Credibility, Reliability and Validity of Data - Essay Example

Validity of data focuses on the whole tentative concept and establishes whether the outcome obtained congregates all the necessities of the scientific research method. If the data fails to meet these requirements, then it is said to be unreliable (Hurtado et al 21). The following are the reasons why data may be lack credibility, reliability, and validity. The principle of credibility tests the believability of data. The credibility of data may be affected by vagueness. Lack of transparency in research may create doubt which may make it difficult to evaluate data.

Failure to consider quality control may also affect the credibility of data. Misspelled words may also be with the meaning of the information. Carelessness and ignorance by the researcher may give rise to such errors. When accuracy tests are carried out, the data may be termed as vague and be disqualified. Failure to give the author’s credentials such as biographic information, contact information, executive authorship, author’s repute, and the site may affect the credibility of data. Timeliness is key since information is fluid in nature.

Information changes as time changes especially in the fields of technology, business, and medicine. The narrowness of data may also contribute to its lack of credibility. If the source fails to reveal the need for wholeness and exactness, it is termed as unbelievable. Another reason is the lack of suitability of the anticipated audience and purpose ((Hurtado et al 22). The intention and the rationale of research should be apposite to the requirement so that you can put them into account. The requirement of testability is that there is duplication of statistically momentous domino effect.

Testability may fail if the experimental conditions are changed. If the tools of research are changed, the outcomes will automatically change. Failure to keep accurate time may produce unreliable data. If the researcher fails to obtain measurements several times, the chances of malfunctioning increase, and this affects the reliability of the collected information. Lack of reliability of data may also arise when the investigator overstates the significance of data drawn from the too diminutive a sample (Hurtado et al 24).

Generally, if the information given is not an exact representation of realism, then the test of reliability fails. The reason is that gives reasons to be doubtful of its accurateness. If the information fails to gauge or reflect the explicit phenomena claimed, it is termed invalid. If the data was initially meant to reflect people’s attitude towards beer and ends up gauging their drinking habit then it is invalid. Failure to go behind all the steps of a scientific research method results in the invalidity of information in terms of the arrangement of its investigational design (Hurtado et al 21).

Invalidity may also be caused by a lack of causal relationships connecting the control groups.