Explain the difference between experimental and non-experimental research?

Answer 1

This answer could have dozens of lines but essentially, in experimental research it's mandatory to manipulate the variables. In non-experimental studies you don't manipulate them.


E.g.: if you want to see if fear is related to low self-esteem you can:


a) create fearful situations, investigate the self-esteem and there you have an experimental study;
or
b) give a questionnaire to a group of persons asking when they feel fear and asking a series of questions that can access self-esteem.

Answer 2

Thus far, we have explained that for experimental research we need: * a hypothesis for a causal relationship; * a control group and a treatment group; * to eliminate confounding variables that might mess up the experiment and prevent displaying the causal relationship; and * to have larger groups with a carefully sorted constituency; preferably randomized, in order to keep accidental differences from fouling things up. But what if we don't have all of those? Do we still have an experiment? Not a true experiment in the strictest scientific sense of the term, but we can have a quasi-experiment, an attempt to uncover a causal relationship, even though the researcher cannot control all the factors that might affect the outcome. A quasi-experimenter treats a given situation as an experiment even though it is not wholly by design. The independent variable may not be manipulated by the researcher, treatment and control groups may not be randomized or matched, or there may be no control group. The researcher is limited in what he or she can say conclusively. The significant element of both experiments and quasi-experiments is the measure of the dependent variable, which it allows for comparison. Some data is quite straightforward, but other measures, such as level of self-confidence in writing ability, increase in creativity or in reading comprehension are inescapably subjective. In such cases, quasi-experimentation often involves a number of strategies to compare subjectivity, such as rating data, testing, surveying, and content analysis. Rating essentially is developing a rating scale to evaluate data. In testing, experimenters and quasi-experimenters use ANOVA (Analysis of Variance) and ANCOVA (Analysis of Co-Variance) tests to measure differences between control and experimental groups, as well as different correlations between groups. For details about these two common types of tests, refer to the Statistics unit. Survey and content analysis are also detailed elsewhere in this Website. Since we're mentioning the subject of statistics, note that experimental or quasi-experimental research cannot state beyond a shadow of a doubt that a single cause will always produce any one effect. They can do no more than show a probability that one thing causes another. The probability that a result is the due to random chance is an important measure of statistical analysis and in experimental research. In the Methods section, we provide more details and a step-by-step scenario, as well as add steps to those so far described.

Answer 3

In quasi-experimental designs, there can be no random assignment. An example would be asking hourly employees how they feel about upper management. There is a limited amount of randomization that can be done. yuppers!!!!!!!!!!!!!

Answer 4

In an Observational study, the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations.

In Observation study, there is no human intervention

In an Experimental study, the researcher manipulates on of the variables and tries to determine how the manipulation influences other variables.

In an Experimental study, there is human intervention.

Answer 5

Experiments study the influence of one or more variable on another. They manipulate the first variable, or variables, which are called independent variables and study the impact it has on the final variable, called the dependent variable. The researchers can make causal conclusions based on the results.

Quasi-experiments are those where the "independent variable" cannot be manipulated by the researchers. These include correlational studies where the researchers examine the relationships between two or more variables that already exist; and studies with subject variables, which are preexisting differences between the subjects or participants being studies and can therefore not be manipulated. Quasi-experiments are still very useful and important, but no causal conclusions can be drawn from them.

ex. An experiment might study the effect of studying on test scores by having two groups of people study for different amounts of time and then write the same test. The independent variable is the amount of time a person studied (say, 1 hour or 5 hours) and the dependent variable is their score on the test. The results might lead the researchers to conclude that studying increases tests scores.

A quasi-experiment might study the relationship between the scores on a test of early-birds and night-owls by having 10 of each group take a test. The subject variable is whether the participant is an early-bird or a night-owl; the experimenter cannot manipulate this variable. The dependent variable is the score on the test. If there is a difference between the groups' scores, the researcher can only say that night-owls and early-birds differ in their test scores.

Answer 6

Experimental research involves manipulation of variables to establish cause-and-effect relationships, with a control group to compare results. Non-experimental research, on the other hand, does not involve manipulation of variables and focuses on observing and describing phenomena without control groups.

Answer 7

Descriptive research does not explain cause-effect. However, as with experimental research, descriptive research may be used to study animal behaviour