Correlations In research

0 Comment

Correlations In research Research refers to the systematic investigation into and study of material and sources in order to establish facts with the aim of finding new conclusions. From this definition, it is evident that for a research to be carried out, a hypothesis must decisively be constructed and either rejected or accepted. Research requires a lot of time to analyze the facts and determine the truths involved in the study. Research data are collected with a number of tools. The most basic among them include observations, interviews, and the use of questionnaires (Sharon and Anthony 96). Depending on the type of the research, the data are later analyzed to draw out provable conclusions. For instance, a research can focus on climate and its effect on agriculture. From the relationship between agricultural productivity and climate, variables can be identified to aid in the process of information collection. In the long run, a relevant hypothesis is then formulated which will be rejected or accepted depending on the outcome of the research.
The favorability of climate is inversely proportional to the quality of agricultural productivity in an area. In proving such a hypothesis, some of the most common working definitions are thus listed herein.
Operational definitions
I. Good climate – a good climate is one which fosters the production of agricultural products. Farming heavily relies on the pattern of rainfall. However, excessive rainfall destroys the crops in the field. The most conducive rainfall pattern, therefore, is one in which it rains moderately. A moderate rainfall is between twenty to sixty millimeters per day. According to this research, a good climate is therefore measured in terms of the volume of rainfall an area receives in a week. An unfavorable climate is characterized by below ten millimeters of rainfall a day.
II. Research productivity-The number of researches carried out in an area defines its productivity in terms of contributing to human development. In this context, the productivity is compared on the number of researches that are produced in the region within a period of one year. A bad productivity infers zero to twenty cases within a year. On the other hand, good productivity refers to the production of fifty to one hundred researches per year (Sharon and Anthony 66).
Operational definitions make it possible for computation of research variables. Furthermore, the definitions of the conflicting factors make it easier for one to carry an effective research on the issues. The goodness of climate is defined in a manner that enables the development of variables in which case the researcher uses the amount of rainfall that a region receives. For a research to be effective, it should be mathematically proven and supported with a graphical presentation. Graphs are easier to interpret and make the research look more professional. The use of the amount of rainfall within a region provides for a variable with analyzed mathematical analysis and a graphical representation. The quality of research solicits a relative definition. However, researches do not work with relativities but follow clearly and concisely defined terms. For affectivity, the terms are turned into mathematical variables. This thus explains the change of the definition of the term to imply the productivity of a region. The addition of productivity makes the whole idea conceive a factor employed in the study as a variable, in which case the researcher simply compares the number of researches completed within a year to come up with either a productive or unproductive regions. The number of researches is plotted on a graph against the amount of rainfall to create a productivity graph.
Work Cited
Sharon, C. and Anthony, H. Design Research in Information Systems: Theory and Practice.
Berlin: Springer US, 2010. Print.