Source: Intro Music by Mark Hannan; Public Domain Graph Created by Paul Hannan
[MUSIC PLAYING] Welcome to this episode of Sociology, Studies of Society. Today's lesson is on conducting sociological research. As always, don't be afraid to pause, stop, rewind, or even fast forward to make sure you get the most out of this tutorial. So today, we're looking at conducting sociological research.
And really, the first kind of way to really start really understanding the way sociologists look at the world is to look at the work of Max Weber. He's a name you might have heard of before if you've listened to some of the other tutorials. And he's a really famous sociologist. And he really had two different general guidelines he stressed for sociologists.
The first thing is that when you're a sociologist and you're choosing what to research, you need to choose value-relevant topics. So you need to choose to study things that matter. Don't study something that isn't going to affect society for the good. You really want to study things that are going to help society.
Now once you've chose your topic, you need to switch and do an about face almost. You need to do value-free research, which means you need to back yourself off and be objective. You want to find the truth, not just find what you hope to find or what you want to find.
Sociology, much like other sciences and social sciences, they use experiments as one way to conduct their research. One of the first things you need when you're conducting an experiment is a hypothesis. And that's just a statement of how two different variables are related. And there are two different types of variables. There are independent variables and dependent variables.
Now independent variables are the cause of the change. I like to think of these as groups. So you have groups that maybe are-- one group is doing something, and another group is not doing something. Or one group is getting something. Another group is not getting something. Or one group is tall people, and one group is short people. Independent variables are what you're trying to say is the cause of the change.
Now the dependent variable is the effect of the change. I like to call it the measurement. So that is what you're seeing as the result-- basing your hypothesis normally-- of that difference in the different groups and the causes of the change. So let's give you an example.
Here's a hypothesis. Spending time reading will increase student test scores on a reading test. Simple hypothesis. So if students spend more time reading per week, they'll do better on reading test than other students who don't. So the independent variable then would be the amount of time spent reading. I divide into three different sub-categories kind of arbitrary. This is a made-up example.
So there's what I call big readers, medium readers, and small readers. And you categorize them as like different number of hours per week or however you're conducting this experiment. And then the dependent variable is what you're measuring, and that's the score on a reading test. So this one I said it's out of 100 possible points.
Now I like to show a graph when you're looking at dependent and independent variables because this can be really helpful to help you categorize which is which. So generally speaking, the dependent variables, the thing that you are measuring, goes up and down. So that is on the y-axis. In this case, it's the average test score out of 100. You see the wording on the right-hand of the screen there.
And the independent variables, the thing that you think is causing the change, is on the bottom. It's on the x-axis. So we have the small readers. We have the medium readers and the big readers. And in this case, when you're looking at this graph, my made-up numbers is saying that, yeah, looks like number of hours reading per week might have an effect on the average test score.
Now one thing about when you're doing experiments, not everyone can be involved in a test. Not everyone can be tested. So part of that idea is that there's a sample. You take a small number of subjects representing a larger population.
So if I want to see the effect of giving extra help to kindergartners for their reading, it's not realistic for me to get every single American to be a part of my survey. No, instead, you take a sample. Hopefully, it's a representative sample, so it's a sample that represents the population that you're trying to apply your findings to. But it's a smaller group because it's not feasible, it's not really possible to test everyone.
And there's a couple different methods for sampling. But one of the terms you really should know is snowball sampling. Now snowball sampling is a way where you find your first people to take your experiment through acquaintances, through people like the researchers know, and then their subjects recommend that study to their friends.
Now it doesn't always give you the most representative sample. But it can be a really good way when you're looking for a really specific group of people that are maybe hard to track down or talking about an issue that can be hard for people to talk about. Those are ways that snowball sampling can be can be really useful.
So today's takeaway message. A hypothesis is just a statement on how two or more variables are related. You have the independent variables, which you're saying are the cause of the change, and the dependent variable, which is the effect of the change. A sample is just a smaller number of subjects representing a larger population. And snowball sampling is a specific way you choose a sample. And you do that by, first, gathering subjects through your acquaintances. And then, those acquaintances go out and recruit other acquaintances to be the subjects.
Lastly, we learned about Max Weber and his value-free research, which is that researchers need to be objective. And then we also learned about value-relevant research that researchers need to choose topics that matter. So that's it for this lesson. Good work. And hopefully, you'll be seeing me on your screen again soon. Peace