Methodology and data collection Sample Clauses

Methodology and data collection. This thesis studies whether to invest Nyhamna as a potential hub, using Ormen Xxxxx Project as a reference. Under various uncertainties, managers have to determine the most appropriate valuation method of this project. The valuation methods include traditional NPV method, simulation, decision tree analysis, financial option and real options. This thesis consists of three main parts, including theory review, gas hub development and Nyhamna case study. The first part is the literature review. This part discusses three approaches for dealing with uncertainties. They are traditional net present value method, financial option method and real option theory. In this part, the shortcomings of traditional net present value have been analyzed. Real option theory could remedy these shortcomings and help managers to make right decisions with flexibility. The second part of this thesis is a link between the theory and reality. This part describes the basic concept in a gas hub system and the basic cost in the hub system. In addition, the decision processes to invest a hub system have been analyzed after understanding the processing facilities in the petroleum industry. The last part contains four sections which are the background of Ormen Xxxxx Project, the onshore and offshore facilities, the advantages and disadvantages of developing a hub and value different real options at Nyhamna based on the limited data. The data collection process is really a challenge for us, especially in the second part and third part. In the first theory review part, the main articles’ sources are from online scientific databases, such as Sciencedirect, ProQuest and so on. There is no problem to find quantitative literature on real option theory because there are a large number of books and articles have been written on this subject over last 15-20 years. However, it is really difficult to find relatively qualities’ articles in real option theory. A lot of articles in this area are based on the model and data analysis. The most challenge for us is to get relevant information and data for the Nyhamna case. Part of data collected in third and fourth parts are from the internet such as the website of different oil companies. Moreover, the interviews, telephone calls and discussions with our supervisor are really helpful for us to deeply understand the problems of our thesis and give us new ideas. In the business as the petroleum industry, large amount of confidential data is owned by different...
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Methodology and data collection. To answer the aforementioned research questions, data are drawn from an obligatory occasion analysis (Xxxxx 1973) of the texts produced by Norwegian university students during the period of 2018-2019. Many of the previous studies on S-V agreement among L2 learners only look at the number of errors (e.g., Xxxxxx 1985; Garshol 2019). However, looking at only the number of errors fails to provide a complete picture of learner language, as frequently mentioned as a limitation of error analysis (see Xxxxx 2008: 60-61). An alternative method for studying learner language is an obligatory occasion analysis, which was first introduced by Xxxxx (1973) for the purpose of studying L1 and had often been used to investigate the order of acquisition (e.g., Xxxxx and Xxxx 1973; Krashen 1974; Xxxxxx-Xxxxxxx 1976, cited in Xxxxx and Barkhuizen 2005). According to Xxxxx and Barkhuizen (2005), Obligatory Occasion Analysis (OOA) is similar to Error Analysis in involving a comparison between the forms used in interlanguage and target language norms. Unlike Error Analysis, however, OOA offers an “analysis of samples of learner language in their totality” by investigating how accurately learners use specific grammatical features by calculating both correct and incorrect uses of the feature (Xxxxx and Barkhuizen 2005: 73). As the forms used by L2 learners in collected samples are measured against target language norms, Xxxxx (1973) considers each obligatory context to be a test item that determines whether L2 learners use the relevant form correctly or not. A criterion for acquisition, according to Xxxxx (1973), is considered to be a 90 per cent accuracy level. With longitudinal data, however, he asserts that for a morpheme to be acquired, a learner has to achieve the 90 per cent accuracy on three consecutive data points, given that children may reach this level at one data point and fall below it at another data point. A weakness of OOA (or any analysis of production data) is that it does not account for what L2 learners know and are capable of (competence), which may not be reflected in their production due to avoidance. Avoidance is one of the strategies L2 learners employ in L2 acquisition when they find certain structures or features to be difficult to use. Studies have shown that this strategy is prevalent in L2 acquisition (e.g., Xxxxxxxx 1978, Dagut and Xxxxxx 1985, cited in Xxxxx 2008). Overcoming such a weakness is to combine the study with a task that also measures L2 ...
Methodology and data collection. 2ś 3.1 The Focus of Research Study: METU Technopolis 2Ŝ 3.2 General Profile of METUTECH 3ŗ 3.3 Data Selection and Sampling Methodology
Methodology and data collection. This thesis differs from other/similar studies in terms of certain features in its methodology and data collection structure. To draw an overall picture of today’s METUTECH (i.e. the population of the study), I requested statistical data from the METUTECH management. After informing the METUTECH management and completing the necessary paperwork between METU Science and Technology Policy Studies Department and the METUTECH management, I received the data that constitutes the basis of this study. During face-to-face interviews with the METUTECH management, it became clear that this data was one of the most comprehensive and detailed compilation they have edited up until now.5 This data is used along with the semi- structured firm interviews (i.e. Firm Evaluation Form) I designed, to create the framework of this study. Before elaborating on the semi-structured interviews, it will be useful to draw a general and current profile for METU Technopolis with reference to METUTECH data mentioned above. It is important to emphasize that through such data collection, I established my research on the most recent and first- hand data on METUTECH. In other words, I gathered this data together from the current statistics (dated March 2013) I obtained as a result of my direct 5 The data is dated as March, 2013. formal and semiformal contracts with the METUTECH management. However, before presenting the statistics that I gathered from the data, I think it is important to tell the reader why I had chosen METUTECH as the focus of this research study.

Related to Methodology and data collection

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  • Details of Data Processing (a) Subject matter: The subject matter of the data processing under this DPA is the Customer Data.

  • Data Collection and Usage The Company and the Employer collect, process and use certain personal information about the Participant, including, but not limited to, the Participant’s name, home address and telephone number, email address, date of birth, social insurance, passport or other identification number, salary, nationality, job title, any Shares or directorships held in the Company, details of all restricted stock units or any other entitlement to Shares or equivalent benefits awarded, canceled, exercised, vested, unvested or outstanding in the Participant’s favor (“Data”), for the legitimate purpose of implementing, administering and managing the Plan. The legal basis, where required, for the processing of Data is the Participant’s consent.

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