Expert answer:PSY 326: Research methods Hypothesis Testing, psyc

Answer & Explanation:Based on the study you designed in the Research Design discussion, apply the scientific method by specifying the five steps of hypothesis testing for your study.Step 1: State the hypothesis.Step 2: Collect the data – For the purpose of this discussion, you will state how you would collect the data.Step 3: Calculate statistics – For the purpose of this discussion, you will indicate the statistical analysis technique(s) you would use.Step 4: Compare to a critical value – For the purpose of this discussion, you will indicate where you would set the alpha value and why.  Note: This step is hypothetical, as you are not actually conducting a statistical analysis. Consequently, you will choose if the results of your hypothetical analysis are above or below the critical value.Step 5: Make a decision – For the purpose of this discussion, you will create a conclusion based on the hypothetical results from Step 4. Be sure to include a recommendation on the effectiveness of the new drug based on the results.Utilize a minimum of two peer-reviewed sources that were published within the last 10 years and are documented in APA style.psy326_chapter02.pdf
psy326_chapter02.pdf

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chapter 2
Design, Measurement, and
Testing Hypotheses
Canabi Hugo/Iconotec/photolibrary
Chapter Contents
• Overview of Research Designs
• Reliability and Validity
• Scales and Types of Measurement
• Hypothesis Testing
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Introduction
I
n the early 1950s, Canadian physician Hans Selye introduced the term stress into both
the medical and popular lexicons. By that time, it had been accepted that humans have
a well-evolved fight-or-flight response, which prepares us to either fight back or flee
from danger, largely by releasing adrenaline and mobilizing the body’s resources more
efficiently. While working at McGill University, Selye began to wonder about the health
consequences of this adrenaline and designed an experiment to test his ideas using rats.
Selye injected rats with doses of adrenaline over
a period of several days and then euthanized
the rats in order to examine the physical effects
of the injections. As expected, the rats that were
exposed to adrenaline had developed ill effects,
such as ulcers, increased arterial plaques, and
decreases in the size of reproductive glands—all
now understood to be consequences of long-term
stress exposure. But there was just one problem.
When Selye took a second group of rats and
injected them with a placebo, they also developed ulcers, plaques, and shrunken reproductive
glands!
Fortunately, Selye was able to solve this scientific
mystery with a little self-reflection. Despite all his
methodological savvy, he turned out to be rather
Canadian physician Hans Selye introduced
the term “stress.”
clumsy when it came to handling rats, occasionally dropping one when he removed it from its
cage for an injection. In essence, the experience
for these rats was one that we would now call stressful, and it is no surprise that they
developed physical ailments in response to it. Rather than testing the effects of adrenaline
injections, Selye was inadvertently testing the effects of being handled by a clumsy scientist. It is important to note that if Selye ran this study in the present day, ethical guidelines
would dictate much more stringent oversight of his study procedures in order to protect
the welfare of the animals.
iStockphoto/Royalty-free
This story illustrates two key points about the scientific process. First, as we discussed in
Chapter 1, it is always good to be attentive to your apparent mistakes because they can
lead to valuable insights. Second, it is absolutely vital to measure what you think you
are measuring. In this chapter, we get more concrete about what it means to do research,
beginning with a broad look at the three types of research design. Our goal at this stage
is to get a general sense of what these designs refer to, when they are used, and the main
differences among them. (Chapters 3, 4, and 5 are each dedicated to one type of research
design and elaborate further on each one.) Following our overview of designs, this chapter covers a set of basic principles that are common to all research designs. Regardless of
the particulars of your design, all research studies involve making sure our measurements
are accurate and consistent and that they are captured using the appropriate type of scale.
Finally, we will discuss the general process of hypothesis testing, from laying out predictions to drawing conclusions.
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Section 2.1 Overview of Research Designs
CHAPTER 2
2.1 Overview of Research Designs
A
s you learned in Chapter 1, scientists can have a wide range of goals going in to a
research project, from describing a phenomenon to attempting to change people’s
behavior. It turns out that these goals lend themselves to different approaches to
answering a research question. That is, you will approach the problem differently when
you want to describe voting patterns than when you want to explain them or predict
them. These approaches are called research designs, or the specific methods that are used
to collect, analyze, and interpret data. The choice of a design is not one to be made lightly;
the way you collect data trickles down to the kinds of conclusions that you can draw
about them. This section provides a brief introduction to the three main types of design—
descriptive, correlational, and experimental.
Descriptive Research
Recall from Chapter 1 that one of the basic goals of research is to describe a phenomenon. If your research question centers around description, then your research design
falls under the category of descriptive research, in which the primary goal is to describe
thoughts, feelings, or behaviors. Descriptive research provides a static picture of what
people are thinking, feeling, and doing at a given moment in time, as seen in the following
examples of research questions:
•
•
•
•
•
What percentage of doctors prefer Xanax for the treatment of anxiety? (thoughts)
What percentage of registered Republicans vote for independent candidates?
(behaviors)
What percentage of Americans blame the president for the economic crisis?
(thoughts)
What percentage of college students experience clinical depression? (feelings)
What is the difference in crime rates between Beverly Hills and Detroit?
(behaviors)
What these five questions have in common is the attempt to get a broad understanding of
a phenomenon without trying to delve into its causes.
The crime rate example highlights the main advantages and disadvantages of descriptive
designs. On the plus side, descriptive research is a good way to get a broad overview of a
phenomenon and can inspire future research. It is also a good way to study things that are
difficult to translate into a controlled experimental setting. For example, crime rates can
affect every aspect of people’s lives, and this importance would likely be lost in an experiment that manipulated income in a laboratory. On the downside, descriptive research
provides a static overview of a phenomenon and cannot dig into the reasons for it. A
descriptive design might tell us that Beverly Hills residents are half as likely as Detroit
residents to be assault victims, but it would not reveal the reasons for this discrepancy.
(If we wanted to understand why this was true, we would use one of the other designs.)
Descriptive research can be either qualitative or quantitative; in fact, the large majority of
qualitative research falls under the category of descriptive designs. Descriptions are quantitative when they attempt to make comparisons and/or to present a random sampling
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of people’s opinions. The majority of our sample questions above would fall into this
group because they quantify opinions from samples of households, or cities, or college
students. Good examples of quantitative description appear in the “snapshot” feature on
the front page of USA Today. The graphics represent poll results from various sources; the
snapshot for August 3, 2011, reveals that only 61%
of Americans turn off the water while they brush
their teeth (i.e., behavior).
Descriptive designs are qualitative when they
attempt to provide a rich description of a particular set of circumstances. A great example of this
approach can be found in the work of neurologist Oliver Sacks. Sacks has written several books
exploring the ways that people with neurological
damage or deficits are able to navigate the world
around them. In one selection from The Man Who
Mistook His Wife for a Hat (1998), Sacks relates
the story of a man he calls William Thompson.
As a result of chronic alcohol abuse, Thompson
developed Korsakov’s syndrome, a brain disease
marked by profound memory loss. The memory
loss was so severe that Thompson had effectively
“erased” himself and could remember only scattered fragments of his past.
Whenever Thompson encountered people, he
would frantically try to determine who he was.
He would develop hypotheses and test them, as
in this excerpt from one of Sacks’s visits:
Erik Charlton
Dr. Oliver Sacks studied how people with
neurological damage formed and retained
memories.
I am a grocer, and you’re my customer, right? Well, will that be paper or plastic? No, wait, why are you wearing that white coat? You must be Hymie, the
kosher butcher. Yep. That’s it. But why are there no bloodstains on your coat?
(Sacks, 1998, p. 112)
Sacks concludes that Thompson is “continually creating a world and self, to replace what
was continually being forgotten and lost” (p. 113). In telling this story, Sacks helps us to
understand Thompson’s experience and to be grateful for our ability to form and retain
memories. This story also illustrates the trade-off in these sorts of descriptive case studies:
Despite all its richness, we cannot generalize these details to other cases of brain damage;
we would need to study and describe each patient individually.
Correlational Research
The second goal of research that we discussed in Chapter 1 was to predict a phenomenon.
If your research question centers around prediction, then your research design falls under
the category of correlational research, in which the primary goal is to understand the
relationships among various thoughts, feelings, and behaviors. Examples of correlational
research questions include:
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CHAPTER 2
Section 2.1 Overview of Research Designs
•
•
•
•
•
Are people more aggressive on hot days?
Are people more likely to smoke when they are drinking?
Is income level associated with happiness?
What is the best predictor of success in college?
Does television viewing relate to hours of exercise?
What each of these questions has in common is that the goal is to predict one variable
based on another. If you know the temperature, can you predict aggression? If you know
a person’s income, can you predict her level of happiness? If you know a student’s SAT
scores, can you predict his college GPA?
These predictive relationships can turn out in one of three ways (more detail on each
one when we get to Chapter 4): A positive correlation means that higher values of one
variable predict higher values of the other variable. As in, more money predicts higher
levels of happiness, and less money predicts lower levels of happiness. The key is that
these variables move up and down together, as shown in the first row of Table 2.1. A
negative correlation means that higher values of one variable predict lower values of
the other variable. As in, more television viewing predicts fewer hours of exercise, and
fewer hours of television predict more hours of exercise. The key is that one variable
increases while the other decreases, as seen in the second row of Table 2.1. Finally, it is
worth noting a third possibility, which is to have no correlation between two variables,
meaning that you cannot predict one variable based on another. The key is that changes
in one variable are not associated with changes in the other, as seen in the third row of
Table 2.1.
Table 2.1: Three Possibilities for Correlational Research
Outcome
Description
Positive Correlation
Variables go up and down
together
For example: Taller people
have bigger feet, and shorter
people have smaller feet
Negative Correlation
One variable goes up and
the other goes down
For example: as the number
of beers consumed goes up,
speed of reactions go down
No Correlation
Height
Shoe size
Reac on
speed
# of beers
The variables have nothing
to do with one another
For example: shoe size
and number of siblings are
completely unrelated
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Visual
# Siblings
Shoe size
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Correlational designs are about prediction, and we are still unable to make causal, explanatory statements (that comes next. . .). A common mantra in the field of psychology is that
correlation does not equal causation. In other words, just because variable A predicts variable B does not mean that A causes B. This is true for two reasons, which we refer to as the
directionality problem and the third variable problem. (See Figure 2.1.)
First, when we measure two variables at the same time, we have no way of knowing
the direction of the relationship. Take the relationship between money and happiness:
It could be true that money makes people happier because they can afford nice things
and fancy vacations. It could also be true that happy people have the confidence and
charm to obtain higher-paying jobs, resulting in more money. In a correlational study, we
are unable to distinguish between these possibilities. Or, take the relationship between
television viewing and obesity: It could be that people who watch more television get
heavier because TV makes them snack more and exercise less. It could also be that people
who are overweight don’t have the energy to move around and end up watching more
television as a consequence. Once again, we cannot identify a cause–effect relationship
in a correlational study.
Second, when we measure two variables as they naturally occur, there is always the possibility of a third variable that actually causes both of them. For example, imagine we find
a correlation between the number of churches and the number of liquor stores in a city. Do
people build more churches to offset the threat of
Figure 2.1: Correlation Is
liquor stores? Do people build more liquor stores
to rebel against churches? Most likely, the link
Not Causation!
involves the third variable of population: The
The Directionality Problem
more people there are living in a city, the more
churches and liquor stores they can support.
A
B
Income
Happiness
The Third Variable Problem
B
A
B
Ice Cream Sales
Homicides
Temperature
Or, consider this example from analyses of
posts on the recommendation website Hunch
.com. One of the cofounders of the website conducted extensive analyses of people’s activity
and brand preferences and found a positive
correlation between how much people liked to
dance and how likely they were to prefer Apple
computers (Fake, 2009). Does this mean that
owning a Mac makes you want to dance? Does
dancing make you think highly of Macs? Most
likely, the link here involves a third variable of
personality: People who are more unconventional may be more likely to prefer both Apple
computers and dancing.
Experimental Research
Finally, recall that the most powerful goal of research is to attempt to explain a phenomenon. When your research goal involves explanation, then your research design falls under
the category of experimental research, in which the primary goal is to explain thoughts,
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feelings, and behaviors and to make causal statements. Examples of experimental research questions include:
•
•
•
•
•
Does smoking cause cancer?
Does alcohol make people more
aggressive?
Does loneliness cause alcoholism?
Does stress cause heart disease?
Can meditation make people healthier?
What these five questions have in common is a
focus on understanding why something happens.
Experiments move beyond asking, for example,
whether alcoholics are more aggressive to ask
whether alcohol causes an increase in aggression.
Photodisc/Kraig Scarbinsky
Experimental designs are able to address the Testing the hypothesis that meditation
improves health requires an experimental
shortcomings of correlational designs because
group and a control group.
the researcher has more control over the environment. We will cover this in great detail in Chapter
5, but for now, experiments are a relatively simple process: A researcher has to control the
environment as much as possible so that all participants in the study have the same experience. She will then manipulate, or change, one key variable and then measure outcomes
in another key variable. The variable that gets manipulated by the experimenter is called
the independent variable. The outcome variable that is measured by the experimenter is
called the dependent variable. The combination of controlling the setting and changing
one aspect of this setting at a time allows her to state with some certainty that the changes
caused something to happen.
Let’s make this a little more concrete. Imagine that you wanted to test the hypothesis
that meditation causes improvements in health. In this case, meditation would be the
independent variable and health would be the dependent variable. One way to test this
hypothesis would be to take a group of people and have half of them meditate 20 minutes
per day for several days while the other half did something else for the same amount of
time. The group that meditates would be the experimental group because it provides
the test of our hypothesis. The group that does not meditate would be the control group
because it provides a basis of comparison for the experimental group. You would want
to make sure that these groups spent the 20 minutes in similar conditions so that the only
difference would be the presence or absence of meditation. One way to accomplish this
would be to have all participants sit quietly for the 20 minutes but give the experimental
group specific instructions on how to meditate. Then, to test whether meditation led
to increased health and happiness, you would give both groups a set of outcome measures—perhaps a combination of survey measures and a doctor’s examination. If you
found differences between these groups on the dependent measures, you could be fairly
confident that meditation caused them to happen. For example, you might find lower
blood pressure in the experimental group; this would suggest that meditation causes a
drop in blood pressure.
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Research: Making an Impact
Helping Behaviors
The 1964 murder of Kitty Genovese in plain sight of her neighbors, none of whom helped, drove
numerous researchers to investigate why people may not help others in need. Are people selfish and
bad, or is there a group dynamic at work that leads to inaction? Is there something wrong with our
culture, or are situations more powerful than we think?
Among the body of research conducted in the late 1960s and 1970s was one pivotal study that
revealed why people may not help others in emergencies. Darley and Latane (1968) conducted an
experiment with various individuals in different rooms, communicating via intercom. In reality, it
was one participant and a number of confederates, one of whom pretends to have a seizure. Among
participants who thought they were the only other person listening over the intercom, more than
80% helped, and they did so in less than 1 minute. However, among participants who thought they
were one of a group of people listening over the intercom, less than 40% helped, and even then only
after more than 2.5 minutes. This phenomenon, that the more people who witness an emergency,
the less likely any of them is to help, has been dubbed the “bystander effect.” One of the main reasons that this occurs is that responsibility for helping gets “diffused” among all of the people present, so that each one feels less personal responsibility for taking action.
This research can be seen in action and has influenced safety measures in today’s society. For example, when witnessing an emergency, no longer does it suffice to simply yell to the group, “Call 9-11!” Because of the bystander effect, we know that most people will believe someone else will do it,
and the call will not be made. Instead, it is necessary to point to a specific person to designate them
as the person to make the call. In fact, part of modern-day CPR training involves making individ …
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