Research is designated as the systematic exploration or study of sources and materials to establish concepts and reach new decisions. The primary purpose of the investigation is to advance knowledge through scientific ideas and theories, thus enhancing society. Studies gather theoretical evidence, inform action, and contribute to underdeveloped information in a field. Scientists conduct experiments to contradict or prove a hypothesis or concept. Experiments show causality, whereas correlational studies establish relationships between variables. In an experimental study, authors manipulate independent variables and measure their effect on the dependent variant. Other variables in the research remain controlled to eliminate their impact on the results. On the other hand, correlational exploration measures variables without altering any of them. Authors test if the variables change simultaneously, but they cannot determine if one variable causes a change in the other. Components of a valid experiment are constants, dependent variables, independent variables, and controls.
Extraneous Variables and Control Groups
Extraneous variables are unexpected things that affect an experiment’s result. Ways in which the impact of the aforementioned parameters can be controlled include randomization, matching, the use of experimental designs, and statistical control. Researchers randomly assign treatments to experimental groups in randomization, especially for large sample sizes. One can also match different confounding variables by distributing them equally. However, sometimes it is challenging to extend matching to all groups, and the matched characteristics might not be relevant to the dependent variable. Thirdly, experimental designs can remove or reduce the impact and role of extraneous variables. Statistical control methods can be employed in situations where all the above options do not result in any significant outcome. For example, the Analysis of Covariance (ANOVA) technique can help lower the extraneous factors’ impact in a study.
The unexpected variables in an experiment tend to affect the dependent variable, thus altering the experiment’s outcome against the researcher’s willingness. Consequently, they provide an alternative explanation to the result. For this reason, it is crucial to control them to ensure the integrity of the study is maintained. Control groups are used in experimental studies to establish the association between the cause and effect by isolating the impact of an independent variable. Investigators alter the treatment group’s independent variable and maintain it constant in the control groups. Subsequently, they compare the results in both units.
Moderator and Mediator
A mediator, also known as the mediating variable, illustrates the process of how and why two different variables (dependent and independent variables) are associated. In contrast, a moderator (moderating variable) alters the direction and strength of the aforementioned relationship. A mediator can be basic, such as a human reaction to a particular stimulus or event. When the consequence of the mediator is extensively represented, the correlation between the variables can disappear. For example, a positive association is established between examination performance and note-taking. The aforementioned relationship can be accounted for by the amount of time spent studying, called the mediating variable.
On the other hand, a moderating variable alters the relation between the criterion and predictor variables. For instance, suppose one wants to fit a regression model where the independent variable is the time spent working out and the dependent variable is the resting heartbeat. It is suspected that the more time one spends exercising, the lower the resting heartbeat. The relationship between the two can be affected by factors such as age and gender, which are the moderators. There is a possibility that every extra hour one spends exercising results in an increased drop in the heart rate among men compared to women.
Laboratory Experiment Versus Field Research
Experimental research can be done in the field or laboratory setting. Laboratory experiments are done under highly controlled circumstances, allowing accurate measurements to be taken. One advantage of the design is that it is easy to replicate because a standardized procedure is used. Furthermore, the process allows for precise control of independent and extraneous variables, making it possible to show a cause-and-effect correlation. However, the artificial setting in which the experiment is conducted can lead to unnatural behaviour that does not represent real-life practice. Consequently, generalization of the finding to natural environments might become impossible. Secondly, the researcher effects or demand characteristics can cause bias, leading to confounding variables that affect the outcomes. Generally, lab experiments have high internal validity, which comes at the cost of low generalizability (external validity).
Types of Validity
Validity in research is defined as how much a method accurately estimates what it is expected to assess. There are three categories of validity: internal, construct and external validity. First, internal validity is explained as the degree to which a study establishes the correct cause-and-effect association between an outcome and a treatment. It is the level to which the independent variable can correctly be declared to result in the observed effect. If the consequence on the dependent variable is only due to the independent variable, then internal validity has been accomplished. Secondly, construct validity is the extent to which the measure acts consistently with the theoretical hypothesis. It shows how well instrument scores reflect the theoretical construct. Lastly, external validity illustrates the degree to which study outcomes can be applied to other settings. In other words, this phenomenon defines how much a research result can be generalized past the sample to different situations and people.