Quantitative Research Quick Notes

Quantitative Research Overview

Collects & analyzes numerical data to study an observable phenomenon.
Employs objective measurement; tests hypotheses with statistical methods.
Typical instruments: polls, questionnaires, surveys, pre-existing datasets, computational techniques.
Focus: generalize findings across populations; explain or predict outcomes.

Data expressed as numbers → analyzed statistically.
Large, representative samples (e.g., n \ge 1000) ensure reliability & generalizability.
Allows replication to verify findings.
Fast data collection & analysis using standardized tools (e.g., SPSS — Statistical Package for Social Science).
Results displayed via tables, charts, graphs for quick interpretation.
Produces estimates with confidence intervals (commonly 95\%).

Descriptive: portrays current status of variables; no manipulation; includes cross-sectional, comparative, etc.
Correlational: measures degree of relationship between \ge 2 variables; no independent-variable manipulation.
Causal-Comparative / Quasi-Experimental: investigates cause–effect with intervention but without random assignment; lower internal validity.
Experimental: manipulates independent variable (X), measures dependent variable (Y); employs random assignment for high internal validity.

Quantitative data can be presented in tables/graphs → TRUE.
Findings can be generalized & used for prediction → TRUE.
Studies are replicable; uniqueness does not prevent repetition → statement claiming otherwise is FALSE.
Data are numerical & statistically analyzed → TRUE.
Participant behaviour observation is secondary; primary focus is on numeric data → statement making it critical is FALSE.
In experiments the independent variable is deliberately manipulated; dependent variable is measured.
Random assignment is characteristic of experimental designs.