Most experiments tend to fall in between the strict and the wide definition. A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.
Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation.
Experimental research is important to society - it helps us to improve our everyday lives. After deciding the topic of interest, the researcher tries to define the research problem. This helps the researcher to focus on a more narrow research area to be able to study it appropriately. The research problem is often operationalizationed , to define how to measure the research problem.
The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.
An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.
Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions. Deciding the sample groups can be done in using many different sampling techniques.
Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing. Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors. Here are some common sampling techniques:. The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test.
The design of the experiment is critical for the validity of the results. It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.
Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment. If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject s.
Those two different pilots are likely to give the researcher good information about any problems in the experiment. An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable s , is measured.
Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables.
Researchers only want to measure the effect of the independent variable s when conducting an experiment , allowing them to conclude that this was the reason for the effect. In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject or item. A cell of the output data is, for example, an average of an effect in many trials for a subject.
The output data is used for statistical analysis, e. The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results. If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results.
Loss of Subjects Mortality — All of the high or low scoring subject may have dropped out or were missing from one of the groups.
If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been. Location — Perhaps one group was at a disadvantage because of their location. The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interferes with our treatment.
Instrumentation Instrument Decay — The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would.
Data Collector Bias — The person collecting data my favors one group, or some characteristic some subject possess, over another. Testing — The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice.
History — Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.
Maturation —There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning.
Hawthorne Effect — The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factor lights increased, so did the work productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.
John Henry Effect — One group may view that it is competition with the other group and may work harder than than they would under normal circumstances. The terms refers to the classic story of John Henry laying railroad track. Resentful Demoralization of the Control Group — The control group may become discouraged because it is not receiving the special attention that is given to the treatment group.
They may perform lower than usual because of this. Regression Statistical Regression — A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.
Implementation —The treatment may not be implemented as intended. Compensatory Equalization of Treatmen t — Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. The control group begins to develop a more positive attitude about mathematics.
Experimental Treatment Diffusion — Sometimes the control group actually implements the treatment.
The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment. This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables.
True experimental research: True experimental research is the actual process of experimentally researching a subject. In true experimental research, sample groups are randomly assigned, one designated control group is assigned and only one variable can be manipulated at a time.
Mar 26, · Effective experimental research design always answers a specific question in a way that controls for differences in treatment populations and for other differences, usually by creating an experimental group and a control group, testing both populations before and after completion of . The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable. There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.
How is a non-experimental design scientific? We will look at what it means to use experimental and non-experimental designs in the course of psychological research. Experimental research designs are the primary approach used to investigate causal (cause/effect) relationships and to study the relationship between one variable and another.