Staged manipulation

1. Which one of the following is an example of an expectation that can cause bias in an experiment? Explain why you think that is so (See instructions below).

a) Experimenter behaves inconsistently with participants
b) Participant wants to look good in the eyes of the experimenter
c) Experimenter is unaware of the hypothesis
d) Participant reads the hypothesis in the informed consent form
e) All of the above

Instructions: Make selection, provide a concept definition (text), and support your opinion on the selection with an example from research that illustrates the concept. Do so in a maximum of 250 words. Use credible and peer reviewed sources. Credible sources include course materials, University Library research that is peer reviewed, and Internet sites ending in .edu or .gov with with the one exception of research pulled from the www.apa.org site. If research is pulled from the APA site, use the www.apa.org

1. GIVE FEEDBACK ON THE PARAGRAPH LISTED BELOW 150-200 WORDS

1.Experimenter bias is something that can ruin all credibility in the outcomes of an experiment. Because experimenters are usually aware of the study that they have to do, they usually end up having certain expectations out of the study, creating experimenter bias or expectancy effects (Cozby 2015). Cozby uses an example in which he says a researcher is more likely to ask certain questions to participants in particular conditions, which would cause the outcome of the results to be biased (Cozby 2015). Personally, I think that experimenter bias is inevitable. The human mind is conditioned to have certain feelings that are associated with certain situations, so it is harder than people think to remove oneself from the research in order to produce objective results. In a study focusing on experimenter bias, a researcher studies how the selection of the participants have a direct effect on the bias that experiments have during the actual study; usually the bias begins in the selection process, with researchers choosing certain people who they feel will produce the greatest results (Forester 2000), therefore proving experimenter bias is inevitable.

Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research (12th ed.). New York, NY: McGraw-Hill.
Forster, K. I. (2000). The potential for experimenter bias effects in word recognition experiments. Memory & Cognition, 28(7), 1109-1115.
https://www.researchgate.net/profile/Kenneth_Forster/publication/12144818_The_potential_for_experimenter_bias_effects_in_word_recognition_experiments/links/0c960528e5baf73778000000.pdf

Conducting Experiments Chapter 8
LEARNING OBJECTIVES
• Distinguish between straightforward and staged manipulations of an independent variable.
• Describe the three types of dependent variables: self-report, behavioral, and physiological.
• Discuss sensitivity of a dependent variable, contrasting floor effects and ceiling effects.
• Describe ways to control participant expectations and experimenter expectations.
• List the reasons for conducting pilot studies.
• Describe the advantages of including a manipulation check in an experiment.
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THE PREVIOUS CHAPTERS HAVE LAID THE FOUNDATION FOR PLANNING A RESEARCH INVESTIGATION. In this chapter, we will focus on some very practical aspects of conducting research. How do you select the research participants? What should you consider when deciding how to manipulate an independent variable? What should you worry about when you measure a variable? What do you do when the study is completed?
SELECTING RESEARCH PARTICIPANTS
The focus of your study may be children, college students, elderly adults, employees, rats, pigeons, or even cockroaches or flatworms; in all cases, the participants or subjects must somehow be selected. The method used to select participants can have a profound impact on external validity. Remember that external validity is defined as the extent to which results from a study can be generalized to other populations and settings.
Most research projects involve sampling research participants from a population of interest. The population is composed of all of the individuals of interest to the researcher. Samples may be drawn from the population using probability sampling or nonprobability sampling techniques. When it is important to accurately describe the population, you must use probability sampling. This is why probability sampling is so crucial when conducting scientific polls. Much research, on the other hand, is more interested in testing hypotheses about behavior: attempting to detect whether X causes Yrather than describing a population. Here, the two focuses of the study are the relationships between the variables being studied and tests of predictions derived from theories of behavior. In such cases, the participants may be found in the easiest way possible using nonprobability sampling methods, also known as haphazard or “convenience” methods. You may ask students in introductory psychology classes to participate, knock on doors in your dorm to find people to be tested, or choose a class in which to test children simply because you know the teacher. Nothing is wrong with such methods as long as you recognize that they affect the ability to generalize your results to some larger population. In Chapter 14, we examine the issues of generalizing from the rather atypical samples of college students and other conveniently obtained research participants.
You will also need to determine your sample size. How many participants will you need in your study? In general, increasing your sample size increases the likelihood that your results will be statistically significant, because larger samples provide more accurate estimates of population values (see Table 7.2, p. 149). Most researchers take note of the sample sizes in the research area being studied and select a sample size that is typical for studies in the area. A more formal approach to selecting a sample size, called power analysis, is discussed in Chapter 13.
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MANIPULATING THE INDEPENDENT VARIABLE
To manipulate an independent variable, you have to construct an operational definition of the variable (see Chapter 4). That is, you must turn a conceptual variable into a set of operations—specific instructions, events, and stimuli to be presented to the research participants. The manipulation of the independent variable, then, is when a researcher changes the conditions to which participants are exposed. In addition, the independent and dependent variables must be introduced within the context of the total experimental setting. This has been called setting the stage (Aronson, Brewer, & Carlsmith, 1985).
Setting the Stage
In setting the stage, you usually have to supply the participants with the information necessary for them to provide their informed consent to participate (informed consent is covered in Chapter 3). This generally includes information about the underlying rationale of the study. Sometimes, the rationale given is completely truthful, although only rarely will you want to tell participants the actual hypothesis. For example, you might say that you are conducting an experiment on memory when, in fact, you are studying a specific aspect of memory (your independent variable). If participants know what you are studying, they may try to confirm (or even deny) the hypothesis, or they may try to look good by behaving in the most socially acceptable way. If you find that deception is necessary, you have a special obligation to address the deception when you debrief the participants at the conclusion of the experiment.
There are no clear-cut rules for setting the stage, except that the experimental setting must seem plausible to the participants, nor are there any clear-cut rules for translating conceptual variables into specific operations. Exactly how the variable is manipulated depends on the variable and the cost, practicality, and ethics of the procedures being considered.
Types of Manipulations
Straightforward manipulations Researchers are usually able to manipulate an independent variable with relative simplicity by presenting written, verbal, or visual material to the participants. Such straightforward manipulations manipulate variables with instructions and stimulus presentations. Stimuli may be presented verbally, in written form, via videotape, or with a computer. Let's look at a few examples.
Goldstein, Cialdini, and Griskevicius (2008) were interested in the influence of signs that hotels leave in their bathrooms encouraging guests to reuse their towels. In their research, they simply printed signs that were hooked on towel shelves in the rooms of single guests staying at least two nights. In a standard message, the sign read “HELP SAVE THE ENVIRONMENT. You can show your respect of nature and help save the environment by reusing Page 182towels during your stay.” In this case, 35% of the guests reused their towels on the second day. Another condition invoked a social norm that other people are reusing towels: “JOIN YOUR FELLOW GUESTS IN HELPING TO SAVE THE ENVIRONMENT. Almost 75% of guests who are asked to participate in our new resource savings program do help by using their towels more than once. You can join your fellow guests in this program to save the environment by reusing your towels during your stay.” This sign resulted in 44% reusing their towels. As you might expect, the researchers have extended this research to study ways that the sign can be even more effective in increasing conservation.
Most memory research relies on straightforward manipulations. For example, Coltheart and Langdon (1998) displayed lists of words to participants and later measured recall. The word lists differed on phonological similarity: Some lists had words that sounded similar, such as cat, map, and pat, and other lists had dissimilar words such as mop, pen, and cow. They found that lists with dissimilar words are recalled more accurately.
Educational programs are most often straightforward. Pawlenko, Safer, Wise, and Holfeld (2013) examined the effectiveness of three training programs designed to improve jurors’ ability to evaluate eyewitness testimony. Subjects viewed one of three 15-minute slide presentations on a computer screen. The Interview-Identification-Eyewitness training focused on three steps to analyze eyewitness evidence: Ask if the eyewitness interviews were done properly, ask if identification methods were proper, and evaluate if the conditions of the crime scene allowed for an accurate identification. A second presentation termed “Biggers training” was a presentation of five eyewitness factors that the Supreme Court determined should be used (developed in a case called Neil v. Biggers). The Jury Duty presentation was a summary of standard information provided to jurors such as the need to be fair and impartial and the importance of hearing all evidence before reaching a verdict. After viewing the presentations, subjects read a trial transcript that included problems with the eyewitness identification procedures. The subjects in the Interview-Identification-Eyewitness conditions were most likely to use these problems in reaching a verdict.
As a final example of a straightforward manipulation, consider a study by Mazer, Murphy, and Simonds (2009) on the effect of college teacher self-disclosure (via Facebook) on perceptions of teacher effectiveness. For this study, students read one of three Facebook profiles that were created for a volunteer teacher, one for each of the high-, medium-, and low-disclosure conditions. Level of disclosure was manipulated by changing the number and nature of photographs, biographical information, favorite movies/books/quotes, campus groups, and posts on “the wall.” After viewing the profile to which they were assigned, participants rated the teacher on several dimensions. Higher disclosure resulted in perceptions of greater caring and trustworthiness; however, disclosure was not related to perceptions of teacher competence.
You will find that most manipulations of independent variables in all areas of research are straightforward. Researchers vary the difficulty of material to Page 183be learned, motivation levels, the way questions are asked, characteristics of people to be judged, and a variety of other factors in a straightforward manner.
Staged manipulations Other manipulations are less straightforward. Sometimes, it is necessary to stage events during the experiment in order to manipulate the independent variable successfully. When this occurs, the manipulation is called a staged manipulation or event manipulation.
Staged manipulations are most frequently used for two reasons. First, the researcher may be trying to create some psychological state in the participants, such as frustration, anger, or a temporary lowering of self-esteem. For example, Zitek and her colleagues studied what is termed a sense of entitlement (Zitek, Jordan, Monin, & Leach, 2010). Their hypothesis is that the feeling of being unfairly wronged leads to a sense of entitlement and, as a result, the tendency to be more selfish with others. In their study, all participants played a computer game. The researchers programmed the game so that some participants would lose when the game crashed. This is an unfair outcome, because the participants lost for no good reason. Participants in the other condition also lost, but they thought it was because the game itself was very difficult. The participants experiencing the broken game did in fact behave more selfishly after the game; they later allocated themselves more money than deserved when competing with another participant.
Second, a staged manipulation may be necessary to simulate some situation that occurs in the real world. Recall the Milgram obedience experiment that was described in Chapter 3. In that study, an elaborate procedure—ostensibly to study learning—was constructed to actually study obedience to an authority. Or consider a study on computer multitasking conducted by Bowman, Levine, Waite, and Gendron (2010), wherein students read academic material presented on a computer screen. In one condition, the participants received and responded to instant messages while they were reading. Other participants did not receive any messages. Student performance on a test was equal in the two conditions. However, students in the instant message condition took longer to read the material (after the time spent on the message was subtracted from the total time working on the computer).
Staged manipulations frequently employ a confederate (sometimes termed an “accomplice”). Usually, the confederate appears to be another participant in an experiment but is actually part of the manipulation (we discussed the use of confederates in Chapter 3). A confederate may be useful to create a particular social situation. For example, Hermans, Herman, Larsen, and Engels (2010) studied whether food intake by males is affected by the amount of food consumed by a companion. Participants were recruited for a study on evaluation of movie trailers. The participant and a confederate sat in a comfortable setting in which they viewed and evaluated three trailers. They were then told that they needed a break before viewing the next trailers; snacks were available if they were interested. In one condition, the confederate took a large serving of snacks. A small serving was taken in another condition, and the confederate Page 184did not eat in the third condition. The researchers then measured the amount consumed by the actual participants; they did model the amount consumed by the confederate but only when they were hungry.

FIGURE 9.1
Example of the Asch line judgment task
The classic Asch (1956) conformity experiment provides another example of how confederates may be used. Asch gathered people into groups and asked them to respond to a line judgment task such as the one in Figure 9.1. Which of the three test lines matches the standard? Although this appears to be a simple task, Asch made it more interesting by having several confederates announce the same incorrect judgment prior to asking the actual participant; this procedure was repeated over a number of trials with different line judgments. Asch was able to demonstrate how easy it is to produce conformity—participants conformed to the unanimous majority on many of the trials even though the correct answer was clear. Finally, confederates may be used in field experiments as well as laboratory research. As described in Chapter 4, Lee, Schwarz, Taubman, and Hou (2010) studied the impact of public sneezing on the perception of unrelated risks by having an accomplice either sneeze or not sneeze (control condition) while walking by someone in a public area of a university. A researcher then approached those people with a request to complete a questionnaire, which they described as a “class project.” The questionnaire measured participants’ perceptions of average Americans’ risk of contracting a serious disease. The researchers found that, indeed, being around a person who sneezes increases self-reported perception of risk.
As you can see, staged manipulations demand a great deal of ingenuity and even some acting ability. They are used to involve the participants in an ongoing social situation that the individuals perceive not as an experiment but as a real experience. Researchers assume that the result will be natural behavior that truly reflects the feelings and intentions of the participants. However, such procedures allow for a great deal of subtle interpersonal communication that is hard to put into words; this may make it difficult for other researchers to replicate the experiment. Also, a complex manipulation is difficult to interpret. If many things happened during the experiment, what one thing was responsible for the results? In general, it is easier to interpret results when the manipulation is relatively straightforward. However, the nature of the variable you are studying sometimes demands complicated procedures.
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Strength of the Manipulation
The simplest experimental design has two levels of the independent variable. In planning the experiment, the researcher has to choose these levels. A general principle to follow is to make the manipulation as strong as possible. A strong manipulation maximizes the differences between the two groups and increases the chances that the independent variable will have a statistically significant effect on the dependent variable.
To illustrate, suppose you think that there is a positive linear relationship between attitude similarity and liking (“birds of a feather flock together”). In conducting the experiment, you could arrange for participants to encounter another person, a confederate. In one group, the confederate and the participant would share similar attitudes; in the other group, the confederate and the participant would be dissimilar. Similarity, then, is the independent variable, and liking is the dependent variable. Now you have to decide on the amount of similarity. Figure 9.2 shows the hypothesized relationship between attitude similarity and liking at 10 different levels of similarity. Level 1 represents the least amount of similarity with no common attitudes, and level 10 the greatest (all attitudes are similar). To achieve the strongest manipulation, the participants in one group would encounter a confederate of level 1 similarity; those in the other group would encounter a confederate of level 10 similarity. This would result in the greatest difference in the liking means—a 9-point difference. A weaker manipulation—using levels 4 and 7, for example—would result in a smaller mean difference.
A strong manipulation is particularly important in the early stages of research, when the researcher is most interested in demonstrating that a relationship does, in fact, exist. If the early experiments reveal a relationship between the variables, subsequent research can systematically manipulate the other levels of the independent variable to provide a more detailed picture of the relationship.

FIGURE 9.2
Relationship between attitude similarity and liking
Page 186The principle of using the strongest manipulation possible should be tempered by at least two considerations. The first concerns the external validity of a study: The strongest possible manipulation may entail a situation that rarely, if ever, occurs in the real world. For example, an extremely strong crowding manipulation might involve placing so many people in a room that no one could move—a manipulation that might significantly affect a variety of behaviors. However, we would not know if the results were similar to those occurring in more common, less crowded situations, such as many classrooms or offices.
A second consideration is ethics: A manipulation should be as strong as possible within the bounds of ethics. A strong manipulation of fear or anxiety, for example, might not be possible because of the potential physical and psychological harm to participants.
Cost of the Manipulation
Cost is another factor in the decision about how to manipulate the independent variable. Researchers who have limited monetary resources may not be able to afford expensive equipment, salaries for confederates, or payments to participants in long-term experiments. Also, a manipulation in which participants must be run individually requires more of the researcher's time than a manipulation that allows running many individuals in a single setting. In this respect, a manipulation that uses straightforward presentation of written or verbal material is less costly than a complex, staged experimental manipulation. Some government and private agencies offer grants for research; because much research is costly, continued public support of these agencies is very important.
MEASURING THE DEPENDENT VARIABLE
In previous chapters, we have discussed various aspects of measuring variables, including reliability, validity, and reactivity of measures; observational methods; and the development of self-report measures for questionnaires and interviews. In this section, we will focus on measurement considerations that are particularly relevant to experimental research.
Types of Measures
The dependent variable in most experiments is one of three general types: self-report, behavioral, or physiological.
Self-report measures Self-reports can be used to measure attitudes, liking for someone, judgments about someone's personality characteristics, intended behaviors, emotional states, attributions about why someone performed well or poorly on a task, confidence in one's judgments, and many other aspects of human thought and behavior. Rating scales with descriptive anchors Page 187(endpoints) are most commonly used. For example, Funk and Todorov (2013) studied the impact of a facial tattoo on impressions of a man accused of assault. The man, Jack, had punched another man in a bar following a dispute over a spilled drink. A description of the incident included a photo of Jack with or without a facial tattoo. After viewing the photo and reading the description, subjects responded to several questions on a 7-point scale that included the following:
How likely is it that Jack is guilty?

Behavioral measures Behavioral measures are direct observations of behaviors. As with self-reports, measurements of an almost endless number of behaviors are possible. Sometimes, the researcher may record whether a given behavior occurs—for example, whether an individual responds to a request for help, makes an error on a test, or chooses to engage in one activity rather than another. Often, the researcher must decide whether to record the number of times a behavior occurs in a given time period—the rate of a behavior; how quickly a response occurs after a stimulus—a reaction time; or how long a behavior lasts—a measure of duration. The decision about which aspect of behavior to measure depends on which is most theoretically relevant for the study of a particular problem or which measure logically follows from the independent variable manipulation.
As an example, consider a study on eating behavior while viewing a food-related or nature television program (Bodenlos & Wormuth, 2013). Participants had access to chocolate-covered candies, cheese curls, and carrots that were weighed before and after the session. More candy was consumed during the food-related program; there were no differences for the other two foods.
Sometimes the behavioral measure is not an actual behavior but a behavioral intention or choice. Recall the study described in Chapter 3 in which subjects decided how much hot sauce another subject would have to consume later in the study (Vasquez, Pederson, Bushman, Kelley, Demeestere, & Miller, 2013). They did not actually pour the hot sauce but they did commit to an action rather than simply indicate their feelings about the other subject.
Physiological measures Physiological measures are recordings of responses of the body. Many such measurements are available; examples include the galvanic skin response (GSR), electromyogram (EMG), and electroencephalogram (EEG). The GSR is a measure of general emotional arousal and anxiety; it measures the electrical conductance of the skin, which changes when sweating occurs. The EMG measures muscle tension and is frequently used as a measure of tension or stress. The EEG is a measure of electrical activity of brain cells. It can be used to record general brain arousal as a response to different situations, such as activity in certain parts of the brain as learning occurs or brain activity during different stages of sleep.
Page 188The GSR, EMG, and EEG have long been used as physiological indicators of important psychological variables. Many other physiological measures are available, including temperature, heart rate, and analysis of blood or urine (see Cacioppo & Tassinary, 1990). In recent years, magnetic resonance imaging (MRI) has become an increasingly important tool for researchers in behavioral neuroscience. An MRI provides an image of an individual's brain structure. It allows scientists to compare the brain structure of individuals with a particular condition (e.g., a cognitive impairment, schizophrenia, or attention deficit hyperactivity disorder) with the brain structure of people without the condition. In addition, a functional MRI (fMRI) allows researchers to scan areas of the brain while a research participant performs a physical or cognitive task. The data provide evidence for what brain processes are involved in these tasks. For example, a researcher can see which areas of the brain are most active when performing different memory tasks. In one study using fMRI, elderly adults with higher levels of education not only performed better on memory tasks than their less educated peers, but they also used areas of their frontal cortex that were not used by other elderly and younger individuals (Springer, McIntosh, Winocur, & Grady, 2005).
Multiple Measures
Although it is convenient to describe single dependent variables, most studies include more than one dependent measure. One reason to use multiple measures stems from the fact that a variable can be measured in a variety of concrete ways (recall the discussion of operational definitions in Chapter 4). In a study on the effects of an employee wellness program on health, the researchers might measure self-reported fatigue, stress, physical activity, and eating habits along with physical measures of blood pressure, blood sugar, cholesterol, and weight (cf. Clark et al, 2011). If the independent variable has the same effect on several measures of the same dependent variable, our confidence in the results is increased. It is also useful to know whether the same independent variable affects some measures but not others. For example, an independent variable designed to affect liking might have an effect on some measures of liking (e.g., desirability as a person to work with) but not others (e.g., desirability as a dating partner). Researchers may also be interested in studying the effects of an independent variable on several different behaviors. For example, an experiment on the effects of a new classroom management technique might examine academic performance, interaction rates among classmates, and teacher satisfaction.
When you have more than one dependent measure, the question of order arises. Does it matter which measures are made first? Is it possible that the results for a particular measure will be different if the measure comes earlier rather than later? The issue is similar to the order effects that were discussed in Chapter 8 in the context of repeated measures designs. Perhaps responding to the first measures will somehow affect responses on the later measures, Page 189or perhaps the participants attend more closely to first measures than to later measures. There are two possible ways of responding to this issue. If it appears that the problem is serious, the order of presenting the measures can be counterbalanced using the techniques described in Chapter 8. Often there are no indications from previous research that order is a serious problem. In this case, the prudent response is to present the most important measures first and the less important ones later. With this approach, order will not be a problem in interpreting the results on the most important dependent variables. Even though order may be a potential problem for some of the measures, the overall impact on the study is minimized.
Making multiple measurements in a single experiment is valuable when it is feasible to do so. However, it may be necessary to conduct a separate series of experiments to explore the effects of an independent variable on various behaviors.
Sensitivity of the Dependent Variable
The dependent variable should be sensitive enough to detect differences between groups. A measure of liking that asks, “Do you like this person?” with only a simple “yes” or “no” response alternative is less sensitive than one that asks, “How much do you like this person?” on a 5- or 7-point scale. With the first measure, people may tend to be nice and say yes even if they have some negative feelings about the person. The second measure allows for a gradation of liking; such a scale would make it easier to detect differences in amount of liking.
The issue of sensitivity is particularly important when measuring human performance. Memory can be measured using recall, recognition, or reaction time; cognitive task performance might be measured by examining speed or number of errors during a proofreading task; physical performance can be measured through various motor tasks. Such tasks vary in their difficulty. Sometimes a task is so easy that everyone does well regardless of the conditions that are manipulated by the independent variable. This results in what is called a ceiling effect—the independent variable appears to have no effect on the dependent measure only because participants quickly reach the maximum performance level. The opposite problem occurs when a task is so difficult that hardly anyone can perform well; this is called a floor effect.
The need to consider sensitivity of measures is nicely illustrated in the Freedman et al. (1971) study of crowding mentioned in Chapter 4. The study examined the effect of crowding on various measures of cognitive task performance and found that crowding did not impair performance. You could conclude that crowding has no effect on performance; however, it is also possible that the measures were either too easy or too difficult to detect an effect of crowding. In fact, subsequent research showed that the tasks may have been too easy; when subjects perform complex cognitive tasks in laboratory or natural settings, crowding does result in lower performance (Bruins & Barber, 2000; Paulus, Annis, Seta, Schkade, & Matthews, 1976).
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Cost of Measures
Another consideration is cost—some measures may be more costly than others. Paper-and-pencil self-report measures are generally inexpensive; measures that require trained observers or elaborate equipment can become quite costly. A researcher studying nonverbal behavior, for example, might have to use a video camera to record each participant's behaviors in a situation. Two or more observers would then have to view the tapes to code behaviors such as eye contact, smiling, or self-touching (two observers are needed to ensure that the observations are reliable). Thus, there would be expenses for both equipment and personnel. Physiological recording devices are also expensive. Researchers need resources from the university or outside agencies to carry out such research.
ADDITIONAL CONTROLS
The basic experimental design has two groups: in the simplest case, an experimental group that receives the treatment and a control group that does not. Use of a control group makes it possible to eliminate a variety of alternative explanations for the results, thus improving internal validity. Sometimes additional control procedures may be necessary to address other types of alternative explanations. Two general control issues concern expectancies on the part of both the participants in the experiment and the experimenters.
Controlling for Participant Expectations
Demand characteristics We noted previously that experimenters generally do not wish to inform participants about the specific hypotheses being studied or the exact purpose of the research. The reason for this lies in the problem of demand characteristics (Orne, 1962), which is any feature of an experiment that might inform participants of the purpose of the study. The concern is that when participants form expectations about the hypothesis of the study, they will then do whatever is necessary to confirm the hypothesis. For example, if you were studying the relationship between political orientation and homophobia, participants might figure out the hypothesis and behave according to what they think you want, rather than according to their true selves.
One way to control for demand characteristics is to use deception—to make participants think that the experiment is studying one thing when actually it is studying something else. The experimenter may devise elaborate cover stories to explain the purpose of the study and to disguise what is really being studied. The researcher may also attempt to disguise the dependent variable by using an unobtrusive measure or by placing the measure among a set of unrelated filler items on a questionnaire. Another approach is simply to assess whether demand characteristics are a problem by asking participants about their perceptions of the purpose of the research. It may be that participants do Page 191not have an accurate view of the purpose of the study; or if some individuals do guess the hypotheses of the study, their data may be analyzed separately.
Demand characteristics may be eliminated when people are not aware that an experiment is taking place or that their behavior is being observed. Thus, experiments conducted in field settings and observational research in which the observer is concealed or unobtrusive measures are used minimize the problem of demand characteristics.
Placebo groups A special kind of participant expectation arises in research on the effects of drugs. Consider an experiment that is investigating whether a drug such as Prozac reduces depression. One group of people diagnosed as depressive receives the drug and the other group receives nothing. Now suppose that the drug group shows an improvement. We do not know whether the improvement was caused by the properties of the drug or by the participants’ expectations about the effect of the drug—what is called a placebo effect. In other words, just administering a pill or an injection may be sufficient to cause an observed improvement in behavior. To control for this possibility, a placebo group can be added. Participants in the placebo group receive a pill or injection containing an inert, harmless substance; they do not receive the drug given to members of the experimental group. If the improvement results from the active properties of the drug, the participants in the experimental group should show greater improvement than those in the placebo group. If the placebo group improves as much as the experimental group, all improvement could be caused by a placebo effect.
Sometimes, participants’ expectations are the primary focus of an investigation. For example, Marlatt and Rohsenow (1980) conducted research to determine which behavioral effects of alcohol are due to alcohol itself as opposed to the psychological impact of believing one is drinking alcohol. The experimental design to examine these effects had four groups: (1) expect no alcohol–receive no alcohol, (2) expect no alcohol–receive alcohol, (3) expect alcohol–receive no alcohol, and (4) expect alcohol–receive alcohol. This design is called a balanced placebo design. Marlatt and Rohsenow's research suggests that the belief that one has consumed alcohol is a more important determinant of behavior than the alcohol itself. That is, people who believed they had consumed alcohol (Groups 3 and 4) behaved very similarly, although those in Group 3 were not actually given any alcohol.