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Indeed, as Muntaner (2003) posited in her research, qualitative methods can be included in a dominantly quantitative research design "in situations where qualitative research adds knowledge that would not be available via quantitative methods" (p. 55). Through a mixed-methods design, the researcher can provide better analyses and stronger interpretations and recommendations through balanced strengths of data reliability and validity -- that is, the achievement of "triangulation" in the research study.
IIA. Based on your reading of books such as Beaglehole (1993), describe what you know about observational epidemiology as a research approach and compare it to experimental studies. Describe some of the designs within each, e.g., RCT, case-control studies, etc.
Observational and analytical/relational studies provide different results and answer different research questions and hypotheses when compared to the experimental approach. In observation and analytical/relational studies, the highest kind of analysis that can be done is correlational and not causal.
This is reflective in the types of methods used under this approach, which include ecological, cross-sectional, case-control and cohort studies.
Ecological studies are conducted as preliminary research that could eventually lead to the development of hypotheses and further research about the phenomenon (i.e., higher level of analysis, such as analytical/relational or experimental). Ecological studies use the descriptive approach because it only shows the behavior of the variables under study; data and information are mainly represented as facts, and if the researcher would like to determine the existence of a relationship among the variables identified, then analytical studies would be the next research approach to use.
Cross-sectional studies are analytical studies that help "measure the prevalence of the disease." It is also known as prevalence studies. This kind of study is useful for analyzing relationship among variables at a given point in time, that is, when getting a "snapshot" of the phenomenon in a specific group/population is needed for that time period. Case-control studies are a longitudinal type of study that looks at specific groups of people within the same population. Commonly used to investigate the causes of diseases, it involves studying case and control groups within the same population over time, allowing the researcher to determine the "estimated relative risk of the disease" based on analyses conducted in these groups. Lastly, cohort is another type of longitudinal study that tracks the outcome of a phenomenon over time over specific groups of people within the population of interest. It is similar to case-control studies only, the path of inquiry is in sync with the progression of time, unlike case-control studies wherein the method of inquiry goes backwards (since the objective is to determine the cause(s) of the disease) (Beaglehole and Bonita, 2006, pp. 41-45).
Under the experimental approach, quantitative methods commonly conducted are randomized controlled trials (or RCTs) and its varieties, field trials and community trials (or community intervention studies) (Beaglehole and Bonita, 2006, p. 40). In RCTs, participants are randomly selected and assigned to either the experimental or control group. The strength of this type of study is that it ensures the randomness of subject's/participant's assignment in the study, "unaffected by the conscious or unconscious biases of the investigators (ibid, p. 50). However, this strength of RTCs is countered by the fact that there are also potentially inherent and common weaknesses within the researcher's study: random error, either through sampling or measurement error. Random error resulting from sampling recognizes that the researcher could have chosen an inappropriate sampling technique, therefore resulting to an "inaccurate measure of association" in the study. Another potential cause for random error is measurement error, which happens when constructs or variables are poorly determined and measured in the study. Both reliability and validity of data become susceptible to random error as a result of poor construction of the variables' measures (ibid, pp. 51-52).
IIB. Discuss why "causation" is important in the field of epidemiology?
In previous discussions, it was established how quantitative studies differed in their approaches: while some studies are observational and seek to describe and analyze links among variables, other studies are experimental and aim to establish causation among variables in the study
. Researchers must be careful to not 'misidentify' analytical from experimental studies, since the former only measures the existence and strength of the relationship among variables. Analytical studies cannot determine which variables cause the event/phenomenon and more so, it cannot identify the direction of the causal relationship that is determined among variables. This caveat is especially critical in the study of social epidemiology: uncovering a relationship between variables does not necessarily mean that one causes the other.
However, once a causal relationship is determined, the researcher will have a stronger empirical evidence to support his/her hypotheses. Establishing causal relationships in research studies is the most important linkage that can be achieved by the researcher. However, creating a causal relationship does not necessarily mean that one variable indeed causes the change in another variable; further into establishing causal links is the identification of the type of causation that resulted from the study. That is, causes of an event/phenomenon are brought about by several factors -- identified and unidentified -- in the study.
To identify the "authenticity of claim" of a causal relationship, it must be "judged" using the following criteria: temporal relationship, plausibility, consistency, strength, dose-response relationship, reversibility and study design
. Temporal relationship establishes whether indeed, the identified cause occurred before the event/phenomenon itself, and not triggered by another variable that was not identified in the research design process. Plausibility, meanwhile, determines if the identified cause is logical and makes sense in the framework of the study (i.e., 'in the whole scheme of things as defined by the researcher). Consistency checks for the test-retest capabilities of the data and the study's results; strength looks at the association between the identified causal variable and variable representing the event/phenomenon. Dose-response relationship enables the researcher determine whether the causal variable goes so far as to establish the direction and level of causal relationship between variables. Reversibility aims to determine if the "removal of a possible cause lead to reduction of disease risk," or in the case of an event/phenomenon, its occurrence or non-occurrence. Lastly, study design looks at the overall research design and its strengths (appropriateness) in answering the research question and hypotheses (Beaglehole and Bonita, 2006, p. 90).
These caveats about causation are important guidelines for the researcher, particularly in the field of social epidemiology, wherein lives are and survival are determined based on results generated from analyses of empirical data collected through quantitative and/or qualitative methods.
At best, researchers must not claim that a causal relationship is established until it passes the abovementioned criteria set in "judging" causation in a research study. The difficulty of successfully establishing causation between variables is evident in the decades-long research on the effects of cigarettes to lung cancer and other respiratory diseases. Causation is important but also a difficult pursuit in medical science, as evidenced in years of cancer research trying to determine what factors (biological and external) causes cancer and its different kinds.
Bonita, R. And R. Beaglehole. (2006). Basic epidemiology. Geneva: WHO Press.
Muntaner, C. (2003). "Qualitative and quantitative research in social epidemiology: is complementarity the only issue?" Gac Sanit, Vol. 17, Supl. 3.
Olsen, J. And K. Christensen. (2010). An introduction to epidemiology for health professionals. NY: Springer.
Rosnow, R. And R. Rosenthal. (1996). Beginning Behavioral Research. NJ: Prentice Hall.
Class notes (faxed material)
Explanation- METHODS Quant
Explanation: Qual Methods
Descriptive/Analytical vs. Experimental
Causation must not be claimed just because it was tested in a research study. The researcher must be able to test the results' "authenticity" as outlined and discussed in the previous section.[continue]
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