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DQ 1: How would you explain the analysis of variance, assuming that your audience has not had a statistics class before?

DQ 1: How would you explain the analysis of variance, assuming that your audience has not had a statistics class before?

HLT 362 Topic 4 DQ 1

How would you explain the analysis of variance, assuming that your audience has not had a statistics class before?

Topic 4 DQ 1

Provide an example of experimental, quasi-experimental, and nonexperimental research from the GCU Library and explain how each research type differs from the others. When replying to peers, evaluate the effectiveness of the research design of the study for two of the examples provided.



Hello Class,

Experimental Research: Experimental or Trial- Research is a form of research or study in which “the investigator directly controls selected conditions or characteristics of the environment, and observes the effects these changes have on other features of the problem at hand in order to determine causal relations” (Stoica, 2021). As the name implies Experimental research relies on running various tests and trials to come to a conclusion.

Example of an Experimental research study:

The Institute of Electrical and Electronics Engineers has conducted a study to research the effectiveness of cable-driven hip joint power-assisted exoskeletons. The experiments conducted to research this required three different conditions. The first condition was no exoskeleton, the second was exoskeleton opening and closing, The third condition was the effects of different experimental conditions on human joint angle, carbon dioxide exhalation, and sEMG. The results of these experiments showed the maximum angle difference of hip and knee was almost halved with the exoskeleton (3.6° with VS 6.1° without). Results also showed a 3.5% decrease in the overall carbon dioxide content in exhaled gas. Lastly, results showed The RMS values of the inferior gauze tail muscle and the quadriceps femoris muscle decreased by 51.40% and 42.55%, respectively (Ma et. al, 2022). With this information a conclusion can be reached that cable-driven hip joint power-assisted exoskeletons showed that motion deviation was small, muscle consumption was greatly reduced, and exoskeletons play a good auxiliary role in human walking.

Quasi-Experimental Research: Quasi-Experimental research is a form of study in which the aim is to evaluate interventions but that does not use randomization. “Quasi-experimental studies encompass a broad range of nonrandomized intervention studies. These designs are frequently used when it is not logistically feasible or ethical to conduct a randomized controlled trial” (Harris et. al, 2006).

Example of a Quasi-Experimental research study:

The National University of Singapore conducted a quasi-experimental research study to find the impact of the Scholarly Project® on medical students’ perception of research skills in Vietnam. To test this “A questionnaire evaluating the perception of fourteen research skills was given to participants in the first week, at midterm, and after finishing the Scholarly Project; students assessed their level on each skill using a 5-point Likert scale from 1 (lowest score) to 5 (highest score)” (Nguyen et. al, 2022). The results showed significantly high scores for 11 skills after participation in the Scholarly Project®.

Non-Experimental Research: Non-experimental research is research without the manipulation of independent variables, random assignment of participants to conditions, and/or orders of conditions.

Example of Non-Experimental research study: 

A non-experimental research study was conducted by the BMJ journal to measure the effect of including OAT in The Joint Commission’s NPSGs on historically low rates of OAT initiation for individuals with incident atrial fibrillation (AF). This test was conducted using North Carolina State Health Plan claims data from 944 500 individuals enrolled between 1 January 2006 and 31 December 2010, supplemented with data from the Area Resource File and Online Survey, Certification and Reporting data network (Beadles et. al, 2014). The results showed OAT initiation was decreased (26.8%) for eligible individuals with incident atrial fibrilation in 2006–2008 but increased after NPSGs implementation (31.7%, p=0.022). OAT initiation was high but was lowered in the positive control group (67.5% vs 62.0%, p=0.003). Multivariate analysis resulted in a relative 11% (95% CI (4% to 18%), p<0.01) increase in OAT initiation for incident AF patients.


Beadles CA, Hassmiller Lich K, Viera AJ, et alA non-experimental study of oral anticoagulation therapy initiation before and after national patient safety goalsBMJ Open 2014;4:e003960. doi: 10.1136/bmjopen-2013-003960

Harris, A. D., McGregor, J. C., Perencevich, E. N., Furuno, J. P., Zhu, J., Peterson, D. E., & Finkelstein, J. (2006). The use and interpretation of quasi-experimental studies in medical informatics. Journal of the American Medical Informatics Association : JAMIA13(1), 16–23. https://doi.org/10.1197/jamia.M1749

Nguyen Tran Minh Duc, Khuu Hoang Viet, & Vuong Thi Ngoc Lan. (2022). Impact of Scholarly Project on students’ perception of research skills: A quasi-experimental study. The Asia Pacific Scholar7(4), 50–58. https://doi-org.lopes.idm.oclc.org/10.29060/TAPS.2022-7-4/OA2748

  1. Ma, A. Zhu, Y. Tu, J. Song, D. Dang and Y. Zhang, “System Design and Experimental Research of Cable-driven Hip Joint Power-assisted Exoskeleton,” 2022 19th International Conference on Ubiquitous Robots (UR), 2022, pp. 237-242, doi: 10.1109/UR55393.2022.9826254.

Stoica, I. (2021). Experimental (Trial) Research. Salem Press Encyclopediahttps://eds-p-ebscohost-com.lopes.idm.oclc.org/eds/detail/detail?vid=1&sid=d01c6dab-6686-47c9-b2fe-2f9a5c973400%40redis&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=89164212&db=ers

HLT 362 Topic 4 DQ 1

HLT 362 Topic 4 DQ 1


Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: DQ 1: How would you explain the analysis of variance, assuming that your audience has not had a statistics class before?

Quasi experimental research aims to establish a cause-effect-relationship between an independent and a dependent variable. Quasi experiment does not rely on random assignment, instead, subjects are assigned to groups based on non-random criteria. Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons. Example: You discover that a few of the psychotherapists in the clinic have decided to try out the new therapy, while others who teat similar patients chose to stick with the normal protocol. Although the groups were not randomly assigned, if you properly account for any systematic differences between them, you can be reasonably confident any differences must arise from the treatment and not other confounding variables.



Lauren Thomas, (2022). Quasi-Experimental Design: Definition, Types & Examples. Retrieved from https://www.scribbr.com/methodology/quasi-experimental-design/


 An independent variable is a variable that is presumed to have an effect on another variable. Within the context of an experimental design it is the variable that the researcher is intentionally manipulating for the experiment (Flannelly et al., 2014). For example if a researcher was trying to experiment with how different forms of purposeful rounding affect overall patient outcome the manipulated variable in this example would be the different forms of purposeful rounding. That is what the researcher is changing in order to make a difference with regard to the overall health outcome. A dependent variable is, quite simply, dependent, in that it depends, in some sense, on an independent variable. It is the dependent variable that the researcher is usually most interested in understanding and possibly interested in predicting (Flannelly et al., 2014). Using the previous example a dependent variable acts as what the researcher is measuring as they change the independent variable so with regard to this case the dependent variable in the experimental design would be the overall health outcome.Those variables, which may or may not have an impact on the participants’ performance but are not included in the set of, are referred to as extraneous variables. (Abdul-Rahman et al., 2020). An example of extraneous variables with regard to the above example would be the specific patient, the patient’s specific health deficit or even the patient relationship with a nurse.

One method that can be used for the controlling of extraneous variables is the employment of constants. The experimenters considered them as extraneous variables, and controlled both of them by using constants (AbdulRahman et al., 2020). This means that outside the purposeful changing of the independent variable the researchers endeavor to keep every other aspect of the experiment the same. Another method to control extraneous variables is to incorporate them or take them into consideration when designing the experiment in the first place; this is called stratification or the organization of data while including extraneous variables as an independent variable. Another method to control extraneous variables is to incorporate them or take them into consideration when designing the experiment. This is called stratification or the organization of data while including extraneous variables as an independent variable. Another way is to incorporate an extraneous variable as an independent variable in the study design. If age, for example, might have an effect on the relationship between the independent variable and dependent variable, the researcher can group participants into subgroups of different ages, say, 20 year-olds, 30 year-olds, 40 year-olds, and so forth. This method is called ‘‘stratification,’’ and the ‘‘effects’’ of stratified variables are usually included in the statistical analyses (Flannelly et al., 2014).


Abdul-Rahman, A., Chen, M., & Laidlaw, D. H. (2020). A survey of variables used in empirical studies for visualization. In Foundations of Data Visualization (pp. 161-179). Springer, Cham.

Flannelly, L., Flannelly, K., & Jankowski, K. B. (2014). Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research. Journal of Health Care Chaplaincy20(4), 161–170. https://doi-org.lopes.idm.oclc.org/10.1080/08854726.2014.959374

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