Essay I need help in paraphrasing

Mathematical logic from numerical data and mathematical statistics of this data to evaluate the strength of the evidence at hand make up the structure of the quantitative research method. Quantifying relationships between variables such as height, performance and time is the functions which is the measurement of a particular sample. Creswell (2003) details this approach as a channel for testing objective theories while continuing with it being numbered data analyzed using statistical procedures. While Thomas (2003) details this focus as being directed on measurements and amounts that are studied on people and events by the researcher. Leedy and Ormond (2010) similarly state quantitative research as being content of differences in one variable related to other distinctions one or more variables. Calculation and clarification is the mindset of the researcher to generalize information to those interested in the research being conducted (Thomas, 2003). Creswell (2003) also conveys quasi-experiments and correlation studies as well as specific single-subject experiments as being factual experiments of quantitative research. Generalizable, reliable and valid measurements are the main components of the quantitative paradigm and are utilized to be a clear prediction of cause and effect (Cassell and Symon, 1994) on variables. Upon obtaining data and quantifying results a final report is then produced which includes a structured introduction, literature, theory, and approaches with results of the data in which all aspects are presented as a discussion (Creswell, 2003) to provoke further thought. Experimental, quasi-experimental, casual comparative, descriptive, correlational and the survey research approaches are all designs listed by Flor (2008). Experimental research is aimed toward obtaining measurements and continuously taking measurements in order to observe a variation. What this means is that researchers intentionally produce circumstances based upon the specifics of the research that outlines testing a theory. Empirically verifying specific data sets represents the groundwork of researching a hypothesis (Frankfort-Nachmias and Nachmias, 1992). There is no room or intent for researchers to include prejudice preferences, values and bias when conducting research with scientific hypotheses because it is intended to be free of values with the quantitative approach. With this in mind, Swanson and Holton (2005) direct the perspective of an additional approach similar to this that details non-experimental research by utilizing an existing condition instead of creating a situation to study phenomena. Very similar to experimental research, casual-comparative calls for a researcher to demonstrate the same technique without maneuvering the variable being examined. Swanson and Holton (2005) present correlation research within organizations and groups to determine any connection among variables of two or more and this is achieved with survey data that do not address causality and descriptive research (p. 33). There is a process of five-steps that portray Swanson and Holton’s (2005) quantitative process which consist of producing the appropriate basic questions of the study first. Second, obtaining participants that are qualified to participate in the study based upon the population and sample criteria. Third, making a choice on the questions that will used to provide results on variables, measures of variables and overall design. Fourthly, deciding upon analysis tools and finally, being knowledgeable of the research and interpretation of the results. It is the researcher’s goal to collect multiple sources of information on the subject at hand, subsequently synthesizing distinctive analysis results in a way that produces inclusive assumptions about the subject that can be illustrated from any analysis within the collection (Thomas, 2003, p.42). Researchers will present a descriptive study from usually hundreds plus sample subjects to gain an accurate estimate; tens of sample subjects for experimental research. When there is a high participation rate within a random sample of subjects it will be less likely to be biased based on the estimate of the relationship. As far as experiments are concerned, when random treatments are assigned to subjects and or if the researcher and subject are blind to the treatments for examples there will then also be the less likely possibility of bias. Swanson and Holton (2005) make a point to address studying larger groups and generalizing beyond the sample being studied is a strong technique of quantitative research. This has been a concern with researchers because of the concern regarding the extent of generalization and the certainty of findings because implication should not exceed the scope and how something was studied during research (Swanson and Holton, 2005). In all studies, characteristics of a subject can have an affect on the relationship that is being investigated and to alleviate making false statements from inaccurate results, limiting the effect by using less heterogeneous subject samples or including characteristic measurements within the analysis is appropriate.