The next step in the research process is immersing oneself in the empirical literature on your research topic to determine the extent to which your research question (identified in Module 1) has been addressed. To this end, please use the Trident University Online Library to conduct a literature search on your research topic.

Remember, your goal is to discover the extent to which a knowledge gap exists with regard to your specific research question. Locate five peer-reviewed journal articles that present studies relevant to your topic, and synthesize these into a four-page paper that summarizes the “current state of knowledge” with regard to your research question. Note specifically,

Study findings relative your research question (positive, negative, and inconclusive) Any additional questions left unanswered by these studies How addressing your research question would help to fill a knowledge gap (should your research question fall short of this, you may need to propose an alternative one) The specific population to which your study will apply (and ultimately, your study’s targeted sample) SLP Assignment Expectations You are expected to consult the scholarly literature in preparing your paper; you are also expected to incorporate relevant background readings. Your paper should be written in your own words. This will enable me to assess your level of understanding. In order to earn full credit, you must clearly show that you have read ALL required background materials. Be sure to cite your references in the text of all papers and on the reference list at the end. For examples, look at the way the references are listed in the modules and on the background reading list. Proofread your paper to be sure grammar and punctuation are correct and that each part of the assignment has been address clearly and completely. Length: 4 pages typed, double-spaced.

Please include 2 extra scholarly references in addition to the ones provided. Note: Wikipedia is not an acceptable source of information.

References

Jepsen, P., Johnsen, S.P., Gillman, M.W., & Sorensen, H.T. (2004). Interpretation of observational studies. Heart, 90,956-960. Retrieved fromhttp://heart.bmj.com/content/90/8/956.full.pdf Retrieved February 23, 2012.

Module Overview Having introduced the concepts of the research question, literature review, and research hypothesis in Module 1, we now arrive at the fourth step in the research process, choosing an appropriate study design.

Study Designs

The study design delineates the method by which you will carry out your study. Among the possible types of designs available to the health sciences research are the observational study designs discussed below and the randomized controlled trial (or the “experiment”). Each of these designs affords the research particular advantages and disadvantages with regard to validity (see below).

Cohort Study Design

Cohort studies follow a group of people, some of whom are exposed to the factor of interest, to determine the association between that exposure and a specific outcome. The researcher is thus able to compare incidence (i.e., the number of new cases of disease that develop in a population of individuals at risk over a specified period) among exposed versus non-exposed groups. Cohort studies can either start with group that is already exposed or with a defined population and wait for exposure to occur. A major advantage of this design is that the exposure is determined prior the disease. A major threat to the validity of findings emerging from cohort studies if confounding. A cohort study can be prospective or retrospective. One of the most famous cohort studies is the Framingham Heart Study, NHLBI, which began in 1948 and continues today.

Click here for more information on cohort studies.

Case-Control Study Design

A case-control study is a study in which persons/group with a given disease, i.e. “cases,” and persons/group without the given disease, i.e., “controls” are compared to ascertain exposure or background of the two groups and compare the proportion of cases affected by a given exposure relative to controls. In doing so, the researcher is able to isolate the specific exposure that contributed to the disease or outcome or interest.

Case control studies are commonly used for initial, inexpensive evaluation of risk factors and are particularly useful for rare conditions or for risk factors with long induction periods. Recall bias is common threat when employing this study design as cases, “sensitized” by their disease status, may “over-attribute” that status to an array of exposures. As an example, a women with breast cancer might attribute her disease to a multitude of factors that, had she not acquired this disease, she might not have even considered.

Click here for more information on case-control studies.

Cross-Sectional Study Design

Cross-sectional studies are conducted at a single point in time or over a short period of time with no follow-up. Exposure status and disease status are measured at one point in time or over a period. Cross-sectional studies are also referred to as “prevalence studies” as they allow the researcher to compare disease prevalence among exposed and non-exposed groups.

A major advantage of this design is that it can be implemented quickly and inexpensively relative to other designs. A major disadvantage of this design is its inability to establish temporality. That is, given that the exposure and outcome are measured at the same time, it is impossible to know if the exposure actually preceded the outcome.

Click here for more information on cross-sectional studies.

Ecologic Study Design

Ecological studies use aggregated secondary data on risk factors and disease prevalence from different population groups to identify associations between the two.

Because all data are aggregated at the group level, relationships at the individual level cannot be inferred. To do so would be to commit the “ecologic fallacy.” This study design thus provides weak empirical evidence.

Randomized Controlled Trial

Randomized controlled trials (RCT) (or “experimental studies”) entail random assignment (also referred to as “randomization” or “random allocation”) of subjects into a treatment or control group. The treatment group receives the intervention or medication; the control group receives “treatment as usual” or a placebo. This process serves to establish equivalence of the two study groups with regard to possible confounders (or by establishing symmetry between the two groups with regard to unknown confounders), thereby eliminating the latter’s effect and permitting the researcher to isolate the effects of the treatment or intervention. In doing so, any observation related to differential outcomes between the treatment and control groups can be attributed solely to the intervention.

The RCT is considered to be the “gold standard” for health studies as, if properly executed, the design provides the strongest evidence for a relationship between exposure and outcome.

Related Concepts (please review):

Confounding:

An important consideration in epidemiologic research is that an observed association (or lack of one) between an exposure and an outcome may be due to the effects of a third factor that is associated with the exposure and independently affects the risk of developing the disease. This is referred to as confounding. The extraneous factor is called a confounding factor or a confounder.

Confounding is the main problem with observational studies. In the health sciences context, this is because healthy or unhealthy behaviors congregate in the same individuals. For example, people who exercise also eat healthier. Thus, if you merely look at the relationship between exercise and health outcomes without taking diet into consideration, the effect of exercise will falsely appear larger than it actually is.

Reliability (reproducibility):

Reliability refers to the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects. In short, it is the repeatability of your measurement. A measure is considered reliable if a person’s score on the same test given twice is similar.

Validity (best available approximation of the truth, accuracy):

Internal Validity: Truth within a study. A study is internally valid if the study’s conclusions represent the truth for the individuals studied because the results were not likely due to the effects of chance, bias, or confounding because the study design, execution, and analysis were correct. The statistical assessment of the effects of chance is meaningless if sufficient bias has occurred to invalidate the study. All studies are flawed to some degree. The crucial question that the reader must answer is whether or not these problems were great enough that the study results are more likely due to the flaws than the hypothesis under investigation.

External Validity (Generalizability): Truth beyond a study. A study is external valid if the study conclusions represent the truth for the population to which the results will be applied because both the study population and the reader’s population are similar enough in important characteristics.

The important characteristics are those that would be expected to have an impact on a study’s results if they were different (e.g., age, previous disease history, disease severity, nutritional status, co-morbidity, …). Whether or not the study is generalizable to the population of interest to the reader is a question only the reader can answer.

Click here for more information on reliability and validity.

Bias (systematic error):

Bias is defined as a deviation in one direction of the observed value from the true value of the construct being measured (as opposed to random error) or any process or effect at any stage of a study, from its design to its execution to the application of information from the study, that produces results or conclusions that differ systematically from the truth. Almost all studies have bias but to varying degrees. The critical question is whether or not the results could be due in large part to bias, thus making the study’s findings invalid. Observational designs are inherently more susceptible to bias as compared to experimental designs.

Here are some examples of bias:

Confounding Bias – results from confounding (described above) Ecological Bias (Fallacy) – Systematic error that occurs when an association observed between variables representing group averages is mistakenly taken to represent the actual association that exists between these variables for individuals (described above) Measurement Bias – Measurement error that affects study groups in a systematically different way. Related Concept: Observer Bias Reader Bias – Systematic errors of interpretation made during inference by the user or reader of clinical information (papers, test results, …). Such biases are due to clinical experience, tradition, credentials, prejudice and human nature. The human tendency is to accept information that supports pre-conceived opinions and to reject or trivialize that which does not support preconceived opinions or that which one does not understand. Selection Bias – Systematic error that occurs when, because of design and execution errors in sampling, selection, or allocation methods, the study comparisons are between groups that differ with respect to the outcome of interest for reasons other than those under study. Recall Bias – Respondents’ selective recalling of past experiences and behavior Case-control studies are particularly prone to selection and recall bias (e.g., cancer patients will remember past behavior differently than controls).