psychological research methods and statistics chapter 2 test

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Understanding Psychology, Student Edition

Richard a. kasschau, psychological research methods and statistics - all with video answers.

What Is Research?

Explain how a psychologist might select a sample for a survey.

In a chart similar to the one below, list and describe the advantages and disadvantages associated with each method of research

What pre-research decisions must a psychologist make?

Why should psychologists question the results of an experiment that they have conducted for the first time?

Suppose you wanted to find out whether there was a correlation between hours spent watching television and test grades in psychology class. Design a plan using one or more of the methods of research to help you study this correlation.

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Ch 2: Psychological Research Methods

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

• Explain the steps of the scientific method
• Describe why the scientific method is important to psychology
• Summarize the processes of informed consent and debriefing
• Explain how research involving humans or animals is regulated

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

The Scientific Process

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Why the scientific method is important for psychology.

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent  form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing  upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

Introduction to Approaches to Research

• Differentiate between descriptive, correlational, and experimental research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
• Describe the strength and weaknesses of archival research
• Compare longitudinal and cross-sectional approaches to research
• Explain what a correlation coefficient tells us about the relationship between variables
• Describe why correlation does not mean causation
• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark  and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize  the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research  is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition  rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does  not  indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants  are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample   (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

• Define reliability and validity
• Describe the importance of distributional thinking and the role of p-values in statistical inference
• Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
• Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Dig deeper:  everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

• Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
• Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
• Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
• Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

• Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
• Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample  that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment  tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

• This was a 14-year study conducted by researchers at the National Cancer Institute.
• The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
• The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
• About 52,000 people died during the course of the study.
• People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
• The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
• Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
• This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

• Video about p-values:  P-Value Extravaganza
• Interactive web applets for teaching and learning statistics
• Inter-university Consortium for Political and Social Research  where you can find and analyze data.
• The Consortium for the Advancement of Undergraduate Statistics
• Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
• Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

• Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
• Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
• Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and  materials  as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
• Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
• Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

• define and apply the scientific method to psychology
• describe the strengths and weaknesses of descriptive, experimental, and correlational research
• define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

• Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read   Imagining Ted Bundy  (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

• Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

• Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
• Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers   recruit a sample of participants and track them for an extended period of time.

• In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study:  researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

• In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design:  two different variables are measured to determine whether there is a relationship between them.

• Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
• Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

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American Psychological Association. (2009). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: Author.

American Psychological Association. (n.d.). Research with animals in psychology. Retrieved from https://www.apa.org/research/responsible/research-animals.pdf

Arnett, J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63(7), 602–614.

Barton, B. A., Eldridge, A. L., Thompson, D., Affenito, S. G., Striegel-Moore, R. H., Franko, D. L., . . . Crockett, S. J. (2005). The relationship of breakfast and cereal consumption to nutrient intake and body mass index: The national heart, lung, and blood institute growth and health study. Journal of the American Dietetic Association, 105(9), 1383–1389. Retrieved from http://dx.doi.org/10.1016/j.jada.2005.06.003

Chwalisz, K., Diener, E., & Gallagher, D. (1988). Autonomic arousal feedback and emotional experience: Evidence from the spinal cord injured. Journal of Personality and Social Psychology, 54, 820–828.

Dominus, S. (2011, May 25). Could conjoined twins share a mind? New York Times Sunday Magazine. Retrieved from http://www.nytimes.com/2011/05/29/magazine/could-conjoined-twins-share-a-mind.html?_r=5&hp&

Fanger, S. M., Frankel, L. A., & Hazen, N. (2012). Peer exclusion in preschool children’s play: Naturalistic observations in a playground setting. Merrill-Palmer Quarterly, 58, 224–254.

Fiedler, K. (2004). Illusory correlation. In R. F. Pohl (Ed.), Cognitive illusions: A handbook on fallacies and biases in thinking, judgment and memory (pp. 97–114). New York, NY: Psychology Press.

Frantzen, L. B., Treviño, R. P., Echon, R. M., Garcia-Dominic, O., & DiMarco, N. (2013). Association between frequency of ready-to-eat cereal consumption, nutrient intakes, and body mass index in fourth- to sixth-grade low-income minority children. Journal of the Academy of Nutrition and Dietetics, 113(4), 511–519.

Harper, J. (2013, July 5). Ice cream and crime: Where cold cuisine and hot disputes intersect. The Times-Picaune. Retrieved from http://www.nola.com/crime/index.ssf/2013/07/ice_cream_and_crime_where_hot.html

Jenkins, W. J., Ruppel, S. E., Kizer, J. B., Yehl, J. L., & Griffin, J. L. (2012). An examination of post 9-11 attitudes towards Arab Americans. North American Journal of Psychology, 14, 77–84.

Jones, J. M. (2013, May 13). Same-sex marriage support solidifies above 50% in U.S. Gallup Politics. Retrieved from http://www.gallup.com/poll/162398/sex-marriage-support-solidifies-above.aspx

Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average (Research Report No. 2008-5). Retrieved from https://research.collegeboard.org/sites/default/files/publications/2012/7/researchreport-2008-5-validity-sat-predicting-first-year-college-grade-point-average.pdf

Lewin, T. (2014, March 5). A new SAT aims to realign with schoolwork. New York Times. Retreived from http://www.nytimes.com/2014/03/06/education/major-changes-in-sat-announced-by-college-board.html.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

• The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
• Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
• While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
• The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 2: Psychological Research Methods and Statistics

Psychology Journal

For the next seven days, observe how statistics are used in the media. In your journal, describe the examples you find. ■

Chapter Overview Visit the Understanding Psychology Web site at glencoe.com and click on Chapter 2—Chapter Overviews to preview the chapter.

34 What Is Research ?

Reader’s Guide ■ Main Idea Exploring Psychology Psychologists must first decide how to approach the research issue. Then psy- Do You Act This Way? chologists conduct the research in one There are some chimps who, far more of a variety of ways to test a hypothesis , than others, constantly seem to try to solve a problem, or confirm previous ingratiate themselves with [win over] their findings. superiors. Melissa, for one, particularly ■ Vocabulary when she was young, used to hurry • sample toward and lay her hand on the back or • naturalistic observation head of an adult male almost every time • case study one passed anywhere near her. If he • survey turned toward her, she often drew her • longitudinal study lips back into a submissive grin as well. • cross-sectional study Presumably Melissa, like the other chimps • correlation who constantly attempt to ingratiate • hypothesis themselves in this way, is simply ill at ease • variable in the presence of a social superior, so that • experimental group she constantly seeks reassurance through • control group physical contact. . . . There is much con- ■ Objectives troversy as to how the human smile has • Describe the process of psychological evolved. It seems fairly certain, though, research and the scientific method . that we have two rather different kinds of • Name the different types of psycho- smiles, . . . We smile when we are amused logical research. and we smile when we are slightly ner- vous, on edge, apprehensive. . . . —from In the Shadow of Man by Jane Goodall, 1988

ane Goodall observed the behavior of chimpanzees in Tanzania, Africa, to obtain data. She observed the behavior of chimps over a period of J30 years and provided much information about the animals’ lives. Whereas Goodall used the research method of naturalistic observation, other scientists conduct experiments and surveys. All of these researchers, however, follow scientific methods.

Chapter 2 / Psychological Research Methods and Statistics 35 Psychologists collect information somewhat like most people do in everyday life—only more carefully and more systematically. When you turn on the television and the picture is out of focus, you experiment with different knobs and dials until you find the one that works. When you ask a number of friends about a movie you are thinking of seeing, you are con- ducting an informal survey. Of course, there is more to doing scientific research than turning dials or asking friends what they think. Over the years psychologists, like other scientists, have transformed these everyday techniques for gathering and analyzing information into more precise tools.

sample: the small group of PRE-RESEARCH DECISIONS participants, out of the total number available, that a Researchers must begin by asking a specific question about a limited researcher studies topic or hypothesis. The next step is to look for evidence. The method a researcher uses to collect information partly depends on the research topic. For Profiles In Psychology example, a social psychol- ogist who is studying the Jane Goodall effects of group pressure is likely to conduct an exper- 1934– iment. A psychologist who is interested in personality “Every individual might begin with intensive matters. Every individ- case studies. Whatever ual has a role to play. approach to gathering data Every individual makes a psychologist selects, a difference.” however, he or she must make certain basic deci- sions in advance. ane Goodall, a British Jzoologist, became known for her work with chimpanzees in the wild. In 1960 she began her research at what is now Gombe Samples Stream National Park in Tanzania. By living among the chim- Suppose a psycholo- panzees, she won their trust, observing their daily activities and gist wants to know how writing detailed reports. She wrote, “The most wonderful thing the desire to get into col- about fieldwork, whether with chimps, baboons or any other lege affects the attitudes of wildlife, is waking up and asking yourself, ‘What am I going to high school juniors and see today?’ ” seniors. It would be impos- Goodall discovered while doing 30 years of research that sible to study every junior chimps hunt and eat larger animals and make and use tools more and senior in the country. than any other species except humans. Goodall also witnessed the Instead, the researcher first known instance in which one group of chimps systematically killed another group, even though the first group’s survival was not would select a sample, a threatened. This discovery surprised naturalists and suggested that relatively small group out behaviors like hunting, using tools, and warfare are not uniquely of the total population human. under study—in this case, all high school juniors and seniors.

36 Chapter 2 / Psychological Research Methods and Statistics A sample must be representative of the population a researcher is studying. For example, if you wanted to know how tall American men were, you would want to make certain that your sample did not include a disproportionately large number of professional basketball players. Such a sample would be nonrepresentative; it would probably not represent American men in general. There are two ways to avoid a nonrepresentative sample. One is to take a purely random sample so that each individual has an equal chance of being represented. For example, a psychologist might choose every twentieth name on school enrollment lists for a study of schoolchildren in a particular town. Random sampling is like drawing names or numbers out of a hat while blindfolded. The second way to avoid a nonrepresentative sample is to deliberately pick individuals who represent the various subgroups in the population being studied. For example, the psychologist doing research on schoolchildren might select students of both sexes, of varying ages, of all social classes, and from all neighborhoods. This is called a stratified sample. In a stratified sample, subgroups in the population are represented proportionately in the sample. For example, Reading Check if about 30 percent of schoolchildren in the United States are ages 5–8, How does a random then in a stratified sample of schoolchildren in the United States, 30 sample differ from a stratified percent of those studied will be ages 5–8. sample?

METHODS OF RESEARCH

The goals of research are to describe behavior, to explain its causes, to predict the circumstances under which certain behaviors may occur again, and to control certain behaviors. Psychologists use various meth- ods of research to accomplish each of these goals.

Naturalistic Observation Researchers need to know how people and animals behave naturally, when they are not conscious of being observed during an experiment. To obtain such information, a psychologist uses naturalistic observation. naturalistic observation: The cardinal rule of naturalistic observation is to avoid disturbing the research method in which the psychologist observes the sub- people or animals you are studying by concealing yourself or by acting as ject in a natural setting without unobtrusively as possible. Otherwise you may observe a performance interfering produced because of the researcher’s presence.

Case Studies A case study is an intensive study of a person or group. Most case study: research method case studies combine long-term observations with diaries, tests, and that involves an intensive investigation of one or more interviews. Case studies can be a powerful research tool. Sigmund Freud ’s participants theory of personality development, discussed in Chapter 14, was based on case studies of his patients. Jean Piaget ’s theory of intellectual development, described in Chapter 3, was based in part on case studies of

Chapter 2 / Psychological Research Methods and Statistics 37 his own children. By itself, however, a case study does DidDid YouYou Know?Know? not prove or disprove anything. The results cannot be A Polling Fiasco To predict the presi- generalized to anyone else. The researcher’s conclu- dential election of 1936, the Literary Digest sions may not be correct. Case studies, though, mailed 10 million ballots as a poll (a survey provide a wealth of descriptive material that may gen- of citizens’ votes). With 23% responding, erate new hypotheses that researchers can then test the Literary Digest predicted Alfred under controlled conditions with comparison groups. M. Landon would win comfortably. But Franklin D. Roosevelt won with 61% of the Surveys popular vote! The Digest sampled mainly owners of telephones and cars and mem- One of the most practical ways to gather data on bers of clubs. This represented a signifi- the attitudes, beliefs, and experiences of large numbers cant over-sampling of the wealthy, who of people is through surveys. A survey may consist of preferred Landon in the election. These, interviews, questionnaires, or a combination of the two. and other sampling errors, created one of Interviews allow a researcher to observe the the greatest polling fiascos of all time. participant and modify questions if the participant seems confused by them. On the other hand, question- naires take less time to administer and the results survey: research method in are more uniform because everyone answers the same questions. which? information is obtained Questionnaires also reduce the possibility that the researcher will influence by asking many individuals a fixed set of questions the participant by unconsciously frowning at an answer he or she does not like. In interviews, there is always a danger that participants will give mis- leading answers in order to help themselves gain approval.

Longitudinal Studies longitudinal study: When conducting longitudinal studies, a psychologist studies research method in which data the same group of people at regular intervals over a period of years to are collected about a group of participants over a number of determine whether their behavior and/or feelings have changed and if years to assess how certain so, how. Longitudinal studies are time-consuming and precarious; characteristics change or participants may disappear in midstudy. Longitudinal studies, however, remain the same during are an ideal way to examine consistencies and inconsistencies in development behavior over time. A good example was the New York Longitudinal Study begun in 1956. Psychologists followed 133 infants as they grew into adulthood, discovering that children are born with different temperaments (Thomas, Chess, & Birch, 1968).

Cross-Sectional Studies An alternative approach to gathering data is cross-sectional studies. cross-sectional study: In a cross-sectional study, psychologists organize individuals into research method in which data groups on the basis of age. Then, these groups are randomly sampled, and are collected from groups of the members of each group are surveyed, tested, or observed participants of different ages and compared so that conclusions simultaneously. Cross-sectional studies are less expensive than longitudinal can be drawn about differences studies and reduce the amount of time necessary for the studies. due to age In 1995 researchers conducted a cross-sectional study in which they showed three-, four-, six-, and seven-year-olds a picture of a serious- looking woman. The psychologists then asked the participants what they thought the woman was thinking about. The psychologists found that the older children seemed to have a clearer picture of mental processes. From

38 Chapter 2 / Psychological Research Methods and Statistics this discovery, the psychologists proposed that as children mature, their understanding of mental processes improves (Flavell, Green, & Flavell, 1995).

Correlations and Explanations A researcher may simply want to observe people or animals and record these observations in a descriptive study. More often, how- ever, researchers want to examine the relationship between two sets of observations—say, between students’ grades and the number of hours they sleep. Scientists use the word correlation to describe how two sets of data correlation: the measure relate to each other. For example, there is a positive correlation between IQ of a relationship between two scores and academic success. High IQ scores tend to go with high grades; variables or sets of data low IQ scores tend to go with low grades. On the other hand, there is a negative correlation between the number of hours you spend practicing your tennis serve and the number of double faults you serve. As the hours of practice increase, errors decrease. In this case, a high rank on one measure tends to go with a low rank on the other (see Figure 2.1). It is important to keep in mind that a correlation describes a relationship between two things. It does not mean, though, that one thing causes the other. In some cases, a third factor exists that may account for the positive correlation. Correlations do not identify what causes what. For example, although you might detect a positive correlation between sunny days and your cheerful moods, this does not mean that sunny days cause good moods. Experiments Why would a researcher choose experimentation over other research methods? Experimentation enables the investigator to control the situation and to decrease the possibility that unnoticed, outside variables will influence the results.

Figure 2.1 A Correlational Study

Positive Correlation Negative Correlation No Correlation

100 100 100 Final Final Final grade in grade in grade in psychology psychology psychology course course course

16 16 16 Hours spent studying Days absent from Minutes spent psychology psychology class brushing teeth These charts display possible correlations between different variables. How does time spent studying psychology correlate to the final grade in a psychology course?

Chapter 2 / Psychological Research Methods and Statistics 39 hypothesis: an educated Every experiment has a hypothesis, or an educated guess, about the guess about the relationship expected outcome; the researcher has some evidence for suspecting a spe- between two variables cific answer. In a hypothesis, a psychologist will state what he or she expects to find. The hypothesis also specifies the important variables of the study. In designing and reporting experiments, psychologists think in variable: any factor that is terms of variables, conditions and behaviors that are subject to change. capable of change There are two types of variables: independent and dependent. The independent variable is the one experimenters change or alter so they can experimental group: the observe its effects. If an effect is found, the dependent variable is the one group to which an independent variable is applied that changes in relation to the independent variable. For example, the number of hours you study (the independent variable) affects your control group: the group performance on an exam (the dependent variable). that is treated in the same way Participants who are exposed to the independent variable are in the as the experimental group except that the experimental experimental group. Participants who are treated the same way as the treatment (the independent experimental group, except that they are not exposed to the independent variable) is not applied variable, make up the control group (see Figure 2.2). A control group is necessary in all experiments. Without it, a researcher cannot be sure the experimental group is reacting to what he Figure 2.2 Experimental Research or she thinks it is reacting to—a change in the independent variable. By comparing Psychology is an experimental science . Psychologists the way control and experimental groups follow the same general procedures when conducting behaved in an experiment (statistically), experimental research. What are the dependent and the researchers can determine whether the independent variables of this experiment? independent variable influences behavior and how it does so. Step 1: Ask Research Question: Does watching violence on TV lead to aggressive behavior? The results of any experiment do not constitute the final word on the Step 2: Form a Hypothesis: subject, however. Psychologists do not People who watch violent TV programs will engage in fully accept the results of their own or more acts of violence than people who don’t. other people’s studies until the results have been replicated—that is, duplicated by Step 3: Determine Variables: at least one other psychologist with People watch violent TV programs (independent variable); people engage in aggressive acts (dependent variable). different participants. Why? Because there is always a chance that the studies may Step 4: Experiment (Testing): have hidden flaws. a. Participants (randomly assigned to groups) Ethical Issues Experimental group Control group spends four hours a day spends four hours a day watching Ethics are the methods of conduct, watching violent programs nonviolent programs or standards, for proper and responsible behavior. In 1992 the American Psychological Association published a set b. Measure aggressive behavior (dependent variable) of of ethical principles regarding the collec- experimental and control groups tion, storage, and use of psychological data. These principles, revised in 2002, include: Step 5: Compare Measurements • Using recognized standards of compe- tence and ethics, psychologists plan Step 6: Interpret Results and Draw Conclusions research so as to minimize the possibility of misleading results. Any ethical

40 Chapter 2 / Psychological Research Methods and Statistics problems are resolved before research is started. The welfare and confidentiality of all participants are to be protected. PSYCHOLOGY • Psychologists are responsible for the dignity and welfare of participants. Psychologists are also responsible for all research they Student Web Activity Visit the Understanding perform or is performed by others under their supervision. Psychology Web site at • Psychologists obey all state and federal laws and regulations as well glencoe.com and click as professional standards governing research. on Chapter 2—Student Web Activities for an • Except for anonymous surveys, naturalistic observations, and similar activity about psychological research, psychologists reach an agreement regarding the rights and research. responsibilities of both participants and researcher(s) before research is started. • When consent is required, psychologists obtain a signed, informed consent before starting any research with a participant. • Deception is used only if no better alternative is available. Under no condition is there deception about (negative) aspects that might influ- ence a participant’s willingness to participate. • Other issues covered include sharing and utilizing data, offering inducements, minimizing evasiveness, and providing participants with information about the study. Recently the use of animals in research has caused much concern and debate. Researchers have attempted to balance the rights of animals with the need for advancing the health of humans through research. While some people oppose subjecting animals to pain for research purposes, others point to the enormous gains in knowledge and reduction in human suffering that have resulted from such research.

1. Review the Vocabulary Explain how a 3. Recall Information What pre-research psychologist might select a sample for a decisions must a psychologist make? survey. 4. Think Critically Why should psychol- 2. Visualize the Main Idea In a chart ogists question the results of an experi- similar to the one below, list and ment that they have conducted for the describe the advantages and disadvan- first time? tages associated with each method of research. 5. Application Activity Suppose you wanted Research Method Description Advantages Disadvantages to find out whether there was a correlation between hours spent watching television and test grades in psychology class. Design a plan using one or more of the methods of research to help you study this correlation.

Chapter 2 / Psychological Research Methods and Statistics 41 Problems and Solutions in Research

Reader’s Guide ■ Main Idea Exploring Psychology The investigation of psychological issues is a painstaking process. Psychologists Was She Doomed? must recognize and resolve errors while One young woman died of fear in a doing research. most peculiar way: When she was born, ■ Vocabulary on Friday the 13th, the midwife who • self-fulfilling prophecy delivered her and two other babies that • single-blind experiment day announced that all three were hexed • double-blind experiment and would die before their 23rd birthday. • placebo effect The other two did die young. As the third woman approached her 23rd birthday, she ■ Objectives checked into a hospital and informed the • Summarize the methodological haz- staff of her fears. The staff noted that she ards of doing research. dealt with her anxiety by extreme hyper- • Examine experimental procedures ventilation (deep breathing). Shortly psychologists use to avoid bias. before her birthday, she hyperventilated to death. —from Introduction to Psychology by James W. Kalat, 2005

nce an expectation is set, we tend to act in ways that are consistent with that expectation. How did the woman in the Oexcerpt above die? Technically, when people do not breathe voluntarily, they breathe reflexively—the amount of carbon dioxide in the blood activates breathing. By breathing so deeply for so long (hyperventilating), the woman exhaled so much carbon dioxide that she did not have enough left in her bloodstream to trigger the breathing reflex. When she stopped breathing voluntarily, she stopped breathing altogether and died. In effect, the woman believed in the Friday the 13th hex and unintentionally fulfilled its prediction. self-fulfilling prophecy: This is what we mean by a self-fulfilling prophecy. A self-fulfilling a situation in which a prophecy involves having expectations about a behavior and then acting in researcher’s expectations influence that person’s own some way, usually unknowingly, to carry out that behavior. behavior, and thereby influence In everyday life, we consciously or unconsciously tip off people as to the participant’s behavior what our expectations of them are. We give them cues, such as nodding

42 Chapter 2 / Psychological Research Methods and Statistics and raising our eyebrows. People pick up on those cues and act as expected. Psychologists must be aware of such cues when conducting experiments. They must not allow their expectations to influence the results. The Hawthorne Study The results must be unbiased. Science is a painstaking, In 1939 a group of industrial psycholo- exacting process. Every researcher must be wary of gists set out to determine how to increase numerous pitfalls that can trap him or her into mis- workers’ productivity at a General Electric takes. In this section, we will look at some of the most plant in Hawthorne, Illinois (Roethlisberger & common problems psychological researchers confront Dickson, 1939). The participants were eight and how they cope with them. assembly line workers. In the first experi- ment, the psychologists gradually increased the lighting in the room (the independent AVOIDING A SELF-FULFILLING variable) and observed the effect on produc- tivity (the dependent variable). Pro- PROPHECY ductivity improved as the lighting was increased. In a second experiment, the par- Sometimes an experimenter’s behavior may unwit- ticipants were permitted to take rest breaks. tingly influence the results. The experimenter may This also increased the productivity of the unintentionally raise an eyebrow or nod when posing workers. Next, the psychologists reduced a question, thus influencing the person being studied. the lighting levels, and again productivity One way to avoid this self-fulfilling prophecy is to use increased. The psychologists found that a double-blind technique. Suppose a psychologist no matter what they did, productivity wants to study the effects of a particular tranquilizer. increased. Why? The psychologists soon She might give the drug to an experimental group and recognized that the participants realized they were receiving special attention. This moti- a placebo (a substitute for the drug that has no med- vated the workers to work harder, thus ical benefits) to a control group. The next step would increasing their productivity. be to compare their performances on a series of tests. The results of the experiment in This is a single-blind experiment. The participants Hawthorne generated studies in human rela- are “blind” in the sense that they do not know whether tions and management that apply to work they have received the tranquilizer or the placebo. situations today. What does it mean, then, if the participants taking the placebo drug report that they feel the effects of the tranquilizer? It means that their expectations have played a role—that single-blind experiment: they felt the effects because they believed they were taking a tranquiliz- an experiment in which the participants are unaware of ing drug, not because of the drug itself. which participants received The researcher will not know who takes the drug or the placebo. She the treatment may, for example, ask the pharmacist to number rather than label the pills. After she scores the tests, she goes back to the pharmacist to learn which participants took the tranquilizer and which took the placebo. This is a double-blind experiment. Neither the participants nor the experimenter double-blind experiment: knows which participants received the tranquilizer. This eliminates the pos- an experiment in which neither the experimenter nor the partic- sibility that the researcher will unconsciously find what she expects to find ipants know which participants about the effects of the drug. The researcher remains unbiased. received which treatment

THE MILGRAM EXPERIMENT

In the 1960s Stanley Milgram wanted to determine whether participants would administer painful shocks to others merely because an authority figure had instructed them to do so. Milgram collected nearly 1,000 male

Chapter 2 / Psychological Research Methods and Statistics 43 Figure 2.3 Single-Blind and Double-Blind Experiments

Researchers must take measures during experimentation to guard against seeing only what they expect to see. Why would a researcher conduct a double-blind experiment?

Experimenter Participants Organizer of Experiment

Single-Blind aware unaware aware Experiment

Double-Blind Experiment unaware unaware aware

participants, including college students and adults in different occupations. Milgram told the group of paid volunteers that he was studying the effects of punishment on learning. Milgram introduced each volunteer to a “learner”—actually someone posing as a learner. The volunteer watched the learner attempt to recite a list of paired words that he supposedly had memorized earlier. Each time the learner made a mistake, the volunteer, or “teacher,” was ordered to push a button to deliver an electric shock to the learner. The volunteers were told that the shocks, mild at first, would increase with each mistake to a painful and dangerous level of 450 volts. The volunteers at this point did not realize that the shocks were false because the learners displayed distress and pain, screaming and begging for the electric shocks to stop. Although the task did not seem easy for them, most of the volunteers delivered a full range of the fake electric shocks to the learners. (Sixty-five percent of the volunteers pushed the shock button until they reached maximum severity.) Reading Check The results implied that ordinary individuals could easily inflict pain on Why can the Milgram others if such orders were issued by a respected authority. Later, Milgram experiment be classified as a informed the volunteers that they had been deceived and that no shocks single-blind experiment? had actually been administered. This was a good example of a single-blind experiment because the participants were unaware that they were not administering a shock. Critics raised the following questions, though. How would you feel if you had been one of Milgram’s participants? Did Milgram violate ethical principles when he placed participants in a position to exhibit harmful behavior? Was the deception Milgram used appropriate? Did the information gained outweigh the deception? Before the start of any experiment today, the experimenter is required to submit a plan to a Human Subjects Committee that can either approve or reject the ethics of the experiment. Milgram’s hypothesis and experiment has been applied in similar studies. In Milgram’s original study, more than half of the participants (26 of 40, or 65 percent) administered the highest level of shock. Researchers at Swarthmore College hypothesized that Milgram’s find- ings were due, in part, to the fact that his participants were mostly

44 Chapter 2 / Psychological Research Methods and Statistics middle-aged, working-class men. Most had probably served in the military during World War II and thus had experience taking orders and obeying authority. Young, liberal, highly educated Swarthmore students would obey less. Yet, surprisingly, 88 percent of the Swarthmore undergraduates administered the highest level of shock!

THE PLACEBO EFFECT

When researchers evaluate the effects of drugs, they must always take into account a possible placebo effect. The placebo effect is a change in a patient’s illness or physical state that results People spend millions of solely from the patient’s knowledge and perceptions of the treatment. The dollars a year on herbal remedies such as these, placebo is some sort of treatment, such as a drug or injection, that resembles which have not been medical therapy yet has no medical effects. proven to cure their ills. In one study (Loranger, Prout, & White, 1961), researchers divided hospitalized psychiatric patients into two experimental groups and a control group. They gave the experimental groups either a “new tranquilizer” or a “new energizer” drug. The control group received no drugs at all. After a placebo effect: a change in six-week period, the researchers evaluated the experimental groups. a participant’s illness or behav- ior that results from a belief that Fifty-three to eighty percent of the experimental groups reported that they the treatment will have an effect had indeed benefited from the drugs. Yet all the drugs administered during rather than from the actual the experiment were placebos. The participants had reacted to their own treatment expectations of how the drug given to them would affect them. Neither the researchers nor the patients were aware that the drugs were placebos until after the experiment.

1. Review the Vocabulary Explain how 3. Recall Information What questions psychologists try to avoid the self- about the Milgram experiment did crit- fulfilling prophecy. ics raise? How are today’s experiments restricted in regards to ethics? 2. Visualize the Main Idea Use a diagram similar to the one below to outline an 4. Think Critically How can the expecta- experiment discussed in this section. tions of the participants bias the results of an experiment? How can the expec- Hypothesis:______▼ tations of the experimenter bias the Independent Dependent results of an experiment? Variables:______Variables: ______▼ Results:______▼ 5. Application Activity Describe a single-blind Conclusions:______experiment you might set up. Explain your hypothesis and the participants’ tasks.

Chapter 2 / Psychological Research Methods and Statistics 45 The Case of horse could not see the questioner. To test his Clever Hans hypothesis, Pfungst fitted the horse with blind- ers. The horse failed to answer the questions. Period of Study: 1911 Eventually Pfungst realized that the questioner would unknowingly give Hans clues as to the Introduction: A horse, Clever Hans, grew right answer. For example, after asking a ques- famous throughout Europe for his startling tion, the questioner would lean forward to watch ability to answer questions. Taught by his owner, Hans’s foot. This was a cue for Hans to start Mr. von Osten, Hans seemed to be able to add, tapping. Pfungst observed that “as the subtract, multiply, divide, spell, and solve prob- experimenter straightened up, Hans would stop lems, even when his tapping, he found that owner was not even the raising of his around. Oskar Pfungst eyebrows was sufficient. decided to investigate Even the dilation of the the humanlike intelli- questioner’s nostrils was gence of the horse. a cue for Hans to stop tapping.” (Pfungst, 1911) Hypothesis: Two Questioners involuntarily different hypotheses performed these actions, are involved in this and Hans responded to case. First, Mr. von the visual signals. Osten, believing that horses could be as Results: Von Osten intelligent as humans, believed that he had hypothesized that he been teaching the horse could teach Hans some problem-solving abili- how to solve problems and answer ties. Pfungst, on the other hand, believed that questions, when in fact he had been teaching horses could not learn such things and, while Hans to make simple responses to simple investigating this theory, developed a hypothe- signals. Pfungst had uncovered errors in sis that Hans, the horse, was reacting to visual von Osten’s experiments. Von Osten had cues to answer questions. practiced a self-fulfilling prophecy—he had unintentionally communicated to Hans how he Method: Mr. von Osten, a German mathe- expected the horse to behave. Pfungst had matics teacher, started by showing Hans an learned the truth by isolating the conditions object while saying “One” and at the same time under which Hans correctly and incorrectly lifting Hans’s foot once. Von Osten would lift answered questions. He had carefully Hans’s foot twice for two objects, and so on. observed the participant’s reactions under Eventually Hans learned to tap his hoof the cor- controlled conditions. rect number of times when von Osten called out a number. For four years, von Osten worked with Hans on more and Analyzing the Case Study more complex problems, until Hans was able to answer any question given him. 1. How did Mr. von Osten test his hypothesis? Upon hearing of the amazing horse, 2. What errors did von Osten make while testing his Pfungst grew skeptical and investigated. hypothesis? Pfungst soon discovered that Hans 3. Critical Thinking If Pfungst had not come along and responded correctly to questions only found the truth, how could we discover today how Hans when the questioner had calculated the answered the questions? answer first. Then Pfungst realized that Hans’s answers proved wrong when the

46 Chapter 2 / Psychological Research Methods and Statistics Statistical Evaluation

Reader’s Guide ■ Main Idea Exploring Psychology Psychologists must collect and evaluate evidence to support their hypotheses. When Statistics Lie ■ Vocabulary Long ago, when Johns Hopkins University had just begun to admit women • statistics • descriptive statistics students, someone not particularly enam- • frequency distribution ored of [happy with] coeducation reported • normal curve a real shocker: Thirty-three and one-third • central tendency percent of the women at Hopkins had • variance married faculty members! The raw figures • standard deviation gave a clearer picture. There were three • correlation coefficient women enrolled at the time, and one of • inferential statistics them had married a faculty man. ■ Objectives —from How to Lie With Statistics by Darrell • Recognize types of descriptive statistics. Huff, 1954 • Describe inferential statistics.

lthough people may use statistics to distort the truth (such as in the example above), people may also use statistics honestly to Asupport their hypotheses. In order to allow statistics to validly support a hypothesis, psychologists must collect meaningful data and evaluate it correctly. How many times have you been told that in order to get good grades, you have to study? A psychology student named Kate has always restricted the amount of TV she watches during the week, particularly before a test. She has a friend, though, who does not watch TV before a test but who still does not get good grades. This fact challenges Kate’s belief. Although Kate hypothesizes that among her classmates, those who watch less TV get better grades, she decides to conduct a survey to test the accuracy of her hypothesis. Kate asks 15 students in her class to write down how many hours of TV they watched the night before a psychology quiz and how many hours they watched on the night after the quiz. Kate collects additional data. She has her participants check off familiar products on a

Chapter 2 / Psychological Research Methods and Statistics 47 list of 20 brand-name items that were advertised on TV the night before the quiz. Kate also asks her participants to Baseball give their height. Statistics When the data are turned in, Kate finds herself overwhelmed with the Let’s look at how statistics are used in one of our most amount of information she has popular sports, baseball. A batting average is the number of collected. Her data are presented in hits per official “at bats” (walks do not count). If a player Figure 2.4. How can she organize it all has a batting average of .250, it means that on average he or she gets a hit every fourth time at the plate. so that it makes sense? How can she The earned run average represents the number of runs analyze it to see whether it supports or a pitcher allows per 9 innings of play. Consider the pitcher contradicts her hypothesis? The who pitches 180 innings in a season and allows 60 runs. On answers to these questions are found in the average, this pitcher allows one run every 3 innings statistics, a branch of mathematics (180 innings divided by 60 runs). One run every 3 innings that enables researchers to organize equals 3 runs every 9 innings, so the earned run average is and evaluate the data they collect. We 3. The next time you watch your favorite sport, think about will explore the statistical procedures the part that statistics plays in it. that help psychologists make sense out of the masses of data they collect. statistics: the branch of mathematics concerned with DESCRIPTIVE STATISTICS summarizing and making meaningful inferences from When a study such as Kate’s is completed, the first task is to organize collections of data the data in as brief and clear a manner as possible. For Kate, this means descriptive statistics: the that she must put her responses together in a logical format. When she listing and summarizing of data does this, she is using descriptive statistics, the listing and summarizing in a practical, efficient way of data in a practical, efficient way, such as through graphs and averages.

Figure 2.4 Kate’s Data

Kate’s data show the Before After Grade* Products Height number of hours of 0.0 1.5 5 2 71 television watched 0.5 2.5 10 4 64 before and after the 0.5 2.5 9 6 69 quiz, the grade on the 1.0 2.0 10 14 60 quiz, the number of 1.0 2.5 8 10 71 products recognized, 1.0 1.5 7 9 63 and participants’ 1.5 3.0 9 7 70 height in inches. How 1.5 2.5 8 12 59 much television did 1.5 2.5 8 9 75 the two students 1.5 3.0 6 14 60 with the best grades 2.0 3.0 5 13 68 watch the night 2.5 2.5 3 17 65 before the quiz? 2.5 3.5 4 10 72 3.0 3.0 0 18 62 * Highest grade 4.0 4.0 4 20 67 possible is 10.

48 Chapter 2 / Psychological Research Methods and Statistics Figure 2.5 A Frequency Distribution A frequency distribution shows how often a particular observation occurs. How many students watched three or more hours of television the night before the quiz?

Hours Frequency Frequency Before* After* 0.0 1 0 0.5 2 0 1.0 3 0 1.5 4 2 2.0 1 1 2.5 2 6 3.0 1 4 3.5 0 1 4.0 1 1 Total 15 15 *Number of students

Distributions of Data One of the first steps that researchers take to organize their data is to create frequency tables and graphs. Tables and graphs provide a rough picture of the data. Are the scores bunched up or spread out? What score occurs most often? Frequency distributions and graphs provide researchers with their initial look at the data. Kate is interested in how many hours of TV her participants watched frequency distribution: the night before and the night after the quiz. She uses the numbers of an arrangement of data that hours of TV viewing as categories, and then she counts how many indicates how often a particular score or observation occurs participants reported each category of hours before and after the quiz. She has created a table called a frequency distribution (see Figure 2.5). A frequency distribution is a way Figure 2.6 A Frequency Polygon of arranging data so that we know how often a particular score or observation occurs. This graph shows the number of hours of TV What can Kate do with this information? watched the night before the quiz and the A commonly used technique is to figure out night after the quiz. How do the two lines compare? percentages. This is done simply by dividing the 6 frequency of participants within a category by the total number of participants and multiplying by Hours of 5 100. Before the quiz, about 13 percent of her par- TV watched before quiz 4 ticipants (2 divided by 15) watched TV for 2.5 3 hours. On the night after the quiz, 40 percent of her Hours of

TV watched Frequency 2 participants watched 2.5 hours of TV (6 divided by after quiz 15). If you are familiar with the use of percentages, of students) (number 1 you know that test grades are often expressed as percentages (the number of correct points divided 0 12345 by the total number of questions times 100). Hours of TV Sometimes frequency distributions include a col- umn giving the percentage of each occurrence.

Chapter 2 / Psychological Research Methods and Statistics 49 Figure 2.7 A Normal Curve It is often easier to visualize frequency infor- mation in the form of a graph. Since Kate is most The maximum frequency lies in the center of a interested in how much TV her classmates range of scores in a perfect normal curve. The watched, she decides to graph the results. Kate frequency tapers off as you reach the edges constructs a histogram. Histograms are very similar of the two sides. Where is the mean located to bar graphs except that histograms show in a normal curve? frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportionate to the corresponding frequencies. Mean Another kind of graph is the frequency polygon or frequency curve. Figure 2.6 is a frequency polygon. It shows the same information presented in a different way. Instead of boxes, a Frequency 34.13% 34.13% point is placed on the graph where the midpoint of

2.15% 2.15% the top of each histogram bar would be. Then the 13.59% 13.59% points are connected with straight lines. –3 –2 –1 0 1 2 3 Frequency polygons are useful because they Scores in standard deviation units provide a clear picture of the shape of the data dis- tribution. Another important feature is that more than one set of data can be graphed at the same time. For example, Kate might be interested in comparing how much TV was watched the night before the quiz with the amount watched the evening after the quiz. She can graph the “after quiz” data using a different kind of line. The com- parison is obvious; in general, her participants watched more TV on the night after the quiz than on the night before the quiz.

Figure 2.8 Measures of Central Tendency My friends’ scores on the last psychology quiz

What is the mean? What is the median? What is the mode? The mean is the “average.” The median is the middle score The mode is the most [55+70+70+86+98+99+ after the scores are ranked from common score. [70] 100=5787=83] highest to lowest. [86] It is often useful to summarize a set of scores by identifying a num- ber that represents the center, average, or most frequently occurring number of the distribution. If your score matched the median on the last psychology quiz, how did you do in comparison to your classmates?

50 Chapter 2 / Psychological Research Methods and Statistics Imagine that Kate could measure how much TV everyone in Chicago watched one night. If she could graph that much information, her graph would probably look something like Figure 2.7. A few people would watch little or no TV, a few would have the TV on all day, while most would watch a moderate amount of TV. Therefore, the graph would be highest in the middle and taper off toward the tails, or ends, of the distribution, giving it the shape of a bell. This curve is called the normal curve (or bell-shaped curve). Many normal curve: a graph of variables, such as height, weight, and IQ, fall into such a curve if enough frequency distribution shaped like a symmetrical, bell-shaped people are measured. The normal curve is symmetrical. This means that curve; a graph of normally if a line is drawn down the middle of the curve, one side of the curve is a distributed data mirror image of the other side. It is an important distribution because of certain mathematical characteristics. We can divide the curve into sections and predict how much of the curve, or what percentage of cases, falls within each section.

Measures of Central Tendency Most of the time, researchers want to do more than organize their data. They want to be able to summarize information about the distribution into statistics. For example, researchers might want to discuss the average height of women or the most common IQ test score. One of the most common ways of summarizing is to use a measure of central tendency— central tendency: a number a number that describes something about the “average” score. We shall use that describes something about the “average” score of Kate’s quiz grades (refer back to Figure 2.4) in the examples that follow. a distribution The mode is the most frequent score. In a graphed frequency distribution, the mode is the peak of the graph. The most frequently occurring quiz grade is 8; that is, more students received an 8 than any other score. Distributions can have more than one mode. The data for Reading Check height presented in Figure 2.4 have two modes: 60 and 71. Distributions What is the difference between the mean and the with two modes are called bimodal. mode? When scores are put in order from least to most, the median is the middle score. Since the median is the midpoint of a set of values, it divides the frequency distribution into two halves. Therefore, 50 percent of the scores fall below the median, and 50 percent fall above the median. For an odd number of observations, the median is the exact middle value. The mean is what most people think of as an average and is the most commonly used measure of – Figure 2.9 central tendency. To find the mean (or X ), add up all Standard Deviation the scores and then divide by the number of scores Two distributions added. The mean equals the sum of the scores on Large SD with the same Small SD variable X divided by the total number of observa- mean and differ- tions. For the quiz grades, the sum of the scores is 96, ent standard devi- and the number of scores is 15. The mean equals 96 ations are shown. What informa-

divided by 15, giving us a mean quiz grade of 6.4. Frequency The mean can be considered the balance point tion does the of the distribution, like the middle of a seesaw, since standard devia- tion supply? it does reflect all the scores in a set of data. If the Scores highest score in a data set is shifted higher, the mean

Chapter 2 / Psychological Research Methods and Statistics 51 will shift upward also. If we change the highest quiz grade from 10 to 20, the mean changes from 6.4 to 7.1.

Transforming Scores Measures of Variability Suppose you take the ACT and score a Distributions differ not only in their average score 26. Then you take the SAT and get a 620. The but also in terms of how spread out, or how variable, the college you want to go to will accept either scores are. Figure 2.9 shows two distributions drawn on test score. Which score should you send? the same axis. Each is symmetrical, and each has the (Which score is better?) To make a compari- same mean. However, the distributions differ in terms of son between two scores that have different their variability. Measures of variability provide an index distributions, different means, and different of how spread out the scores of a distribution are. variabilities, you must transform the scores. Two commonly used measures of variability are the ACT range and the standard deviation. To compute the range, subtract the lowest score in a data set from the highest score and add 1. The highest quiz score is 10 and the lowest is 0, so the range is 11, representing 11 possible

18 = Mean scores 0–10. The range uses only a small amount of infor- 6 = Standard deviation mation, and it is used only as a crude measure. SAT The standard deviation is a better measure of vari- ability because, like the mean, it uses all the data points in its calculation. It is the most widely used measure of vari- ability. The standard deviation is a measure of distance. It is like (but not exactly like) an average distance of every 500 = Mean 100 = Standard deviation score to the mean of the scores. This distance is called a – deviation and is written: X – X . Scores above the mean If you look at the distributions of the will have a positive deviation; scores below the mean will ACT and SAT, you will find that the ACT has have a negative deviation. The size of the typical devia- a mean of 18 and a standard deviation of 6. tion depends on how variable, or spread out, the distrib- So you take your score on the ACT (26) and ution is. If the distribution is very spread out, deviations subtract the mean from it (26 18) to get 8; tend to be large. If the distribution is bunched up, devia- 8 is 1.33 standard deviations above the tions tend to be small. The larger the standard deviation, mean (8/6). Do the same for your SAT score [620500 = 120; so 620 is 1.2 standard devi- the more spread out the scores (see Figure 2.9). ations above the mean (120/100)]. So which score would you submit to the college of your Correlation Coefficients choice? (The correct answer is your ACT A correlation coefficient describes the direction score because 1.33 is greater than 1.2.) and strength of the relationship between two sets of What we just did was to make a standard score. A standard score is a transformed score observations (recall the discussion of correlations in that provides information about its location Section 1). The most commonly used measure is the in a distribution. Pearson correlation coefficient (r). A coefficient with a plus () sign indicates a positive correlation. This means that as one variable increases, the second variable also variability: a measure of increases. For example, the more you jog, the better your cardiovascular difference, or spread of data system works. A coefficient with a minus () sign indicates a negative cor- relation; as one variable increases, the second variable decreases. For exam- standard deviation: a mea- ple, the more hours a person spends watching TV, the fewer hours are sure of variability that describes available for studying. Correlations can take any value between 1 and 1 an average distance of every score from the mean including 0. An r near 1 or 1 indicates a strong relationship (either positive or negative), while an r near 0 indicates a weak relationship.

52 Chapter 2 / Psychological Research Methods and Statistics Generally, an r from 0.60 to 1.0 indicates a strong correlation, from correlation coefficient: 0.30 to 0.60 a moderate correlation, and from 0 to 0.30 a weak describes the direction and strength of the relationship correlation. A correlation of 1.0 indicates a perfect relationship between between two sets of variables two variables and is very rare. To get an idea of how her data look, Kate draws some scatterplots. A scatterplot is a graph of participants’ scores on the two variables, and it demonstrates the direction of the relationship between them. Figure 2.10 illustrates one of Kate’s correlations. Note that each point represents one person’s score on two variables.

INFERENTIAL STATISTICS

The purpose of descriptive statistics is to describe the characteristics of a sample. Psychologists, however, are not only interested in the information they collect from their participants, but they also want to make generalizations about the population from which the participants come. To make such generalizations, they need the tools of inferential statistics. Using inferential statistics, researchers can determine inferential statistics: whether the data they collect support their hypotheses, or whether their numerical methods used to determine whether research results are merely due to chance outcomes. data support a hypothesis or whether results were due to Probability and Chance chance If you toss a coin in the air, what is the probability that it will land with heads facing up? Since there are only two possible outcomes, the probability of heads is 0.50. If you toss a coin 100 times, you would expect 50 heads and 50 tails. If the results were 55 heads and 45 tails, would you think the coin is fair? What if it were 100 heads and zero tails? When a researcher completes an experiment, he or she is left with lots of data to analyze. The researcher must determine whether the findings Figure 2.10 A Scatterplot from the experiment support the hypothesis (for When there is little or no relationship between example, the coin is fair) or whether the results are two variables, the points in the scatterplot due to chance. To do this, the researcher must do not seem to fall into any pattern. What perform a variety of statistical tests, called measures conclusions can you draw from this of statistical significance. When researchers conclude scatterplot? that their findings are statistically significant, they are 80 stating, at a high level of confidence, that their results are not due to chance.

Statistical Significance 70 For many traits in a large population, the frequency distribution follows a characteristic 60 pattern, called the normal curve (see Figure 2.7). For Height in inches example, if you measured the heights of 500 students chosen at random from your high school, you would 0 12345 find very few extremely tall people and very few HoursHours of TV ofwatched TV before before quiz quiz extremely short people. The majority of students

Chapter 2 / Psychological Research Methods and Statistics 53 would fall somewhere in the middle. Suppose Kate wants to know if her classmates watch more TV than the “average American.” Since daily TV Do some people really have psychic viewing is probably normally distributed, she powers? can compare her results to the normal A well-known psychic sometimes begins his distribution if she knows the population’s performance by saying the following: “Think of a mean number of TV viewing hours. number between 1 and 50. Both digits must be odd When psychologists evaluate the results numbers, but they must not be the same. For of their studies, they ask: Could the results example, it could be 15 but it could not be 11. Please choose a number and I will tell you what be due to chance? What researchers really number you are thinking of.” want to know is whether the results are so extreme, or so far from the mean of the Procedure distribution, that they are more likely due 1. Develop a hypothesis that explains how the to their independent variable, not to chance. psychic is performing this feat. (Hint: The The problem is that this question cannot psychic uses statistics, not magic.) be answered with a yes or no. This is why 2. Try out the psychic’s act on several of researchers use some guidelines to evaluate your classmates and record their responses. probabilities. Many researchers say that if the Analysis probability that their results were due to chance 1. Based on the psychic’s directions, decide is less than 5 percent (0.05), then they are confi- which numbers can be used and which dent that the results are not due to chance. Some numbers will most likely be used. researchers want to be even more certain, and so 2. How do your observations they use 1 percent (0.01) as their level of confidence. support or contradict your When the probability of a result is 0.05 or 0.01 (or hypothesis? whatever level the researcher sets), we say that the result is statistically significant. It is important to remember that See the Skills Handbook, page 622, for an probability tells us how likely it is that an event or outcome is explanation of designing due to chance, but not whether the event is actually due to chance. an experiment. When does a statistically significant result not represent an important finding? Many statistical tests are affected by sample size. A small difference between groups may be magnified by a large sample and may result in a statistically significant finding. The difference, however, may be so small that it is not a meaningful difference. Assessment

1. Review the Vocabulary What is the 3. Recall Information What is the impor- difference between a frequency distri- tance of the normal curve? bution and a histogram? Between a 4. Think Critically What does correlation normal curve and a scatterplot? tell you about the relationship between 2. Visualize the Main Idea two variables? Using an organizer similar 1 to the one at right, list 5. Application Activity Conduct a class or family 2 and describe the survey on an issue, then display your findings in a measures of central 3 frequency distribution, frequency polygon, or tendency. scatterplot. Apply evaluation rules. What conclu- sions can you reach from your results?

54 Chapter 2 / Psychological Research Methods and Statistics Summary and Vocabulary

Psychologists learn about what they do not know by carefully and systematically collecting information. They then must describe Chapter Vocabulary and analyze their research findings through various statistical sample (p. 36) measurements and interpret their results. naturalistic observation (p. 37) What Is Research? case study (p. 37) survey (p. 38) Main Idea: Psycholo- ■ Researchers begin their research by asking a spe- longitudinal study (p. 38) gists must first decide cific question about a limited topic; determining how to approach the the validity of a claim, hypothesis, or theory; and cross-sectional study (p. 38) research issue. Then choosing an unbiased sample. correlation (p. 39) psychologists conduct ■ Psychologists use several methods of research to hypothesis (p. 40) accomplish their research goals. These methods the research in one of a variable (p. 40) include naturalistic observation, case studies, sur- variety of ways to test a veys, and experiments. experimental group (p. 40) hypothesis, solve a pro- ■ Psychologists follow a set of ethical principles control group (p. 40) blem, or confirm previ- that govern their research. ous findings. self-fulfilling prophecy (p. 42) single-blind experiment (p. 43) Problems and Solutions in Research double-blind experiment (p. 43) Main Idea: The investi- ■ In a self-fulfilling prophecy, an experimenter has gation of psychological expectations about a participant’s behavior and placebo effect (p. 45) issues is a painstaking then acts in some way, usually unknowingly, to statistics (p. 48) process. Psychologists influence that behavior. descriptive statistics (p. 48) ■ must recognize and In single-blind experiments, the participants do frequency distribution (p. 49) not know which participants have received the resolve errors while normal curve (p. 51) doing research. treatment. ■ Researchers can avoid a self-fulfilling prophecy central tendency (p. 51) by using the double-blind technique in their variability (p. 52) experiments. standard deviation (p. 52) ■ When researchers evaluate the effects of drugs, they must always take into account a possible correlation coefficient (p. 52) placebo effect. inferential statistics (p. 53)

Statistical Evaluation Main Idea: Psycholo- ■ Researchers use descriptive statistics to organize gists must collect and data in a practical, efficient way. evaluate evidence to test ■ Descriptive statistics include distributions of data, their hypotheses. measures of central tendency, measures of vari- ability, and correlation coefficients. ■ Researchers use inferential statistics to make gen- eralizations about the population from which the participants come. ■ Researchers perform a variety of statistical tests, called measures of statistical significance, to determine whether findings from their experi- ment support the hypothesis or whether the results are due to chance.

Chapter 2 / Psychological Research Methods and Statistics 55 Assessment

PSYCHOLOGY 10. In a(n) ______, a researcher studies a group of people over a period of years. Self-Check Quiz Visit the Understanding Psychology Web site at glencoe.com and click on Chapter 2—Self-Check Recalling Facts Quizzes to prepare for the Chapter Test. 1. What are two ways that a researcher can avoid a biased sample? Reviewing Vocabulary 2. When do researchers use naturalistic observation? Choose the letter of the correct term or concept 3. How does a self-fulfilling prophecy present a below to complete the sentence. problem for researchers? a. variability f. double-blind 4. Using a graphic organizer similar to the one b. sample experiment below, identify and explain the kinds of descrip- c. longitudinal study g. placebo effect tive statistics. d. control group h. statistics e. single-blind i. normal experiment j. frequency distribution DESCRIPTIVE STATISTICS 1. ______is a branch of mathematics that helps researchers organize and evaluate data. 2. In a(n) ______, only the participants of the experiment do not know whether they are in 5. Why do researchers use inferential statistics? the experimental group or the control group. How do inferential statistics describe data 3. Measures of ______indicate how spread differently than descriptive statistics? out the scores of a distribution are. 4. A bell-shaped curve is a(n) ______curve. Critical Thinking 5. In an experiment, the ______includes the 1. Synthesizing Information How could you participants who are not exposed to experimen- attempt to disprove the following hypothesis? tal variables. You can raise blood pressure by making a 6. The ______is a change in a patient’s participant anxious. physical state that results from the patient’s 2. Analyzing Statements Explain the following perceptions of the treatment. statement: “Correlation does not imply 7. Researchers use a(n) ______to arrange causation.” data so that they know how often a particular 3. Making Inferences What correlation would observation occurs. you expect between students’ grades and class 8. Researchers generally select a(n) ______, attendance? which is a relatively small group of the total 4. Applying Concepts How are statistics used population that is being studied. within your classroom? Within your school? 9. In a(n) ______, neither the participants nor 5. Analyzing Information Various kinds of the experimenter knows whether the partici- statistics are used in sports. Provide examples of pants are in the experimental group or the statistics from various sports. control group.

56 Chapter 2 / Psychological Research Methods and Statistics Assessment

Psychology Projects 1. What Is Research? Choose a traffic intersec- 30 25 Technology Activity 20 15 10 5 tion near your home or school that has a stop 0 sign. Design a study to assess whether or not Does smoking cause lung cancer? motorists stop at the posted sign. Consider the Some scientists cite animal studies research questions you need to answer, such as as proving that it does. Representatives of the how to determine whether motorists comply tobacco industry state that animal studies cannot be with the sign, the number of vehicles, and the generalized to humans. Search the Internet to find time of day. Conduct your study and record arguments and data from each side of this debate. your observations. Use that information to support both viewpoints in an essay. 2. Statistical Evaluation Collect heights from 20 women and 20 men. Create a frequency distribution for each group, and divide them Psychology Journal into 5-inch intervals before counting. Graph your For each of the examples of statistics you data for men and women separately as listed in your journal (at the beginning of frequency polygons on the same axis. Compute the chapter), indicate whether you feel that enough means, medians, modes, ranges, and standard information was provided to evaluate the validity of deviations for women and men separately. How any reported claims. What other information are the two distributions alike and different? should have been provided? How might additional information change the reported conclusions?

Building Skills Internet Use Interpreting Graphs Adult Internet users Review the graphs, then 90 Type of Internet answer the questions that follow. connection 75 High-speed 1. What does each of the graphs illustrate? 60 (broadband) 73% 42% 2. Which of the age groups shown is least likely to 45

use the Internet? 30

3. How has the number of adult Internet users Percentage of adults 15 Dial-up changed since 1996? 0 58% 4. Do most Internet users today use a dial-up con- '96 '06 nection or a broadband connection? How do you Use the Internet think this will change in the future? (by age group) 5. Do you think a higher percentage of teens use the Internet than the age groups shown? Explain.

Practice and assess key social studies skills with Glencoe Skillbuilder 88% 84% 71% 32% 73% Interactive Workbook CD-ROM, Level 2. 18-29 30-49 50-64 65+ All ages See the Skills Handbook, page 628, for an Source: Pew Internet & American Life Project, 2006. explanation of interpreting graphs.

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