Please write the “Reasearch Methodology” of the “Financial planning to prepare senior year students in Thailand for stability in early working life”
Chapter 3 begins with a brief description of what will be covered in this chapter. Consider this chapter to be somewhat like a recipe. The methodology that you provide should be detailed enough such that you could provide the chapter to a colleague in another country, and by following your methodology they would conduct the study exactly as you did and arrive at the same findings just as a person in Thailand could make an stir-fried dish that had a very distinctive flavor and a person in Canada could make that same dish using that recipe and obtain the same flavor result. This requires that the methodology be very specific.
3.1 Data Sample
In this section, explain the data you have selected for the study. Most of the data employed in finance and economic research are time series. Description must be provided to clarify why the data are selected as a benchmark of variables. You should describe the sources of data (where/which database you have retrieved your data from and why this is the appropriate source for the data required in this study). This information can be summarized in a table followed by a discussion assisting the reader with a better understanding of what is presented in that table. The time period covered by the sample and how often samples are drawn should be reasonably defined. The rationale and justification for methods used in this Chapter should be derived from the discussion of similar studies analyzed in Chapter 2.
3.2 Research Methodology
This section will begin by explaining how the data will be collected and providing information regarding sample size to be representative of the population of data from which the sample is drawn. Many finance research will be done with secondary data. However, when appropriate, a student may choose to work with primary data (interview, focus groups, surveys, etc.). In that case, the student can refer to guidelines from templates for Firm and Industry Specific Research Plans or studies using Qualitative Methodology for this section.
3.2.1 Data Preparation
In a time-series model, researchers might need to transform raw data until they are qualified for the analysis model/equation in the study. This first part of methodology will describe data treatment that is necessary for your analysis. However, this treatment depends on assumptions or requirements of the analysis model you plan to utilize. The Review of Literature presented in Chapter 2 should have detailed these requirements and will help the reader understand the model requirements and necessary treatment of the data. For example, data must be checked using the unit root test to satisfy the basic principle of cointegration that the data are non-stationary while multiple regression requires stationary data series. If these assumptions are not met the results of the analysis are likely to be spurious.
3.2.2 Analysis Model
This is the section where you clarify model/equation construction. The process must be clear enough so that anyone reading your paper can understand and perform the same analysis in the same manner. You have to explain each of your variables and how to derive them. Whenever possible you should employ methodology suggested by prior scholarly research. The discussion regarding the model(s) chosen must provide a reasonable basis for having chosen this methodology instead of others. Each equation must be numbered and well explained. For example:
In this chapter, we will provide a detailed account of the methodology employed to investigate and address the question of financial planning for senior year students in Thailand, preparing them for stability in their early working life. Just as a recipe provides clear instructions for replicating a dish’s flavor, our methodology aims to be specific enough for researchers anywhere to replicate this study with the same rigor and obtain consistent findings.
3.1 Data Sample
In this section, we will elucidate the data selected for our study. Given the nature of financial and economic research, our primary dataset comprises time series data. We have carefully chosen this data to serve as benchmarks for our variables. Below, we outline the key aspects of our data sample:
3.1.1 Data Sources
The data utilized in this study have been sourced from reputable databases and repositories relevant to the study’s focus. We have chosen these sources because they contain comprehensive and reliable data necessary for our research objectives. A summary of these sources is presented in Table 3.1.
| Data Source | Description |
|---|---|
| Thailand’s Ministry of Education Database | Academic and demographic data of senior year students. |
| Central Bank of Thailand | Economic indicators and inflation data. |
| National Employment Agency | Employment statistics and job market data. |
Table 3.1: Summary of Data Sources
We have meticulously selected these sources to ensure that the data are representative of the population and are pertinent to the study’s objectives. The time period covered by our data sample ranges from 2010 to 2022, with quarterly updates. These choices are justified based on the need for recent and relevant data, as discussed in Chapter 2.
3.2 Research Methodology
3.2.1 Data Preparation
In this subsection, we will outline the crucial steps in data preparation, particularly in the context of time-series analysis. Data treatment is essential to ensure that the data meet the prerequisites of the analysis models. The treatment process depends on the assumptions and requirements of the chosen models, as discussed in Chapter 2.
Unit Root Testing: To meet the basic principles of cointegration, which is essential for some of our analysis models, we will perform unit root tests to determine whether the data are stationary or non-stationary. Non-stationary data could lead to spurious results, which must be avoided.
Data Transformation: Depending on the requirements of our analysis models, we may need to transform the raw data. Common transformations include differencing, scaling, or log transformations to achieve stationarity, linearity, or other model-specific prerequisites.
Outlier Detection: We will identify and address any outliers in the data to ensure that they do not unduly influence our analysis results.
3.2.2 Analysis Model
In this subsection, we will elucidate the construction of our analysis models, providing clear and comprehensive explanations to facilitate replication by other researchers. We will discuss each variable’s definition and derivation and explain the rationale behind our model selection.
Multiple Regression Model: Our primary analysis model will be a multiple regression model, widely employed in financial planning studies. We will specify the model equation, enumerate the variables, and provide detailed explanations of their significance and interpretation.
Model Equation: �=�0+�1�1+�2�2+…+����+�
Where:
We have chosen this model based on prior research in the field, as discussed in Chapter 2, and its suitability for examining the relationships between financial planning and stability among senior year students in Thailand.
In summary, this chapter’s methodology is designed to be comprehensive and detailed, allowing researchers from diverse backgrounds to replicate the study accurately. It covers data selection, preparation, and the analysis model, providing clear instructions akin to a well-crafted recipe, ensuring consistent and reliable outcomes.
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