Discussion
| Title | Discussion | Preferred Language style | English (U.S.) |
|---|---|---|---|
| Type of document | Essay | Number of pages/words | 2 Pages Double Spaced (approx 275 words per page) |
| Subject area | Psychology | Academic Level | Master |
| Style | APA | Number of sources/references | 1 |
| Order description: | |||
Discussion: (Jackson, S. L. (2017). Statistics plain and simple. (4th ed.). Boston, MA: Cengage Learning). Use the reference given. Be careful with grammar and spelling. Discuss elaborate and give examples on the questions below. 150 words please
- 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.
- What is the independent variable? 150 words
- What is the dependent variable? 150 words
- Is the independent variable a participant variable or a true manipulated variable? 150 words
Review this week’s course materials and learning activities and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs: 200 words
Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
Describe how you will apply that learning in your daily life, including your work life.
Describe what may be unclear to you, and what you would like to learn.
Questions 1, 2 and 3 should be in paragraph and use transition. From question 1 to 3 should be connected to each other.
Reference Chapter 1
Learning Objectives
- 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 whyit 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 hypothesis is a prediction regarding the outcome of a study. This prediction concerns the potential relationship between at least two variables (a variable 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.
hypothesis A prediction regarding the outcome of a study involving the potential relationship between at least two variables.
variable An event or behavior that has at least two values.
In science, the goal of testing hypotheses is to arrive at or test a theory—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.
theory 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 hypothesis 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 theory 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.
Description Description 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.
description Carefully observing behavior in order to describe it.
Prediction Prediction 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.
prediction Identifying the factors that indicate when an event or events will occur.
Explanation Finally, explanation 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.
explanation Identifying the causes that determine when and why a behavior occurs.
An Introduction to Research Methods in Science
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 observational method—simply making observations of human or other animal behavior. Scientists approach observation in two ways. Naturalistic observation 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. Laboratory observation 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.
observational method Making observations of human or other animal behavior.
A second descriptive method is the case study method. 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.
case study method An in-depth study of one or more individuals.
The third method that relies on description is the survey method—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 sample) is representative of all the people about whom the study is meant to generalize (the population). This concern can usually be overcome through random sampling. A random sample is achieved when, through random selection, each member of the population is equally likely to be chosen as part of the sample.
survey method Questioning individuals on a topic or topics and then describing their responses.
sample The group of people who participate in a study.
population All of the people about whom a study is meant to generalize.
Predictive (Relational) Methods
Two methods allow researchers to not only describe behaviors but also predict from one variable to another. The first, the correlational method, 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.
correlational method 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. Correlation does not imply causation. 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 positive relationship: As one variable (height) increases, we observe an increase in the second variable (weight). Some correlations indicate a negative relationship: 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.
positive relationship A relationship between two variables in which an increase in one variable is accompanied by an increase in the other variable.
negative relationship 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 method. 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 subject or participant variable—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 causes 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.
quasi-experimental method 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, alternative explanations for this relationship. An alternative explanation 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.
alternative explanation 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 experimental method 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.
experimental method 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 independent variable) and measures at least one variable (known as the dependent variable). 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 conditions as the control group and the experimental group. The control group 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 experimental group 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.
independent variable The variable in a study that is manipulated by the researcher.
dependent variable The variable in a study that is measured by the researcher.
control group The group of participants that does not receive any level of the independent variable and serves as the baseline in a study.
experimental group 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 random assignment—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).
random assignment 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 control 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.
control Manipulating the independent variable in an experiment or any other extraneous variables that could affect the results of a study.
AN INTRODUCTION TO RESEARCH METHODS
| Goal Met | Research Methods | Advantages/Disadvantages |
| Description | Observational method | Descriptive methods allow description of behavior(s) |
| Case study method | Descriptive methods do not support reliable predictions | |
| Survey method | Descriptive methods do not support cause-and-effect explanations | |
| Prediction | Correlational method | Predictive methods allow description of behavior(s) |
| Quasi-experimental method | Predictive methods support reliable predictions from one variable to another Predictive methods do not support cause-and-effect explanations |
|
| Explanation | Experimental method | Allows 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).
On this task imagine that you are a police officer responsible for making sure the drinking-age rule is being followed. The four cards below represent information about four people sitting at a table. One side of a card indicates what the person is drinking and the other side of the card indicates the person’s age. The rule is: “If a person is drinking alcohol, then the person is 21 or over.” In order to check that the rule is true or false, which card or cards below would you turn over? Turn over only the card or cards that you need to check to be sure.
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.
alternative explanation (p. 6)
case study method (p. 4)
control (p. 8)
control group (p. 8)
correlational method (p. 5)
dependent variable (p. 7)
description (p. 3)
experimental group (p. 7)
experimental method (p. 6)
explanation (p. 3)
hypothesis (p. 2)
independent variable (p. 7)
negative relationship (p. 5)
observational method (p. 4)
population (p. 5)
positive relationship (p. 5)
prediction (p. 3)
quasi-experimental method (p. 6)
random assignment (p. 7)
sample (p. 5)
survey method (p. 4)
theory (p. 2)
variable (p. 2)
(Answers to odd-numbered questions appear in Appendix B.)
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?
CRITICAL THINKING CHECK ANSWERS
Critical Thinking Check 1.1
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
| MODULE 2 |
Learning Objectives
- 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.
Operationally Defining Variables
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 operational definition of hunger—a definition of the variable in terms of the operations (activities) the researcher uses to measure or manipulate it.
operational definition 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 terms of the number of hours without food is an operational definition, whereas defining hunger as that “gnawing feeling” is not an operational definition.
In research, it is necessary to operationally define all variables—those measured (dependent variables) and those manipulated (independent variables). One reason for doing so is to ensure that the variables are measured consistently or manipulated in the same way during the course of the study. Another reason is to help us communicate our ideas to others. For example, what if a researcher said that she measured anxiety in her study? I would need to know how she defined anxiety operationally because it can be defined in many different ways. Thus, it can be measured in many different ways. For example, anxiety could be defined as the number of nervous actions displayed in a 1-hour time period, as a person’s score on a GSR (galvanic skin response) machine, as a person’s heart rate, or as a person’s score on the Taylor Manifest Anxiety Scale. Some measures are better than others—better meaning more consistent and valid. Once I understand how a researcher has defined a variable operationally, I can replicate the study if I desire. I can begin to have a better understanding of the study and whether or not it may have problems. I can also better design my study based on how the variables were operationally defined in other research studies.
In addition to operationally defining independent and dependent variables, you must consider the level of measurement of the dependent variable. There are four levels of measurement, each based on the characteristics or properties of the data. These properties include identity, magnitude, equal unit size, and absolute zero. When a measure has the property of identity, objects that are different receive different scores. For example, if participants in a study had different political affiliations, they would receive different scores. Measurements have the property of magnitude (also called ordinality) when the ordering of the numbers reflects the ordering of the variable. In other words, numbers are assigned in order so that some numbers represent more or less of the variable being measured than others.
identity A property of measurement in which objects that are different receive different scores.
magnitude A property of measurement in which the ordering of numbers reflects the ordering of the variable.
Measurements have an equal unit size when a difference of 1 is the same amount throughout the entire scale. For example, the difference between people who are 64 inches tall and 65 inches tall is the same as the difference between people who are 72 inches tall and 73 inches tall. The difference in each situation (1 inch) is identical. Notice how this differs from the property of magnitude. Were we to simply line up and rank a group of individuals based on their height, the scale would have the properties of identity and magnitude, but not equal unit size. Can you think about why this would be so? We would not actually measure people’s height in inches, but simply order them in terms of how tall they appear, from shortest (the person receiving a score of 1) to tallest (the person receiving the highest score). Thus, our scale would not meet the criteria of equal unit size. In other words, the difference in height between the two people receiving scores of 1 and 2 might not be the same as the difference in height between the two people receiving scores of 3 and 4.
equal unit size A property of measurement in which a difference of 1 means the same amount throughout the entire scale.
Lastly, measures have an absolute zero when assigning a score of 0 indicates an absence of the variable being measured. For example, time spent studying would have the property of absolute zero because a score of 0 on this measure would mean an individual spent no time studying. However, a score of 0 is not always equal to the property of absolute zero. As an example, think about the Fahrenheit temperature scale. That measurement scale has a score of 0 (the thermometer can read 0 degrees), but does that score indicate an absence of temperature? No, it indicates a very cold temperature. Hence, it does not have the property of absolute zero.
absolute zero A property of measurement in which assigning a score of 0 indicates an absence of the variable being measured.
Scales (Levels) of Measurement
As noted previously, the level or scale of measurement depends on the properties of the data. There are four scales of measurement (nominal, ordinal, interval, and ratio), and each of these scales has one or more of the properties described in the previous section. We will discuss the scales in order, from the one with the fewest properties to the one with the most properties—that is, from least to most sophisticated. As we will see in later modules, it is important to establish the scale of measurement of your data in order to determine the appropriate statistical test to use when analyzing the data.
Nominal Scale A nominal scale is one in which objects or individuals are broken into categories that have no numerical properties. Nominal scales have the characteristic of identity but lack the other properties. Variables measured on a nominal scale are often referred to as categorical variables because the measuring scale involves dividing the data into categories. However, the categories carry no numerical weight. Some examples of categorical variables, or data measured on a nominal scale, include ethnicity, gender, and political affiliation.
nominal scale A scale in which objects or individuals are broken into categories that have no numerical properties.
We can assign numerical values to the levels of a nominal variable. For example, for ethnicity, we could label Asian Americans as 1, African Americans as 2, Latin Americans as 3, and so on. However, these scores do not carry any numerical weight; they are simply names for the categories. In other words, the scores are used for identity, but not for magnitude, equal unit size, or absolute value. We cannot order the data and claim that 1s are more than or less than 2s. We cannot analyze these data mathematically. It would not be appropriate, for example, to report that the mean ethnicity was 2.56. We cannot say that there is a true zero where someone would have no ethnicity. We can, however, form frequency distributions based on the data, calculate a mode, and use the chi-square test to analyze data measured on a nominal scale. If you are unfamiliar with these statistical concepts, don’t worry. They will be discussed in later modules.
Ordinal Scale An ordinal scale is one in which objects or individuals are categorized and the categories form a rank order along a continuum. Data measured on an ordinal scale have the properties of identity and magnitude but lack equal unit size and absolute zero. Ordinal data are often referred to as ranked data because the data are ordered from highest to lowest, or biggest to smallest. For example, reporting how students did on an exam based simply on their rank (highest score, second highest, and so on) would be an ordinal scale. This variable would carry identity and magnitude because each individual receives a rank (a number) that carries identity, and beyond simple identity it conveys information about order or magnitude (how many students performed better or worse in the class). However, the ranking score does not have equal unit size (the difference in performance on the exam between the students ranked 1 and 2 is not necessarily the same as the difference between the students ranked 2 and 3), or an absolute zero. We can calculate a mode or a median based on ordinal data; it is less meaningful to calculate a mean. We can also use nonparametric tests such as the Wilcoxon rank-sum test or a Spearman rank-order correlation coefficient (again, these statistical concepts will be explained in later modules).
ordinal scale A scale in which objects or individuals are categorized and the categories form a rank order along a continuum.
interval scale A scale in which the units of measurement (intervals) between the numbers on the scale are all equal in size.
Interval Scale An interval scale is one in which the units of measurement (intervals) between the numbers on the scale are all equal in size. When using an interval scale, the properties of identity, magnitude, and equal unit size are met. For example, the Fahrenheit temperature scale is an interval scale of measurement. A given temperature carries identity (days with different temperatures receive different scores on the scale), magnitude (cooler days receive lower scores and hotter days receive higher scores), and equal unit size (the difference between 50 and 51 degrees is the same as that between 90 and 91 degrees.) However, the Fahrenheit scale does not have an absolute zero. Because of this, we are not able to form ratios based on this scale (for example, 100 degrees is not twice as hot as 50 degrees). Because interval data can be added and subtracted, we can calculate the mean, median, or mode for interval data. We can also use t tests, ANOVAs, or Pearson product-moment correlation coefficients to analyze interval data (once again, these statistics will be discussed in later modules).
Ratio Scale A ratio scale is one in which, in addition to order and equal units of measurement, there is an absolute zero that indicates an absence of the variable being measured. Ratio data have all four properties of measurement—identity, magnitude, equal unit size, and absolute zero. Examples of ratio scales of measurement include weight, time, and height. Each of these scales has identity (individuals who weigh different amounts would receive different scores), magnitude (those who weigh less receive lower scores than those who weigh more), and equal unit size (1 pound is the same weight anywhere along the scale and for any person using the scale). These scales also have an absolute zero, which means a score of 0 reflects an absence of that variable. This also means that ratios can be formed. For example, a weight of 100 pounds is twice as much as a weight of 50 pounds. As with interval data, mathematical computations can be performed on ratio data. This means that the mean, median, and mode can be computed. In addition, as with interval data, t tests, ANOVAs, or the Pearson product-moment correlation can be computed.
ratio scale A scale in which, in addition to order and equal units of measurement, there is an absolute zero that indicates an absence of the variable being measured.
Notice that the same statistics are used for both interval and ratio scales. For this reason, many behavioral scientists simply refer to the category as interval-ratio data and typically do not distinguish between these two types of data. You should be familiar with the differences between interval and ratio data but aware that the same statistics are used with both types of data.
FEATURES OF SCALES OF MEASUREMENT
| SCALE OF MEASUREMENT | ||||
| Nominal | Ordinal | Interval | Ratio | |
| Examples | Ethnicity Religion Sex |
Class rank Letter grade |
Temperature (Fahrenheit and Celsius) Many psychological tests |
Weight Height Time |
| Properties | Identity | Identity Magnitude |
Identity Magnitude Equal unit size |
Identity Magnitude Equal unit size Absolute zero |
| Mathematical Operations | Determine whether = or * | Determine whether = or * Determine whether < or > |
Determine whether = or * Determine whether < or > Add Subtract |
Determine whether = or * Determine whether < or > Add Subtract Multiply Divide |
| Typical Statistics Used |
Mode Chi-square |
Mode Median Wilcoxon tests |
Mode Median Mean t test ANOVA |
Mode Median Mean t test ANOVA |
1.Provide several operational definitions of anxiety. Include nonverbal measures and physiological measures. How would your operational definitions differ from a dictionary definition?
2.Identify the scale of measurement for each of the following:
a.Phone area code
b.Grade of egg (large, medium, small)
c.Amount of time spent studying
d.Score on the SAT
e.Class rank
f.Number on a volleyball jersey
g.Miles per gallon
Discrete and Continuous Variables
Another means of classifying variables is in terms of whether they are discrete or continuous in nature. Discrete variables usually consist of whole-number units or categories. They are made up of chunks or units that are detached and distinct from one another. A change in value occurs a whole unit at a time, and decimals do not make sense with discrete scales. Most nominal and ordinal data are discrete. For example, gender, political party, and ethnicity are discrete scales. Some interval or ratio data can be discrete. For example, the number of children someone has would be reported as a whole number (discrete data), yet it is also ratio data (you can have a true zero and form ratios).
discrete variables Variables that usually consist of whole-number units or categories and are made up of chunks or units that are detached and distinct from one another.
Continuous variables usually fall along a continuum and allow for fractional amounts. The term continuous means that it “continues” between the whole-number units. Examples of continuous variables are age (22.7 years), height (64.5 inches), and weight (113.25 pounds). Most interval and ratio data are continuous in nature.
continuous variables Variables that usually fall along a continuum and allow for fractional amounts.
absolute zero (p. 15)
continuous variables (p. 18)
discrete variables (p. 18)
equal unit size (p. 14)
identity (p. 14)
interval scale (p. 16)
magnitude (p. 14)
nominal scale (p. 15)
operational definition (p. 14)
ordinal scale (p. 16)
ratio scale (p. 16)
(Answers to odd-numbered questions appear in Appendix B.)
1.What does it mean to define variables operationally?
2.Which of the following is the best operational definition of depression?
a.Depression is defined as that low feeling you get sometimes.
b.Depression is defined as what happens when a relationship ends.
c.Depression is defined as your score on a 50-item depression inventory.
d.Depression is defined as the number of boxes of tissues that you cry your way through.
3.Identify and describe the four properties of measurement.
4.Describe the similarities and differences between a nominal scale and an ordinal scale.
5.Describe the similarities and differences between an interval scale and a ratio scale.
6.Identify the type of scale of measurement for each of the following.
a.Number correct on a 100-point exam
b.Distance walked (in miles) on a treadmill
c.Religious affiliation
d.Placement in a beauty contest
7.Is number of college classes completed a discrete or continuous variable? Explain your answer. Identify the scale of measurement for this variable.
CRITICAL THINKING CHECK ANSWERS
Critical Thinking Check 2.1
1.Some operational definitions are suggested in the text. These definitions are quantifiable and based on measurable events. They are not conceptual, as a dictionary definition would be.
2.nominal
ordinal
ratio
interval
ordinal
nominal
ratio
| CHAPTER ONE SUMMARY AND REVIEW |
CHAPTER SUMMARY
We began the chapter by stressing the importance of statistics to scientists and researchers. The three goals of science (description, prediction, and explanation) were discussed and related to the research methods used by behavioral scientists. Methods that are descriptive in nature include observation, case study, and survey methods. Those that are predictive in nature include correlational and quasi-experimental methods. The experimental method allows for explanation of cause-and-effect relationships. The practicalities of doing research and proof and disproof in science were discussed, including the idea that testing a hypothesis involves attempting to falsify it. Lastly, we discussed defining variables operationally, identifying the scale of measurement for dependent variables, and the difference between discrete and continuous variables.
CHAPTER 1 REVIEW EXERCISES
(Answers to exercises appear in Appendix B.)
Fill-in Self-Test
Answer the following questions. If you have trouble answering any of the questions, restudy the relevant material before going on to the multiple-choice self-test.
1.A _____ is a prediction regarding the outcome of a study that often
involves a prediction regarding the relationship between two variables in a study.
2.The three goals of science are _____, _____, and
3.A _____ is an in-depth study of one or more individuals.
4.All of the people about whom a study is meant to generalize make up the
5.The _____ method is a method in which the degree of relationship between at least two variables is assessed.
6.A characteristic inherent in the participants that cannot be changed is known as a _____ variable.
7.The variable in a study that is manipulated is the _____ variable.
8.The _____ group is the group of participants that serves as the baseline in a study.
9.A definition of a variable in terms of the activities a researcher uses to measure or manipulate it is an _____.
10._____ is a property of measurement in which the ordering of numbers reflects the ordering of the variable.
11.A(n) _____ scale is a scale in which objects or individuals are broken into categories that have no numerical properties.
12.A(n) _____ scale is a scale in which the units of measurement between the numbers on the scale are all equal in size.
Multiple-Choice Self-Test
Select the single best answer for each of the following questions. If you have trouble answering any of the questions, restudy the relevant material.
1.A prediction regarding the outcome of a study is to _____ and an organized system of assumptions and principles that attempts to explain certain phenomena and how they are related is to _____.
a.theory; hypothesis
b.hypothesis; theory
c.independent variable; dependent variable
d.dependent variable; independent variable
2.Ray was interested in the mating behavior of squirrels, so he went into the field to observe them. Ray is using the _____ method of research.
a.case study method
b.laboratory observational
c.naturalistic observational
d.correlational
3.Negative correlation is to _____ and positive correlation is to
a.increasing or decreasing together; moving in opposite directions
b.moving in opposite directions; increasing or decreasing together
c.independent variable; dependent variable
d.dependent variable; independent variable
4.Which of the following is a participant (subject) variable?
a.Amount of time given to study a list of words
b.Fraternity membership
c.The number of words in a memory test
d.All of the above
5.If a researcher assigns subjects to groups based on, for example, their earned GPA, the researcher would be using
a.a manipulated independent variable.
b.random assignment.
c.a participant variable.
d.a manipulated dependent variable.
6.In an experimental study of the effects of time spent studying on grade, time spent studying would be the
a.control group.
b.independent variable.
c.experimental group.
d.dependent variable.
7.Baseline is to treatment as _____ is to _____.
a.independent variable; dependent variable
b.dependent variable; independent variable
c.experimental group; control group
d.control group; experimental group
8.In a study of the effects of alcohol on driving performance, driving performance would be the
a.control group.
b.independent variable.
c.experimental group.
d.dependent variable.
9.Gender is to the _____ property of measurement and time is to the _____ property of measurement.
a.magnitude; identity
b.equal unit size; magnitude
c.absolute zero; equal unit size
d.identity; absolute zero
10.Arranging a group of individuals from heaviest to lightest represents the _____ property of measurement.
a.identity
b.magnitude
c.equal unit size
d.absolute zero
11.Letter grade on a test is to the _____ scale of measurement and height is to the _____ scale of measurement.
a.ordinal; ratio
b.ordinal; nominal
c.nominal; interval
d.interval; ratio
12.Weight is to the _____ scale of measurement and political affiliation is to the _____ scale of measurement.
a.ratio; ordinal
b.ratio; nominal
c.interval; nominal
d.ordinal; ratio
13.Measuring in whole units is to _____ and measuring in whole units and/or fractional amounts is to _____.
a.discrete variable; continuous variable
b.continuous variable; discrete variable
c.nominal scale; ordinal scale
d.Both b and c
