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Secondary Data's Reliability And Validity
The majority of research projects begin with a thorough overview of all previously published research on the topic of interest. What researchers, both academic and commercial find, is that secondary data's reliability and validity is only as solid and applicable to the specific needs of a project if the original methodology was well planned and executed. Further, the methodologies to originally create the secondary data need to be clearly described and critically reviewed if the resulting data is to be of the greatest value. Secondary data without a reference to how it was created is often not as viable as data which has sourcing noted, and if primary research was involved and analyzing, a description of those methodologies as well.
The ability to be critically evaluative of the source of secondary data is also a valuable skill that researchers develop over time. This specifically refers to questioning why the secondary data was created in the first place; answering in essence what need the data was collected to fulfill in the first. The most important aspect of being critically evaluative is to understand how the original need for the information may have influenced the development of the initial research. The critical task then is to evaluate through critical judgment how closely aligned or far removed the original goal that the secondary data was originally created to respond to. There is also the need to evaluate the credibility and biases of the authors and publishers of the secondary data. Answering this question is also critical to keep the correct context of the secondary data as well. Above all, when working with secondary data, the underlying unmet need that lead to its development in the first place, the methodologies that primary data integrated into the secondary data relied on, and any biases of the writers and publishers need to be taken into account. The bottom line is that secondary data, to have been created, was precisely aligned with an unmet information need. It is up to the researcher to ascertain how closely aligned or how far removed their specific objectives are from the secondary data's original catalyst for its creation.
E-Groceries Primary Data Collection Secondary Data Collection Performance evaluation of the optimized supply chains McLane e-grocery Carrefour Ooshop e-grocery Logistics Optimization Structural decisions items of operations strategy in logistics Hardware of the firm Operational Facilities Operational Capacity Process technology Supply Network Infrastructure decision areas Software of the organization Planning and control Quality Organizational Structure Comparison of structural and infrastructural logistical operations management decisions Horizons Capital Investment Globalization and Logistics Optimization Logistical optimization models Challenges in e-grocery Logistics E-grocery logistical solution Store-based order picking model Figure: Store-based order picking model Store-based order picking for attended goods reception Store-based
By assigning the numerical value to all data, researcher is able to present data in tables, graph and chart to assist the presentation of the research findings. In addition, the data presented are used to compare the performances of the experimental group and control group. The graph, chart, and table are also used to make comparison of the two groups. Curricular Innovation Development The findings of the research seek to improve
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
For example, with respect to the leadership support area developed by Bryk, Korkmaz (2006) reports that in some cases, collecting primary data are required to make informed decisions, particularly with respect to human resource decisions. In his study of 842 teachers working in 42 elementary schools, Korkmaz operationalized the dimensions in this area as described in Table 2 below. Table 2 Operationalization of Elementary School Climate and Leadership Dimensions Dimension Definition Instruments Used School vision This dimension
When the different levels of functionality were compared highly functioning individuals took 55% of the academic courses the difference between the groups was significant (p< .01). Moderate functioning individuals took 46% of their classes as academic classes and for low-functioning persons only 40% of their classes were academic. When consideration was given to the setting in which the courses was taken it was found that 92% of the sample took
446). Again, since data collection methods are relevant to specific research studies based on their questions, hypotheses, and literature reviews, this funneling of "phenomena of interest" should ideally suit most research studies -- so long as the those methods are tailored to meet a particular study. When researchers have narrowed down certain characteristics or behaviors that they are looking to observe in their study, they can do with a