Good Example Of Research Proposal On Social Media

Social media facilitates better communication amongst co-workers through the use of smartphones rather than desktop computers are an issue that has the potential to reduce cost and increase productivity in the office. In order to properly assess the argument, this study will assess the viability of the hypothesis as a starting point for a useable form of study that provides an applicable solution to the issue of how best to increase inter office communication and efficiency. This can be proven using a true experiment that will provide real world data from which to prove or disprove the hypotheses. This hypothesis makes economic and business sense in that there is revenue to be generated from creating a method for relating to the needs of the consumer and the industry which in turn provides the best manner of moving forward and making progress in the area by creating solutions that have the potential to apply to topical issues.
Social media inclusion in the office lends itself easily to further inquiry, with several prior studies confirming that emerging technology has the potential to reduce cost and increase availability of key employees (Bingham, 2010). This is a direct indication that there is recourse for testing available, which will in turn prove or disprove the hypothesis. In this case, a inter office experiment involving twenty participants, ten with smartphones and ten without, the control group will then provide the evidence necessary for a proper assessment based on real world developments. This hypothesis, or suggested explanation, is linked to the Organisational Informational Theory, or tested explanation, in that both are concerned with the communication, management and enhancement of the inter offices personnel efficiency. Written in a declarative form that is both clear and concise, this hypothesis is easy to understand and relate to, leaving many options for the beginning.
Variables incorporated into the model testing the validity of this hypothesis include independent, dependent and other (Druckman, 2011). This translates into the input or causes being the independent variables which in this case include the random participants chosen for this study. Those which must be tested to are the dependant variables, or the results of this study, which in turn illustrate the effect of the hypothesis (Evans et al, 1993; Dekkers et al, 2010). The term blocking variable denotes the breaking down of the model into similar units, such as two similar groups for the purposes of this study, in order to better gauge the impact on each group (Dekkers et al, 2010). Extraneous variables will incorporate any element outside the design of the model (Dekkers et al, 2010).
Internal validity will be addressed by reducing research bias, and minimizing systemic error (Evans et al, 1993). This will allow for a solution that is balanced and true to the nature of the model and not the desires of the researcher. Cause and effect must be viable and reasonable in order to be accepted as internally valid (Druckman, 2011). External validity will be addressed as the situation warrants, with a desire to increase the internal validity of the model at the expense of the external validity that enables a more general use of the results (Druckman, 2011). There is every reason to conclude that the results will apply to the larger and more general population, but the study must not be biased with that assumption from the outset.
3 References
Bingham, T., & Conner, M. (2010). The new social learning. Alexandria, Va.: ASTD Press.
Dekkers, O., von Elm, E., Algra, A., Romijn, J., & Vandenbroucke, J. (2010). How to assess the external validity of therapeutic trials: a conceptual approach. International Journal Of Epidemiology, 39(1), 89--94.
Druckman, J. (2011). Cambridge Handbook of Experimental Political Science. Cambridge: Cambridge University Press.
Evans, M., Hastings, N., Peacock, J., & Hastings, N. (1993). Statistical distributions. New York: J. Wiley.
Kleinbaum, D., & Kupper, L. (1978). Applied regression analysis and other multivariable methods. North Scituate, Mass.: Duxbury Press.
Wilcox, R. (2005). Introduction to robust estimation and hypothesis testing. Amsterdam: Elsevier/Academic Press.