Next, the researchers turn to selective coding, which is "the process that links all categories and sub-categories to the core category thus facilitating the emergence of the 'storyline' or theory" (Corbin & Strauss, 2008, p. 155). This style of coding focuses on making relationships out of the emerging categories of more abstract qualitative data. Last, there is axial coding, which strengthens relationships between core concepts and categories. According to the research, "this process is used to make connections between categories and sub-categories and allows a conceptual framework to emerge" (Corbin & Strauss, 2008, p. 154). By using such coding methods, core concepts can help point to meaningful answers. Moreover, data collection can be overlapping with data analysis within the context of qualitative research. This is much different than in quantitative contexts, where all the research data must first be collected before any sort of analysis can actually begin. Here, the research suggests that "By contrast, in qualitative evaluation, data collection and...
Essentially, time frames overlap within qualitative research contexts. Often, the data collection period is much longer and is determined by several rounds of data collection, including follow up interviews or observations. During this period, researchers can begin to analyze their first round of data in order to better tailor second round efforts. Concepts and frequent occurrences first encountered in the first round can help guide second round practices towards finding more details about particular core concepts.
ERP Implementation Approach The study collects data from 5 business units of the company. The data collection method is through both qualitative and quantitative data analysis, and the study collects data to enhance greater understanding of the ERP implementation approach carried out by the company. As being discussed previously, the methodology used to collect data is through qualitative and quantitative approach, and the study collects data from the following business units: Accounting
data collection to solve the problems arising from the impact of mass media on terrorism following the reviewing of the case study titled "Threat of Terrorism: Weighing Public Safety in Seattle." (Lundberg, 2002 p 1). The case discusses the possibility of terrorist attack at Seattle following the arrest of the Ressam at the U.S.-Canadian border for the possession of the explosive bomb. The follow-up investigation reveals that Rassam was connected
Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive
This reduces response bias for better reliability of the information gathered and a higher anticipated response rate for an adequate sample size. A one month time frame gives better assurance of an appropriate response rate adequate analysis of results. Research Questions Patient wait time: How long did you wait before being register? < 5 min, 5 min, 10 min, longer How long did you wait to be called after being registered? <
) However, additional observation visits in the site could help provide more in-depth information that will yield sub-categories for the category, "Activities in the skateboard park." Under this category, observations were identified as "skateboarders taking breaks," "skateboarding," "hanging out," "smoking marijuana," and "video- or phone video- taking." Additional visits to the site will determine if there are other activities done in the skateboard park apart from those identified already after the
Harnessing Unstructured Data in Radiology The harnessing of unstructured data is vital to moving the field of radiology forward. There are methods used for the mining of unstructured data, with one of the most common being Natural Language Processing (NLP). However, there are some difficulties with the use of NLP in the radiology field, because NLP lacks the capacity to analyze free-text radiology reports and images. There is too much uncertainty
Our semester plans gives you unlimited, unrestricted access to our entire library of resources —writing tools, guides, example essays, tutorials, class notes, and more.
Get Started Now