09 Apr 2018
Experimental Design Strengths
Experimental designs sets the precedence for being the first standard prototypes for researchers. Researchers can regulate independent variables and dispose of superfluous variables more so than other research design methods. With the ability to manipulate contact to independent variables experimental design allows a researcher to conclude causal relationships within the design. With the ability to watch cause and effect and the influence of the independent variable on the dependent one. (Fort-Nachmias & Nachmias, 2008) Since the conditions are controlled in experimental design this permits duplication that allows the researcher to authenticate their results. New research is contingent upon experimental design methods because when this research experiments are replicated it allows other researchers to have confidence in a study’s results. (Fort-Nachmias & Nachmias, 2008)
Experimental Design Limitations
Experimental design strives to remove or governs extraneous variables yet this may not always be probable. Especially when the study has to do with real world situations since it’s next to impossible to replicate a natural environment; this weakens external validity. (Levy, Ellis, & Cohen, 2011) When dealing with the selection process it may not be random which is necessary to control variables. Samples sizes may not me indigenous of a wider population making results too general to the population of interest. (Fort-Nachmias & Nachmias, 2008) This methodology can prove to be unethical or impossible to apply arbitrary treatments to patients and or control populations.
Quasi-Experimental Design Strengths
A quasi-experimental designed studies lets researchers examine behavior in ordinary situations that aren’t ordinarily pliable to experimental designs. The external validity is increased when using natural samples. (Fort-Nachmias & Nachmias, 2008) In this week’s assignments Marques and Lima (2011) studied the effects of living in industrial neighborhoods. Marques and Lima (2011) took a look at how living in an industrial neighborhood can psychologically effect its inhabitants.
Since the researchers didn’t use random assignments to compare groups this would be considered a quasi-experimental design. Marques and Lima were unable to control where those they were studying lived. Researchers who create comparison groups through a non-random methods comparing groups are using quasi-experimental design. Quasi-experimental design is a substitute for experimental design since experimental design uses random assignment to groups. For instance a researcher may inquire of the differences of participants in the groups due to gender, marital status, age, or education level. In this particular article the difference was years of residing in a particular town. There was no other significant difference in the four samples. (Marques & Lima, 2011)
Quasi-Experimental Design Limitations
Since quasi-experimental design doesn’t use random selection to allocate assignments to participants to comparison groups, the person conducting the research must be mindful of how the non-random selection process might affect the study results. (Shannon, Goldenhar, & Hale, 2001) When determining the differences between groups with factors like age, socio-economic status, gender, marital status, education level or other differences a researcher might want to know if there are intrinsic differences. As a researcher it is important to take into account these differences since they could possibly affect participant responses to the support group to which they are linked. (Shannon et al., 2001) It is also important to note that these differences must be accounted for and detailed for statistical analysis. (Shannon et al., 2001).
Unchosen Research Designs
Quasi-experimental versus experimental differs in that experimental always uses a control group to which research participants are randomly assigned, given treatments executed by the researcher, and followed by an assessment of the effects of treatment. (Levy, Ellis, & Cohen, 2011) Experimental designs measure the connection between the independent and dependent variables of which researchers have complete control of independent variable’s effects on the dependent variables. (Fort-Nachmias & Nachmias, 2008)
Cross-sectional deigns surveillances and uses random sampling of research participants and tend to be connected to survey research. (Fort-Nachmias & Nachmias, 2008) Information is recorded from the survey without manipulation of variables or exposure of groups to a single treatment. Cross-sectional research designs can give researchers a nominal tool to examine many individualities concurrently like age, gender socio-economic status between the supposed group that is participating in a researched activity and the group that isn’t. This design is descriptive rather than causal. Experimental design manipulates variables unlike cross-sectional designed research.
My Research Design for Quantitative Study
The point of my study is to define the individualities of a sample of young caregivers of Alzheimer’s and dementia patients between the ages of 28 and 40 to conclude the effects of self-esteem and familial support on stress management. The sample will be made up of caregivers' responses to a flyer announcement for participants displayed at a local Adult Daycare center. Contingent on the number of responses, all, or an unsystematically selected number of participants will be carefully chosen.
This population was chosen because of the supposed impact of stress on younger Alzheimer’s/Dementia caregivers. The survey design will be a self-administered questionnaire for the data collection in this study. The partiality for survey design is steered by the necessity to gather caregiver responses concerning the personal experiences of these younger caregivers in demanding circumstances with concern to their use of self-esteem and having familial support. The survey tool used was designed specifically for this research. The dependent variable in both hypotheses is the management of stress. The independent variables are self-esteem and familial support.
The survey content is designed to identify to what extent participants utilize their family networks. The survey will also ascertain how their self-esteem is effected in stressful situations as a caregiver. The data will be analyzed and interpreted reporting the results of the returned surveys as well as the amount of the non-returned surveys. A wave analysis will be utilized where we will examine returns on select items week by week to determine if average responses change from beginning to the end of the process. (Creswell, 2009 pg. 152). Descriptive analysis will be provided for the independent and dependent variables in the study and will identify the means, standard deviations, and range of scores for these variables. (Creswell, 2009 pg. 152) An analysis of variance will be applied to make contrasts between the independent variables. (Creswell, 2009 pg. 152)
Burger, J. M. (2009). Replicating Milgram: Would people still obey today? American Psychologist, 64(1), 1-11. doi:10.1037/a0010932.
Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd Ed.). Thousand Oaks, CA: Sage Publications.
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). New York: Worth.
Levy, Y., Ellis, T. J., & Cohen, E. (2011). A Guide for Novice Researchers on Experimental and Quasi-Experimental Studies in Information Systems Research. Interdisciplinary Journal Of Information, Knowledge & Management, 6151-161.
Marques, S., & Lima, M. L. (2011). Living in grey areas: Industrial activity and psychological health. Journal of Environmental Psychology, 31(4), 314-322. doi:10.1016/j.jenvp.2010.12.002
Shannon, H. S., Goldenhar, L. M., & Hale, A. R. (2001). Chapter 4 Quasi-experimental and experimental designs: More powerful evaluation designs. In L. S. Robson (Author), Guide to evaluating the effectiveness of strategies for preventing work injuries: How to show whether a safety intervention really works. (pp. 29-42). Cincinnati: NIOSH.
Trochim, W. M. (2006). Quasi-experimental design. The Research Methods Knowledge Base. Retrieved December 21, 2013, from http://www.socialresearchmethods.net/kb/quasiexp.php
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