Threats to Validity of Data Interpretation and Solutions - Essay Example

Some of the elements of validity are accuracy and precision. Validity however faces constant threats and this paper discusses unwarranted generalization of data and effect size as factors that may threaten the validity of data interpretation. Possible strategies for resolving the threats and reasons for success of the strategies are also discussed. Effects size is significant to interpretation validity because it relates to the meaning of analyzed data. It defines the degree of strength of an occurrence which interpretation, especially in quantitative research, undertakes.

Like other tools in inferential statistics such as p-value and confidence interval, effect size helps in determination of whether to accept or reject a null hypothesis. It however becomes a threat when a researcher interprets significance, through decision on hypothesis, without regard to effect size and may lead to a wrong interpretation for significance of a relationship when such a relationship is actually insignificant. Similarly, failure to consider effect size may lead to overestimation of significance of a relationship in a study (Onwuegbuzie, Jiao and Bostick, 2004).

A statistical approach to estimation of effect size, based on power analysis approach, is a solution to the threat to validity in data interpretation. This ensures that sufficient effect size is used in a study to inform sample size. Consequently, a minimum sample size and confidence interval can be calculated to ensure that determination of significance of relationships is accurate and precise. The strategy promises effectiveness because of its statistical approach that theory supports. The objective scope of its application in power analysis and eliminates potential bias or underestimation by a researcher to establish a high level of accuracy (Aberson, 2011).

Unwarranted generalization is another threat to validity at the interpretation stage of a study. It involves making inferences, from findings, to a population when the findings are not reflexive of the population. Application of a non-representative sample is the major cause of the threat that may result from sampling bias or from use of non-probability sampling technique (Munck, 2009). Convenience sampling is an example and uses readily available participants but this may not generate a reflexive sample to the target population.

Researcher bias towards an already identified theory to a study or a desired outcome may also influenced selection of a non-representative sample. Using probability-sampling techniques such as random sampling approach, stratified random sampling, and systematic sampling are some of the ways that can help in eliminating the bias to generate a representative sample and eliminate chances of unwarranted generalization. The proposed solution is effective because the proposed sampling approached applied statistical theories and therefore guarantees a representative sample from which valid generalization can be made (Babbie, 2012).