Collection and Presentation of Data

Collection and Presentation of Data

Collection and Presentation of Data

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Data Types & Sources - The Raw Material

  • Data: Raw facts & figures.
  • Types of Data:
    • Qualitative (Categorical): Describes qualities.
      • Nominal: Unordered categories (blood groups, gender).
      • Ordinal: Ordered categories (Likert scale, disease severity: mild/moderate/severe).
    • Quantitative (Numerical): Measurable quantities.
      • Discrete: Countable, whole numbers (number of children, admissions).
      • Continuous: Any value in a range (height, weight, BP).
  • Sources of Data:
    • Primary Data: First-hand collection by investigator (surveys, interviews, experiments).
    • Secondary Data: Already collected (hospital records, census, NFHS).

      ⭐ Hospital records are a vital secondary data source for retrospective studies and health trends.

Sampling Methods - Picking Your Players

Goal: Select a representative subset from a population.

  • Probability Sampling (Unbiased):
    • Simple Random (SRS): Equal chance for all (e.g., lottery).
    • Systematic: Every k-th unit selected ($k = N/n$).
    • Stratified: Population divided into homogenous strata; SRS from each. ↑Precision.
    • Cluster: Random clusters selected; all units in selected clusters sampled. Often ↑Efficiency, ↓Cost.
    • Multistage: Sampling in multiple phases (e.g., national survey).
  • Non-Probability Sampling (Bias prone):
    • Convenience: Readily available participants.
    • Purposive (Judgmental): Investigator's choice based on specific criteria/judgment.
    • Quota: Predefined quotas for subgroups to mirror population proportions.
    • Snowball: Participants recruit future subjects. Useful for hidden/rare populations.

Cluster sampling: The unit of sampling is a group (e.g., village, school) rather than an individual. It is particularly useful for large, geographically dispersed populations where SRS is impractical.

Multistage sampling diagram

Tabular Presentation - Order in the Court

  • Systematic data arrangement in rows & columns; aids comparison, analysis.
  • Key Parts (📌 Mnemonic: Tall Captains Shout Boldly From Ships):
    • Title: Clear, concise (What, Where, When, How classified).
    • Captions (Column heads) & Stubs (Row heads).
    • Body: Numerical data.
    • Footnote (Explanations) & Source Note (Origin).
  • Types:
    • Simple (One-way): 1 characteristic.
    • Complex: Two-way (cross-tab), Three-way, Manifold.
  • Principles: Logical, clear, units stated, totals, avoid clutter.

    ⭐ Cross-tabulation (two-way table) is pivotal for exploring associations between two categorical variables. A well-structured table with labeled parts like title, caption, stubs, body, footnote, and source note.)

Graphical Presentation - A Picture's Worth

  • Visuals for rapid data interpretation: patterns, trends, relationships.
  • Histogram:
    • Continuous quantitative data (e.g., age). Bars adjacent (touch).
    • Area of bar proportional to frequency. X-axis: class intervals; Y-axis: frequency.
  • Bar Chart:
    • Qualitative or discrete data (e.g., gender). Bars separated.
    • Length of bar proportional to frequency.
    • Types: Simple, Multiple, Component/Proportional.
  • Pie Chart (Sector Diagram):
    • Proportions of whole (categorical data, e.g., causes of death).
    • Total angle 360°. Sector angle = (Component value / Total value) × 360°.
  • Line Chart/Graph:
    • Trends over time (time-series data, e.g., disease cases/year).
  • Frequency Polygon:
    • Joins histogram tops' midpoints. Smooths data; compares distributions.
  • Scatter Diagram (Correlation Plot):
    • Relationship & strength between two quantitative variables. Pattern shows correlation.
  • Box & Whisker Plot:
    • Min, Q1, Median, Q3, Max. Shows data spread, central tendency, outliers.
    • Compares group distributions. Pie and Bar Charts of SNP Counts

⭐ Ogives (cumulative frequency curves) graphically determine median from grouped data.

High‑Yield Points - ⚡ Biggest Takeaways

  • Primary data: First-hand collection. Secondary data: Pre-existing information.
  • Sampling: Simple random (equal chance), stratified (subgroup representation).
  • Qualitative data: Nominal (e.g., blood type), Ordinal (e.g., pain scale).
  • Quantitative data: Discrete (e.g., hospital admissions), Continuous (e.g., height).
  • Presentation: Histograms for continuous data; Bar charts for categorical/discrete data.
  • Frequency polygon joins histogram midpoints; Ogive (cumulative frequency) for percentiles.
  • Pie charts for proportions; Scatter plots for relationships between two variables.

Practice Questions: Collection and Presentation of Data

Test your understanding with these related questions

Which of the following is obtained by joining the midpoints of histogram blocks in statistics?

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Flashcards: Collection and Presentation of Data

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The horizontal line on either side of rectangles in the Forest plot represent _____

TAP TO REVEAL ANSWER

The horizontal line on either side of rectangles in the Forest plot represent _____

confidence intervals

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