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Don't have time for it all now? No problem, save it as a course and come back to it later. Share this page on your website: This article is a part of the guide: Select from one of the other courses available: Don't miss these related articles:. Check out our quiz-page with tests about: Back to Overview "Research Design". Related articles Related pages: Search over articles on psychology, science, and experiments.
Leave this field blank: Want to stay up to date? Get all these articles in 1 guide Want the full version to study at home, take to school or just scribble on? Get PDF Download electronic versions: Save this course for later Don't have time for it all now? Experimental studies are also known as longitudinal or repeated-measures studies, for obvious reasons. They are also referred to as interventions , because you do more than just observe the subjects.
In the simplest experiment, a time series , one or more measurements are taken on all subjects before and after a treatment. A special case of the time series is the so-called single-subject design , in which measurements are taken repeatedly e. Time series suffer from a major problem: For example, subjects might do better on the second test because of their experience of the first test, or they might change their diet between tests because of a change in weather, and diet could affect their performance of the test.
The crossover design is one solution to this problem. Normally the subjects are given two treatments, one being the real treatment, the other a control or reference treatment. Half the subjects receive the real treatment first, the other half the control first.
After a period of time sufficient to allow any treatment effect to wash out, the treatments are crossed over. Any effect of retesting or of anything that happened between the tests can then be subtracted out by an appropriate analysis. Multiple crossover designs involving several treatments are also possible. If the treatment effect is unlikely to wash out between measurements, a control group has to be used. In these designs, all subjects are measured, but only some of them--the experimental group --then receive the treatment.
All subjects are then measured again, and the change in the experimental group is compared with the change in the control group.
If the subjects are assigned randomly to experimental and control groups or treatments, the design is known as a randomized controlled trial.
Random assignment minimizes the chance that either group is not typical of the population. If the subjects are blind or masked to the identity of the treatment, the design is a single-blind controlled trial. The control or reference treatment in such a study is called a placebo: Blinding of subjects eliminates the placebo effect , whereby people react differently to a treatment if they think it is in some way special. In a double-blind study, the experimenter also does not know which treatment the subjects receive until all measurements are taken.
Blinding of the experimenter is important to stop him or her treating subjects in one group differently from those in another.
In the best studies even the data are analyzed blind, to prevent conscious or unconscious fudging or prejudiced interpretation. Ethical considerations or lack of cooperation compliance by the subjects sometimes prevent experiments from being performed. For example, a randomized controlled trial of the effects of physical activity on heart disease may not have been performed yet, because it is unethical and unrealistic to randomize people to 10 years of exercise or sloth.
But there have been many short-term studies of the effects of physical activity on disease risk factors e. The various designs differ in the quality of evidence they provide for a cause-and-effect relationship between variables. Cases and case series are the weakest. A well-designed cross-sectional or case-control study can provide good evidence for the absence of a relationship.
But if such a study does reveal a relationship, it generally represents only suggestive evidence of a causal connection. A cross-sectional or case-control study is therefore a good starting point to decide whether it is worth proceeding to better designs. Prospective studies are more difficult and time-consuming to perform, but they produce more convincing conclusions about cause and effect. Experimental studies provide the best evidence about how something affects something else, and double-blind randomized controlled trials are the best experiments.
Confounding is a potential problem in descriptive studies that try to establish cause and effect. Confounding occurs when part or all of a significant association between two variables arises through both being causally associated with a third variable. For example, in a population study you could easily show a negative association between habitual activity and most forms of degenerative disease. But older people are less active, and older people are more diseased, so you're bound to find an association between activity and disease without one necessarily causing the other.
To get over this problem you have to control for potential confounding factors. For example, you make sure all your subjects are the same age, or you include age in the analysis to try to remove its effect on the relationship between the other two variables.
You almost always have to work with a sample of subjects rather than the full population. But people are interested in the population, not your sample. To generalize from the sample to the population, the sample has to be representative of the population.
The safest way to ensure that it is representative is to use a random selection procedure. You can also use a stratified random sampling procedure, to make sure that you have proportional representation of population subgroups e. When the sample is not representative of the population, selection bias is a possibility. A statistic is biased if the value of the statistic tends to be wrong or more precisely, if the expected value--the average value from many samples drawn using the same sampling method--is not the same as the population value.
A typical source of bias in population studies is age or socioeconomic status: Thus a high compliance the proportion of people contacted who end up as subjects is important in avoiding bias. Failure to randomize subjects to control and treatment groups in experiments can also produce bias. If you let people select themselves into the groups, or if you select the groups in any way that makes one group different from another, then any result you get might reflect the group difference rather than an effect of the treatment.
For this reason, it's important to randomly assign subjects in a way that ensures the groups are balanced in terms of important variables that could modify the effect of the treatment e.
Human subjects may not be happy about being randomized, so you need to state clearly that it is a condition of taking part. Often the most important variable to balance is the pre-test value of the dependent variable itself. You can get close to perfectly balanced randomization for this or another numeric variable as follows: If you have male and female subjects, or any other grouping that you think might affect the treatment, perform this randomization process for each group ranked separately.
Data from such pair-matched studies can be analyzed in ways that may increase the precision of the estimate of the treatment effect. Watch this space for an update shortly. When selecting subjects and designing protocols for experiments, researchers often strive to eliminate all variation in subject characteristics and behaviors. Their aim is to get greater precision in the estimate of the effect of the treatment.
The problem with this approach is that the effect generalizes only to subjects with the same narrow range of characteristics and behaviors as in the sample.
Depending on the nature of the study, you may therefore have to strike a balance between precision and applicability. If you lean towards applicability, your subjects will vary substantially on some characteristic or behavior that you should measure and include in your analysis. How many subjects should you study? Popular Lessons Biotransformation of Drugs: Create an account to start this course today. Like this lesson Share. Browse Browse by subject. Upgrade to Premium to enroll in Psychology Research Methods in Psychology.
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Types of Quantitative Design. Descriptive research seeks to describe the current status of an identified variable. These research projects are designed to provide systematic information about a phenomenon. The researcher does not usually begin with an hypothesis, but is likely to develop one after collecting data.
There are four main types of quantitative research designs: descriptive, correlational, quasi-experimental and experimental. The differences between the four types primarily relates to the degree the researcher designs for control of the variables in the experiment.
In quantitative research your aim is to determine the relationship between one thing (an independent variable) and another (a dependent or outcome variable) in a population. Quantitative research designs are either descriptive (subjects usually measured once) or experimental (subjects measured before and after a treatment). Descriptive Research Design: Definition, Examples & Types. Descriptive research is a study designed to depict the participants in an accurate way. More simply put, descriptive research is all.
Quantitative research design also tends to generate only proved or unproven results, with there being very little room for grey areas and uncertainty. For the social sciences, education, anthropology and psychology, human nature is a lot more complex than just a simple yes or no response. Types of quantitative research question. Dissertations that are based on a quantitative research design attempt to answer at least one quantitative research considerableaps.tk some cases, these quantitative research questions will be followed by either research hypotheses or null considerableaps.tkr, this article focuses solely on quantitative research .