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Correlational studies provide a valuable method for examining how two quantitative variables interact without manipulating the natural environment. This article gets into the intricacies of correlational research, exploring its methodologies, applications, and the considerations researchers must keep in mind.
Correlational research is a non-experimental approach used to measure the statistical relationship between two or more quantitative variables.
Unlike experimental research, which involves manipulating one variable to observe its effect on another, correlational studies observe variables as they naturally occur. This method is particularly useful when ethical or practical constraints prevent manipulation of variables.
Quantitative variables are numerical and can be measured and compared statistically. In correlational research, these variables are essential because they allow researchers to calculate correlation coefficients, which indicate the strength and direction of the relationship between variables. For example, a researcher might examine the relationship between socioeconomic status and shopping habits to understand consumer behavior better.
Correlations can be positive or negative, indicating the direction of the relationship between variables.
Collecting accurate and reliable data is crucial in correlational studies. Researchers employ various methods to gather data without influencing the variables under investigation. For example, utilizing tools such as the best wix referral apps can enhance survey distribution and data collection efficiency.
Surveys are a common tool in correlational research, allowing researchers to collect data on behaviors, attitudes, and characteristics from a large number of participants efficiently. By designing surveys that measure multiple variables, researchers can analyze how these variables relate to each other.
For example, a survey might collect data on participants’ socioeconomic status and their shopping habits to explore any potential relationships. Ensuring that participants remain anonymous can encourage honest responses, enhancing the reliability of the data.
Naturalistic observation involves observing subjects in their natural environment without any manipulation or interference. This method provides insights into how variables interact in real-world settings.
For instance, a cognitive psychologist might observe how people interact with different puzzles in an escape room to understand the relationship between the difficulty of the puzzles and the team dynamics that emerge during the problem-solving process. By making little or no effort to manipulate variables, the researcher can collect data that reflects natural behaviors.
Utilizing existing records, such as social security records or performance reports, allows researchers to analyze relationships over time without the need for new data collection. Archival data can be a cost-effective way to conduct correlational research, especially when studying historical trends.
For example, a researcher interested in the relationship between first-year college performance and high school grades might analyze archival data from educational institutions.
The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. It ranges from -1 to +1.
Understanding the correlation coefficient helps researchers interpret the data accurately. For example, if a study finds a strong negative correlation between stress levels and sleep quality, it suggests that higher stress is associated with poorer sleep.
In correlational research, it’s essential to consider the potential impact of third variables, also known as confounders, which may influence the relationship between the variables under study.
A third variable is an external factor that affects both variables being studied, potentially leading to a spurious correlation. For example, there might be a correlation between ice cream sales and drowning incidents, but the third variable—hot weather—increases both, creating a misleading relationship.
Researchers must be cautious of the third variable problem to avoid incorrect conclusions about the relationship between variables.
To address potential confounders, researchers can use statistical methods and careful study design. Techniques like multiple regression analysis allow researchers to control for other variables, isolating the relationship between the primary variables of interest.
By acknowledging and adjusting for confounding variables, researchers enhance the internal validity of their study, increasing confidence in their findings.
One limitation of correlational research is the difficulty in determining causation. Just because two variables are correlated does not mean that one causes the other.
The directionality problem refers to the challenge of determining which variable influences the other. For instance, if there’s a correlation between exercise frequency and happiness levels, it’s unclear whether exercising more leads to increased happiness or happier people tend to exercise more.
Correlational studies can identify relationships but cannot establish cause and effect due to the potential influence of third variables and the directionality problem. To establish causation, experimental research designs that manipulate the independent variable and control extraneous variables are necessary.
Correlational research has broad applications across various fields, providing valuable insights that inform theory and practice.
In psychology, researchers use correlational studies to explore relationships between mental processes and behaviors. For example, a cognitive psychologist might investigate the relationship between participants’ scores on a brief extraversion test and their performance on memory recall tasks. Such studies can help identify patterns that contribute to our understanding of cognitive functions.
In healthcare, correlational research can evaluate the effectiveness of electronic health interventions. By examining the relationship between patients’ use of e-health services and their health outcomes, researchers can assess the impact of technology on patient care.
Businesses utilize correlational studies to understand consumer behavior. Analyzing the relationship between socioeconomic status and shopping habits can help companies tailor their marketing strategies . For instance, a positive correlation between income levels and the purchase of luxury goods can inform targeted advertising campaigns.
Ethical considerations are paramount in correlational research to protect participants and ensure the integrity of the study.
Researchers must ensure that participants’ rights are upheld throughout the study.
As correlational data becomes increasingly sophisticated with advances in technology, future correlational studies will present both exciting opportunities and significant challenges. Researchers interested in exploring relationships between two or more variables will need to adopt innovative methods to enhance internal and external validity.
The traditional researcher measures may evolve as cognitive psychologists compare digital assessments with conventional brief tests. Field research could see a transformation where a research assistant collects data using real-time sensors, making studies more dynamic.
In complex studies where a food scientist studies nutritional impacts, isolating the dependent variable from other variables will be crucial to establish clearer cause and effect relationships. Most researchers will grapple with distinguishing correlation from causation, especially when variables are strongly correlated but not causally linked. Ensuring research remains ethically acceptable will be paramount, particularly in areas like eHealth evaluation. Embracing an evidence-based approach will help navigate these complexities, leading to more robust and reliable findings in correlational research.
A wellness company conducted a study to determine if their new exercise program could reduce employee stress levels . Participants took a brief test measuring their stress before starting the program. Over eight weeks, they engaged in regular physical exercises and participated in weekly group discussions about wellness strategies.
At the end of the study, numerical values from stress assessments showed a significant decrease in reported stress levels among participants. The researchers concluded a causal relationship existed between the exercise program (the independent variable) and stress reduction (the dependent variable).
However, this conclusion overlooked the impact of the other variable—the group discussions. In terms of independent variables, both the exercise regimen and the wellness discussions could have contributed to the stress reduction. The failure to isolate the exercise program’s effect meant the study couldn’t definitively establish which factor was responsible.
This case highlights the importance of controlling for other variables to avoid incorrectly attributing cause and effect. Without isolating the independent variable, it’s misleading to claim a direct causal relationship based solely on correlational data. Proper study design should account for all influencing factors to ensure accurate conclusions.
Correlational research plays a significant role in survey research by providing a means to explore and understand the relationships between variables.
Correlational studies are a valuable tool in survey research, offering insights into how variables relate within their natural context. By carefully designing studies, collecting data responsibly, and analyzing statistical relationships thoughtfully, researchers can uncover meaningful patterns that contribute to our understanding of complex phenomena. While correlational research has limitations in establishing causation, it remains a foundational method for exploring relationships that inform theory, practice, and future research directions.
A correlational study examines the relationship between variables without manipulating them, unlike studies focused on causal relationships that determine how one variable directly affects another.
A cognitive psychologist compares variables by measuring research participants’ responses on brief tests, analyzing how one variable relates to another without altering independent variables.
Numerical values indicate the strength and direction of relationships in a correlational study, helping researchers understand how closely variables are related.
First-year performance reports can be used in correlational studies to find patterns, but they don’t establish causation since other variables may influence outcomes.
Through exercises and discussions, researchers collect participants’ feedback on how a stick shift feels, using this correlational data to study relationships without manipulating variables.
In correlational studies, the independent variable is observed rather than manipulated, allowing researchers to see how it relates to other variables naturally.
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Learning objectives.
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.
In a between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university 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 assigns 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 matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.
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. Table 5.2 shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.
4 | B |
5 | C |
6 | A |
Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so 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 possibility 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 confound 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.
An alternative to simple random assignment of participants to conditions is the use of a matched-groups design . Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study. If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health.
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 . However, not all experiments can use a within-subjects design nor would it be desirable to do so.
One disadvantage of within-subjects experiments is that they 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 knowledge 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.”
The primary disadvantage of within-subjects designs is that they can result in order effects. An order effect occurs when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed. One type of order effect is a carryover effect. 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 type of effect is called a context effect (or contrast 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.
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. The best method of counterbalancing is complete counterbalancing in which an equal number of participants complete each possible order of conditions. For example, half of the participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others half 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 four conditions, there would be 24 different orders; with five conditions there would be 120 possible 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.
A more efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:
A | B | C | D |
B | C | D | A |
C | D | A | B |
D | A | B | C |
You can see in the diagram above that the square has been constructed to ensure that each condition appears at each ordinal position (A appears first once, second once, third once, and fourth once) and each condition preceded and follows each other condition one time. A Latin square for an experiment with 6 conditions would by 6 x 6 in dimension, one for an experiment with 8 conditions would be 8 x 8 in dimension, and so on. So while complete counterbalancing of 6 conditions would require 720 orders, a Latin square would only require 6 orders.
Finally, when the number of conditions is large experiments can use random counterbalancing in which the order of the conditions is randomly determined for each participant. Using this technique every possible order of conditions is determined and then one of these orders is randomly selected for each participant. This is not as powerful a technique as complete counterbalancing or partial counterbalancing using a Latin squares design. Use of random counterbalancing will result in more random error, but if order effects are likely to be small and the number of conditions is large, this is an option available to researchers.
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.
Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”. One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [1] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this difference is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).
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.
Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility 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 design 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 difficulty 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 take exactly this type of mixed methods approach.
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Learning objectives.
Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.
Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.
Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:
Some true experiments are more complex. Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.
In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.
In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.
In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.
The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test . In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.
Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.
An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.
In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963). The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.
Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.
Group 1 | X | X | X |
Group 2 | X | X | |
Group 3 | X | X | |
Group 4 | X |
Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.
Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we will discuss in the next section–can be used. However, the differences in rigor from true experimental designs leave their conclusions more open to critique.
You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals. For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change. There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013). Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments. For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.
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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Andrew gellert.
Correlational methodologies and experimental ones are the two approaches to doing research. Experimental studies allow the researcher to control the variables in the study, while correlational ones involve just looking at the data that already exists. Experimental studies allow the researcher to draw conclusions about one variable causing changes in another.
In a basic study, the researcher has two variables of interest -- the dependent variable and the independent variable -- and the researcher believes that the independent variable affects the dependent variable. For example, in a study about how using fertilizer increases the amount of wheat grown on a farm, the amount of fertilizer used is the independent variable that affects the amount of wheat that grows, which is the dependent variable.
In a correlation study, the researcher or research team does not have control over the variables in the study. The researcher simply measures the data that she finds in the world. This allows her to see if the two variables are correlated -- whether changes in one are associated with changes in the other. Experimenters in such a study collect existing data, such as economic data from governments, and analyze it using statistical tools. Sometimes, the results of correlation studies can inspire hypothesis that can be tested with a more specific experimental one.
In a controlled experiment, the research team has control over the independent variable and other aspects of the experiment. This allows the researchers to make conclusions about whether the independent variable really affects the dependent variable, as opposed to the variables changing at the same time through coincidence. The researcher can also eliminate the effects of other variables. For example, the researcher can add precise amounts of fertilizer to different areas of the same wheat field, and measure the differences in wheat yield, as the other other factors, such as rainfall, sun exposure and soil make-up, are the same.
The strength of the experimental treatment is that it isolates the relationship between the independent and dependent variable. In a correlation study, there might be other influences on the variables that make it hard to measure how strong the relationship between the two really is. Experimental studies are often more expensive and difficult to run. Correlation studies can explore a relationship to see if it is worth the later expense of a controlled experiment, as well as study a larger data set than may be feasible in an experiment. Some researchers will use both methods in a study, conducting an experiment and then carrying out correlation analysis on the results.
Andrew Gellert is a graduate student who has written science, business, finance and economics articles for four years. He was also the editor of his own section of his college's newspaper, "The Cowl," and has published in his undergraduate economics department's newsletter.
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8.3 complex correlational designs, learning objectives.
As we have already seen, researchers conduct correlational studies rather than experiments when they are interested in noncausal relationships or when they are interested in causal relationships where the independent variable cannot be manipulated for practical or ethical reasons. In this section, we look at some approaches to complex correlational research that involve measuring several variables and assessing the relationships among them.
We have already seen that factorial experiments can include manipulated independent variables or a combination of manipulated and nonmanipulated independent variables. But factorial designs can also include only nonmanipulated independent variables, in which case they are no longer experiments but correlational studies. Consider a hypothetical study in which a researcher measures both the moods and the self-esteem of several participants—categorizing them as having either a positive or negative mood and as being either high or low in self-esteem—along with their willingness to have unprotected sexual intercourse. This can be conceptualized as a 2 × 2 factorial design with mood (positive vs. negative) and self-esteem (high vs. low) as between-subjects factors. (Willingness to have unprotected sex is the dependent variable.) This design can be represented in a factorial design table and the results in a bar graph of the sort we have already seen. The researcher would consider the main effect of sex, the main effect of self-esteem, and the interaction between these two independent variables.
Again, because neither independent variable in this example was manipulated, it is a correlational study rather than an experiment. (The similar study by MacDonald and Martineau [2002] was an experiment because they manipulated their participants’ moods.) This is important because, as always, one must be cautious about inferring causality from correlational studies because of the directionality and third-variable problems. For example, a main effect of participants’ moods on their willingness to have unprotected sex might be caused by any other variable that happens to be correlated with their moods.
Most complex correlational research, however, does not fit neatly into a factorial design. Instead, it involves measuring several variables—often both categorical and quantitative—and then assessing the statistical relationships among them. For example, researchers Nathan Radcliffe and William Klein studied a sample of middle-aged adults to see how their level of optimism (measured by using a short questionnaire called the Life Orientation Test) relates to several other variables related to having a heart attack (Radcliffe & Klein, 2002). These included their health, their knowledge of heart attack risk factors, and their beliefs about their own risk of having a heart attack. They found that more optimistic participants were healthier (e.g., they exercised more and had lower blood pressure), knew about heart attack risk factors, and correctly believed their own risk to be lower than that of their peers.
This approach is often used to assess the validity of new psychological measures. For example, when John Cacioppo and Richard Petty created their Need for Cognition Scale—a measure of the extent to which people like to think and value thinking—they used it to measure the need for cognition for a large sample of college students, along with three other variables: intelligence, socially desirable responding (the tendency to give what one thinks is the “appropriate” response), and dogmatism (Caccioppo & Petty, 1982). The results of this study are summarized in Table 8.1 “Correlation Matrix Showing Correlations Among the Need for Cognition and Three Other Variables Based on Research by Cacioppo and Petty” , which is a correlation matrix showing the correlation (Pearson’s r ) between every possible pair of variables in the study. For example, the correlation between the need for cognition and intelligence was +.39, the correlation between intelligence and socially desirable responding was −.02, and so on. (Only half the matrix is filled in because the other half would contain exactly the same information. Also, because the correlation between a variable and itself is always +1.00, these values are replaced with dashes throughout the matrix.) In this case, the overall pattern of correlations was consistent with the researchers’ ideas about how scores on the need for cognition should be related to these other constructs.
Table 8.1 Correlation Matrix Showing Correlations Among the Need for Cognition and Three Other Variables Based on Research by Cacioppo and Petty
Need for cognition | Intelligence | Social desirability | Dogmatism | |
---|---|---|---|---|
Need for cognition | — | |||
Intelligence | +.39 | — | ||
Social desirability | +.08 | +.02 | — | |
Dogmatism | −.27 | −.23 | +.03 | — |
When researchers study relationships among a large number of conceptually similar variables, they often use a complex statistical technique called factor analysis . In essence, factor analysis organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters. Each cluster is then interpreted as multiple measures of the same underlying construct. These underlying constructs are also called “factors.” For example, when people perform a wide variety of mental tasks, factor analysis typically organizes them into two main factors—one that researchers interpret as mathematical intelligence (arithmetic, quantitative estimation, spatial reasoning, and so on) and another that they interpret as verbal intelligence (grammar, reading comprehension, vocabulary, and so on). The Big Five personality factors have been identified through factor analyses of people’s scores on a large number of more specific traits. For example, measures of warmth, gregariousness, activity level, and positive emotions tend to be highly correlated with each other and are interpreted as representing the construct of extroversion. As a final example, researchers Peter Rentfrow and Samuel Gosling asked more than 1,700 college students to rate how much they liked 14 different popular genres of music (Rentfrow & Gosling, 2008). They then submitted these 14 variables to a factor analysis, which identified four distinct factors. The researchers called them Reflective and Complex (blues, jazz, classical, and folk), Intense and Rebellious (rock, alternative, and heavy metal), Upbeat and Conventional (country, soundtrack, religious, pop), and Energetic and Rhythmic (rap/hip-hop, soul/funk, and electronica).
Two additional points about factor analysis are worth making here. One is that factors are not categories. Factor analysis does not tell us that people are either extroverted or conscientious or that they like either “reflective and complex” music or “intense and rebellious” music. Instead, factors are constructs that operate independently of each other. So people who are high in extroversion might be high or low in conscientiousness, and people who like reflective and complex music might or might not also like intense and rebellious music. The second point is that factor analysis reveals only the underlying structure of the variables. It is up to researchers to interpret and label the factors and to explain the origin of that particular factor structure. For example, one reason that extroversion and the other Big Five operate as separate factors is that they appear to be controlled by different genes (Plomin, DeFries, McClean, & McGuffin, 2008).
Another important use of complex correlational research is to explore possible causal relationships among variables. This might seem surprising given that “correlation does not imply causation.” It is true that correlational research cannot unambiguously establish that one variable causes another. Complex correlational research, however, can often be used to rule out other plausible interpretations.
The primary way of doing this is through the statistical control of potential third variables. Instead of controlling these variables by random assignment or by holding them constant as in an experiment, the researcher measures them and includes them in the statistical analysis. Consider some research by Paul Piff and his colleagues, who hypothesized that being lower in socioeconomic status (SES) causes people to be more generous (Piff, Kraus, Côté, Hayden Cheng, & Keltner, 2011). They measured their participants’ SES and had them play the “dictator game.” They told participants that each would be paired with another participant in a different room. (In reality, there was no other participant.) Then they gave each participant 10 points (which could later be converted to money) to split with the “partner” in whatever way he or she decided. Because the participants were the “dictators,” they could even keep all 10 points for themselves if they wanted to.
As these researchers expected, participants who were lower in SES tended to give away more of their points than participants who were higher in SES. This is consistent with the idea that being lower in SES causes people to be more generous. But there are also plausible third variables that could explain this relationship. It could be, for example, that people who are lower in SES tend to be more religious and that it is their greater religiosity that causes them to be more generous. Or it could be that people who are lower in SES tend to come from ethnic groups that emphasize generosity more than other ethnic groups. The researchers dealt with these potential third variables, however, by measuring them and including them in their statistical analyses. They found that neither religiosity nor ethnicity was correlated with generosity and were therefore able to rule them out as third variables. This does not prove that SES causes greater generosity because there could still be other third variables that the researchers did not measure. But by ruling out some of the most plausible third variables, the researchers made a stronger case for SES as the cause of the greater generosity.
Many studies of this type use a statistical technique called multiple regression . This involves measuring several independent variables ( X 1 , X 2 , X 3 ,…X i ), all of which are possible causes of a single dependent variable ( Y ). The result of a multiple regression analysis is an equation that expresses the dependent variable as an additive combination of the independent variables. This regression equation has the following general form:
b 1 X 1 + b 2 X 2 + b 3 X 3 + … + b i X i = Y .
The quantities b 1 , b 2 , and so on are regression weights that indicate how large a contribution an independent variable makes, on average, to the dependent variable. Specifically, they indicate how much the dependent variable changes for each one-unit change in the independent variable.
The advantage of multiple regression is that it can show whether an independent variable makes a contribution to a dependent variable over and above the contributions made by other independent variables. As a hypothetical example, imagine that a researcher wants to know how the independent variables of income and health relate to the dependent variable of happiness. This is tricky because income and health are themselves related to each other. Thus if people with greater incomes tend to be happier, then perhaps this is only because they tend to be healthier. Likewise, if people who are healthier tend to be happier, perhaps this is only because they tend to make more money. But a multiple regression analysis including both income and happiness as independent variables would show whether each one makes a contribution to happiness when the other is taken into account. (Research like this, by the way, has shown both income and health make extremely small contributions to happiness except in the case of severe poverty or illness; Diener, 2000.)
The examples discussed in this section only scratch the surface of how researchers use complex correlational research to explore possible causal relationships among variables. It is important to keep in mind, however, that purely correlational approaches cannot unambiguously establish that one variable causes another. The best they can do is show patterns of relationships that are consistent with some causal interpretations and inconsistent with others.
Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42 , 116–131.
Diener, E. (2000). Subjective well-being: The science of happiness, and a proposal for a national index. American Psychologist , 55 , 34–43.
MacDonald, T. K., & Martineau, A. M. (2002). Self-esteem, mood, and intentions to use condoms: When does low self-esteem lead to risky health behaviors? Journal of Experimental Social Psychology, 38 , 299–306.
Piff, P. K., Kraus, M. W., Côté, S., Hayden Cheng, B., & Keltner, D. (2011). Having less, giving more: The influence of social class on prosocial behavior. Journal of Personality and Social Psychology , 99 , 771–784.
Plomin, R., DeFries, J. C., McClearn, G. E., & McGuffin, P. (2008). Behavioral genetics (5th ed.). New York, NY: Worth.
Radcliffe, N. M., & Klein, W. M. P. (2002). Dispositional, unrealistic, and comparative optimism: Differential relations with knowledge and processing of risk information and beliefs about personal risk. Personality and Social Psychology Bulletin , 28 , 836–846.
Rentfrow, P. J., & Gosling, S. D. (2008). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology , 84 , 1236–1256.
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Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."
Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.
Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.
Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.
In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.
Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.
Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.
So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.
Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.
For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?
In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!
Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.
Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.
Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.
Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.
Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.
Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.
Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.
In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.
With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.
Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.
So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.
Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.
Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.
Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.
Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.
Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.
Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.
Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.
Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.
Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!
Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.
Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.
Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:
So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.
Let's get into examples of experimental designs.
In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.
Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.
No sneaky biases here!
The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.
However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?
The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.
When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"
So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.
So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.
Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.
In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.
Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.
For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.
Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"
Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.
In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.
Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.
Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.
So, what's the deal with pre-experimental designs?
Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.
It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.
Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.
A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.
But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.
This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.
So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.
Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.
Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.
In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.
It's like cooking with several spices to see how they blend together to create unique flavors.
Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.
This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.
However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.
Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.
And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.
So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.
Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.
You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.
With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.
This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.
The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.
So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.
But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.
Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.
So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.
Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.
In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.
This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.
You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.
So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.
Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.
Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.
So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.
Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.
In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.
The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.
This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.
One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.
This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.
But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.
Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.
So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.
Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.
If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.
Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.
The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.
You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.
For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.
The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.
However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.
Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.
So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.
Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.
In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.
So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.
Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.
The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.
Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.
For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.
One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.
So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.
Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.
Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.
The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.
Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.
The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.
But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.
A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.
In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.
Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.
In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.
This design is like the utility player on our team—versatile, flexible, and really good at adapting.
The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.
The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.
What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.
A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.
In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.
Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.
This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.
Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.
Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.
Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.
There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.
A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.
In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.
Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.
Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!
Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.
Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'
But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'
So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.
But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.
A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).
In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.
Now, let's turn our attention to Multivariate Design, the multitasker of the research world.
If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.
Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.
Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.
So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.
But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.
Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.
A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.
In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.
Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?
Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.
This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.
In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."
What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.
But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.
Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.
One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.
Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.
Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.
Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!
What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.
The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.
Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).
Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.
The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.
Now, let's talk about Adaptive Designs, the chameleons of the experimental world.
Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.
In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.
This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.
But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.
Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.
For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.
The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.
In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.
Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.
Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.
Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.
Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.
In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.
One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.
However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.
Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.
Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.
This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.
Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.
Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.
Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.
In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.
Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.
The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.
But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.
This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.
Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.
In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.
For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.
Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.
Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.
Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.
In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.
The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.
Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.
However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.
This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.
In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.
Next up is Sequential Design, the dynamic and flexible member of our experimental design family.
Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.
In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.
This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.
One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.
However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.
In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.
This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.
On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.
Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.
Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.
Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.
Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.
On one hand, the results often give us a better understanding of how things work outside the lab.
While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.
On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.
Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.
Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.
Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.
From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.
We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.
Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.
Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.
So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.
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Chapter 8: Complex Research Designs
Learning Objectives
As we have already seen, researchers conduct correlational studies rather than experiments when they are interested in noncausal relationships or when they are interested in causal relationships where the independent variable cannot be manipulated for practical or ethical reasons. In this section, we look at some approaches to complex correlational research that involve measuring several variables and assessing the relationships among them.
We have already seen that factorial experiments can include manipulated independent variables or a combination of manipulated and nonmanipulated independent variables. But factorial designs can also include only nonmanipulated independent variables, in which case they are no longer experiments but correlational studies. Consider a hypothetical study in which a researcher measures both the moods and the self-esteem of several participants—categorizing them as having either a positive or negative mood and as being either high or low in self-esteem—along with their willingness to have unprotected sexual intercourse. This can be conceptualized as a 2 × 2 factorial design with mood (positive vs. negative) and self-esteem (high vs. low) as between-subjects factors. Willingness to have unprotected sex is the dependent variable. This design can be represented in a factorial design table and the results in a bar graph of the sort we have already seen. The researcher would consider the main effect of sex, the main effect of self-esteem, and the interaction between these two independent variables.
Again, because neither independent variable in this example was manipulated, it is a correlational study rather than an experiment. (The similar study by MacDonald and Martineau [2002] [1] was an experiment because they manipulated their participants’ moods.) This is important because, as always, one must be cautious about inferring causality from correlational studies because of the directionality and third-variable problems. For example, a main effect of participants’ moods on their willingness to have unprotected sex might be caused by any other variable that happens to be correlated with their moods.
Most complex correlational research, however, does not fit neatly into a factorial design. Instead, it involves measuring several variables—often both categorical and quantitative—and then assessing the statistical relationships among them. For example, researchers Nathan Radcliffe and William Klein studied a sample of middle-aged adults to see how their level of optimism (measured by using a short questionnaire called the Life Orientation Test) relates to several other variables related to having a heart attack (Radcliffe & Klein, 2002) [2] . These included their health, their knowledge of heart attack risk factors, and their beliefs about their own risk of having a heart attack. They found that more optimistic participants were healthier (e.g., they exercised more and had lower blood pressure), knew about heart attack risk factors, and correctly believed their own risk to be lower than that of their peers.
This approach is often used to assess the validity of new psychological measures. For example, when John Cacioppo and Richard Petty created their Need for Cognition Scale—a measure of the extent to which people like to think and value thinking—they used it to measure the need for cognition for a large sample of college students, along with three other variables: intelligence, socially desirable responding (the tendency to give what one thinks is the “appropriate” response), and dogmatism (Caccioppo & Petty, 1982) [3] . The results of this study are summarized in Table 8.1, which is a correlation matrix showing the correlation (Pearson’s r ) between every possible pair of variables in the study. For example, the correlation between the need for cognition and intelligence was +.39, the correlation between intelligence and socially desirable responding was +.02, and so on. (Only half the matrix is filled in because the other half would contain exactly the same information. Also, because the correlation between a variable and itself is always +1.00, these values are replaced with dashes throughout the matrix.) In this case, the overall pattern of correlations was consistent with the researchers’ ideas about how scores on the need for cognition should be related to these other constructs.
Need for cognition | Intelligence | Social desireability | Dogmatism | |
---|---|---|---|---|
Need for cognition | — | |||
Intelligence | +.39 | — | ||
Social desirability | +.08 | +.02 | — | |
Dogmatism | −.27 | −.23 | +.03 | — |
When researchers study relationships among a large number of conceptually similar variables, they often use a complex statistical technique called factor analysis . In essence, factor analysis organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters. Each cluster is then interpreted as multiple measures of the same underlying construct. These underlying constructs are also called “factors.” For example, when people perform a wide variety of mental tasks, factor analysis typically organizes them into two main factors—one that researchers interpret as mathematical intelligence (arithmetic, quantitative estimation, spatial reasoning, and so on) and another that they interpret as verbal intelligence (grammar, reading comprehension, vocabulary, and so on). The Big Five personality factors have been identified through factor analyses of people’s scores on a large number of more specific traits. For example, measures of warmth, gregariousness, activity level, and positive emotions tend to be highly correlated with each other and are interpreted as representing the construct of extroversion. As a final example, researchers Peter Rentfrow and Samuel Gosling asked more than 1,700 university students to rate how much they liked 14 different popular genres of music (Rentfrow & Gosling, 2008) [4] . They then submitted these 14 variables to a factor analysis, which identified four distinct factors. The researchers called them Reflective and Complex (blues, jazz, classical, and folk), Intense and Rebellious (rock, alternative, and heavy metal), Upbeat and Conventional (country, soundtrack, religious, pop), and Energetic and Rhythmic (rap/hip-hop, soul/funk, and electronica).
Two additional points about factor analysis are worth making here. One is that factors are not categories. Factor analysis does not tell us that people are either extraverted or conscientious or that they like either “reflective and complex” music or “intense and rebellious” music. Instead, factors are constructs that operate independently of each other. So people who are high in extroversion might be high or low in conscientiousness, and people who like reflective and complex music might or might not also like intense and rebellious music. The second point is that factor analysis reveals only the underlying structure of the variables. It is up to researchers to interpret and label the factors and to explain the origin of that particular factor structure. For example, one reason that extraversion and the other Big Five operate as separate factors is that they appear to be controlled by different genes (Plomin, DeFries, McClean, & McGuffin, 2008) [5] .
Another important use of complex correlational research is to explore possible causal relationships among variables. This might seem surprising given that “correlation does not imply causation.” It is true that correlational research cannot unambiguously establish that one variable causes another. Complex correlational research, however, can often be used to rule out other plausible interpretations.
The primary way of doing this is through the statistical control of potential third variables. Instead of controlling these variables by random assignment or by holding them constant as in an experiment, the researcher measures them and includes them in the statistical analysis. Consider some research by Paul Piff and his colleagues, who hypothesized that being lower in socioeconomic status (SES) causes people to be more generous (Piff, Kraus, Côté, Hayden Cheng, & Keltner, 2011) [6] . They measured their participants’ SES and had them play the “dictator game.” They told participants that each would be paired with another participant in a different room. (In reality, there was no other participant.) Then they gave each participant 10 points (which could later be converted to money) to split with the “partner” in whatever way he or she decided. Because the participants were the “dictators,” they could even keep all 10 points for themselves if they wanted to.
As these researchers expected, participants who were lower in SES tended to give away more of their points than participants who were higher in SES. This is consistent with the idea that being lower in SES causes people to be more generous. But there are also plausible third variables that could explain this relationship. It could be, for example, that people who are lower in SES tend to be more religious and that it is their greater religiosity that causes them to be more generous. Or it could be that people who are lower in SES tend to come from certain ethnic groups that emphasize generosity more than other ethnic groups. The researchers dealt with these potential third variables, however, by measuring them and including them in their statistical analyses. They found that neither religiosity nor ethnicity was correlated with generosity and were therefore able to rule them out as third variables. This does not prove that SES causes greater generosity because there could still be other third variables that the researchers did not measure. But by ruling out some of the most plausible third variables, the researchers made a stronger case for SES as the cause of the greater generosity.
Many studies of this type use a statistical technique called multiple regression . This involves measuring several independent variables ( X1, X2, X3,…Xi ), all of which are possible causes of a single dependent variable ( Y ). The result of a multiple regression analysis is an equation that expresses the dependent variable as an additive combination of the independent variables. This regression equation has the following general form:
b 1 X 1 + b 2 X 2 + b 3 X 3 + … + b i X i = Y
The quantities b1 , b2 , and so on are regression weights that indicate how large a contribution an independent variable makes, on average, to the dependent variable. Specifically, they indicate how much the dependent variable changes for each one-unit change in the independent variable.
The advantage of multiple regression is that it can show whether an independent variable makes a contribution to a dependent variable over and above the contributions made by other independent variables. As a hypothetical example, imagine that a researcher wants to know how the independent variables of income and health relate to the dependent variable of happiness. This is tricky because income and health are themselves related to each other. Thus if people with greater incomes tend to be happier, then perhaps this is only because they tend to be healthier. Likewise, if people who are healthier tend to be happier, perhaps this is only because they tend to make more money. But a multiple regression analysis including both income and happiness as independent variables would show whether each one makes a contribution to happiness when the other is taken into account. (Research like this, by the way, has shown both income and health make extremely small contributions to happiness except in the case of severe poverty or illness; Diener, 2000. [7] )
The examples discussed in this section only scratch the surface of how researchers use complex correlational research to explore possible causal relationships among variables. It is important to keep in mind, however, that purely correlational approaches cannot unambiguously establish that one variable causes another. The best they can do is show patterns of relationships that are consistent with some causal interpretations and inconsistent with others.
Key Takeaways
A statistical technique that organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters.
The researcher measures potential third variables and includes them in the statistical analysis.
Measuring several independent variables, all of which are possible causes of a single dependent variable. This results in an equation that expresses the dependent variable as an additive combination of the independent variables.
Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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Controlled experiments establish causality, whereas correlational studies only show associations between variables. In an experimental design, you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can't impact the results. In a correlational design, you measure variables ...
Descriptive, correlational, and experimental research designs are used to collect and analyze data. Descriptive designs include case studies, surveys, and naturalistic observation.
Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.
Correlational research and experimental research are two different research approaches used in social sciences and other fields of research..
Experiments are used to study causal relationships. You manipulate one or more independent variables and measure their effect on one or more dependent variables. Experimental design create a set of procedures to systematically test a hypothesis. A good experimental design requires a strong understanding of the system you are studying.
Correlational vs. experimental research Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in data collection methods and the types of conclusions you can draw.
As you have seen in this module, there are many ways to do research in psychology. Now let's. carefully compare three common approaches to answering the same research question and. review their advantages and disadvantages. We'll look at a correlational approach, an ex post. facto study, and an experimental approach.
What's the difference between correlational and experimental research? Controlled experiments establish causality, whereas correlational studies only show associations between variables. In an experimental design, you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can ...
Correlational research is a non-experimental approach used to measure the statistical relationship between two or more quantitative variables. Unlike experimental research, which involves manipulating one variable to observe its effect on another, correlational studies observe variables as they naturally occur.
Learning Objectives Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question. Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it. Define what a ...
Learning Objectives Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question. Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it Define several ...
Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning.
Correlational methodologies and experimental ones are the two approaches to doing research. Experimental studies allow the researcher to control the variables in the study, while correlational ones involve just looking at the data that already exists. Experimental studies allow the researcher to draw conclusions about ...
Experimental research design is centrally concerned with constructing research that is high in causal (internal) validity. Randomized experimental designs provide the highest levels of causal validity. Quasi-experimental designs have a number of potential threats to their causal validity. Yet, new quasi-experimental designs adopted from fields ...
We believe that differential research designs can employ control procedures not available in straight correlational research and therefore should be conceptualized as somewhere between quasi-experimental and cor relational designs.
Learning Objectives Explain some reasons that researchers use complex correlational designs. Create and interpret a correlation matrix. Describe how researchers can use correlational research to explore causal relationships among variables—including the limits of this approach.
What Is Experimental Design? Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is. Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues.
Explain some reasons that researchers use complex correlational designs. Create and interpret a correlation matrix. Describe how researchers can use correlational research to explore causal relationships among variables—including the limits of this approach.
2. In thinking about the importance of studying life-span development, research has found that: A) massage therapy decreases the immune system functioning of preterm infants. B) secure attachment to parents in adolescence is linked with a host of negative outcomes.