Research design: Rival causal factors

  1. Research design is basically the plan for a study in which it gives the who, what, where, why and how of a study and/or investigation. The rival causal factors are variables other than the independent variable that may be responsible for the outcome of the study. There are two types of rival causal factors which are internal and external factors. Internal factors are within the study itself that tend to invalidate the conclusions. On the other hand, external factors are elements outside one’s immediate study that may impair the conclusion. The difference is that external factor may limit one’s ability to larger populations and with internal factor, another variable might be present. Examples of internal factors are selection bias, history, testing, maturation, statistical regression, etc. Examples of external factors are testing effects, reactivity, multiple treatment effect, etc. The rival causal factor that impacts most studies is selection bias. It is a kind of error that takes places when the researcher decides who is going to be studied. Selection bias can also occur when individuals volunteer for a study. An individual can have a characteristic that makes them different from the rest of the participants which can influence in the outcome. 
  • Rival causal factors are variables that could also explain what the researcher was studying, and could make people skeptical of the findings due to possible other explanations. In research, many factors need to be explored within a study and the correlations between things should be explained so that these factors are all addressed. By exploring geographical location, gender, race, social events, and other common things that are explored in studies, every aspect of the situation can be looked at and adequately addressed. History can affect a study if a major event related to the subject occurs, whereas maturation may affect the subjects in a way where their actions change as a result of time passing. Within these factors, the most common rival causal factor is selection bias. Selection bias is a problem as well because the researcher may be picking subjects that fit the bias of how they feel the study will go, which may be self-fulfilling by the end. 
  • Rival causal factors are variables that might be responsible for the outcome without being the treatment and affect the validity of the research. There are eight internal validity threats: history, maturation, testing, instrumentation, regression, selection bias, experimental mortality, and selection maturation interaction. Then there are four external validity threats: testing effects, reactivity, multiple-treatment interference, and selection bias. The history factor deals with specific events that happened before the first and second measurements. Whereas, maturation is the fact that participants change on their own within time. The classic experimental design controls for history and maturation by having the random assignment, first observation, treatment, then the second observation. There will also be a control group that goes through the same process except without the treatment. Having a control group is important because that is how they can minimize threats. Maturation tends to have more of an impact on longitudinal studies because time passes and certain aspects of the population’s lives can change which could have an impact on the outcome.  
  • Research design is the planning of the study and takes into account the who, what where, when, why, and how of the study. Within a study there are natural oppositions that can influence the study and are known as the rival causal factors. Rival casual factors are broken down to two sections internal and external, these are variables outside of the treatment that may be responsible for the outcome or results. The way in which the classical experimental design controls for history and maturation can be seen within its five regiments for conducting a study. Classical experimental design uses random assignment, an experimental group, a control group, pre-testing, and a post testing. With all of this it can take into account for maturation which is the time passing from when the experiment was first conducted, if the first subject study was done five years ago the participant may have matured in different aspects such as the way they think or their physical abilities, having a pretest and a post test can help identify the changes of the participants actions or physical abilities from the initial experiment and the experiment done after a five-year gap. The rival casual factor that impacts most studies is selection bias. The study can become corrupt due to the researchers selection of the people within a study and could potentially ruin the study if a person has a unique characteristic that makes them stand out from the rest and can influence the study in a negative way.
  • Research design control is defined mostly as control being the key word. It highlights the work done by the researcher. It is mostly looking for the effort the researcher put into the paper to remove any unwanted influences. Unwanted influences would be bias as well. Mostly this can be avoided by having an efficient control or also known as a placebo group. Rival factors feed into any unwanted influences as stated above. This could simply be anything that could cause a different result that isn’t the specific treatment at hand. Two big rival casual factors are history and maturation. History is when observed events are affected by previous/historical events instead of the specific treatment. An example of this is if you are testing a student on their math program at school. A lack of knowledge could stem from a student who failed to study for the examination and not reflect the direct treatment. In this case, this could be avoided by having each student create a log of study time and set an amount of time that studying needed to be performed or not performed at all. Maturation is when observed events are changed by natural maturation which is not related to the treatment. Using the same example would be if one student was simply more intelligent then the other. It does not effectively test the math program because they are at different intellectual levels. These two elements are extremely difficult to avoid and at most times impossible. In this case a simple IQ test could be a requirement prior to participating in the study to ensure all students have a similar intellectual level. The rival factor that influences the most studies is  bias. Bias is something some individuals may not even realize they have and that is what makes it even more difficult to decent and eliminate from the study at hand.
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