Quantitative Methods Final Assignment MSc CFM Semeste 389173

Quantitative Methods Final Assignment MSc CFM – Semester 2 The object

The objective of this assignment is to provide students with the opportunity to apply the quantitative methods reviewed during the course to real data in view of answering a research question (option 1), or to plan out the data collection and analysis processes in view of answering a research question (option 2). Students are encouraged to take advantage of this exercise to incorporate quantitative methods into the analysis to be included in their dissertation. This assignment may be completed in groups of two or three students. It must be submitted by email to [email protected] before midnight of Friday April 27, 2018.

Option 1: Modelling exercise

Present the research question and any hypotheses to be tested. Justify the interest in answering the question and assessing the hypotheses. Present the model to estimate. Explain the model and how it allows one to assess the hypotheses and answer the research question. Collect the relevant data. Document all data sources, as well as the data cleaning and/or preparation process(es). Justify the estimation procedure to be implemented, including any diagnostics that were carried out. Present and analyse the results. Use the results to assess the hypotheses and answer the research question. Write up the whole process in the form of a professional report. Make sure to discuss any limitations encountered along the way. The report should include the code implemented in R, along with the results generated by R, but what is even more important is the careful discussion of why certain decisions were made during the research process.

Option 2: Questionnaire design

Present the research question and any hypotheses to be tested. Justify the interest in answering the question and assessing the hypotheses. Present the model to estimate. Explain the model and how it allows one to assess the hypotheses and answer the research question. Develop the questionnaire and design the surveying process. Carefully document which principles of survey design were taken into account and explain how they were taken into account. Present any data preparation that is anticipated and justify the estimation procedure to be implemented. Include a detailed discussion of how the hypotheses will be assessed and the research question answered. Write up the whole process in the form of a professional report. Make sure to discuss any limitations encountered along the way. It is crucially important to discuss why certain decisions were made during the research process.

Paper For Above instruction

The quantitative methods course provides a foundational framework for conducting empirical research, accommodating both modeling and data collection procedures. This assignment offers students a practical opportunity to apply the theoretical knowledge acquired, either by analyzing real data through a modeling approach or by meticulously planning the data collection process via questionnaire design. Such engagement not only reinforces the technical skills necessary for quantitative analysis but also enhances critical thinking regarding research design, data integrity, and statistical inference.

Option 1: Modeling Exercise

The first option focuses on conducting a comprehensive modeling exercise. Students are tasked with formulating a clear research question complemented by testable hypotheses. A compelling research question might involve examining the impact of socioeconomic status on educational attainment, or the effect of marketing strategies on consumer behavior. Establishing hypotheses, such as “Higher income levels positively influence educational outcomes,” lays the groundwork for empirical testing.

Justification for the research interest is crucial. For instance, understanding determinants of educational success can inform policy development, allocate resources effectively, or identify at-risk populations. Once the research question and hypotheses are established, students must select an appropriate econometric or statistical model—such as linear regression, logistic regression, or more advanced techniques depending on data characteristics. Explaining the model’s structure and how it tests the hypotheses demonstrates critical understanding and forms the core of the analysis.

Data collection involves sourcing data from credible databases, surveys, or institutional records, with detailed documentation. Data cleaning and preparation could include handling missing values, encoding categorical variables, and outlier management. Justifying the choice of estimation procedures—such as Ordinary Least Squares (OLS)—and implementing diagnostic tests like residual analysis, multicollinearity checks, and heteroskedasticity tests ensure the robustness of results.

The results are then analyzed to confirm or refute hypotheses. For example, a statistically significant coefficient for income in a regression model could support the hypothesis that income impacts educational outcomes. Discussing limitations—such as data restrictions, measurement error, or model assumptions—adds transparency and credibility to the research. The entire process, including R code and output, must be documented thoroughly, with reflections on the decision-making process and its implications.

Option 2: Questionnaire Design

The second approach involves designing a questionnaire aligned with the research question. Defining clear hypotheses—for instance, “The level of customer satisfaction influences repurchase intention”—guides the survey's focus. Justification for the research interest clarifies its importance for managerial decisions, policy formulation, or academic inquiry.

Developing the questionnaire requires adherence to principles of sound survey design. These include clarity of questions, avoidance of leading or biased wording, appropriate question types (Likert scales, multiple choice), and sequencing that maintains respondent engagement. Pre-testing the questionnaire ensures validity and reliability, while considering sampling strategies and mode of administration enhances representativeness.

Additionally, outlining the data preparation process involves planning for coding qualitative responses, managing missing data, and ensuring data quality. For analysis, students must justify the choice of statistical techniques suitable for the data type and research question—such as t-tests, ANOVA, correlation analysis, or regression models.

Hypotheses will be tested through appropriate statistical procedures, and the results interpreted within the context of the research question. Limitations such as potential response bias, sampling errors, or questionnaire design flaws must be transparently discussed. Documenting the entire research process, including decision rationales, ensures academic rigor and practical relevance.

Conclusion

This assignment fosters critical engagement with quantitative research processes, emphasizing the importance of methodological rigor, transparency, and reflective decision-making. Whether analyzing real data or designing surveys, students learn to integrate theory with practical application, preparing them for future research endeavors or professional roles requiring empirical analysis.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Harrell, F. E. (2015). Regression Modeling Strategies. Springer.
• Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling. Guilford Publications.
• Bailey, K. D. (1998). Methods of Social Research. The Free Press.
• Brace, N., & Snelgar, R. (2016). Questionnaire Design Course. Routledge.
• DeVellis, R. F. (2016). Scale Development: Theory and Applications. SAGE Publications.
• Groves, R. M., et al. (2009). Survey Methodology. Wiley.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Perreault, W. D., & McCarthy, E. J. (2014). Basic Marketing. McGraw-Hill Education.
• DeVellis, R. F. (2016). Scale Development: Theory and Applications. SAGE Publications.