Discussion Forum
Introduction
Researchers identify different purposes of conducting a study. However, most purposes entail collecting data and using it to make certain conclusions. Different data collection approaches enable the researcher to draw different conclusions from the research. The collection of data determines the type of information that a researcher can draw from a study; the best approach enables the researcher to make analysis and comparisons between data.
Issues in presenting data without scientific interpretation
Quality data representation fails to highlight the correlation between different variables in the research study. The importance of understanding any correlation revolves around establishing the determinant factors in a particular phenomenon. Further, the researcher manages to perform a measure of relationships on the data obtained when by using scientific interpretation (Nieswiadomy, 2012). Failure to implement the scientific approaches in evaluating the data means that a research cannot draw the pattern of events leading to the research topic/idea. Therefore, the quality-data representation technique limits the researcher from performing comparisons, which lead to an understanding of the situation.
The scientific approaches involve several mathematical operations on the data in order to establish various facts. These operations enable a researcher to analyze the data significantly. For example, by calculating the different percentiles in the research data, a researcher manages to compare the data collected with that collected from other researches or sources (Nieswiadomy, 2012). In addition, by measuring the standard deviation on data, one manages to determine the average variance of the set of values from the mean of the same values.
Largely, the scientific measures enable a research to perform comparisons on data using different techniques. The data collection approaches differ however; comparison enables researchers to have a basis for understanding relationships and patterns between data (Nieswiadomy, 2012).
Survey data involve brief descriptive summaries of a research phenomenon. Although the survey research bases its data on the real-world observations, it lacks details on the topic of investigation (Greenwald, & Hart, 2008). Successful research requires the researcher to have an in-depth description of the topic in order to present the findings to other parties. Survey research fails to provide this assurance.
In this technique, the researcher tends to focus on the range of coverage. From this approach, he or she fails to determine the significance of the collected data. Further, in such a study, on may fail to search for adequate information from theories, problems, and relevant issues. In addition, one does not establish the implication of the data on other relevant issues. In survey research, researchers often fail to secure a high response rate, mostly when they carry out the research in a post (Greenwald, & Hart, 2008). It also becomes challenging and hard to control the survey when it takes place face-to-face. Therefore, the high amount of data collected from the survey fails to match up to the expected results. From this perspective, the researcher collects a high amount of data that fails to highlight significant findings.
Conclusion
The research process requires a determination of the best approach before initiation. Survey research provides the research with plenty of information but it does not present quality and significant findings. Further, quality data fails to provide a correlation between different aspects of the research. In situations, researchers cannot perform mathematical analysis on the data and cannot determine the patterns in a research phenomenon.
References
Greenwald, H., & Hart, L. (2008). Issues in survey data on medical practice: some empirical comparisons. Public Health Reports, 105(5), 540-546.
Nieswiadomy, R. M. (2012). Foundations of nursing research (6th ed.). Upper Saddle River, N.J.: Pearson/Prentice Hall.
