Quantitative Research Methods Overview

Highly Reliable Outcome

• Quantitative research methods are preferred in industrial research due to their reliability.
• Outcomes are easily interpreted and understood, typically represented through tables and graphs, facilitating quick management approval.

Reusable Outcome

• Data collected in quantitative research is versatile and can be applied to future studies on similar issues.
• Previous findings can inform new research problems, saving both time and resources, for instance, monetary value assessments for different electronic products.

Close-Ended Questions

• Close-ended questions yield specific responses, making them more reliable compared to open-ended questions, which may result in scattered and detailed answers.
• Respondents tend to prefer close-ended questions, leading to more uniform data collection.

Numerical Outcome

• Results from quantitative research are presented in numerical formats such as percentages and ranges, aiding in clarity and understanding.
• Numerical data facilitates straightforward conclusions, contrasting with more complex narrative outcomes.

Generalization of Outcome

• Quantitative research outcomes can easily be generalized to a larger population, enhancing the applicability of results.

Limitations of Quantitative Research

• Quantitative methods may overlook contextual nuances, utilizing a rigid approach that limits data discovery.
• Standardized questions can introduce "structural bias," potentially misrepresenting participants' true views.
• Numeric results provide less insight into behaviors, attitudes, or motivations and focus more on superficial data.
• Research is often conducted in controlled settings, which may not reflect real-world situations, leading to "laboratory results."
• Preset answers may not accurately capture true feelings, producing results closer to researchers' hypotheses than participants' actual views.

Kinds of Quantitative Research

Descriptive Research

• Aims to systematically describe current statuses of identified variables, providing systematic information about phenomena.

Correlational Research

• Examines relationships between two or more variables using statistical data without establishing causation.
• Focuses on identifying trends and patterns, often involving manipulation of specific variables.

Causal-Comparative/Quasi-Experimental Research

• Aims to establish cause-and-effect relationships among variables, using pre-existing or naturally formed groups without random assignment.
• Observes differences among groups affected by various circumstances.

Experimental Research

• Involves true experiments that aim to control variables to establish cause-effect relationships.
• Subjects are randomly assigned to conditions, allowing for hypothesis testing.

Importance of Quantitative Research Across Fields

• Education: Assesses performance metrics for students and teachers.
• Business: Enhances marketing strategies and product development decisions.
• Science and Technology: Critical in experiments for new devices and evaluating efficiency.
• Medical and Health: Generates statistics that improve patient recovery rates and treatment efficacy.

Reasons Why Quantitative Research Is Important

• Provides reliable and objective findings based on statistical analysis.
• Simplifies complex problems into manageable variables, facilitating the establishment of relationships and causality.
• Facilitates theory testing with a representative sample of the population.
• While offering less detail than qualitative methods, it appreciates researchers' objectivity in methodology.