Quasi-Experimental Research Design
One of the most frequently used types of quasi-experimental research design is the pretest-posttest design. This occurs when one group is given a pretest, which could be a medication, treatment, or procedure. This medication, treatment, or procedure is considered the independent variable. Patients are assessed pretest or prior to administration of a medication, treatment, or procedure. After the independent variable (medication, treatment, or procedure) is given or performed, a posttest is given, or the patient is reassessed. The differences between the status of the patient’s pretest (prior administration of medication, treatment, or procedure) and posttest (group after the administration of medication, treatment, or procedure) is the result of the study. There is no control group or randomization. All research participants receive the medication, treatment, or procedure. This study design is not considered experimental because there was no randomization or control and is considered quasi-experimental research (see Figure 4.5).
Quasi-Experimental Design Pretest/Posttest
Another type of quasi-experimental research design is the historical comparison design. Because quasi-experimental groups do not use randomization or control groups, the researcher may use historical group data as a control for comparison. This can be done by a retrospective chart review . For example, 15 years ago, patients were turned every 2 hours, but the mattress that existed then was just a standard mattress. Through a retrospective chart review, it was found that 10% of the patient population that was bedbound in the Intensive Care Unit (ICU) developed pressure ulcers. The mattresses in the hospital were changed this year to alternating pressure mattresses. The patients continued to be turned every 2 hours. It was found that only 5% of the bedbound population in the ICU developed pressure ulcers. This is an example of a historical comparison study. The researcher collected the same data on the same patient population, just 15 years apart (see Figure 4.6). This is also considered quasi-experimental research because no control or randomization occurred.
Quasi-Experimental Historical Comparison Design
A common type of nonexperimental research is a correlational design . Correlational design looks at the association or relationship between variables. It is not like a quasi-experimental design study or randomized control trial because there is nothing new introduced in the design of the study. There is no new medication, treatment, or procedure introduced. The correlational study looks at variables and the relationships that variables may have with each other. By seeing how variables exist naturally, one can evaluate or theorize what would happen if one of the variables were manipulated. Would there be change, and what type of change would occur? The results of a correlational study describe the relationships between the variables. The data collected can be retrospective or prospective and can be used to formulate a theory or as a foundation for a randomized control trial. There does not have to be causation with correlation as demonstrated by the example in Table 4.2 and Figure 4.7.
Correlation vs. Causation Table
|Eating a Healthy Diet(times per month)||2||3||4||5||6||7||8||9||10||11||12||9|
|Filling Gas Tank(times per month)||3||4||5||6||7||8||9||10||11||12||13||11|
Correlation vs. Causation Graph
A correlational study can be simple, comparative, longitudinal, or cross-sectional. Each study’s purpose is the same—to describe the relationships between variables—but the designs differ in time periods and groups of variables. For a simple correlational design, data are collected from one group of variables over one period of time. In a comparative correlational design study, data are collected on two or more groups of variables, still over one period of time. For a longitudinal design study, data are collected for one group of variables over two or more periods of time. Lastly, cross-sectional correlational design research collects data from whatever groups the researcher has selected over just one period of time.
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