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What do you think of when you hear the word "laboratory"? Do you picture people in white coats and goggles and gloves standing over a table with beakers and tubes? Well, that picture is pretty close to reality in some cases. In others, laboratory experiments, especially in psychology, focus more on observing behaviours in highly controlled settings to establish causal conclusions. Let's explore lab experiments further. 

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The aim of lab experiments is to identify if observed changes in the      are caused by the        .

Is it difficult to generalise results from lab experiments to real-life settings? 

Demand characteristics lower the           of the research.

True or false: there is more likelihood of demand characteristics influencing lab experiments than field experiments.

A researcher wanted to explore how driving conditions affected speeding. Which type of experimental method is the researcher more likely to use? 

A researcher wanted to explore if sleep deprivation affected cognitive abilities. Which type of experimental method is the researcher more likely to use? 

Are lab experiments easy to replicate? 

True or false: Participants are aware that they are taking part in the lab experiment and sometimes may not know the aim of the investigation.

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• We are going to delve into the topic of lab experiments in the context of psychology.
• We will start by looking at the lab experiment definition and how lab experiments are used in psychology.
• Moving on from this, we will look at how lab experiment examples in psychology and cognitive lab experiments may be conducted.
• And to finish off, we will also explore the strengths and weaknesses of lab experiments.

Lab Experiment Psychology Definition

You can probably guess from the name that lab experiments occur in lab settings. Although this is not always the case, they can sometimes occur in other controlled environments. The purpose of lab experiments is to identify the cause and effect of a phenomenon through experimentation.

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to accurately measure how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

In lab experiments, the IV is what the researcher predicts as the cause of a phenomenon, and the dependent variable is what the researcher predicts as the effect of a phenomenon.

Lab Experiment: P sychology

Lab experiments in psychology are used when trying to establish causal relationships between variables . For example, a researcher would use a lab experiment if they were investigating how sleep affects memory recall.

The majority of psychologists think of psychology as a form of science. Therefore, they argue that the protocol used in psychological research should resemble those used in the natural sciences. For research to be established as scientific , three essential features should be considered:

• Empiricism - the findings should be observable via the five senses.
• Reliability - if the study was replicated, similar results should be found.
• Validity - the investigation should accurately measure what it intends to.

But do lab experiments fulfil these requirements of natural sciences research? If done correctly, then yes. Lab experiments are empirical as they involve the researcher observing changes occurring in the DV. Reliability is established by using a standardised procedure in lab experiments .

A standardised procedure is a protocol that states how the experiment will be carried out. This allows the researcher to ensure the same protocol is used for each participant, increasing the study's internal reliability.

Standardised procedures are also used to help other researchers replicate the study to identify if they measure similar results.

Dissimilar results reflect low reliability.

Validity is another feature of a lab experiment considered. Lab experiments are conducted in a carefully controlled setting where the researcher has the most control compared to other experiments to prevent extraneous variables from affecting the DV .

Extraneous variables are factors other than the IV that affect the DV; as these are variables that the researcher is not interested in investigating, these reduce the validity of the research.

There are issues of validity in lab experiments, which we'll get into a bit later!

Lab Experiment Examples: Asch's Conformity Study

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question. Participants were given two pieces of paper, one depicting a 'target line' and another three, one of which resembled the 'target line' and the others of different lengths.

The participants were put in groups of eight. Unknown to the participants, the other seven were confederates (participants who were secretly part of the research team) who were instructed to give the wrong answer. If the actual participant changed their answer in response, this would be an example of conformity .

Asch controlled the location where the investigation took place, constructed a contrived scenario and even controlled the confederates who would affect the behaviour of the actual participants to measure the DV.

Some other famous examples of research that are lab experiment examples include research conducted by Milgram (the obedience study) and Loftus and Palmer's eyewitness testimony accuracy study . These researchers likely used this method because of some of their strengths , e.g., their high level of control .

Lab Experiment Examples: Cognitive Lab Experiments

Let's look at what a cognitive lab experiment may entail. Suppose a researcher is interested in investigating how sleep affects memory scores using the MMSE test. In the theoretical study , an equal number of participants were randomly allocated into two groups; sleep-deprived versus well-rested. Both groups completed the memory test after a whole night of sleep or staying awake all night.

In this research scenario , the DV can be identified as memory test scores and the IV as whether participants were sleep-deprived or well-rested.

Some examples of extraneous variables the study controlled include researchers ensuring participants did not fall asleep, the participants took the test at the same time, and participants in the well-rested group slept for the same time.

Lab Experiment Advantages and Disadvantages

It's important to consider the advantages and disadvantages of laboratory experiments . Advantages include the highly controlled setting of lab experiments, the standardised procedures and causal conclusions that can be drawn. Disadvantages include the low ecological validity of lab experiments and demand characteristics participants may present.

Strengths of Lab Experiments: Highly Controlled

Laboratory experiments are conducted in a well-controlled setting. All the variables, including extraneous and confounding variables , are rigidly controlled in the investigation. Therefore, the risk of experimental findings being affected by extraneous or confounding variables is reduced . As a result, the well-controlled design of laboratory experiments implies the research has high internal validity .

Internal validity means the study uses measures and protocols that measure exactly what it intends to, i.e. how only the changes in the IV affect the DV.

Strengths of Lab Experiments: Standardised Procedures

Laboratory experiments have standardised procedures, which means the experiments are replicable , and all participants are tested under the same conditions. T herefore, standardised procedures allow others to replicate the study to identify whether the research is reliable and that the findings are not a one-off result. As a result, the replicability of laboratory experiments allows researchers to verify the study's reliability .

Strengths of Lab Experiments: Causal Conclusions

A well-designed laboratory experiment can draw causal conclusions. Ideally, a laboratory experiment can rigidly control all the variables , including extraneous and confounding variables. Therefore, laboratory experiments provide great confidence to researchers that the IV causes any observed changes in DV.

Weaknesses of Lab Experiments

In the following, we will present the disadvantages of laboratory experiments. This discusses ecological validity and demand characteristics.

Weaknesses of Lab Experiments: Low Ecological Validity

Laboratory experiments have low ecological validity because they are conducted in an artificial study that does not reflect a real-life setting . As a result, findings generated in laboratory experiments can be difficult to generalise to real life due to the low mundane realism. Mundane realism reflects the extent to which lab experiment materials are similar to real-life events.

Weaknesses of Lab Experiments: Demand Characteristics

A disadvantage of laboratory experiments is that the research setting may lead to demand characteristics .

Demand characteristics are the cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

The participants are aware they are involved in an experiment. So, participants may have some ideas of what is expected of them in the investigation, which may influence their behaviours. As a result, the demand characteristics presented in laboratory experiments can arguably change the research outcome , reducing the findings' validity .

Lab Experiment - Key takeaways

The lab experiment definition is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affect the dependent variable (DV; variable measured).

Psychologists aim to ensure that lab experiments are scientific and must be empirical, reliable and valid.

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question.

The advantages of lab experiments are high internal validity, standardised procedures and the ability to draw causal conclusions.

The disadvantages of lab experiments are low ecological validity and demand characteristics.

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Frequently Asked Questions about Lab Experiment

What is a lab experiment?

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

What is the purpose of lab experiments?

Lab experiments investigate cause-and-effect. They aim to determine the effect of changes in the independent variable on the dependent variable. 

What is a lab experiment and field experiment?

A field experiment is an experiment conducted in a natural, everyday setting. The experimenter still controls the IV; however, extraneous and confounding variables may be difficult to control due to the natural setting.

Similar, to filed experiments researchers, can control the IV and extraneous variables. However, this takes place in an artificial setting such as a lab. 

Why would a psychologist use a laboratory experiment? 

A psychologist may use a lab experiment when trying to establish the causal relationships between variables to explain a phenomenon. 

Why is lab experience important?

Lab experience allows researchers to scientifically determine whether a hypothesis/ theory should be accepted or rejected. 

What is a lab experiment example? 

The research conducted by Loftus and Palmer (accuracy of eyewitness testimony) and Milgram (obedience) used a lab experiment design. These experimental designs give the researcher high control, allowing them to control extraneous and independent variables.

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17 Advantages and Disadvantages of Experimental Research Method in Psychology

There are numerous research methods used to determine if theories, ideas, or even products have validity in a market or community. One of the most common options utilized today is experimental research. Its popularity is due to the fact that it becomes possible to take complete control over a single variable while conducting the research efforts. This process makes it possible to manipulate the other variables involved to determine the validity of an idea or the value of what is being proposed.

Outcomes through experimental research come through a process of administration and monitoring. This structure makes it possible for researchers to determine the genuine impact of what is under observation. It is a process which creates outcomes with a high degree of accuracy in almost any field.

The conclusion can then offer a final value potential to consider, making it possible to know if a continued pursuit of the information is profitable in some way.

The pros and cons of experimental research show that this process is highly efficient, creating data points for evaluation with speed and regularity. It is also an option that can be manipulated easily when researchers want their work to draw specific conclusions.

List of the Pros of Experimental Research

1. Experimental research offers the highest levels of control. The procedures involved with experimental research make it possible to isolate specific variables within virtually any topic. This advantage makes it possible to determine if outcomes are viable. Variables are controllable on their own or in combination with others to determine what can happen when each scenario is brought to a conclusion. It is a benefit which applies to ideas, theories, and products, offering a significant advantage when accurate results or metrics are necessary for progress.

2. Experimental research is useful in every industry and subject. Since experimental research offers higher levels of control than other methods which are available, it offers results which provide higher levels of relevance and specificity. The outcomes that are possible come with superior consistency as well. It is useful in a variety of situations which can help everyone involved to see the value of their work before they must implement a series of events.

3. Experimental research replicates natural settings with significant speed benefits. This form of research makes it possible to replicate specific environmental settings within the controls of a laboratory setting. This structure makes it possible for the experiments to replicate variables that would require a significant time investment otherwise. It is a process which gives the researchers involved an opportunity to seize significant control over the extraneous variables which may occur, creating limits on the unpredictability of elements that are unknown or unexpected when driving toward results.

4. Experimental research offers results which can occur repetitively. The reason that experimental research is such an effective tool is that it produces a specific set of results from documented steps that anyone can follow. Researchers can duplicate the variables used during the work, then control the variables in the same way to create an exact outcome that duplicates the first one. This process makes it possible to validate scientific discoveries, understand the effectiveness of a program, or provide evidence that products address consumer pain points in beneficial ways.

5. Experimental research offers conclusions which are specific. Thanks to the high levels of control which are available through experimental research, the results which occur through this process are usually relevant and specific. Researchers an determine failure, success, or some other specific outcome because of the data points which become available from their work. That is why it is easier to take an idea of any type to the next level with the information that becomes available through this process. There is always a need to bring an outcome to its natural conclusion during variable manipulation to collect the desired data.

6. Experimental research works with other methods too. You can use experimental research with other methods to ensure that the data received from this process is as accurate as possible. The results that researchers obtain must be able to stand on their own for verification to have findings which are valid. This combination of factors makes it possible to become ultra-specific with the information being received through these studies while offering new ideas to other research formats simultaneously.

7. Experimental research allows for the determination of cause-and-effect. Because researchers can manipulate variables when performing experimental research, it becomes possible to look for the different cause-and-effect relationships which may exist when pursuing a new thought. This process allows the parties involved to dig deeply into the possibilities which are present, demonstrating whatever specific benefits are possible when outcomes are reached. It is a structure which seeks to understand the specific details of each situation as a way to create results.

List of the Cons of Experimental Research

1. Experimental research suffers from the potential of human errors. Experimental research requires those involved to maintain specific levels of variable control to create meaningful results. This process comes with a high risk of experiencing an error at some stage of the process when compared to other options that may be available. When this issue goes unnoticed as the results become transferable, the data it creates will reflect a misunderstanding of the issue under observation. It is a disadvantage which could eliminate the value of any information that develops from this process.

2. Experimental research is a time-consuming process to endure. Experimental research must isolate each possible variable when a subject matter is being studied. Then it must conduct testing on each element under consideration until a resolution becomes possible, which then requires data collection to occur. This process must continue to repeat itself for any findings to be valid from the effort. Then combinations of variables must go through evaluation in the same manner. It is a field of research that sometimes costs more than the potential benefits or profits that are achievable when a favorable outcome is eventually reached.

3. Experimental research creates unrealistic situations that still receive validity. The controls which are necessary when performing experimental research increase the risks of the data becoming inaccurate or corrupted over time. It will still seem authentic to the researchers involved because they may not see that a variable is an unrealistic situation. The variables can skew in a specific direction if the information shifts in a certain direction through the efforts of the researchers involved. The research environment can also be extremely different than real-life circumstances, which can invalidate the value of the findings.

4. Experimental research struggles to measure human responses. People experience stress in uncountable ways during the average day. Personal drama, political arguments, and workplace deadlines can influence the data that researchers collect when measuring human response tendencies. What happens inside of a controlled situation is not always what happens in real-life scenarios. That is why this method is not the correct choice to use in group or individual settings where a human response requires measurement.

5. Experimental research does not always create an objective view. Objective research is necessary for it to provide effective results. When researchers have permission to manipulate variables in whatever way they choose, then the process increases the risk of a personal bias, unconscious or otherwise, influencing the results which are eventually obtained. People can shift their focus because they become uncomfortable, are aroused by the event, or want to manipulate the results for their personal agenda. Data samples are therefore only a reflection of that one group instead of offering data across an entire demographic.

6. Experimental research can experience influences from real-time events. The issue with human error in experimental research often involves the researchers conducting the work, but it can also impact the people being studied as well. Numerous outside variables can impact responses or outcomes without the knowledge of researchers. External triggers, such as the environment, political stress, or physical attraction can alter a person’s regular perspective without it being apparent. Internal triggers, such as claustrophobia or social interactions, can alter responses as well. It is challenging to know if the data collected through this process offers an element of honesty.

7. Experimental research cannot always control all of the variables. Although experimental research attempts to control every variable or combination that is possible, laboratory settings cannot reach this limitation in every circumstance. If data must be collected in a natural setting, then the risk of inaccurate information rises. Some research efforts place an emphasis on one set of variables over another because of a perceived level of importance. That is why it becomes virtually impossible in some situations to apply obtained results to the overall population. Groups are not always comparable, even if this process provides for more significant transferability than other methods of research.

8. Experimental research does not always seek to find explanations. The goal of experimental research is to answer questions that people may have when evaluating specific data points. There is no concern given to the reason why specific outcomes are achievable through this system. When you are working in a world of black-and-white where something works or it does not, there are many shades of gray in-between these two colors where additional information is waiting to be discovered. This method ignores that information, settling for whatever answers are found along the extremes instead.

9. Experimental research does not make exceptions for ethical or moral violations. One of the most significant disadvantages of experimental research is that it does not take the ethical or moral violations that some variables may create out of the situation. Some variables cannot be manipulated in ways that are safe for people, the environment, or even the society as a whole. When researchers encounter this situation, they must either transfer their data points to another method, continue on to produce incomplete results, fabricate results, or set their personal convictions aside to work on the variable anyway.

10. Experimental research may offer results which apply to only one situation. Although one of the advantages of experimental research is that it allows for duplication by others to obtain the same results, this is not always the case in every situation. There are results that this method can find which may only apply to that specific situation. If this process is used to determine highly detailed data points which require unique circumstances to obtain, then future researchers may find that result replication is challenging to obtain.

These experimental research pros and cons offer a useful system that can help determine the validity of an idea in any industry. The only way to achieve this advantage is to place tight controls over the process, and then reduce any potential for bias within the system to appear. This makes it possible to determine if a new idea of any type offers current or future value.

Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

When conducting research, it is important to create an aim and a hypothesis,  click here  to learn more about the formation of aims and hypotheses.

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Experimental Research

23 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.

What Is an Experiment?

As we saw earlier in the book, an  experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in one variable (referred to as an independent variable ) cause a change in another variable (referred to as a dependent variable ). Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher exerts control over, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions are often referred to as a single factor two-level design .  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design .  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s experiment used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres [1] . Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger lesbian women would apply to older gay men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Treatment and Control Conditions

In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This intervention includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bed sheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008) [2] .

Placebo effects are interesting in their own right (see Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 5.2 shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 5.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This difference is what is shown by a comparison of the two outer bars in Figure 5.4 .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a wait-list control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [3] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [4] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. Note that the IRB would have carefully considered the use of deception in this case and judged that the benefits of using it outweighed the risks and that there was no other way to answer the research question (about the effectiveness of a placebo procedure) without it. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

• Knecht, S., Dräger, B., Deppe, M., Bobe, L., Lohmann, H., Flöel, A., . . . Henningsen, H. (2000). Handedness and hemispheric language dominance in healthy humans. Brain: A Journal of Neurology, 123 (12), 2512-2518. http://dx.doi.org/10.1093/brain/123.12.2512 ↵
• Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88. ↵

A type of study designed specifically to answer the question of whether there is a causal relationship between two variables.

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

The different levels of the independent variable to which participants are assigned.

Holding extraneous variables constant in order to separate the effect of the independent variable from the effect of the extraneous variables.

Any variable other than the dependent and independent variable.

Changing the level, or condition, of the independent variable systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times.

An experiment design involving a single independent variable with two conditions.

When an experiment has one independent variable that is manipulated to produce more than two conditions.

An extraneous variable that varies systematically with the independent variable, and thus confuses the effect of the independent variable with the effect of the extraneous one.

Any intervention meant to change people’s behavior for the better.

The condition in which participants receive the treatment.

The condition in which participants do not receive the treatment.

An experiment that researches the effectiveness of psychotherapies and medical treatments.

The condition in which participants receive no treatment whatsoever.

A simulated treatment that lacks any active ingredient or element that is hypothesized to make the treatment effective, but is otherwise identical to the treatment.

An effect that is due to the placebo rather than the treatment.

Condition in which the participants receive a placebo rather than the treatment.

Condition in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it.

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How to Conduct a Psychology Experiment

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Laboratory Experimentation

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• Torsten Schubert 3  

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In laboratory experimentation the causal influence of at least one actively manipulated independent variable on at least on dependent variable is tested in a controlled envorinment.

Introduction

The main objective of the experimental approach is to causally relate changes in one or more independent variables to changes in one or more dependent variables. The condition assignment is usually randomized, and researchers aim to eliminate or control the potential effect(s) of extraneous variables on the data of interest. By analyzing the manifestation of individual differences in the data variability with elaborated methods, the advantages of an experimental approach can be combined with research methods designed to understand the individual realization of the investigated phenomena and their emergence in the corresponding experimental condition.

Experimental Method

Scientific research aims to gather information objectively and systematically such that valid conclusions can be...

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Bittrich, K., & Blankenberger, S. (2011). Experimentelle Psychologie: Experimente planen realisieren, präsentieren . Weinheim: Beltz.

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7 Advantages and Disadvantages of Experimental Research

There are multiple ways to test and do research on new ideas, products, or theories. One of these ways is by experimental research. This is when the researcher has complete control over one set of the variable, and manipulates the others. A good example of this is pharmaceutical research. They will administer the new drug to one group of subjects, and not to the other, while monitoring them both. This way, they can tell the true effects of the drug by comparing them to people who are not taking it. With this type of research design, only one variable can be tested, which may make it more time consuming and open to error. However, if done properly, it is known as one of the most efficient and accurate ways to reach a conclusion. There are other things that go into the decision of whether or not to use experimental research, some bad and some good, let’s take a look at both of these.

The Advantages of Experimental Research

1. A High Level Of Control With experimental research groups, the people conducting the research have a very high level of control over their variables. By isolating and determining what they are looking for, they have a great advantage in finding accurate results.

2. Can Span Across Nearly All Fields Of Research Another great benefit of this type of research design is that it can be used in many different types of situations. Just like pharmaceutical companies can utilize it, so can teachers who want to test a new method of teaching. It is a basic, but efficient type of research.

3. Clear Cut Conclusions Since there is such a high level of control, and only one specific variable is being tested at a time, the results are much more relevant than some other forms of research. You can clearly see the success, failure, of effects when analyzing the data collected.

4. Many Variations Can Be Utilized There is a very wide variety of this type of research. Each can provide different benefits, depending on what is being explored. The investigator has the ability to tailor make the experiment for their own unique situation, while still remaining in the validity of the experimental research design.

The Disadvantages of Experimental Research

1. Largely Subject To Human Errors Just like anything, errors can occur. This is especially true when it comes to research and experiments. Any form of error, whether a systematic (error with the experiment) or random error (uncontrolled or unpredictable), or human errors such as revealing who the control group is, they can all completely destroy the validity of the experiment.

2. Can Create Artificial Situations By having such deep control over the variables being tested, it is very possible that the data can be skewed or corrupted to fit whatever outcome the researcher needs. This is especially true if it is being done for a business or market study.

3. Can Take An Extensive Amount of Time To Do Full Research With experimental testing individual experiments have to be done in order to fully research each variable. This can cause the testing to take a very long amount of time and use a large amount of resources and finances. These costs could transfer onto the company, which could inflate costs for consumers.

Important Facts About Experimental Research

• Experimental Research is most used in medical ways, with animals.
• Every single new medicine or drug is testing using this research design.
• There are countless variations of experimental research, including: probability, sequential, snowball, and quota.

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9 Chapter 9: Simple Experiments

Simple experiments.

What Is an Experiment?

As we saw earlier, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. Do changes in an independent variable cause changes in a dependent variable? Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

9.1  Experiment Basics

Internal Validity

Recall that the fact that two variables are statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise and be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The basic logic is this: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial or unlike “real life” (Stanovich, 2010). In many psychology experiments, the participants are all college undergraduates and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had college students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998). At first, this might seem silly. When will college students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to. Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of college students in a laboratory at a selective college who merely judged the appeal of various colors presented on a computer screen. If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this is relevant to grocery shoppers’ cereal-buying decisions.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005). These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions, and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to do an experiment on the effect of early illness experiences on the development of hypochondriasis. This does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using non-experimental approaches. We will discuss this in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

An extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situation or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

One way to control extraneous variables is to hold them constant. This can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, straight, female, right-handed, sophomore psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger straight women would apply to older gay men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Consider the results of a hypothetical study in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. If IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Key Takeaways

·         An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.

·         Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.

·         Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.

9.2  Experimental Design

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment, each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment, which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization. In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition, in which they receive the treatment, or a control condition, in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial.

There are different types of control conditions. In a no-treatment control condition, participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right, but they also pose a serious problem for researchers who want to determine whether a treatment works. Fortunately, there are several solutions to this problem. One is to include a placebo control condition, in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations.

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition, in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

Within-Subjects Experiments

In a within-subjects experiment, each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect, where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect, where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect. For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing, which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

·         Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.

·         Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.

·         Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

9.3  Conducting Experiments

The information presented so far in this chapter is enough to design a basic experiment. When it comes time to conduct that experiment, however, several additional practical issues arise. In this section, we consider some of these issues and how to deal with them. Much of this information applies to non-experimental studies as well as experimental ones.

Recruiting Participants

Of course, you should be thinking about how you will obtain your participants from the beginning of any research project. Unless you have access to people with schizophrenia or incarcerated juvenile offenders, for example, then there is no point designing a study that focuses on these populations. But even if you plan to use a convenience sample, you will have to recruit participants for your study.

There are several approaches to recruiting participants. One is to use participants from a formal subject pool—an established group of people who have agreed to be contacted about participating in research studies. For example, at many colleges and universities, there is a subject pool consisting of students enrolled in introductory psychology courses who must participate in a certain number of studies to meet a course requirement. Researchers post descriptions of their studies and students sign up to participate, usually via an online system. Participants who are not in subject pools can also be recruited by posting or publishing advertisements or making personal appeals to groups that represent the population of interest. For example, a researcher interested in studying older adults could arrange to speak at a meeting of the residents at a retirement community to explain the study and ask for volunteers.

The Volunteer Subject

Even if the participants in a study receive compensation in the form of course credit, a small amount of money, or a chance at being treated for a psychological problem, they are still essentially volunteers. This is worth considering because people who volunteer to participate in psychological research have been shown to differ in predictable ways from those who do not volunteer. Specifically, there is good evidence that on average, volunteers have the following characteristics compared with non-volunteers (Rosenthal Rosnow, 1976):

·         They are more interested in the topic of the research.

·         They are more educated.

·         They have a greater need for approval.

·         They have higher intelligence quotients (IQs).

·         They are more sociable.

·         They are higher in social class.

This can be an issue of external validity if there is reason to believe that participants with these characteristics are likely to behave differently than the general population. For example, in testing different methods of persuading people, a rational argument might work better on volunteers than it does on the general population because of their generally higher educational level and IQ.

In many field experiments, the task is not recruiting participants but selecting them. For example, researchers Nicolas Guéguen and Marie-Agnès de Gail conducted a field experiment on the effect of being smiled at on helping, in which the participants were shoppers at a supermarket. A confederate walking down a stairway gazed directly at a shopper walking up the stairway and either smiled or did not smile. Shortly afterward, the shopper encountered another confederate, who dropped some computer diskettes on the ground. The dependent variable was whether or not the shopper stopped to help pick up the diskettes (Guéguen & de Gail, 2003). Notice that these participants were not “recruited,” but the researchers still had to select them from among all the shoppers taking the stairs that day. It is extremely important that this kind of selection be done according to a well-defined set of rules that is established before the data collection begins and can be explained clearly afterward. In this case, with each trip down the stairs, the confederate was instructed to gaze at the first person he encountered who appeared to be between the ages of 20 and 50. Only if the person gazed back did he or she become a participant in the study. The point of having a well-defined selection rule is to avoid bias in the selection of participants. For example, if the confederate was free to choose which shoppers he would gaze at, he might choose friendly-looking shoppers when he was set to smile and unfriendly-looking ones when he was not set to smile. As we will see shortly, such biases can be entirely unintentional.

Standardizing the Procedure

It is surprisingly easy to introduce extraneous variables during the procedure. For example, the same experimenter might give clear instructions to one participant but vague instructions to another. Or one experimenter might greet participants warmly while another barely makes eye contact with them. To the extent that such variables affect participants’ behaviour, they add noise to the data and make the effect of the independent variable more difficult to detect. If they vary across conditions, they become confounding variables and provide alternative explanations for the results. For example, if participants in a treatment group are tested by a warm and friendly experimenter and participants in a control group are tested by a cold and unfriendly one, then what appears to be an effect of the treatment might actually be an effect of experimenter demeanour.

Experimenter Expectancy Effects

It is well known that whether research participants are male or female can affect the results of a study. But what about whether the experimenter is male or female? There is plenty of evidence that this matters too. Male and female experimenters have slightly different ways of interacting with their participants, and of course participants also respond differently to male and female experimenters (Rosenthal, 1976). For example, in a recent study on pain perception, participants immersed their hands in icy water for as long as they could (Ibolya, Brake, & Voss, 2004). Male participants tolerated the pain longer when the experimenter was a woman, and female participants tolerated it longer when the experimenter was a man.

Researcher Robert Rosenthal has spent much of his career showing that this kind of unintended variation in the procedure does, in fact, affect participants’ behaviour. Furthermore, one important source of such variation is the experimenter’s expectations about how participants “should” behave in the experiment. This is referred to as an experimenter expectancy effect (Rosenthal, 1976). For example, if an experimenter expects participants in a treatment group to perform better on a task than participants in a control group, then he or she might unintentionally give the treatment group participants clearer instructions or more encouragement or allow them more time to complete the task. In a striking example, Rosenthal and Kermit Fode had several students in a laboratory course in psychology train rats to run through a maze. Although the rats were genetically similar, some of the students were told that they were working with “maze-bright” rats that had been bred to be good learners, and other students were told that they were working with “maze-dull” rats that had been bred to be poor learners. Sure enough, over five days of training, the “maze-bright” rats made more correct responses, made the correct response more quickly, and improved more steadily than the “maze-dull” rats (Rosenthal & Fode, 1963). Clearly it had to have been the students’ expectations about how the rats would perform that made the difference. But how? Some clues come from data gathered at the end of the study, which showed that students who expected their rats to learn quickly felt more positively about their animals and reported behaving toward them in a more friendly manner (e.g., handling them more).

The way to minimize unintended variation in the procedure is to standardize it as much as possible so that it is carried out in the same way for all participants regardless of the condition they are in. Here are several ways to do this:

·         Create a written protocol that specifies everything that the experimenters are to do and say from the time they greet participants to the time they dismiss them.

·         Create standard instructions that participants read themselves or that are read to them word for word by the experimenter.

·         Automate the rest of the procedure as much as possible by using software packages for this purpose or even simple computer slide shows.

·         Anticipate participants’ questions and either raise and answer them in the instructions or develop standard answers for them.

·         Train multiple experimenters on the protocol together and have them practice on each other.

·         Be sure that each experimenter tests participants in all conditions.

Another good practice is to arrange for the experimenters to be “blind” to the research question or to the condition that each participant is tested in. The idea is to minimize experimenter expectancy effects by minimizing the experimenters’ expectations. For example, in a drug study in which each participant receives the drug or a placebo, it is often the case that neither the participants nor the experimenter who interacts with the participants know which condition he or she has been assigned to. Because both the participants and the experimenters are blind to the condition, this is referred to as a double-blind study. (A single-blind study is one in which the participant, but not the experimenter, is blind to the condition.) Of course, there are many times this is not possible. For example, if you are both the investigator and the only experimenter, it is not possible for you to remain blind to the research question. Also, in many studies the experimenter must know the condition because he or she must carry out the procedure in a different way in the different conditions.

Record Keeping

It is essential to keep good records when you conduct an experiment. As discussed earlier, it is typical for experimenters to generate a written sequence of conditions before the study begins and then to test each new participant in the next condition in the sequence. As you test them, it is a good idea to add to this list basic demographic information; the date, time, and place of testing; and the name of the experimenter who did the testing. It is also a good idea to have a place for the experimenter to write down comments about unusual occurrences (e.g., a confused or uncooperative participant) or questions that come up. This kind of information can be useful later if you decide to analyze sex differences or effects of different experimenters, or if a question arises about a particular participant or testing session.

It can also be useful to assign an identification number to each participant as you test them. Simply numbering them consecutively beginning with 1 is usually sufficient. This number can then also be written on any response sheets or questionnaires that participants generate, making it easier to keep them together.

Pilot Testing

It is always a good idea to conduct a pilot test of your experiment. A pilot test is a small-scale study conducted to make sure that a new procedure works as planned. In a pilot test, you can recruit participants formally (e.g., from an established participant pool) or you can recruit them informally from among family, friends, classmates, and so on. The number of participants can be small, but it should be enough to give you confidence that your procedure works as planned. There are several important questions that you can answer by conducting a pilot test:

·         Do participants understand the instructions?

·         What kind of misunderstandings do participants have, what kind of mistakes do they make, and what kind of questions do they ask?

·         Do participants become bored or frustrated?

·         Is an indirect manipulation effective? (You will need to include a manipulation check.)

·         Can participants guess the research question or hypothesis?

·         How long does the procedure take?

·         Are computer programs or other automated procedures working properly?

·         Are data being recorded correctly?

Of course, to answer some of these questions you will need to observe participants carefully during the procedure and talk with them about it afterward. Participants are often hesitant to criticize a study in front of the researcher, so be sure they understand that this is a pilot test and you are genuinely interested in feedback that will help you improve the procedure. If the procedure works as planned, then you can proceed with the actual study. If there are problems to be solved, you can solve them, pilot test the new procedure, and continue with this process until you are ready to proceed.

·         There are several effective methods you can use to recruit research participants for your experiment, including through formal subject pools, advertisements, and personal appeals. Field experiments require well-defined participant selection procedures.

·         It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects.

·         It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.

References from Chapter 9

Birnbaum, M. H. (1999). How to show that 9 221: Collect judgments in a between-subjects design. Psychological Methods, 4, 243–249.

Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer. Retrieved from  http://www.psychologicalscience.org/observer/getArticle.cfm?id=1762 .

Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75, 269–284.

Guéguen, N., & de Gail, Marie-Agnès. (2003). The effect of smiling on helping behavior: Smiling and good Samaritan behavior. Communication Reports, 16, 133–140.

Ibolya, K., Brake, A., & Voss, U. (2004). The effect of experimenter characteristics on pain reports in women and men. Pain, 112, 142–147.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … & Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347, 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59, 565–590.

Rosenthal, R. (1976). Experimenter effects in behavioral research (enlarged ed.). New York, NY: Wiley.

Rosenthal, R., & Fode, K. (1963). The effect of experimenter bias on performance of the albino rat. Behavioral Science, 8, 183-189.

Rosenthal, R., & Rosnow, R. L. (1976). The volunteer subject. New York, NY: Wiley.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician. Baltimore, MD: Johns Hopkins University Press.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn Bacon.

Research Methods in Psychology & Neuroscience Copyright © by Dalhousie University Introduction to Psychology and Neuroscience Team. All Rights Reserved.

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Chapter 6: Experimental Research

6.1 experiment basics, learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. Do changes in an independent variable cause changes in a dependent variable? Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

Internal and External Validity

Internal validity.

Recall that the fact that two variables are statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise and be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The basic logic is this: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial or unlike “real life” (Stanovich, 2010). In many psychology experiments, the participants are all college undergraduates and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had college students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998). At first, this might seem silly. When will college students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to. Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of college students in a laboratory at a selective college who merely judged the appeal of various colors presented on a computer screen. If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this is relevant to grocery shoppers’ cereal-buying decisions.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005). These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to do an experiment on the effect of early illness experiences on the development of hypochondriasis. This does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

An extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situation or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” , which makes the effect of the independent variable is easier to detect (although real data never look quite that good).

Table 6.1 Hypothetical Noiseless Data and Realistic Noisy Data

One way to control extraneous variables is to hold them constant. This can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, straight, female, right-handed, sophomore psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger straight women would apply to older gay men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6.1 “Hypothetical Results From a Study on the Effect of Mood on Memory” shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory

Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.

Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.

• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/observer/getArticle.cfm?id=1762 .

Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn & Bacon.

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Lab Experiments – The Benefits and the Drawbacks

14 Friday Oct 2011

Posted by jrwpsychology15 in Uncategorized

≈ 9 Comments

Lab experiments are a fundamental research method conducted in highly controlled conditions by psychologists. It involves the manipulation of variables to discover cause and effect (meaning lab experiments differ from non-experimental methods).

One major benefit of lab experiments is that psychologists can make statements about cause and effect because they involve the deliberate manipulation of one variable, while trying to keep the other variables constant, unlike non-experimental methods. For example, in the Bobo doll experiment where children witnessed an adult being aggressive towards an inflated Bobo doll, Bandura controlled the type of aggression that the children saw the model display. Bandura, by doing this, made sure that all the children participants saw exactly the same routine of aggressive behaviour. This therefore means that any changes in the children’s aggressive behaviour could be more easily attributed to the aggression that they saw thus enabling cause and effect to be established.

Lab experiments can also be replicated due to the standardised procedures and measures, ultimately making it easier to check reliability of the research/results and prove/disprove results if the research is conducted by another researcher.

Although lab experiments have many benefits there are also several drawbacks. A key drawback of conducting a lab experiment is that it is not typical to real life and lacks mundane realism. They are generally set in contrived environments, often strange to the participants taking part. Participants are often asked to perform unusual tasks not typical to day to day life. Therefore, the results gathered from a lab experiment can be difficult to generalise and ultimately the findings will lack ecological validity.

 A further drawback is the due to the unnatural environments people may become aware of the aims of the study and therefore act in the way that they feel the researcher may want, showing demand characteristics. For example, Samuel and Bryant’s study (which aimed to see if younger children would cope with Piaget’s conservation task) was conducted in a lab; the children taking part were tested by an experimenter who was ultimately a stranger to them. Meaning the children may have been nervous, felt under pressure and unlikely to show their natural behaviour, instead they showed demand characteristics by giving instant answers to the questions they were asked and wouldn’t have thought the answer through. This is different to what the children would normally go through as in class they may be given more time to think through their answers. This problem further decreases the ecological validity of the research.

I personally believe that lab experiments are useful and an effective way to test and gather data. Even though they can often lack in ecological validity and mundane realism they can easily be replicated and results can be retested, helping to prove/disprove data, ultimately leading to the creation of practical applications. In lab experiments the participants are also aware that the study is taking place and therefore raises less ethical issues than other experimental and non experimental research methods would.

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9 thoughts on “lab experiments – the benefits and the drawbacks”.

October 17, 2011 at 7:57 pm

If I couldn’t sit on the fence, I would argue that the advantages outweight the limitations, and lab experiments the best type of method overall. I like that because the study takes place in a laboratory, it can be easily replicated in any laboratory across the world. This ensures that the experiment can take place in the same way in different countries, and therefore remains culturally unbiased. This is a huge advantage as other than questionnaires (which have many limitations) no other methods can achieve accurately. Overall this is a really well written post which is clear and easy to understand. I feel that it would be appropriate for other audiences as well as us psychologists.

October 18, 2011 at 10:04 am

Good post, and an interesting take on how lab experiments are more ethical, makes allot of sense. I agree with everything that can make lab experiments less valid, but it doesn’t necessarily have to be that way. With environmental controls in place, it is possible to create the environment in which you are trying to study. This is often the case with social experiments such as The Stanford prison experiment and Haslam & Riecher’s study. Both of these lab experiements artificially created a prison environment which worked rather well. With the right funding, it’s possible.

Lab experiements to not necessarily have to be conducted in a labratory setting.

October 18, 2011 at 8:13 pm

I agree with JC there are ways and means, but its expensive! Sometimes I guess as psychologists there isnt really a choice. For example when Pilavin studied the reactions of bystanders when someone was in need of help. That kind of study needs high ecological validity only found outside the lab in real world situations. I think we should always strive to improve conditions in experiments outside the lab because the results are more representative of the general public. I feel to make real improvements in our pschological knowledge real world situations shouldnt be avoided if possible.

October 18, 2011 at 11:27 pm

I would say the main drawback would be the lack of ecological validity because it can be difficult to do studies on real-life aspects within a lab, along with the fact that control over variables is high within the lab. Although, I agree with the comment above in that it can be possible to create some settings like the Stanford Prison experiment. But, other issues you could have mentioned would be that, problems with lab experiments may include that demand charecteristics were created, this would in turn effect the validity of the the results. Also, in lab experiments, participants are more likely to be decieved for the study, which means ethical issues may be raised. But overall, a good blog 🙂

October 19, 2011 at 9:45 am

I think that lab experiments are often easier to do. This means that we tend to do more of them. I think that they are good in many situations but often they do not hold the ecological validity needed. When using children as opposed to adults in lab experiments I think they are more valid. This is beacause children possibly don’t think so much into the situation and therefore you do not get so high a risk of demand characteristics. Children are also less likely to be scared or anxious as they may think it is some kind of game. Therefore I think that lab experiments are useful but more valid when using children. I thought your blog was really well written.

October 19, 2011 at 1:40 pm

I feel lab experiments are often one of the more useful ways of conducting research within psychology as the variables can often be controlled easily meaning less interference and therefore we are more able to determine cause – a troublesome area in psychology. However I do believe the benefits of lab experiments outweigh the drawbacks of the research, making it more useful to the field of psychology. As others of have said, there are ways to overcome these problem like validity, but really are they practical?

October 19, 2011 at 4:36 pm

You have portrayed both the negatives and positives of using a lab experiments, and they can be quite useful. However, unless the Psychologist is specifically looking to see how the participants behave in a lab experiment environment then using a lab experiment would be completely reliable. This is not the case for most researchers as they want to be able to apply their results to the real world. No matter how reliable the results will be, they will not be valid when applied to a real life situation as there are too many variables to try to predict and control, therefore using a lab experiment can be a massive drawback for certain researchers.

October 19, 2011 at 9:50 pm

In my opinion, lab experiments are the best type of methodology to use. They are easy to do, you’ll most likely get the same results each time and the variables can be easily controlled. Now I know that the argument is that they lack ecological validity, but isn’t that worth sacrificing sometimes when you’re looking for specific results? However, thinking about it now, many lab experiments should only be used in certain circumstances due to real life applications. This blog definitely made me think about my overall views of lab experiments…nice one! 🙂

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What do you think of when you hear the word "laboratory"? Do you picture people in white coats and goggles and gloves standing over a table with beakers and tubes? Well, that picture is pretty close to reality in some cases. In others, laboratory experiments, especially in psychology, focus more on observing behaviours in highly controlled settings to establish causal conclusions. Let's explore lab experiments further. 

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The aim of lab experiments is to identify if observed changes in the      are caused by the        .

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Demand characteristics lower the           of the research.

True or false: there is more likelihood of demand characteristics influencing lab experiments than field experiments.

A researcher wanted to explore how driving conditions affected speeding. Which type of experimental method is the researcher more likely to use? 

A researcher wanted to explore if sleep deprivation affected cognitive abilities. Which type of experimental method is the researcher more likely to use? 

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• We are going to delve into the topic of lab experiments in the context of psychology.
• We will start by looking at the lab experiment definition and how lab experiments are used in psychology.
• Moving on from this, we will look at how lab experiment examples in psychology and cognitive lab experiments may be conducted.
• And to finish off, we will also explore the strengths and weaknesses of lab experiments.

Lab Experiment Psychology Definition

You can probably guess from the name that lab experiments occur in lab settings. Although this is not always the case, they can sometimes occur in other controlled environments. The purpose of lab experiments is to identify the cause and effect of a phenomenon through experimentation.

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to accurately measure how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

In lab experiments, the IV is what the researcher predicts as the cause of a phenomenon, and the dependent variable is what the researcher predicts as the effect of a phenomenon.

Lab Experiment: P sychology

Lab experiments in psychology are used when trying to establish causal relationships between variables . For example, a researcher would use a lab experiment if they were investigating how sleep affects memory recall.

The majority of psychologists think of psychology as a form of science. Therefore, they argue that the protocol used in psychological research should resemble those used in the natural sciences. For research to be established as scientific , three essential features should be considered:

• Empiricism - the findings should be observable via the five senses.
• Reliability - if the study was replicated, similar results should be found.
• Validity - the investigation should accurately measure what it intends to.

But do lab experiments fulfil these requirements of natural sciences research? If done correctly, then yes. Lab experiments are empirical as they involve the researcher observing changes occurring in the DV. Reliability is established by using a standardised procedure in lab experiments .

A standardised procedure is a protocol that states how the experiment will be carried out. This allows the researcher to ensure the same protocol is used for each participant, increasing the study's internal reliability.

Standardised procedures are also used to help other researchers replicate the study to identify if they measure similar results.

Dissimilar results reflect low reliability.

Validity is another feature of a lab experiment considered. Lab experiments are conducted in a carefully controlled setting where the researcher has the most control compared to other experiments to prevent extraneous variables from affecting the DV .

Extraneous variables are factors other than the IV that affect the DV; as these are variables that the researcher is not interested in investigating, these reduce the validity of the research.

There are issues of validity in lab experiments, which we'll get into a bit later!

Lab Experiment Examples: Asch's Conformity Study

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question. Participants were given two pieces of paper, one depicting a 'target line' and another three, one of which resembled the 'target line' and the others of different lengths.

The participants were put in groups of eight. Unknown to the participants, the other seven were confederates (participants who were secretly part of the research team) who were instructed to give the wrong answer. If the actual participant changed their answer in response, this would be an example of conformity .

Asch controlled the location where the investigation took place, constructed a contrived scenario and even controlled the confederates who would affect the behaviour of the actual participants to measure the DV.

Some other famous examples of research that are lab experiment examples include research conducted by Milgram (the obedience study) and Loftus and Palmer's eyewitness testimony accuracy study . These researchers likely used this method because of some of their strengths , e.g., their high level of control .

Lab Experiment Examples: Cognitive Lab Experiments

Let's look at what a cognitive lab experiment may entail. Suppose a researcher is interested in investigating how sleep affects memory scores using the MMSE test. In the theoretical study , an equal number of participants were randomly allocated into two groups; sleep-deprived versus well-rested. Both groups completed the memory test after a whole night of sleep or staying awake all night.

In this research scenario , the DV can be identified as memory test scores and the IV as whether participants were sleep-deprived or well-rested.

Some examples of extraneous variables the study controlled include researchers ensuring participants did not fall asleep, the participants took the test at the same time, and participants in the well-rested group slept for the same time.

Lab Experiment Advantages and Disadvantages

It's important to consider the advantages and disadvantages of laboratory experiments . Advantages include the highly controlled setting of lab experiments, the standardised procedures and causal conclusions that can be drawn. Disadvantages include the low ecological validity of lab experiments and demand characteristics participants may present.

Strengths of Lab Experiments: Highly Controlled

Laboratory experiments are conducted in a well-controlled setting. All the variables, including extraneous and confounding variables , are rigidly controlled in the investigation. Therefore, the risk of experimental findings being affected by extraneous or confounding variables is reduced . As a result, the well-controlled design of laboratory experiments implies the research has high internal validity .

Internal validity means the study uses measures and protocols that measure exactly what it intends to, i.e. how only the changes in the IV affect the DV.

Strengths of Lab Experiments: Standardised Procedures

Laboratory experiments have standardised procedures, which means the experiments are replicable , and all participants are tested under the same conditions. T herefore, standardised procedures allow others to replicate the study to identify whether the research is reliable and that the findings are not a one-off result. As a result, the replicability of laboratory experiments allows researchers to verify the study's reliability .

Strengths of Lab Experiments: Causal Conclusions

A well-designed laboratory experiment can draw causal conclusions. Ideally, a laboratory experiment can rigidly control all the variables , including extraneous and confounding variables. Therefore, laboratory experiments provide great confidence to researchers that the IV causes any observed changes in DV.

Weaknesses of Lab Experiments

In the following, we will present the disadvantages of laboratory experiments. This discusses ecological validity and demand characteristics.

Weaknesses of Lab Experiments: Low Ecological Validity

Laboratory experiments have low ecological validity because they are conducted in an artificial study that does not reflect a real-life setting . As a result, findings generated in laboratory experiments can be difficult to generalise to real life due to the low mundane realism. Mundane realism reflects the extent to which lab experiment materials are similar to real-life events.

Weaknesses of Lab Experiments: Demand Characteristics

A disadvantage of laboratory experiments is that the research setting may lead to demand characteristics .

Demand characteristics are the cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

The participants are aware they are involved in an experiment. So, participants may have some ideas of what is expected of them in the investigation, which may influence their behaviours. As a result, the demand characteristics presented in laboratory experiments can arguably change the research outcome , reducing the findings' validity .

Lab Experiment - Key takeaways

The lab experiment definition is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affect the dependent variable (DV; variable measured).

Psychologists aim to ensure that lab experiments are scientific and must be empirical, reliable and valid.

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question.

The advantages of lab experiments are high internal validity, standardised procedures and the ability to draw causal conclusions.

The disadvantages of lab experiments are low ecological validity and demand characteristics.

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Frequently Asked Questions about Lab Experiment

What is a lab experiment?

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

What is the purpose of lab experiments?

Lab experiments investigate cause-and-effect. They aim to determine the effect of changes in the independent variable on the dependent variable. 

What is a lab experiment and field experiment?

A field experiment is an experiment conducted in a natural, everyday setting. The experimenter still controls the IV; however, extraneous and confounding variables may be difficult to control due to the natural setting.

Similar, to filed experiments researchers, can control the IV and extraneous variables. However, this takes place in an artificial setting such as a lab. 

Why would a psychologist use a laboratory experiment? 

A psychologist may use a lab experiment when trying to establish the causal relationships between variables to explain a phenomenon. 

Why is lab experience important?

Lab experience allows researchers to scientifically determine whether a hypothesis/ theory should be accepted or rejected. 

What is a lab experiment example? 

The research conducted by Loftus and Palmer (accuracy of eyewitness testimony) and Milgram (obedience) used a lab experiment design. These experimental designs give the researcher high control, allowing them to control extraneous and independent variables.

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The aim of lab experiments is to identify if observed changes in the      are caused by the      .

Demand characteristics lower the         of the research.

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Field Experiments

Last updated 22 Mar 2021

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Experiments look for the effect that manipulated variables (independent variables) have on measured variables (dependent variables), i.e. causal effects.

Field experiments are conducted in a natural setting (e.g. at a sports event or on public transport), as opposed to the artificial environment created in laboratory experiments. Some variables cannot be controlled due to the unpredictability of these real-life settings (e.g. the public interacting with participants), but an independent variable will still be altered for a dependent variable to be measured against.

Evaluation of field experiments:

- Field experiments generally yield results with higher ecological validity than laboratory experiments, as the natural settings will relate to real life.

- Demand characteristics are less of an issue with field experiments than laboratory experiments (i.e. participants are less likely to adjust their natural behaviour according to their interpretation of the study’s purpose, as they might not know they are in a study).

- Extraneous variables could confound results due to the reduced control experimenters have over them in non-artificial environments, which makes it difficult to find truly causal effects between independent and dependent variables.

- Ethical principles have to be considered, such as the lack of informed consent; if participants are not made aware of their participation in an experiment, privacy must be respected during observations and participants must be debriefed appropriately when observations come to an end.

- Precise replication of the natural environment of field experiments is understandably difficult, so they have poor reliability, unlike laboratory experiments where the exact conditions can be recreated.

- Field experiments are more susceptible to sample bias, as participants are often not randomly allocated to experimental conditions (i.e. participants’ groups are already pre-set rather than randomly assigned).

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Controlled Experiment

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) – is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables that are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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AQA ALEVEL PSYCHOLOGY - Social Influence: Zimbardo’s (1973) Stanford Prison Experiment

Subject: Psychology

Age range: 16+

Resource type: Unit of work

Last updated

4 September 2024

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This is a complete set of notes for Zimbardo’s Stanford Prison Experiment in AQA Alevel Psychology in the subtopic of social influence. This includes his aim, procedure, findings, conclusion and evaluation points. I hope this helps you with your revision. If you have any questions please message me or leave a reviews. Thank you

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