Discussion: The Logic of Inference: The Science of Uncertainty

Example Week One Discussion and One Response

Research Article

Surani et al. (2017) assessed social media use by healthcare workers; they wanted to understand

how the healthcare workers used social media in their daily practice and if they recommended

the use of social media to their patients. It was a useful article that provided insight into how this

group of clinicians were using social media and how the groups of providers differed in their use.

Variables

Dietz and Kalof (2009) note that the dependent variable is represented by the Y in the Y= f(X) +

E notation and that X is the independent variable while E is the error. In this study, the

independent variables were age, profession, and gender. The dependent variables were the type

of social media used, time using social media, understanding of organizational policies and

recommendations made for patients to use social media.

Sources of Error

In the study cited, there are two potential sampling errors. The sample size was small with only

366 participants, and it was in one geographic region in the US Southwest. (Surani et al., 2017).

One sampling error is the small sample size, and the other is the limited region represented by

the sample (Dietz & Kalof, 2009). Both of these errors would limit the author's ability to

generalize results outside the region. The authors do acknowledge these sampling limitations and

recommend that the study is repeated with a larger and more diverse audience (Surani et al.,

2017).

The Model

Thinking about how the model could be wrong for this study could relate to the potential errors

in the study. It was a small and local sample that lacked generalizability, but, did offer them

insights into the social media culture in their organization. So ultimately, they were able to better

understand how their clinicians were using social media. In that regard, the data helped them

understand their world (Dietz & Kalof, 2009).

Student Name

References

Dietz, T., & Kalof, L. (2009). Introduction to social statistics: The logic of statistical

reasoning. West Sussex, United Kingdom: Wiley-Blackwell.

Surani, Z., Hirani, R., Elias, A., Quisenberry, L., Varon, J., Surani, S., & Surani, S. (2017).

Social media usage among health care providers. BMC Research Notes,10(1), 1-5.

doi:10.1186/s13104-017-2993-y

Wk 1 response R8210

Hi Classmate,

As a social change agent, your work in understanding how emotional intelligence can impact

nursing students and student outcomes is important. The article you mentioned supports the

importance of incorporating emotional intelligence in school of nursing curricula (Benson et al.,

2009). Your research will lend more support to this important need and hopefully improve

nursing school student retention and retention of new graduate nurses.

You mentioned several errors related to sampling and randomization. The authors did limit the

sample to 25 students in each of the four years of an undergraduate program (Benson et al.,

2009). It is likely that the study is generalizable beyond the program or perhaps the region where

the school is located (Dietz & Kalof, 2009). However, the study still makes a positive

contribution to our understanding of the importance of integrating emotional intelligence in

curricula. It is also important because the results were significant across the four groups which

support repeating the study with a larger sample.

Student name

References

Benson, G., Ploeg, J., & Brown, B. (2009). A cross-sectional study of emotional intelligence in

baccalaureate nursing students. Nurse Education Today, 30(1), 49-53.

https://doi.org/10.1016/j.nedt.2009.06.006

Dietz, T., & Kalof, L. (2009). Introduction to social statistics: The logic of statistical

reasoning. Wiley-Blackwell.