Methodological Rigor - Assignment Example

Methodological Rigor Preamble Validity is an important component in quantitative research, and its essence and necessity cannot be overemphasized. However, there are common threats to validity that negatively impact on the quantitative research study. Self-selection, attrition, communication subjects, history, volunteer and maturation effects are some of the threats to validity in quantitative research. Common threats to validity in quantitative research Attrition is a validity threat that is attributed to the failure by subjects to seize participation in a particular study.

Under these circumstances, participants either choose not to be part of the program or totally failure to comply with the procedures of the research program. Self-selection develops when there is no representation of the samples. The self-selection process gives discretion to the subjects to belong to any group without proper consideration of representation (Vogt, 2007). Essentially, subjects in the study can decide the group to belong, or individuals may assign themselves. The third threat to validity in quantitative research is the volunteer effects (Vogt, 2007).

The threat develops when individuals do not give consent for them to be studied. The subjects who participate in the study give different information from the ones who do not give consent. The communication among subjects exhibits multiple complications and can extensively alter information. Maturation occurs due to more time being spent in a study. History effect is a validity threat because it results in extended time being surpassed during a research study (Christensen et al., 2011). Ways Of Mitigating Validity Threats There are diverse ways through which the inherent validity threats to quantitative research can be mitigated.

Vogt (2007) notes that it is important for a researcher to individual assign subjects to respective groups to deter self-selection. Subjects should be screened carefully to avoid attrition effect. History effect is a fundamental problem that can be dealt with by ensuring that the measurements are taken on every interval. Subjects should not be allowed to control the proceedings of a study (Vogt, 2007). Statistical power and How does it reduces Type I and Type II errors Statistical power refers to the capability of a test to determine with precision an effect and the authenticity of its existence (Black, 1999).

Statistical power aid in reducing type I and II errors by correctly determining existence of a relationship or effect (Black, 1999). Ideally, it reduces the incorrect rejection of a null hypothesis in type I error (Peck et al., 2011). Consequently, it reduces the rejection of a null hypothesis because it correctly determines the effect or the relationship. ReferencesBlack, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics.

London, England: Sage Publications.Christensen, L. B., Johnson, R. B., & Turner, L. A. (2011). Research methods, design, and analysis (11th ed.). Boston, MA: Allyn & Bacon.Peck, Roxy and Jay L. Devore (2011). Statistics: The Exploration and Analysis of Data. Cengage Learning.Vogt, W. P. (2007). Quantitative research methods for professionals. Boston, MA: Pearson.