2. What Makes “Good” Research?
Good research should be valid, reliable, and
generalizable:
Validity: does the study measure what it is intended to
measure?
Reliability: if you conduct the study again, will you get the
same results?
Generalizability: will the findings of this study apply to some
other population or group of people?
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3. Research Methods
Research methods are standard rules that social scientists follow
when trying to establish a causal relationship between social
elements.
Quantitative methods seek to Qualitative methods attempt to collect
obtain information about the information about the social
social world that is in, or can be world that cannot be readily
converted to, numeric form. converted to numeric form.
4. Approaches to Research
A deductive approach to
research:
1)starts with a theory.
2)develops a hypothesis.
3)makes empirical observations.
4)analyzes the data collected through
observation to confirm, reject, or modify
the original theory.
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6. Approaches to Research
An inductive approach to
research:
1) starts with empirical observation.
2) then works to form a theory.
3) determines if a correlation exists by noticing
if a change is observed in two things
simultaneously.
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7. Inductive reasoning is more open-ended and exploratory, especially during the early stages.
Sometimes called a “bottom up” approach.
8. In research it is often a
combination..
He noticed that Protestant countries consistently had higher suicide rates
than Catholic ones.
His theoretical interpretations in turn led His initial observations led him to inductively
him to deductively create more create a theory of religion, social integration,
hypotheses and collect more anomie, and suicide.
observations.
9. Causality vs. Correlation
Causality is the idea that a change in one factor results
in a corresponding change in another factor.
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12. Causality vs. Correlation
Sociologists conduct research to try to prove
causation.
To prove causation, correlation and time order are
established and alternative explanations are ruled out.
1.Correlation
2. Time order
3. Alternate Explanations.
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13. Variables – What Are We Studying?
A dependent variable is the outcome that a
researcher is trying to explain.
An independent variable is a measured factor that
the researcher believes has a causal impact on the
dependent variable.
Example: a person’s income (dependent variable) may vary according
to age, gender and social class (independent variables).
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14. The Hypothesis (if…then)
A hypothesis is a proposed relationship between
two variables, represented by either the null
hypothesis or an alternative hypothesis.
Null Hypothesis (sometime called no-difference)
•Hyperactivity is unrelated to eating sugar.
The null hypothesis is good for experimentation
because it's simple to disprove. If you disprove a
null hypothesis, that is evidence for a relationship
between the variables you are examining.
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15. Marijuana and serious mental illness
(SMI) research
Prevalence of Past Year SMI among Adults Aged 18 or Prevalence of Past Year SMI among Lifetime Marijuana Users Aged 18
Older, by Gender and Age Group: 2002 and 2003 or Older, by Age at First Marijuana Use: 2002 and 2003
17. Hollywood Goes to High School:
Urban High School Films Central
Theme
“utilitarian individualism”
Lean On Me Dangerous Minds High School High
18. Suburban School Films Central
Theme
“expressive individualism”
The New Guy Teaching Mrs. Tingle Ferris Bueller's Day Off
19. Private School Films Central Theme
“expressive and utilitarian individualism
coexist”
Making The Grade Rushmore School Ties
Notas del editor
It is important when we conduct a study that the results mean something to other people even if they weren’t involved in the study. As such, we look for validity, reliability, and generalizability to help us determine if the results of the study are applicable to the larger social world.
There are different ways to study social phenomena. If you wanted to study poverty, for instance, you could do a quantitative analysis by picking a neighborhood, getting the census data, and seeing how much money the average household makes. Then you could compare that to the federal poverty line to determine how many people are in poverty. On the other hand, you might not get a complete picture just by looking at the numbers. Some families have high incomes (maybe $100,000/year or more), but if you asked them, they might tell you that they don’t have enough money to get by. If you just look at the numbers, you might exclude these people from your study, yet you might be able to learn something interesting about social life by talking to these people. As a result, many studies include both quantitative and qualitative methods in order to produce more thorough data.
For example, you read somewhere that college graduates are likely to have higher incomes than non-college graduates, so you hypothesize that graduation from college increases salary. You collect some data and analyze it to determine whether your theory is correct.
In this case, you notice that one of your friends is making more money than one of your other friends, even though they have similar jobs. You have no idea why this could be, but you are interested in figuring it out. You think of all the differences between these two people. They are both females, they are from the same state, they like the same music, they work in the same area – but then you remember that one went to college and the other did not. You look back at their work history to see if there was always a big difference in the amount of income they made. You then see that they were making the same salaries while in high school, but after the first friend graduated from college, she got a huge raise. You can conclude that there is a correlation (or a connection) between college graduation and salary!
For example, in Chapter 1, you read about famous college dropouts like Woody Allen and Bill Gates. We might ask ourselves if people become successful because they go to college or if they would have been successful whether or not they went to college. In order to study success, we would want to determine if college caused them to be successful or if college was simply a coincidence and did not cause success. The cases that we mentioned (Allen and Gates) lead us to believe that successful people might be successful regardless of whether they finish college, but we would have to conduct a more thorough study to make a determination. We could do a quantitative study (maybe by looking at SAT scores before college, and salaries later in life), or a qualitative study (possibly by talking to individuals who are successful to find out how college did or didn’t influence their success). Image: http://commons.wikimedia.org/wiki/File:Domino_Cascade.JPG
Causation is a stronger assertion than correlation. Let’s say you have noticed that people who have fender-benders (small car accidents) on their way to work are in a bad mood. But you wonder, did the car accident put them in a bad mood, or were they already in a bad mood, which caused them to have an accident? You see that there is a correlation –- bad moods and car accidents are related to each other (there is an association), but which causes the other? You would want to know which came first, the car accident or the bad mood. If you find that many people who have car accidents were actually already in a bad mood, you might prove causation: that being in a bad mood actually causes car accidents!
In our previous example, car accidents would be the dependent variable. We’re trying to explain whether mood changes the outcome (a car accident or no car accident). Therefore, mood is an independent variable. We want to see if mood has a causal impact on the dependent variable.
A null hypothesis states that there is no relationship between the variables. If we are studying the impact of mood on car accidents, the null hypothesis is that mood does not affect car accidents (there is no effect of mood). The alternative hypothesis is that, as we thought, mood does affect car accidents.
