Skip to Main Content

Research Methods & Studies

Quantitative Research Examples

In a Cohort Study, subjects are enrolled or grouped on the basis of their exposure, then are followed to document occurrence of disease. Differences in disease rates between the exposed and unexposed groups lead investigators to conclude that exposure is associated with disease.  In an observational study, the investigator does not intervene and rather simply “observes” and assesses the strength of the relationship between an exposure and disease variable.

Cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a time dimension (i.e. prospective or retrospective):

  • Prospective Cohort Studies are carried out from the present time into the future. Because prospective studies are designed with specific data collection methods, it has the advantage of being tailored to collect specific exposure data and may be more complete. The disadvantage: the long follow-up period while waiting for events or diseases to occur.
  • Retrospective Cohort Studies, also known as historical cohort studies, are carried out at the present time and look to the past to examine medical events or outcomes. The primary disadvantage of this study design is the limited control the investigator has over data collection. The existing data may be incomplete, inaccurate, or inconsistently measured between subjects. However, because of the immediate availability of the data, this study design is comparatively less costly and shorter than prospective cohort studies.

Cohort studies are particularly advantageous for examining rare exposures because subjects are selected by their exposure status. Additionally, the investigator can examine multiple outcomes simultaneously. Disadvantages include the need for a large sample size and the potentially long follow-up duration of the study design resulting in a costly endeavor.

Bias: 

Experimental research works to provide strong evidence to prove hypothesis about a causal relationship between independent and dependent variables. It is a method borrowed from the physical and natural sciences and is characterized by three components: manipulation, control, and randomization.

Experimental research roughly consists of five phases:

  1. Identifying a research problem
  2. Planning an experimental research study
  3. Conducting the experiment
  4. Analyzing the data
  5. Presentation of the findings

Like quasi-experimental research, experiments are dependent on probabilities, meaning they can only present the probability that one thing causes another. Thus, experiments cannot claim to be able to prove a single effect will always be the result of a single variable.

Further Reading:

Looking for additional readings on or examples of experimental research? Click the image below or here for a list of relevant resources.

 

 

Abraham, I., & MacDonald, K. (2011). Experimental research. In J. J. Fitzpatrick, Encyclopedia of nursing research (3rd ed.). New York, NY: Springer Publishing Company. Retrieved from http://ezproxyles.flo.org/login?url=https://search.credoreference.com/content/entry/spennurres/experimental_research/0?institutionId=1429
experimental method. (2006). In B. S. Turner (Ed.), Cambridge dictionary of sociology. Cambridge, UK: Cambridge University Press. Retrieved from http://ezproxyles.flo.org/login?url=https://search.credoreference.com/content/entry/cupsoc/experimental_method/0?institutionId=1429
Colorado State University Writing Center. Experimental and Quasi-Experimental Research. Retrieved from https://writing.colostate.edu/guides/guide.cfm

While it can be used as both a qualitative and quantitative method, descriptive research is typically classified as the latter for its focus on gathering quantifiable data to statistically analyze a target group, concept, or phenomenon. These studies make use of the following tools:
           • surveys
           • measurement tools
           • chart or record reviews
           • physiological measurements
           • meta-analyses
           • secondary data analyses

Descriptive research is used when there is very little information available about a phenomenon or to increase understanding of a well-research phenomenon by providing a new perspective. It has minimal interpretation to keep its findings objective, and thus more readily accepted as a factual representation.

 

Looking for additional readings on or examples of descriptive research? Click the image below or here for a list of relevant resources.

 

Descriptive research. (2010). In A. B. Powers, Dictionary of nursing theory and research (4th ed.). New York, NY: Springer Publishing Company. Retrieved from http://ezproxyles.flo.org/login?url=https://search.credoreference.com/content/entry/spnurthres/descriptive_research/0?institutionId=1429
Tarzian, A. J., & Zichi Cohen, M. (2011). Descriptive research. In J. J. Fitzpatrick, Encyclopedia of nursing research (3rd ed.). New York, NY: Springer Publishing Company. Retrieved from http://ezproxyles.flo.org/login?url=https://search.credoreference.com/content/entry/spennurres/descriptive_research/0?institutionId=1429

Randomized Control Trials (RCT) are trials in which subjects are randomly assigned to one of two groups: one (the experimental group) receiving the intervention that is being tested, and the other (the comparison group or control) receiving an alternative (conventional) treatment. RCTs can demonstrate the superiority of a new treatment over an existing standard treatment or a placebo.

In clinical research, RCTs are the gold standard for ascertaining the safety and efficacy of new treatments. The basis of every RCT is the study protocol that describes the medical/scientific background, the risk: benefit assessment, the study design, the study methods, and the overall planning, conduct and analysis.

Bias can be mitigated but never truly eradicated.  Ways to mitigate bias:

  • Randomization & Blinding (a study may be double blind, single blind, or open)
  • Double-blind study: neither patient nor study physician knows to which treatment the patient has been assigned.
    • Double-blind studies are advantageous if knowledge of the treatment might influence the course and therefore the results of the study. It’s important that the study physician is blinded to treatment if the endpoints are subjective.
  • Blinding of patients to their treatment is important, for example, if their attitude could potentially affect their reliability in taking the test medication (compliance) or even their response to treatment.

If only one party, either patient or study physician, is blinded to the treatment, the study is called single blind; a study with no blinding is described as open. The highest possible degree of blinding should be chosen to minimize bias.

RCTs in surgery are subject to their own particular challenges and sets of biases - typical caveats of surgical trials include limitations such as low external validity (poor generalizability), difficulty of blinding patients and investigators, co-intervention bias, lost-to-follow-up bias, and performance bias.

  • Selection Bias - Those who are less ill could get allocated to the preferred treatment if allocation is not concealed from both patient and investigators (can be mitigated by blinding).
  • Performance Bias - The participants could cooperate better in taking a new treatment (can be mitigated by blinding).
  • Detection Bias – refers to the risk of how the evaluation of the outcome bias effects: researchers who are aware of the actual treatment may unconsciously or intentionally alter their assessment – this can either cause an overestimate or underestimate of the size of the effect (can be mitigated by blinding).
  • Measurement Bias – Sometimes researchers evaluate outcomes that are easy to measure, rather than the outcomes that are relevant (measurement bias). One variant of this is the time term bias in which short-term outcomes are measured rather than the important long-term outcomes.
  • Attrition Bias - Attrition bias is a systematic error caused by unequal loss of participants. In clinical trials, participants might withdraw due to unsatisfactory treatment efficacy, intolerable adverse events, or even death. The patients who drop out of trials tend to be those who are doing badly, thus improving the average result in those who are left in that trial arm.
  • Reporting Bias - Highly significant results are more likely to be fully reported in papers than those that are uninteresting because they are not significant.

Quasi-experimental research (also known as causal-comparative research) seeks to determine cause-and-effect relationships between two or more variables. While similar to the experimental research design, quasi-experimental differs in that there is no control group, no random selection, no random assignment, and/or no active manipulation. Essentially, quasi-experimental is an experimental design that is restricted by a lack of manipulation of one of the components, yet still seeks to determine a casual relationship between variables.

Like experimental research, quasi-experiments are dependent on probabilities, meaning they can only present the probability that one thing causes another. Thus, quasi-experiments cannot claim to be able to prove a single effect will always be the result of a single variable.

Further Reading:

Looking for additional readings on or examples of quasi-experimental research? Click the image below or here for a list of relevant resources.

 

Abraham, I., & MacDonald, K. (2011). Quasi Experimental research. In J. J. Fitzpatrick, Encyclopedia of nursing research (3rd ed.). New York, NY: Springer Publishing Company. Retrieved from http://ezproxyles.flo.org/login?url=https://search.credoreference.com/content/entry/spennurres/quasi_experimental_research/0?institutionId=1429
Colorado State University Writing Center. Basic concepts of Experimental and Quasi-Experimental Research. Retrieved from https://writing.colostate.edu/guides/page.cfm?pageid=1361&guideid=64