Share a question you have that you would like to research. What is the population of interest? What type of sample would you collect for this study? Explain what guidelines you used to select that sample. • Explain the goals of science. • Identify and compare descriptive methods. • Identify and compare predictive (relational) methods. • Describe the explanatory method. Your description should include independent variable, dependent variable, control group, and experimental group. • Explain how we “do” science and how proof and disproof relate to doing science. You may be wondering why you are enrolled in a statistics class. Most students take statistics because it is a requirement in their major field, and often students do not understand it is a requirement. Scientists and researchers use statistics to describe data and draw inferences. Thus, no matter whether your major is in the behavioral sciences, the natural sciences, or in more applied areas such as business or education, statistics are necessary to your discipline. Why? Statistics are necessary because scientists and researchers collect data and test hypotheses with these data using statistics. A is a prediction regarding the outcome of a study. This prediction concerns the potential relationship between at least two variables (a is an event or behavior that has at least two values). Hypotheses are stated in such a way that they are testable. When we test our hypothesis, statistics may lead us to conclude that our hypothesis is or is not supported by our observations. A prediction regarding the outcome of a study involving the potential relationship between at least two variables. An event or behavior that has at least two values. In science, the goal of testing hypotheses is to arrive at or test a —an organized system of assumptions and principles that attempts to explain certain phenomena and how they are related. Theories help us to organize and explain the data gathered in research studies. In other words, theories allow us to develop a framework regarding the facts in a certain area. For example, Darwin’s theory organizes and explains facts related to evolution. In addition to helping us organize and explain facts, theories also help in producing new knowledge by steering researchers toward specific observations of the world. An organized system of assumptions and principles that attempts to explain certain phenomena and how they are related. Students are sometimes confused about the differences between a hypothesis and a theory. A is a prediction regarding the outcome of a single study. Many hypotheses may be tested and several research studies conducted before a comprehensive theory on a topic is put forth. Once a is developed, it may aid in generating future hypotheses. In other words, researchers may have additional questions regarding the theory that help them to generate new hypotheses to test. If the results from these additional studies further support the theory, we are likely to have greater confidence in the theory. However, every time we test a hypothesis, statistics are necessary. Scientific research has three basic goals: (1) to describe, (2) to predict, and (3) to explain. All of these goals lead to a better understanding of behavior and mental processes. begins with careful observation. Behavioral scientists might describe patterns of behavior, thought, or emotions in humans. They might also describe the behavior(s) of other animals. For example, researchers might observe and describe the type of play behavior exhibited by children or the mating behavior of chimpanzees. Description allows us to learn about behavior and when it occurs. Let’s say, for example, that you were interested in the channel-surfing behavior of males and females. Careful observation and description would be needed in order to determine whether or not there were any gender differences in channel-surfing. Description allows us to observe that two events are systematically related to one another. Without description as a first step, predictions cannot be made. Carefully observing behavior in order to describe it. allows us to identify the factors that indicate when an event or events will occur. In other words, knowing the level of one variable allows us to predict the approximate level of the other variable. We know that if one variable is present at a certain level, then there is a greater likelihood that the other variable will be present at a certain level. For example, if we observed that males channel-surf with greater frequency than females, we could then make predictions about how often males and females might change channels when given the chance. Identifying the factors that indicate when an event or events will occur. Finally, allows us to identify the causes that determine when and why a behavior occurs. In order to explain a behavior, we need to demonstrate that we can manipulate the factors needed to produce or eliminate the behavior. For example, in our channel-surfing example, if gender predicts channel-surfing, what might cause it? It could be genetic or environmental. Maybe males have less tolerance for commercials and thus channel-surf at a greater rate. Maybe females are more interested in the content of commercials and are thus less likely to change channels. Maybe the attention span of females is greater. Maybe something associated with having a Y chromosome increases channel-surfing, or something associated with having two X chromosomes leads to less channel-surfing. Obviously the possible explanations are numerous and varied. As scientists, we test these possibilities to identify the best explanation of why a behavior occurs. When we try to identify the best explanation for a behavior, we must systematically eliminate any alternative explanations. To eliminate alternative explanations, we must impose control over the research situation. We will discuss the concepts of control and alternative explanations shortly. Identifying the causes that determine when and why a behavior occurs. The goals of science map very closely onto the research methods that scientists use. In other words, there are methods that are descriptive in nature, predictive in nature, and explanatory in nature. I will briefly introduce these methods here. Behavioral scientists use three types of descriptive methods. First is the —simply making observations of human or other animal behavior. Scientists approach observation in two ways. involves observing humans or other animals behave in their natural habitat. Observing the mating behavior of chimpanzees in their natural setting would be an example of this approach. involves observing behavior in a more contrived and controlled situation, usually the laboratory. Bringing children to a laboratory playroom to observe play behavior would be an example of this approach. Observation involves description at its most basic level. One advantage of the observational method, as well as other descriptive methods, is the flexibility to change what one is studying. A disadvantage of descriptive methods is that the researcher has little control. As we use more powerful methods, we gain control but lose flexibility. Making observations of human or other animal behavior. A second descriptive method is the . A case study is an in-depth study of one or more individuals. Freud used case studies to develop his theory of personality development. Similarly, Jean Piaget used case studies to develop his theory of cognitive development in children. This method is descriptive in nature, as it involves simply describing the individual(s) being studied. An in-depth study of one or more individuals. The third method that relies on description is the —questioning individuals on a topic or topics and describing their responses. Surveys can be administered by mail, over the phone, on the Internet, or as a personal interview. One advantage of the survey method over the other descriptive methods is that it allows researchers to study larger groups of individuals more easily. This method has disadvantages, however. One concern has to do with the wording of questions. Are they easy to understand? Are they written in such a manner that they bias the respondents’ answers? Such concerns relate to the validity of the data collected. Another concern relevant to the survey method (and most other research methods) is whether the group of people who participate in the study (the ) is representative of all the people about whom the study is meant to generalize (the ). This concern can usually be overcome through random sampling. A is achieved when, through random selection, each member of the population is equally likely to be chosen as part of the sample. Questioning individuals on a topic or topics and then describing their responses. The group of people who participate in a study. All of the people about whom a study is meant to generalize. Two methods allow researchers to not only describe behaviors but also predict from one variable to another. The first, the , assesses the degree of relationship between two measured variables. If two variables are correlated with each other, we can predict from one variable to the other with a certain degree of accuracy. For example, height and weight are correlated. The relationship is such that an increase in one variable (height) is generally accompanied by an increase in the other variable (weight). Knowing this, we can predict an individual’s approximate weight, with a certain degree of accuracy, given the person’s height. A method that assesses the degree of relationship between two variables. One problem with correlational research is that it is often misinterpreted. Frequently, people assume that because two variables are correlated, there must be some sort of causal relationship between the variables. This is not so. . Remember that a correlation simply means that the two variables are related in some way. For example, being a certain height does not cause you to also be a certain weight. It would be nice if it did, because then we would not have to worry about being either under- or overweight. What if I told you that watching violent TV and displaying aggressive behavior were correlated? What could you conclude based on this correlation? Many people might conclude that watching violent TV causes one to act more aggressively. Based on the evidence given (a correlational study), however, we cannot draw this conclusion. All we can conclude is that those who watch more violent television programs also tend to act more aggressively. It is possible that the violent TV causes aggression, but we cannot draw this conclusion based only on correlational data. It is also possible that those who are aggressive by nature are attracted to more violent television programs, or that some other variable is causing both aggressive behavior and violent TV watching. The point is that observing a correlation between two variables simply means that they are related to each other. The correlation between height and weight, or violent TV and aggressive behavior, is a : As one variable (height) increases, we observe an increase in the second variable (weight). Some correlations indicate a : As one variable increases, the other variable systematically decreases. Can you think of an example of a negative relationship between two variables? Consider this: As mountain elevation increases, temperature decreases. Negative correlations also allow us to predict from one variable to another. If I know the mountain elevation, it will help me predict the approximate temperature. A relationship between two variables in which an increase in one variable is accompanied by an increase in the other variable. A relationship between two variables in which an increase in one variable is accompanied by a decrease in the other variable. Besides the correlational method, a second method that allows us to describe and predict is the . Quasi-experimental research allows us to compare naturally occurring groups of individuals. For example, we could examine whether alcohol consumption by students in a fraternity or sorority differs from that of students not in such organizations. You will see in a moment that this method differs from the experimental method, described below, in that the groups studied occur naturally. In other words, we do not assign people to join a Greek organization or not. They have chosen their groups on their own, and we are simply looking for differences (in this case, in the amount of alcohol typically consumed) between these naturally occurring groups. This is often referred to as a or —a characteristic inherent in the participants that cannot be changed. Because we are using groups that occur naturally, any differences that we find may be due to the variable of being a Greek member or not, or the differences may be due to other factors that we were unable to control in this study. For example, maybe those who like to drink more are also more likely to join a Greek organization. Once again, if we find a difference between these groups in amount of alcohol consumed, we can use this finding to predict what type of student (Greek or non-Greek) is likely to drink more. However, we cannot conclude that belonging to a Greek organization one to drink more because the participants came to us after choosing to belong to these organizations. In other words, what is missing when we use predictive methods such as the correlational and quasi-experimental methods is control. Research that compares naturally occurring groups of individuals; the variable of interest cannot be manipulated. When using predictive methods, we do not systematically manipulate the variables of interest; we only measure them. This means that, although we may observe a relationship between variables (such as that described between drinking and Greek membership), we cannot conclude that it is a causal relationship. Why? Because there could be other, for this relationship. An is the idea that it is possible that some other, uncontrolled, extraneous variable may be responsible for the observed relationship. For example, maybe those who choose to join Greek organizations come from higher-income families and have more money to spend on such things as alcohol. Or maybe those who choose to join Greek organizations are more interested in socialization and drinking alcohol before they even join the organization. Thus, because these methods leave the possibility for alternative explanations, we cannot use them to establish cause-and-effect relationships. The idea that it is possible that some other, uncontrolled, extraneous variable may be responsible for the observed relationship. When using the experimental method, researchers pay a great deal of attention to eliminating alternative explanations by using the proper controls. Because of this, the allows researchers not only to describe and predict but also to determine whether there is a cause-and-effect relationship between the variables of interest. In other words, this method enables researchers to know when and why a behavior occurs. Many preconditions must be met in order for a study to be experimental in nature. Here, we will simply consider the basics—the minimum requirements needed for an experiment. A research method that allows a researcher to establish a cause-and-effect relationship through manipulation of a variable and control of the situation. The basic premise of experimentation is that the researcher controls as much as possible in order to determine whether there is a cause-and-effect relationship between the variables being studied. Let’s say, for example, that a researcher is interested in whether cell phone use while driving affects driving performance. The idea behind experimentation is that the researcher manipulates at least one variable (known as the ) and measures at least one variable (known as the ). In our study, what should the researcher manipulate? If you identified the use of cell phones while driving, then you are correct. If cell phone use while driving is the independent variable, then driving performance is the dependent variable. For comparative purposes, the independent variable has to have at least two groups or conditions. We typically refer to these two groups or condition
s as the control group and the experimental group. The is the group that serves as the baseline or “standard” condition. In our study of cell phone use while driving, the control group is the group that does not use a cell phone use while driving. The is the group that receives the treatment—in this case, those who use cell phones while driving. Thus, in an experiment, one thing that we control is the level of the independent variable that participants receive. The variable in a study that is manipulated by the researcher. The variable in a study that is measured by the researcher. The group of participants that does not receive any level of the independent variable and serves as the baseline in a study. The group of participants that receives some level of the independent variable. What else should we control to help eliminate alternative explanations? Well, we need to control the type of subjects in each of the treatment conditions. We should begin by drawing a random sample of subjects from the population. Once we have our sample of subjects, we have to decide who will serve in the control group versus the experimental group. In order to gain as much control as possible, and eliminate as many alternative explanations as possible, we should use —assigning participants to conditions in such a way that every subject has an equal probability of being placed in any condition. How does random assignment help us to gain control and eliminate alternative explanations? By using random assignment we should minimize or eliminate differences between the groups. In other words, we want the two groups of participants to be as alike as possible. The only difference we want between the groups is that of the independent variable we are manipulating—either using or not using cell phones while driving. Once participants are assigned to conditions, we measure driving performance for subjects in each condition using a driving simulator (the dependent variable). Studies such as this one have already been completed by researchers. What researchers have found is that cell phone use while driving has a negative effect on driving performance (Beede & Kass, 2006; Dula, Martin, Fox, & Leonard, 2011). Assigning participants to conditions in such a way that every participant has an equal probability of being placed in any condition. Let’s review some of the controls we have used in the present study. We have controlled who is in the study (we want a sample representative of the population about whom we are trying to generalize), who participates in each group (we should randomly assign participants to the two conditions), and the treatment each group receives as part of the study (some drive while using a cell phone and some do not). Can you identify other variables that we might need to consider controlling in the present study? How about past driving record, how long subjects have driven, age, and their proficiency with cell phones? There are undoubtedly other variables we would need to control if we were to complete this study. The basic idea is that when using the experimental method, we try to as much as possible by manipulating the independent variable and controlling any other extraneous variables that could affect the results of the study. Randomly assigning participants also helps to control for subject differences between the groups. What does all of this control gain us? If, after completing this study with the proper controls, we find that those in the experimental group (those who drove while using a cell phone) did in fact have lower driving performance scores than those in the control group, we would have evidence supporting a cause-and-effect relationship between these variables. In other words, we could conclude that driving while using a cell phone negatively impacts driving performance. Manipulating the independent variable in an experiment or any other extraneous variables that could affect the results of a study. DescriptionObservational methodDescriptive methods allow description of behavior(s)Case study methodDescriptive methods do not support reliable predictionsSurvey methodDescriptive methods do not support cause-and-effect explanationsPredictionCorrelational methodPredictive methods allow description of behavior(s)Quasi-experimental methodPredictive methods support reliable predictions from one variable to another Predictive methods do not support cause-and-effect explanationsExplanationExperimental methodAllows description of behavior(s) Supports reliable predictions from one variable to another Supports cause-and-effect explanations 1. In a recent study, researchers found a negative correlation between income level and incidence of psychological disorders. Jim thinks this means that being poor leads to psychological disorders. Is he correct in his conclusion? Why or why not? 2. In a study designed to assess the effects of exercise on life satisfaction, participants were assigned to groups based on whether they reported exercising or not. All participants then completed a life satisfaction inventory. a. What is the independent variable? b. What is the dependent variable? c. Is the independent variable a participant variable or a true manipulated variable? 3. What type of method would you recommend researchers use to answer the following questions? a. What percentage of cars run red lights? b. Do student athletes spend as much time studying as student nonathletes? c. Is there a relationship between type of punishment used by parents and aggressiveness in children? d. Do athletes who are randomly assigned to a group using imagery techniques perform better than those who are randomly assigned to a group not using such techniques? Although the experimental method can establish a cause-and-effect relationship, most researchers would not wholeheartedly accept a conclusion from only one study. Why is that? Any one of a number of problems can occur in a study. For example, there may be control problems. Researchers may believe they have controlled for everything but miss something, and the uncontrolled factor may affect the results. In other words, a researcher may believe that the manipulated independent variable caused the results when, in reality, it was something else. Another reason for caution in interpreting experimental results is that a study may be limited by the technical equipment available at the time. For example, in the early part of the 19th century, many scientists believed that studying the bumps on a person’s head allowed them to know something about the internal mind of the individual being studied. This movement, known as phrenology, was popularized through the writings of physician Joseph Gall (1758–1828). At the time that it was popular, phrenology appeared very “scientific” and “technical.” With hindsight and with the technological advances that we have today, the idea of phrenology seems laughable to us now. Finally, we cannot completely rely on the findings of one study because a single study cannot tell us everything about a theory. The idea of science is that it is not static; the theories generated through science change. For example, we often hear about new findings in the medical field, such as “Eggs are so high in cholesterol that you should eat no more than two a week.” Then, a couple of years later, we might read, “Eggs are not as bad for you as originally thought. New research shows that it is acceptable to eat them every day,” followed a few years later by even more recent research indicating that “two eggs a day are as bad for you as smoking cigarettes every day” (Spence, Jenkins, & Davignon, 2012). You may have heard people confronted with such contradictory findings complain, “Those doctors, they don’t know what they’re talking about. You can’t believe any of them. First they say one thing, and then they say completely the opposite. It’s best to just ignore all of them.” The point is that when testing a theory scientifically, we may obtain contradictory results. These contradictions may lead to new, very valuable information that subsequently leads to a theoretical change. Theories evolve and change over time based on the consensus of the research. Just because a particular idea or theory is supported by data from one study does not mean that the research on that topic ends and that we just accept the theory as it currently stands and never do any more research on that topic. When scientists test theories, they do not try to prove them true. Theories can be supported based on the data collected, but obtaining support for something does not mean it is true in all instances. Proof of a theory is logically impossible. As an example, consider the following problem, adapted from Griggs and Cox (1982). This is known as the Drinking Age Problem (the reason for the name will become readily apparent). Does turning over the beer card and finding that the person is 21 years of age or older prove that the rule is always true? No—the fact that one person is following the rule does not mean that it is always true. How, then, do we test a hypothesis? We test a hypothesis by attempting to falsify or disconfirm it. If it cannot be falsified, then we say we have support for it. Which cards would you choose in an attempt to falsify the rule in the drinking age problem? If you identified the beer card as being able to falsify the rule, then you were correct. If we turn over the beer card and find that the individual is under 21 years of age, then the rule is false. Is there another card that could also falsify the rule? Yes, the 16 years of age card can. How? If we turn that card over and find that the individual is drinking alcohol, then the rule is false. These are the only two cards that can potentially falsify the rule. Thus, they are the only two cards that need to be turned over. Even though disproof or disconfirmation is logically sound in terms of testing hypotheses, falsifying a hypothesis does not always mean that the hypothesis is false. Why? There may be design problems in the study, as described earlier. Thus, even when a theory is falsified, we need to be cautious in our interpretation. We do not want to completely discount a theory based on a single study. (p. 6) (p. 4) control (p. 8) control group (p. 8) (p. 5) (p. 7) (p. 3) experimental group (p. 7) (p. 6) (p. 3) (p. 2) (p. 7) (p. 5) (p. 4) (p. 5) (p. 5) (p. 3) (p. 6) random assignment (p. 7) (p. 5) (p. 4) (p. 2) (p. 2) (Answers to odd-numbered questions appear in .) 1. After describing your medical symptoms to your doctor, he claims he has a possible “theory” to explain your symptoms. What is wrong with his statement? How might he better state his beliefs? 2. Identify and briefly describe the three goals of science. 3. Identify advantages and disadvantages of naturalistic observation versus laboratory observation. 4. Identify the two predictive (relational) methods and describe each. 5. In a study of the effects of type of study on exam performance, participants are randomly assigned to one of two conditions. In one condition, participants study alone using notes they took during class lectures. In a second condition, participants study in interactive groups with notes from class lectures. The amount of time spent studying is held constant. All students then take the same exam on the material. a. What is the independent variable in this study? b. What is the dependent variable in this study? c. Identify the control and experimental groups in this study. d. Is the independent variable manipulated or a participant variable? 6. Researchers interested in the effects of caffeine on anxiety have randomly assigned participants to one of two conditions in a study, the no caffeine condition or the caffeine condition. After drinking two cups of either regular or decaffeinated coffee, participants will take an anxiety inventory. a. What is the independent variable in this study? b. What is the dependent variable in this study? c. Identify the control and experimental groups in this study. d. Is the independent variable manipulated or a participant variable? 7. Gerontologists interested in the effects of aging on reaction time have two groups of participants take a test in which they must indicate as quickly as possible whether a probe word was a member of a previous set of words. One group of participants is between the age of 25 and 45, whereas the other group of participants is between the age of 55 and 75. The time it takes to make the response is measured. a. What is the independent variable in this study? b. What is the dependent variable in this study? c. Identify the control and experimental groups in this study. d. Is the independent variable manipulated or a participant variable? 1. Jim is incorrect because he is inferring causation based on correlational evidence. He is assuming that because the two variables are correlated, one must be causing changes in the other. In addition, he is assuming the direction of the inferred causal relationship—that a lower income level causes psychological disorders, not that having a psychological disorder leads to a lower income level. The correlation simply indicates that these two variables are related in an inverse manner. That is, those with psychological disorders also tend to have lower income levels. 2. a. The independent variable is exercise. b. The dependent variable is life satisfaction. c. The independent variable is a participant variable. 3. a. Naturalistic observation b. Quasi-experimental method c. Correlational method d. Experimental method • Explain and give examples of an operational definition. • Explain the four properties of measurement and how they are related to the four scales of measurement. • Explain the difference between a discrete variable and a continuous variable. An important step when designing a study is to define the variables in your study. A second important step is to determine the level of measurement of the dependent variable, which will ultimately help to determine which statistics are appropriate for analyzing the data collected. Some variables are fairly easy to define, manipulate, and measure. For example, if a researcher were studying the effects of exercise on blood pressure, she could manipulate the amount of exercise by varying the length of time that individuals exercised or by varying the intensity of the exercise (as by monitoring target heart rates). She could also measure blood pressure periodically during the course of the study; a machine already exists that will take this measure in a consistent and accurate manner. Does this mean that the measure will always be accurate? No. There is always the possibility for measurement error. In other words, the machine may not be functioning properly, or there may be human error contributing to the measurement error. Now let’s suppose that a researcher wants to study a variable that is not as concrete or easily measured as blood pressure. For example, many people study abstract concepts such as aggression, attraction, depression, hunger, or anxiety. How would we either manipulate or measure any of these variables? My definition of what it means to be hungry may be quite different from yours. If I decided to measure hunger by simply asking participants in an experiment if they were hungry, the measure would not be accurate because each individual may define hunger in a different way. What we need is an of hunger—a definition of the variable in terms of the operations (activities) the researcher uses to measure or manipulate it. A definition of a variable in terms of the operations (activities) a researcher uses to measure or manipulate it. As this is a somewhat circular definition, let’s reword it in a way that may make more sense. An operational definition specifies the activities of the researcher in measuring and/or manipulating a variable (Kerlinger, 1986). In other words, we might define hunger in terms of specific activities, such as not having eaten for 12 hours. Thus, one operational definition of hunger could be that simple: Hunger occurs when 12 hours have passed with no food intake. Notice how much more concrete this definition is than simply saying hunger is that “gnawing feeling” that you get in your stomach. Specifying hunger in t