Placebo effects and participant demand often occur unintentionally. Even experimenter expectations can influence the outcome of a study. One way to prevent these confounds from affecting the results of a study is to use a double-blind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier.
When scientists passively observe and measure phenomena it is called correlational research. Here, we do not intervene and change behavior, as we do in experiments.
In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less. You could use a correlational design—which is exactly what Professor Dunn did, too. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related?
The more money people reported spending on others, the happier they were. To find out how well two variables correspond, we can plot the relation between the two scores on what is known as a scatterplot Figure 1.
In the scatterplot, each dot represents a data point. Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month x-axis and how happy the person felt in the past month y-axis. Which variable is plotted on which axis does not matter. The association between two variables can be summarized statistically using the correlation coefficient abbreviated as r. A correlation coefficient provides information about the direction and strength of the association between two variables.
For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.
With a positive correlation, the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right just as they do in Figure 1. The r value for a positive correlation is indicated by a positive number although, the positive sign is usually omitted. Here, the r value is. A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down.
Figure 2 shows the association between the average height of males in a country y-axis and the pathogen prevalence or commonness of disease; x-axis of that country. In this scatterplot, each dot represents a country.
Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus — sign in front of it. Here, it is —. The strength of a correlation has to do with how well the two variables align.
The more money people reported spending on others, the happier they reported to be. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable!
Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong.
The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line. The r value of a strong correlation will have a high absolute value. In other words, you disregard whether there is a negative sign in front of the r value, and just consider the size of the numerical value itself. If the absolute value is large, it is a strong correlation. A weak correlation is one in which the two variables correspond some of the time, but not most of the time.
Figure 3 shows the relation between valuing happiness and grade point average GPA. People who valued happiness more tended to earn slightly lower grades, but there were lots of exceptions to this. The r value for a weak correlation will have a low absolute value. If two variables are so weakly related as to be unrelated, we say they are uncorrelated, and the r value will be zero or very close to zero. In the previous example, is the correlation between height and pathogen prevalence strong?
Compared to Figure 3, the dots in Figure 2 are tighter and less dispersed. The absolute value of —. Therefore, it is a strong negative correlation. Can you guess the strength and direction of the correlation between age and year of birth? If you said this is a strong negative correlation, you are correct!
Older people always have lower years of birth than younger people e. In fact, this is a perfect correlation because there are no exceptions to this pattern. I challenge you to find a year-old born before ! If generosity and happiness are positively correlated, should we conclude that being generous causes happiness?
Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two.
For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists. Qualitative designs, including participant observation, case studies, and narrative analysis are examples of such methodologies. For example, Festinger, Riecken, and Shacter were very interested in the psychology of a particular cult. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult.
Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. Another qualitative method for research is the case study, which involves an intensive examination of specific individuals or specific contexts. Sigmund Freud, the father of psychoanalysis, was famous for using this type of methodology; however, more current examples of case studies usually involve brain injuries.
At the same time, there are too few people who have this type of injury to conduct correlational research. In such an instance, the researcher may examine only one person with this brain injury, but in doing so, the researcher will put the participant through a very extensive round of tests.
Hopefully what is learned from this one person can be applied to others; however, even with thorough tests, there is the chance that something unique about this individual other than the brain injury will affect his or her happiness.
But with such a limited number of possible participants, a case study is really the only type of methodology suitable for researching this brain injury. The final qualitative method to be discussed in this section is narrative analysis.
Narrative analysis centers around the study of stories and personal accounts of people, groups, or cultures. These stories may be written, audio-recorded, or video-recorded, and allow the researcher not only to study what the participant says but how he or she says it.
Every person has a unique perspective on the world, and studying the way he or she conveys a story can provide insight into that perspective. What if you want to study the effects of marriage on a variable? For example, does marriage make people happier? Can you randomly assign some people to get married and others to remain single? So how can you study these important variables? You can use a quasi-experimental design.
A quasi-experimental design is similar to experimental research, except that random assignment to conditions is not used. Instead, we rely on existing group memberships e. As a result, with quasi-experimental designs causal inference is more difficult. For example, married people might differ on a variety of characteristics from unmarried people.
If we find that married participants are happier than single participants, it will be hard to say that marriage causes happiness, because the people who got married might have already been happier than the people who have remained single. Imagine you want to know who is a better professor: Here, the independent variable is the professor Dr. Again, the key difference is random assignment to the conditions of the independent variable.
For example, maybe students heard Dr. Smith sets low expectations, so slackers prefer this class, whereas Dr. Khan sets higher expectations, so smarter students prefer that one. It is also the biggest drain on time and resources, and is often impossible to perform for some fields, because of ethical considerations. The Tuskegee Syphilis Study was a prime example of experimental research that was fixated on results, and failed to take into account moral considerations.
In other fields of study, which do not always have the luxury of definable and quantifiable variables - you need to use different research methods.
These should attempt to fit all of the definitions of repeatability or falsifiability , although this is not always feasible.
Opinion based research methods generally involve designing an experiment and collecting quantitative data. For this type of research, the measurements are usually arbitrary, following the ordinal or interval type. Questionnaires are an effective way of quantifying data from a sample group, and testing emotions or preferences. This method is very cheap and easy, where budget is a problem, and gives an element of scale to opinion and emotion. These figures are arbitrary, but at least give a directional method of measuring intensity.
By definition, this experiment method must be used where emotions or behaviors are measured, as there is no other way of defining the variables. Whilst not as robust as experimental research , the methods can be replicated and the results falsified. Observational research is a group of different research methods where researchers try to observe a phenomenon without interfering too much. Observational research methods, such as the case study , are probably the furthest removed from the established scientific method.
Observational research tends to use nominal or ordinal scales of measurement. Observational research often has no clearly defined research problem , and questions may arise during the course of the study. Observation is heavily used in social sciences, behavioral studies and anthropology, as a way of studying a group without affecting their behavior. Whilst the experiment cannot be replicated or falsified , it still offers unique insights, and will advance human knowledge.
Case studies are often used as a pre-cursor to more rigorous methods, and avoid the problem of the experiment environment affecting the behavior of an organism. Observational research methods are useful when ethics are a problem. In an ideal world, experimental research methods would be used for every type of research, fulfilling all of the requirements of falsifiability and generalization.
However, ethics , time and budget are major factors, so any experimental design must make compromises. As long as a researcher recognizes and evaluates flaws in the design when choosing from different research methods, any of the scientific research methods are valid contributors to scientific knowledge.
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PART I WHAT IS RESEARCH DESIGN? 1 THE CONTEXT OF DESIGN Before examining types of research designs it is important to be clear about the role and purpose of research design.
The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data.
Mixed Research Designs. A mixed research design involves having both a quantitative design and qualitative design. Mixed designs is the best approach if the study requires both quantitative and qualitative designs to address the problem statement. A research design will typically include how data is to be collected, what instruments will be employed, how the instruments will be used and the intended means for analyzing data collected. manipulated var.
A research method is a general framework guiding a research project. Different methods can be used to tackle different questions. Research design is a specific outline detailing how your chosen method will be applied to answer a particular research question. Research methods are generalized and. Design research was originally constituted as primarily research into the process of design, developing from work in design methods, but the concept has been expanded to include research embedded within the process of design, including work concerned with the context of designing and research-based design practice. The concept retains a sense.