Sampling Techniques Quiz

Sampling Techniques

• Stratified Sampling: Dividing a population into subgroups (strata) and randomly selecting from each.
• Systematic Sampling: Selecting subjects at regular intervals from a larger group.
• Multi-stage Sampling: Combining different sampling methods. Population divided into clusters, then further subgroups (strata) based on similarities; sampling one or more clusters from each stratum.
• Convenience Sampling: Choosing participants based on availability.
• Purposive Sampling: Selecting participants based on specific characteristics relevant to the study.
• Quota Sampling: Ensuring sample mirrors population characteristics (proportionately).
• Snowball Sampling: Finding and recruiting participants through referrals.
• Simple Random Sampling: Every individual has an equal chance of selection.

Slovin's Formula

• Formula used for calculating sample size.
• Variables: 'n' (sample size), 'N' (population size), 'e' (margin of error).
• Formula: n = N / (1 + Ne²)

Margin of Error

• 1% Margin of Error: Requires a large sample size for high precision.
• 5% Margin of Error: Common for balancing precision and practical sample size.
• 10% Margin of Error: Fewer respondents used, but less precise results.
• Confidence Levels: 90%, 95%, and 99%. Different confidence levels affect sample size needs.

Sampling Techniques

• Probability Sampling: Equal chances selected.
• Non-probability Sampling: Biased method of selection.

Types of Questions

• Yes/No: Recognition.
• Completion: (filling in blanks).
• Coding: (classifying).
• Subjective: (opinion-based).
• Combination: (mixing question types).
• Likert Scale: (measuring agreement).
• Semantic Differential Scale: (measuring attitude using bipolar adjectives).

Validity and Reliability

• Validity: Measures what it intends to measure. (e.g., does a test accurately measure what it intends).
• Reliability: Consistency of results when repeated. (e.g., produces similar outcomes if repeated)
• Types of Validity: Face validity, content validity, construct validity, criterion validity.
• Types of validity illustrated in context: Face validity checks if the items appear relevant and appropriate to the aims. Content validity ensures a test fully represents the subject matter being tested. Construct Validity establishes if the measure is appropriately capturing the concept being tested (e.g., does a depression questionnaire accurately measure depression). Criterion Validity aligns with previous established tests, testing if the instrument results align with those of a similar or validated test of the same thing (e.g., new writing test correlates with earlier established writing tests).
• Two types of Criterion Validity: Concurrent and Predictive.
  • Concurrent Validity: Instrument produces results similar to an already validated instrument in the immediate present.
  • Predictive Validity: Instrument's results correlate with future outcomes of the phenomenon measured by the already validated instrument.

Quantitative Data Analysis

• Descriptive Statistics: Describes aspects of data. Calculate measures like mean, median, mode, and variance which helps to understand where data points reside in relation to each other or to a reference point, and whether or not there are correlations or regressions between or among variables.
• Inferential Statistics: Makes conclusions and draws inferences or generalizations based on the specific data collected from a sample to the whole population. Includes tests like the t-test, ANOVA, and Z-test.
• T-Test: Compares two means.
• ANOVA: Compares means of more than two groups.
• Z-Test: Same as t-test but for larger sample sizes (more than 30).
• Parameters discussed: Mean, Median, Mode, Percentage, Frequency, Range, P-Value, Alpha Level (Significance Level)

Data Collection Procedure

• Before Data Collection: Develop instruments and materials, obtain permissions, select participants and samples (using appropriate sampling methods), and obtain participant consent and permission.
• During Data Collection: Explain the process to participants, administer the instruments and implement any assigned interventions as needed, and ensure data collection remains consistent across participants.
• After Data Collection: Data should be encoded immediately, kept confidential, and analyzed using appropriate statistical methods and tools.

Types of Reliability

• Test-retest Reliability: Checking consistency of results when re-testing.
• Parallel Forms Reliability: Comparing results of equivalent test versions.
• Inter-rater Reliability: Monitoring the consistency of judgments between two or more raters.

Test your understanding of various sampling techniques such as stratified, systematic, and purposive sampling. This quiz also covers Slovin's formula for calculating sample size, making it essential for research methodology studies. Explore the nuances of each method and solidify your knowledge of effective sampling strategies.