A study note on the empirical cycle in quantitative research methods: for MBA and Housing Studies students

A study note on the empirical cycle in quantitative research methods: for MBA and Housing Studies students (information from perplexity.ai)

My question 1: "What is "the empirical cycle" in quantitative research methods all about with reference to MBA and Housing dissertation projects?"

The reply: "The empirical cycle in quantitative research methods refers to a systematic, iterative process used to generate and test hypotheses based on empirical data. This cycle consists of five interconnected stages:

1. Observation: Gathering empirical data through observation or measurement of phenomena, which sparks ideas for hypotheses.

2. Induction: Using reasoning to form general hypotheses or explanations based on observed data.

3. Deduction: Deriving specific predictions from the general hypotheses using logical reasoning.

4. Testing: Conducting experiments or collecting quantitative data to test these predictions empirically.

5. Evaluation: Analyzing the results to confirm, refute, or refine the hypotheses, which then leads to new observations and the cycle repeats.

This cycle characterizes the hypothetico-deductive approach and supports rigorous, structured research where theories are continuously tested and refined based on data.

In the context of MBA dissertations and housing-related research projects, the empirical cycle guides researchers to systematically investigate phenomena such as market behaviors, housing demand and supply factors, pricing patterns, or consumer preferences. For example, an MBA student studying housing markets might:

• Observe patterns or trends in housing prices or buyer behavior.

• Induce a hypothesis that interest rates affect housing demand.

• Deduce testable predictions like "a one-percent increase in interest rates reduces housing purchases by X%."

• Test this using quantitative data from housing market surveys or sales records.

• Evaluate the data statistically to support or reject the hypothesis and discuss implications for business strategy or policy.

Thus, the empirical cycle ensures MBA and housing dissertation projects are grounded in observable evidence, follow a logical progression, and produce valid conclusions that can inform real-world decision-making.

In summary, the empirical cycle is the backbone of quantitative empirical research, facilitating disciplined hypothesis generation and testing, crucial for MBA-level research and housing market dissertations where data-driven insights are essential."

My question 2: "Please clarify the topic a bit more with illustrative empirical cycle practice on the following research topics:

Topic 1 (MBA related): "to evaluate how AI could affect the Hong Kong Banking Sector to recruit fresh part-time MBA graduates as managerial employees in 2025"

Topic 2 (Housing Studies related): "to evaluate how negative home equity affect the divorce rate of Generation Y couples in Hong Kong in 2025"."

The reply: "Here is a clarification of the empirical cycle with illustrative practice applied to your two research topics in MBA and Housing Studies:

The Empirical Cycle consists of five iterative stages: Observation, Induction, Deduction, Testing, and Evaluation. It guides systematic, evidence-based investigation by starting from data observation to theory generation, hypothesis formulation, empirical testing, and results evaluation.

Topic 1 (MBA related):

Research: "To evaluate how AI could affect the Hong Kong banking sector to recruit fresh part-time MBA graduates as managerial employees in 2025."

1. Observation
   Look at current trends: Hong Kong banks are increasingly adopting AI technologies (especially generative AI) for automation, risk management, and customer service. MBA graduates are often recruited into managerial roles traditionally supported by human decision-making.

2. Induction
   Hypothesize that the rise of AI tools in banks may change recruitment needs, either by automating tasks or enhancing data-driven decision-making, therefore affecting hiring of part-time MBA graduates into managerial roles.

3. Deduction
   Formulate testable predictions such as:

   • Banks adopting AI extensively might recruit fewer part-time MBA graduates for managerial roles if AI replaces routine decision functions.

   • Conversely, banks using AI to augment managerial tasks might hire more MBAs skilled in AI management and strategy.

4. Testing
   Collect quantitative data (e.g., recruitment figures for part-time MBA graduates in target banks, AI adoption levels, managerial task analyses) through surveys, HR records, and interviews in 2025.

5. Evaluation
   Analyze if recruitment trends correlate negatively or positively with AI adoption. Conclusions may confirm, refine, or reject hypotheses about AI's impact on MBA recruitment.

Topic 2 (Housing Studies related):

Research: "To evaluate how negative home equity affects the divorce rate of Generation Y couples in Hong Kong in 2025."

1. Observation
   Observe current housing market data and divorce statistics among Generation Y couples in Hong Kong, noting trends in negative home equity (when mortgage debt exceeds home value)[No direct snippet but assumed from domain knowledge].

2. Induction
   Hypothesize that couples experiencing negative home equity might have higher divorce rates due to financial stress.

3. Deduction
   Predict:

   • Couples with negative equity are more likely to divorce within a specified timeframe than those with positive equity.

   • The correlation might be stronger among certain subgroups (e.g., lower income, first-time buyers).

4. Testing
   Use quantitative datasets from government housing records, mortgage data, and divorce registry to statistically test the relationship using methods such as regression analysis.

5. Evaluation
   Based on analytical results, evaluate if negative equity significantly predicts higher divorce rates for Gen Y couples and discuss possible confounding factors or policy implications.

In both cases, the empirical cycle emphasizes grounding research in actual data (observation), generating informed hypotheses (induction), deriving testable predictions (deduction), collecting and analyzing data systematically (testing), and critically assessing results to refine knowledge (evaluation). This enables rigorous, data-driven MBA and housing dissertations addressing real-world issues in Hong Kong’s context."

A collection of blog notes on using chatgpt for research purpose.