables. You don't need to know how to find these things out or anything, but you need to know that the closer that number is to one, that r equals plus 0.37, is the direction or relationship of positive or negative.
So you see the plus, that's a positive 37, which means it's going up. And the zero to one indicates the strength of the relationship. When we're doing correlations, we will scatter plots of the relationship, kind of like that forgetting curve that you saw earlier in the week. So that's how we relate the information. We'll mess around with these charts in class and give you scatter plots to determine whether or not temperament and height um have a relationship.
Here you can say they have a moderate positive correlation. One tricky thing about correlation is it does not imply causation. You can repeat that statement after me.
Correlation does not imply causation. Your turn. Right. Just because two things are related does not mean it is a cause.
For instance, we talked about the scientific method, low self-esteem and depression. Those two things are correlated. It does not mean... prove a cause and effect relationship.
The only way that we can find a cause and effect relationship is to actually perform an experiment where we, well, we have to perform an experiment. The reason why correlation does not cause equal causation, here is one place why it's important to differentiate between causation and correlation. So in Taiwan, birth control or by the toaster method, They found that there was a positive correlation between births or lower births, a small appliance ownership increase, so the use of birth control.
So this is a direct correlation. Are they cause and effect? No. It's just one of those things that are related. So there may be another variable in these correlational studies that we didn't account for, like, family income, if you're buying more toasters, you're not having as many children because now you can buy also birth control.
Correlation and causation, there's positive correlation between low self-esteem and depression. I mentioned that just a bit ago. In order for us to prove one could cause the other, we have to do an experiment again. Here in this graph, this is showing you the correlational data. So low self-esteem...
could cause depression. Depression could cause low self-esteem or, you know, distressing events or biological disposition could cause both. So we cannot accurately pinpoint depression to low self-esteem.
All right, more on that in class.