Phenomenology, grounded theory, or ethnography: which approach is best?

When considering which method of qualitative research to use, it really is the question that matters. Take the example: What is the experience of waiting for service in a hospital emergency room? Asking this will undoubtedly raise more unknowns than answers. We will frame the question three ways in order to fit phenomenology, grounded theory, and ethnography.
If we leave the it unchanged, the experience of waiting for service in a hospital emergency room (ER) is best suited for an approach such as phenomenology. This broad design probes people’s general experience and perceived meanings associated with phenomena (Smith, 2008). Knowing how people identify with waiting in the ER can provide researchers with data to improve hospital experiences.
There are three basic elements to phenomenology. The first is a process known as bracketing. Researchers must table their preconceived ideas in order to gain more representative views of other people’s perspectives and beliefs, which leads to the second second element of phenomenology known as intuiting. These two foundational principles enable collected data to be qualified through the third step, analyzing and describing. This is when the researcher categorizes and extracts significant meaning from an experience. Once variables of the experience are qualified, more specific methods of inquiry could be used. Two of these methods are grounded theory and ethnography.
The grounded theory is not suited for the general experience of waiting in an ER since it is geared toward accounting or understanding people’s actions in a scenario (Polit & Beck, 2012). If the question was framed: How do people cope with the experience of waiting for service in a hospital room, then the grounded theory could be used to identify coping mechanisms or people’s actions within the experience of waiting in the ER.
If the question was designed to identify the behaviors of a specific population, ethnography could be used. Ethnography is the evaluation of a specific culture’s framework within phenomena (Hoey, 2011). The data would be all over the place if we used the general population. A target population needs to be identified before using an ethnographic approach. Our country is the great melting pot, so the question would need restructuring. One example of an ethnographic approach could be: How do Amish people experience waiting for service in a hospital emergency room?
Phenomenology, grounded theory, and ethnography are all excellent research methods in their own right. It is the framing of the question that determines the method of inquiry and the ability to explain the silhouettes that shape our human experience.
Hoey, B. (2011). What is ethnography? Retrieved February 20, 2012 from
Polit, D. and Beck, C. (2012). Nursing research: Generating and assessing evidence for nursing practice [9th ed.]. Philadelphia, PA: Lippincott Williams & Wilkins.
Smith, D. (2008). The Stanford Encyclopedia of Philosophy. Phenomenology. Retrieved February 20, 2012 from

Random Bias vs. Systemic Bias

I tried to keep this short and limited the controversial elements as this was a topic of discussion in my nursing research class:

Random and systemic bias can occur any time in any study. Early apology for ranting but this is my pet peeve. Surprisingly, bias has been shown to exist too often in renowned journals such as the Journal of the American Medical Association (JAMA). This problem has been building upon itself for decades (Freedman, 2011). As a result, we have been travelling down a road filled with iatrogenic injury and death. Amazingly enough, this has also been reported in JAMA (Starfield, 2000) with medical care listed as the third leading cause of death in the United States.  Starfield (2000) points out that complexities related to this problem have multiple factors of which we all agree. Unfortunately, what we cannot count out are the implications of EBP from historically biased research data. Freedman also notes that biased research has helped shape modern standards of practice such as hormone replacement therapy, coronary stents, and low-dose aspirin used to mitigate cardiovascular events. According to Freedman (2011) Dr. John Ioannidis has demonstrated a 40% error rate in 49 of the most respected and cited research articles in the last 13 years.
When it comes to real-world practical prevention of bias, it is extremely difficult to eliminate in the medical field since research is often driven from inventions that incorporate pharmaceutical or mechanical means and funding sources often drive the research. Nursing tends to follow medical, so we are just as vulnerable. Even the gold standard, randomized trials, has a reported rate of 25% inaccuracies due to bias (Freedman, 2011).
Random bias occurs when a participant or researcher makes an assumption about data (Polit & Beck, 2012). The assumption is not an accurate representation of what is or has occurred in the study (Peterson, 2009). One example is food diaries. They have been reported to often be inaccurate. People forget and underrepresent consumption or over represent their food intake. One strategy to reduce bias here would be to photograph every meal. The researcher could review the diary, make comparisons with pictures and have tighter correlation with reality.
Systemic bias is a little different. One example is The China Study. The primary researcher Dr. T. Colin Campbell conducted the study under the premise that there was something inherently harmful with consuming animal protein. The data he presented in his book revealed this correlation (Campbell & Campbell, 2006). Further analysis of the data reveals an inverse correlation with animal protein consumption and other foods were found to correlate positively with all cause mortality, (Minger, 2011). How do we avoid this type of bias? The solution may be three-fold: Utilization and  re-utilization of multiple null-hypotheses based upon ongoing collection of data and representation of all data positive, negative, and ambiguous.
During any study we need to remain vigilant in order to reduce random or systemic bias. Multiple contrarian viewpoints should be integrated as this my help identify confounding factors or other variables in need of deeper analysis.
Bogl, L., Kaprio J., Korkeila, M., Pietilainen, K., Rissanen, A., Westerterp, K., and Yki-Jarvinen, H. (2010). Inaccuracies in food and physical activity diaries of obese subjects: complementary evidence from doubly labeled water and co-twin assessments. International Journal of Obesity. Mar;34(3):437-45.
Campbell, T. and Campbell, T. (2006. The China Study: Startling implications for diet, weight loss, and long-term health. Dallas, TX: BenBella Books
Freedman, D. (2011). Lies, Lies, Damned Lies, and Medical Science. The Atlantic Monthly. Retrieved February 6, 2012 from
Minger, D. (2011). The China Study, wheat, and heart disease; [sic] Oh my! Raw Food SOS: Troubleshooting on the raw food diet. Retrieved February 18, 2012 from
Peterson, (2009). The Mathematical Tourist. Mathematical Association of America. Retrieved February 18, 2012 from
Polit, D. and Beck, C. (2012). Nursing research: Generating and assessing evidence for nursing practice [9th ed.]. Philadelphia, PA: Lippincott Williams & Wilkins.
Starfield, B. (2000). Is US Health Really the Best in the World? The Journal of American Medical Association. 284(4):483-485. doi: 10.1001/jama.284.4.483

A Research Idea

I was doing my rounds reading some of the great blogs I follow and came across one topic I am really starting to appreciate, the importance of our gut physiology. 
My real love and curiosity is related to optimum physiological functioning arising from primary and secondary holistic preventive methodologies. This goes well beyond nursing and includes a variety of scientific disciplines and causeways. The connection to nursing is often opaque and unrefined, even though the relationship is stronger than we realize.
One clinical problem requiring greater understanding are the long-term implications antibiotics have on our immunity, metabolism, and disease progression. This is not generally considered, since antibiotic use is standard practice and routinely administered prophylactically and for nonemergent circumstances. I am not implying they should not be used. The salient point here is not their short-term benefit, rather their long-term implications.
This type of study would be observational in nature, reviewing decades of antibiotic administration and bowel disorder data. It could take on meta-analysis like characteristics. Factors affecting feasibility would not include cost since the study could follow current and past administration of an antibiotic use. The focus could be narrowed to ciprofloxacin since it is considered one of the most benign perturbative antibiotics (Dethlefsen, Huse, Relman & Sogin, 2008). Pooling available data would generally require just the researcher’s time.
Further narrowing the focus: identifying previously known perturbations antibiotics play on our gut flora may elucidate implications with gastrointestinal disorders such as celiac spree, inflammatory bowel disease, and Crohn’s disease. Feasibility of the implications applies to the type of practicing clinician. How preventive and holistic a practitioner may be may determine the research’s applicability. From an integrative or holistic perspective, this information can provide significant implications for treatment.
Other problems that may arise: confounding the extent antibiotics play a role when consideration other factors such as vaginal or cesarean birth (Bessi, et Al., 2010), underlying gastrointestinal disorders, chronic disease status, and functional or structural alterations. There may be other factors requiring further exploration. The goal with this type of inquiry is twofold: to reveal potential harm unnecessary use of antibiotics have on our health and to understand the importance healthy gut flora diversity contributes to our long-term health.
Dethlefsen, L., Huse, S., Relman, D., and Sogin, M. (2008). The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biology. 6(11): e280. Doi: 10.1371/journal.pbio.0060280
Bessi, E., Biasucci, G., Morelli, L., Retetangos, C., Riboni, S., and Rubini, M. (2010). Mode of delivery affects the bacterial community in the newborn gut. [Abstract].  Early Human Development. Jul(86), Suppl. 1:13-15. Retrieved February 10, 2012 from

Qualitative vs. Quantitative Research

Qualitative research (QlR) and quantitative research (QnR) each have their own strengths and weaknesses. When it comes to the rubber meeting the road, both types of research are immensely important. Some researchers have argued that neither is genuinely independent of each other while others go as far as proclaiming, “There is no such thing as qualitative data” (Writing@CSU, 2012). I personally believe that both are so intricately linked, one without the other is generally inadequate for effective evidenced-based treatment outcomes. Both types should be considered in order to address behavior and clinical issues. Now on to my brief and limited perspective about some of the shortcomings . . .
If you are a strict researcher interested in facts substantiated by definitive measurements, then qualitative research is not your thing. Qualitative research focuses on holistic processes through a narrative and subjective analysis (Polit & Beck, 2012). Since people’s perspectives are the receptacle of inquiry, numerical data, questionnaires, and other inventories are generally unnecessary. The researcher often works with the subjects in the field to learn about the interested phenomena. The results can often reflect the researchers’ original bias or the subjects’ worldview. It may be difficult to determine exact mechanisms of underlying principles since the researcher is striving to find out the how or why phenomena occur in the absence of scientific experimentation (Polit & Beck, 2012). It is generally observational in nature and as a result is challenged when determining causal factors.
Quantitative research seeks to understand variables and causal pathways with quantifiable data and controls (Answers Research, 2011). What QnR often fails to do is see the 30,000-foot picture since it is focusing on specific variables. Since the bigger picture is usually missed, additional or alternative causal factorsn, and confounding variables are overlooked. Even though this type of inquiry’s lens is limited, some of the things touted as strengths are statistical power and large amounts of data. This can be expensive and complicated. As a result, financial interests can creep into bias, reported, data, and outcomes (Freedman, 2011). QnR also tends to be shorter in duration not allowing for deeper, long-term statistical intervention analysis. In addition, misuse of data, simple errors, and research bias can reduce the validity and accuracy of the underlying hypothesis (Freedman, 2011).

Answers Research, (2011). Articles: Quantitative vs. qualitative. Retrieved February 6, 2012 from
Freedman, D. (2011). Lies, Lies, Damned Lies, and Medical Science. The Atlantic Monthly. Retrieved February 6, 2012 from archive/2010/11/lies-damned-lies-and-medical-science/8269
Polit, D. and Beck, C. (2012). Nursing research: Generating and assessing evidence for nursing practice [9th ed.]. Philadelphia, PA: Lippincott Williams & Wilkins.
Writing@CSU, (2012). The qualitative vs. quantitative research debate. Colorado State University. Retrieved February 6, 2012 from http// gentrans/pop2f.cfm