Nature of Inquiry & Quantitative Research – Focused Review

Quantitative vs Qualitative Research

  • Quantitative: systematic investigation of observable phenomena; collects numerical data; analyzed with statistical methods.
  • Qualitative: exploratory; interprets meaning of information; flexible report structure; researcher’s reflections & biases visible.

Core Statistical Methods

  • r (Pearson’s r): strength & direction of relationship between two variables.
  • t-test: checks statistical difference between 2 means.
  • ANOVA (Analysis of Variance): tests difference among \ge 3 groups.
  • Multiple Regression: predicts a dependent variable from several independent variables.

Main Goals of Quantitative Research

  • Test hypotheses.
  • Explore causal relationships.
  • Make predictions.
  • Generalize findings to a population.

Strengths

  • Replicable across contexts.
  • Results generalizable to large populations.
  • Establishes causality more conclusively.
  • Generates predictions from numerical data.
  • Faster analysis via statistical software.
  • Less demanding data gathering; low subjectivity; measurable validity & reliability.

Weaknesses

  • Limited depth for complex phenomena & rich descriptions.
  • Hard to analyze intangible factors (e.g., socio-economic norms).
  • Rigid design; responses confined to preset items.
  • Self-report bias can reduce accuracy.

Types of Quantitative Research

  • ## Non-Experimental
    • Descriptive: observes & reports phenomena; no manipulation.
    • Correlational: identifies relationships; no causation.
    • Ex post facto: infers causes of already-occurred events.
    • Developmental: studies changes over time.
  • ## Experimental
    • True Experimental: random assignment of individuals; strong cause-effect evidence.
    • Quasi-Experimental: uses intact groups; limited randomization.

Importance Across Fields

  • Social Inquiry, Arts, ICT, Science, Agriculture & Fisheries, Sports, Business.

Variables & Their Uses

  • Variable: measurable element (quantity or quality).
  • Discrete: countable, whole-number values (e.g., frequency).
  • Continuous (Interval): ranges, can include fractions & negatives (e.g., temperature).
  • Ratio: continuous with true zero (e.g., age, height, weight).
  • Qualitative (Categorical): non-numeric groupings.
    • Dichotomous: 2 categories (yes/no).
    • Nominal: >2 unordered categories (hair color).
  • Ordinal: ranked categories (e.g., Likert 1–5 scale).
  • Dependent: measured for change; presumed effect.
  • Independent: manipulated; presumed cause.
  • Extraneous: undesired variable influencing results.
  • Confounding: uncontrolled extraneous variable that threatens validity.