Notes CH 6 (Lesson 1-5) PSY 101 Study.COM Notes & Quizes PDF

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Chapter 6: Research in Psychology Lesson 1: Types of Research Designs in Psychology What are the three main research designs, and what are their advantages and disadvantages? In this lesson, you'll explore the different goals behind descriptive, correlational and experimental research designs. Psychological studies begin as questions. 'How does a person with severe brain damage behave?' 'Do smart parents have smart children?' 'How does reminding someone of their race or gender change their performance on a test?'

Psychologists turn these questions into hypotheses: 'Do smart parents have smart children?' is changed to 'Parents who have high scores on intelligence tests have children with similarly high scores.' Then they design a study to test the hypothesis in an efficient way that reduces potential confounds, or factors that could explain the results but aren't directly measured or addressed by the study. Depending on the question, and on the hypothesis, psychologists will choose one of three main types of research designs.

A first type of research design is called descriptive. Descriptive studies aim only to gather data to present a complete picture of a given subject. Psychologists might use a survey to assess the state of mental health on college campuses. The results wouldn't tell them anything about the causes of mental illness in college students, but it would give a complete picture of the problem. To answer one of the questions we began with, 'How does a person with severe brain damage behave?' psychologists might use a case study, or a close examination of one person with a particular problem. Phineas Gage, a railroad construction foreman in the nineteenth century, is a classic example of such a case study: in an accident at a railroad construction site, he had a large metal rod driven through his head. He not only survived but was fully-functioning and lived for another twelve years. But several people close to him remarked that they noticed his personality had changed, that he'd become irritable and unable to hold a job. Though Gage's case alone could not prove anything definitive about his particular brain injury and emotion regulation, it did help psychologists make better hypotheses about the relationship between these things for future studies. Descriptive studies often form the basis for later correlational or experimental research.

Correlational studies try to figure out the relationship between two or more variables, which could be anything you can measure like behavior, age, gender, etc. To answer the question, 'Do smart parents have smart children,' two variables psychologists might measure are parents' IQ and children's IQ. Psychologists would then administer IQ tests to a very large number of families and determine statistically how related the parents' scores were to the children's scores--this 'relatedness' is known as the 'correlation.' A correlation is represented by a number called the Pearson correlation coefficient that is between -1 to 1. If two variables have a correlation of 0, there is no relation between them --for example, something like your birthday and the color of your hair would likely have a correlation very close to 0 because these two things have nothing to do with each other. A correlation between 0 and 1 is called positive, and it means that as the first variable increases, so does the second one. This turns out to be true in the case of parent and child IQ; one study reports a moderately positive correlation of .35. A correlation between -1 and 0 indicates a negative correlation, and means that as the first variable increases, the second decreases. Age and memory function are likely to be negatively correlated; as age increases, the ability to remember things clearly tends to decrease.

Correlation is not causation; for example, high parental IQ does not necessarily cause a high child IQ

An important shortcoming of correlational research is the problem of determining causation. As tempting as it might be to assume so, correlation is not causation. Though there is a moderate positive correlation between parents' IQ and children's IQ, this does not mean that a high parental IQ causes a high child's IQ. Though in this particular case it seems unlikely that the parents' high IQ's are caused by the child's, this is a possibility that cannot be ruled out by correlational research. It is also possible, even likely, that both high IQ's are caused by a third external factor, like high income and socioeconomic status. To determine causation, psychologists must conduct experimental research. This also involves variables, but in this case they are distinguished as independent and dependent. Psychologists change the independent variable and look at what happens to the dependent variable. Some psychologists hypothesized that women and racial minorities might experience something called stereotype threat when they take tests. Since these groups are stereotyped as not being as good at math as white men, the anxiety they experience by worrying that their performance will confirm this

negative stereotype actually makes them end up doing worse. Psychologists have tested this in many situations, using awareness of race or gender identity as the independent variable and test performance as the dependent variable. One group administered a difficult section of a standardized test to two groups of African-American and European-American students. The first group was led to believe that the test measured intelligence; the second was not. In the first group, there was a wide performance gap between African-American and European-American students; in the second group, this gap was greatly reduced. The researchers concluded that worrying about confirming negative stereotypes about intelligence had actually made the AfricanAmerican students perform worse. Similar results have been found for women in chess competitions and entrepreneurship.

Variables of Experimental Research

Experimental research is powerful but limited in an important way: psychologists simply can't manipulate all the variables they're interested in. Sometimes it's just impossible. If psychologists are interested in the differences between female and male leadership, they can't just instate a female leader in a company formerly led by a man; the company board might have something to say about that. Sometimes experimental research is possible but highly unethical; to experimentally determine what kinds of trauma were most likely to cause post-traumatic stress disorder, for example, psychologists would have to submit large numbers of people to different kinds of trauma and potentially debilitate them with PTSD symptoms for the rest of their lives. Let's quickly go over the three types of research designs once again. Descriptive studies seek only to document; a case study like Phineas Gage is an example of this. Correlational studies try to establish a relationship between two variables, like parents' IQ and children's IQ, though correlation does not equal causation. Finally, experimental research looks to study causation, like with the example of stereotype threat and test performance, but can't be used in every situation due to practical and ethical concerns.

Lesson 2: Random Sample in Psychology: Example & Definition A random sample is a very important component in research. In this lesson, learn more about random samples, some advantages and disadvantages of each, and look at some examples.

What is a Random Sample? A random sample is a sample in which each member of the population has an equal chance of being selected to represent the whole. In order for us to truly understand what a random sample is, we must first distinguish between a sample and a population. A population is all members of a defined group that have certain characteristics or attributes that we are interested in studying. Suppose we were interested in studying the sleep habits of college males in the United States. Our population would consist of all males who attend college in the U.S. Since there are over 9 million men in this population, it would be virtually impossible for us to collect data from every single member. But how then do we collect our research data? We collect data from a sample, which is a portion of the population that's used to represent the entire population. In order to study our population, we can take 560 U.S. college males and collect data from them. A sample allows us to collect data from a few members that represent the whole population. When a sample is truly representative of a population, we can make inferences that apply to the entire population. The best way to obtain a representative sample is through the use of a random sample. In order for our sample to be random, two things must occur: 1. Every member of the population must have an equal chance of being selected to be a part of the sample. 2. The selection of one member of the population is not dependent upon the selection of another member. Suppose you wanted to research the effects of receiving a bad grade on the morale of 100 high school freshmen. You might assign each student a number from 1-100, place the numbers in a hat, then choose 30 numbers from the hat to be in your sample. This is known as the lottery method. Because every person has the same chance of being picked, it helps make sure that all possible biases and viewpoints are represented. Since each number was only placed on one piece of paper and all the papers were the same size, each person had the same chance of being picked. Also, since the numbers were written on separate sheets of paper, the selection of one person was not dependent on the selection of another.

Advantages and Disadvantages There are several advantages to using a random sample, including: • It's highly representative of the population • It decreases bias since the process of selecting the sample is done randomly • Data analysis and interpretation is simpler in random samples

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There are also several disadvantages to using a random sample, including: • It's not possible if you do not have a complete list of the members in the population • It can take a lot of time and money if you are sampling data from a large population • The characteristics of the sample and/or the population can change over time

Lesson Summary Let's review. In a random sample, each member of the population has the same probability of being selected for the sample. In addition, the selection of one member of the population is completely independent of the selection of another. There are several advantages to using a random sample including a decrease in bias and having data that is highly representative of the population. There are also disadvantages, including the potential costs, time that it may take to find a truly representative sample, and the difficulty of creating a complete list of all members in a population.

Lesson 3: Descriptive Research Design: Definition, Examples & Types This lesson explores the different ways that a researcher can understand individuals or groups of people, both in terms of psychological research as well as general research in other fields.

Descriptive Research: Definitions Sometimes an individual wants to know something about a group of people. Maybe the individual is a would-be senator and wants to know who they're representing or a surveyor who is looking to see if there is a need for a mental health program. Descriptive research is a study designed to depict the participants in an accurate way. More simply put, descriptive research is all about describing people who take part in the study. There are three ways a researcher can go about doing a descriptive research project, and they are:

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Observational, defined as a method of viewing and recording the participants Case study, defined as an in-depth study of an individual or group of individuals Survey, defined as a brief interview or discussion with an individual about a specific topic Let's look at specific ways we can use each of these.

Observational If I say, 'chimpanzees,' what do you think? Okay, after you think of bananas. Okay, after you remember that their babies are adorable. Yes! Jane Goodall - the researcher who spent years observing chimpanzees in the wild. Observational studies are all about watching people, and they come in two flavors. Naturalistic, also known as field observation, is a study where a researcher observes the subject in its natural environment. This is basically what Jane Goodall did; she observed the chimpanzees in their natural environment and drew conclusions from this. This makes the observations more true to what happens in the chaotic, natural world. But, it also means you have less control over what happens. The other flavor is laboratory observation, where a researcher observes the subject in a laboratory setting. This gives the researcher a little more control over what happens so they don't have to fly out to some tiny little island in the middle of a war zone to observe something. However, it does ruin some of the naturalness that one might get from field observation. An example of a laboratory observation in psychology would be done to understand something about children at a certain age, such as the process of how a child learns to speak and mimic sounds.

Survey A survey comes in different flavors, be it interviewing people face to face or handing out questionnaires to fill out. The main difference between surveys and observations is that in a survey, you don't watch people; you ask them about themselves. Surveys are useful because they don't take as long as an observational study since you're asking people about themselves instead of spending weeks observing them. On the other hand, if the person doesn't know very much about themselves or if they lie, then you run into a problem. For instance, if I asked you how often you pick your nose, you will likely deny you have ever done so. But, I bet you have, and I bet you do it regularly.

Case Study Case studies are a little more in-depth than an observation and typically a little more holistic. So, instead of just watching chimps frolic in the field, you may be interested in a particular chimp that was born an albino or a troop of chimps that are interacting with people. You are attempting to study something more in depth. If you have people involved in the study, it is a combination of the observation and survey method. Case studies are more often used when a researcher encounters a rare issue that they cannot recreate on their own. For instance, if someone had a rod of metal blasted through their skull and survived, we would likely do a case study on him. We cannot ethically recreate this issue, so we have to study it when it comes up. This is the case of Phineas Gage, to some extent. A full case study was not done on him, so there are a lot of issues and retellings of his story.

Lesson Summary Descriptive research is a study designed to depict the participants in an accurate way. The three main ways to collect this information are:

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Observational, defined as a method of viewing and recording the participants Case study, defined as an in-depth study of an individual or group of individuals Survey, defined as a brief interview or discussion with the individuals about a specific topic

Learning Outcomes After watching this video lesson, you might be able to:

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Realize the purpose of descriptive research Mention three ways to do descriptive research Emphasize two types of observational research Note the contrasts between case studies and observations Highlight ways of performing survey research

Lesson 4: Correlational Research: Definition, Purpose & Examples This lesson explores, with the help of two examples, the basic idea of what a correlation is, the general purpose of using correlational research, and how a researcher might use it in a study.

Definitions in Correlational Research Your brain can do some really cool things. For instance, you learn that a particular jingle means the ice cream trucks are nearby. The louder the jingle, the closer it is. And if you were lucky enough to have several types of ice cream trucks, you will recognize which jingle goes with which ice cream truck. The world is full of things where if thing A happens, then there is a good chance that thing B will happen. If thing A is the jingle, then there is a good chance that thing B, the ice cream truck, is close by. We can also make things more complicated by thing A being the loudness of the jingle and thing B being the distance to the ice cream truck. As the loudness increases, the distance shrinks. As the distance increases, the loudness goes down. This is kind of a silly example, but it's an example of how you naturally correlate one event with another. A correlation is simply defined as a relationship between two variables. The whole purpose of using correlations in research is to figure out which variables are connected. I'm also going to start referring to the things as variables; it's a more scientific name. This simple definition is the basis of several statistical tests that result in a correlation coefficient, defined as a numerical representation of the strength and direction of a relationship. Correlation research is looking for variables that seem to interact with each other, so that when you can see one changing, you have an idea of how the other will change. This often entails the researcher using variables that they can't control. For example, a researcher may be interested in studying the preference for ice cream based on age. If we cannot assign age, does that mean we have to scrap the whole correlation? Nope! Since the researcher cannot assign certain variables, this would mean the researcher is performing a quasi-experimental study. A quasi-experimental study is defined as an experiment in which participants are not randomly assigned. There are different techniques for how we might overcome this, and I encourage you to explore this in other lessons. 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 variable A increases, variable B will decrease. Please note that I did not say how much the other variable moves when the first variable changes.

Example 1 When looking for correlations, a researcher will look for patterns - what they have seen happen again and again. A simple pattern known to every teacher, but unfortunately not every student, is the link between studying and grades. The studious student who studies is more likely to score a higher score on their test. Students who don't study much are less likely to score as high as those who do. You may be sitting there doubting what I've said because you've taken tests before where you didn't study and did just fine. And, there are others who do study and still don't get good grades. This is because there isn't a perfect correlation, or a perfect 1:1 relationship, between the items. There is just too much going on in the real world for this to be a perfect connection. Things like personal talents, distractions, familiarity with the subject and brain stuff make everyone a little different. This interference in a correlation is known as an extraneous variable, which is simply defined as a variable that is influencing the study. They are something to watch out for when you're looking at correlations because nothing in the math or experiment will say, 'Here it is; this is messing up your experiment.'

Example 2 The previous example was a good example for a positive correlation, but what about a negative correlation? Sticking with the grades and people, did you ever know that person who did nothing but watch TV? The person who watches too much television usually doesn't do well on their tests. This means as they watch more television, thei...