Factorial Design This Study Seeks to Answer

Factorial Design

This study seeks to answer the question of what individual treatment modes have been found to be the effective for treating this population as stated by the study. Complementing that question is the fact that the study is also attempting to determine whether certain treatments are more effective than other treatments for adolescent boys between the ages of 14 -- 16 who have been diagnosed with conduct disorder. In order to ascertain if one of the proposed treatments is more effective than the other, the study is collecting qualitative and quantitative data which will then be analyzed by the researchers. In addition to the collection of data, variables will be presented and tested using a variety of methodologies.

One method that will be employed by the study is the experimental design most commonly referred to as a factorial design method. The factorial design method is a methodology that allows the researcher(s) to determine the effects of a number of different treatments. For example; in this study the factors that could be studied include; family therapy, social skills training, administration of placebos, and administration of fluoxetine. The factorial design methodology allows the researcher to look at the two or more factors simultaneously, while at the same time determining the effect (if any) the factors have on the subjects. Since there are at least four different factors in which this study is interested, a factorial design provides the researcher with the knowledge of what factors actually influence the outcome, and what factors do not.

One recent study determined that "traditional behavioral intervention studies are typically large-scale randomized controlled trials (RCT's) in which the goal is to confirm the superiority of a new program over an existing one" (Nair, Strecher, Fagerlin, Ubel, Resnicow, Murphy, Little, Chakraborty, Zhang, 2008, p. 1355).

The study has as its objective to determine whether one method of treating CD is more effective than another, especially when certain treatments are paired with other treatments (such as behavioral intervention paired with pharmacology). The study does not meet the large-scale randomized controlled trial requirement, therefore the results may be somewhat biased. To overcome that bias, the experiment will be administered to all participants notwithstanding the particular treatment received.

The study is seeking to determine what factors cause the most (and the least) effect on the participants. The reason behind using the factorial design methodology in this particular case is that it is known as a method for increasing the amount of data and knowledge gathered from a study without materially increasing the number of participants or expenses. By designing a method for determining the influence of specific factors a researcher can tell more about interactions especially in behavioral studies. As Nair et al. determined however, "FFDs (factorial fractional designs) should be supplemented with follow-up experiments in the refining phase so any critical assumptions about interactions can be verified" (p. 1354).

One of the advantages of the factorial design is that it provides information that physicians and other medical professionals find helpful regarding medical treatments in behavioral scenarios (Everitt, Moss-Morris, Sibelli, Tapp, Coleman, Yardley, Smith, Little, 2010). In this case, the effects of the drug factors being studied will help those providing treatments to male adolescents diagnosed with CD is well worth the efforts made to study those factors.

The experiment will necessarily include fractional factor design due to the already accomplished screening process in which the participants have been selected.

There are nine participants receiving one of three treatments. A factorial design would allow for data gatherings tools such as an additional survey be completed by all nine participants. The survey or questionnaire could details the types of drugs that they have (or have not) abused in the past, as well as the timeframe in which such abuse took place. Once the survey or questionnaire has been completed, the data can be compared to the other data gathered to determine if the treatments are influenced in any manner by the specific drug abuse, and to what degree. A graph of the answers as broken down by category and question can then be created from the data. Factorial designs allow for a much stronger analysis of the data. The graph(s) helps in determining exactly what influence the various factors have on the outcome.