Credible Information and Data

Credible Information and Data

The credibility of resources can be evaluated using different criteria. Notably, the CRAAP model can be used in this evaluation. This model focuses on the currency, relevance, accuracy, authority, and purpose of the resources (Esparrago-Kalidas, 2021). However, bias can affect the results obtained in any research, which alters the credibility of resources. Accordingly, this paper discusses different types of bias and explains how they can affect the results obtained during the research.

Sampling Bias

According to Andringa and Godfroid (2020), sampling bias happens when the likelihood of selecting specific members of the target population is higher than that of choosing other members. Generalizability is impeded by sampling bias (Andringa & Godfroid, 2020). Generalizability refers to the applicability of the findings of a study to other scenarios or the entire population (Andringa & Godfroid, 2020). Furthermore, this type of bias occurs in both non-probability and probability sampling.

According to Andringa and Godfroid (2020), sampling bias is categorized into non-response, undercover age, self-selection, and survivorship. Self-selection occurs when there is a higher likelihood of respondents with specific manifestations participating in a study than the members of the general population (Andringa & Godfroid, 2020). Non-response occurs when there is a difference in characteristics between respondents who refuse to participate and those who participate in the study (Andringa & Godfroid, 2020). An example of sampling bias can occur in studies involving pain research. Thrill-seekers are more likely to enroll, unlike the general population. Findings from this study can be skewed and fail to reflect the general population’s perceptions.

Selection Bias

According to Nohr and Liew (2018), selection bias occurs when a researcher chooses the respondents inappropriately. The chosen respondents are not representative of the general population (Nohr & Liew, 2018). Findings from this type of study fail to accurately reflect the target population’s characteristics. Types of selection bias include self-selection, protopathic, prevalence, and referral (Nohr & Liew, 2018). An example of selection bias can occur when people volunteer to participate in a study (Nohr & Liew, 2018). These volunteers may exhibit characteristics that are different from those of the majority of the target population. An example is volunteers in a study to assess and improve dietary habits. These volunteers are most likely to be health-conscious. Therefore, findings from the study lack generalizability.

Interviewer Bias

According to Jager et al. (2020), interviewer bias occurs when the interviewer’s opinions, preferences, or expectations impede the objectivity of the interview and skew the findings. The interviewer uses an unconscious criterion and preconceived ideas to assess the respondents (Jager et al., 2020). This may lead to the rejection of skilled and competent respondents at the expense of those with lower qualifications (Jager et al., 2020). Types of interviewer bias include affinity, confirmation, anchor, and stereotyping (Jager et al., 2020). An example is when an interviewer prefers female candidates to fill a specific position, such as the job of a receptionist. A competent and skilled male applicant or respondent who scored higher in the interview is most likely to be turned down in favor of a female applicant. This type of bias can be mitigated using a standardized guide or questions during interviews and adequate training of interviewers (Jager et al., 2020).

Response Bias

According to Fleming (2021), response bias occurs when respondents intentionally provide misleading and inaccurate answers to questions. This is common when online surveys and questionnaires are used to collect data (Jager et al., 2020). Response bias is common when researchers fail to display neutrality in the structured questions. When the questions are judgmental, respondents are most likely to give wrong feedback. Furthermore, it can be caused by the respondents’ perceptions that researchers need questions to be answered in a specific manner (Jager et al., 2020). This can be averted by upholding neutrality and ensuring respondents’ anonymity, privacy, and confidentiality (Jager et al., 2020). An example of response bias can occur when a survey is conducted among high school students to evaluate the prevalence of drug and substance use (Fleming, 2021).

Observation Bias

According to Andringa and Godfroid (2020), observation bias occurs when the opinions or expectations of the researcher interfere with the accuracy of data collection and recording. It is common in observational studies that involve the study of the behavior of respondents (Fleming, 2021). Observer bias can be minimized via different methods, including blinding, triangulation, and adequate training (Fleming, 2021). Blinding occurs when both the observer and respondents are unaware of the study and control group. Triangulation entails the utility of numerous observers (Fleming, 2021). Examples include when a researcher fails to report a deterioration of participants in the control group but detects changes in the study group (Fleming, 2021).

Leading Questions and Wording Bias

According to Andringa and Godfroid (2020), leading questions and wording bias occur when a researcher internationally formulates questions that influence the type of responses obtained from participants. Findings from these types of questions are skewed and fail to give the respondents accurate information, knowledge, or opinions (Andringa & Godfroid, 2020). This can be disastrous to organizations, especially when these findings guide policy formulation or specific interventions (Andringa & Godfroid, 2020). Wording bias and leading questions always aim to fulfill the researcher’s goals and objectives. This bias can be mitigated by using various techniques, such as asking open-ended questions or using rating scales to frame questions (Fleming, 2021).

Sponsor Bias

According to Jefferson (2020), sponsor bias occurs when the findings or conclusions drawn from a study are distorted to fulfill the interests of the sponsor of the study. This type of bias is common when the sponsor is a company or industry with vested interests, such as producing or supplying a specific good or service (Jefferson, 2020). Such bias is deleterious to research because it misleads the general public and puts them at risk of harm that may accompany the implementation of the distorted findings. An example may occur when researchers fail to report an adverse effect of a drug detected during preclinical trials to facilitate its entry into clinical trials (Jefferson, 2020). This puts the well-being of human subjects at risk.

Conclusion

Bias alters the credibility of findings obtained in research. Researchers and study participants should aim to minimize bias to improve the study’s credibility. Examples of these interventions include researcher training, maintaining neutrality, and blinding. Study bias includes sampling, selection, sponsor, wording, observation, response, and interviewer bias.

References

Andringa, S., & Godfroid, A. (2020). Sampling Bias and the Problem of Generalizability in Applied Linguistics. Annual Review of Applied Linguistics, 40, 134–142. https://doi.org/10.1017/S0267190520000033

Esparrago-Kalidas, A. J. (2021). The Effectiveness of CRAAP Test in Evaluating Credibility of Sources. International Journal of TESOL & Education, 1(2), 1–14.

Fleming, S. T. (2021). Managerial Epidemiology Cases and Concepts. https://drive.google.com/file/d/1cEbXDcnG2dgCVSey6m7523CSumwGGj75/view

Jager, K. J., Tripepi, G., Chesnaye, N. C., Dekker, F. W., Zoccali, C., & Stel, V. S. (2020). Where to look for the most frequent biases? Nephrology, 25(6), 435–441. https://doi.org/10.1111/nep.13706

Jefferson, T. (2020). Sponsorship Bias in Clinical Trials: Growing Menace rr Dawning Realisation? Journal of the Royal Society of Medicine, 113(4), 148–157. https://doi.org/10.1177/0141076820914242

Nohr, E. A., & Liew, Z. (2018). How to investigate and adjust for selection bias in cohort studies. Acta Obstetricia et Gynecologica Scandinavica, 97(4), 407–416. https://doi.org/10.1111/aogs.13319

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So far in the course, we have learned that healthcare leaders and managers must seek out credible information and data when decisions are to be made and when educating others. It is vital that they review at least three, preferably more, sources to determine the credibility of information. In order to know they are using credible data, it is important for them to have a solid understanding of how data and information are collected and compiled. Knowing what processes and procedures should take place to produce credible data is important. Familiarity with the scientific method is a must.

All studies are subject to bias, some more than others. Even with potential bias, research is critical to literally everything we know and do in the healthcare services field. Knowing research is not perfect, leaders and managers can still use data effectively.

There are many types of bias that can affect research outcomes. For your project this week, thoroughly research and discuss the following types of bias that can affect the credibility of resources. Please give specific examples of how these types of bias can alter results of studies.

Sampling bias
Selection bias
Interviewer bias
Response bias
Observation bias
Leading questions and wording bias
Sponsor bias
To support your work, use your course and textbook readings, credible Internet sources, and also use the South University Online Library. As in all assignments, cite your sources in your work and provide references for the citations in APA format.

Submission Details:
Your assignment should be addressed in a 3–4-page document, not including your title and reference page. Depth and discussion from thorough research is needed to complete this heavily weighted assignment.

Submit your documents to the Submissions Area by the due date assigned.