Research Methodology in Sampling

Population

• Entire group of objects, elements, people, or characteristics being studied
• All members share common characteristics relevant to the research
• Researchers aim to understand these shared characteristics

Sample Selection

• Approaches: Choosing a smaller group (sample) from the larger population (all members)
• Time & Cost: Dealing with the whole population is time-consuming and expensive
• Descriptive & Comparative Research: Need to consider sample size and participant numbers

Types of Populations

• Target Population: The entire group the researcher wants to study
• Accessible Population: The portion of the target population the researcher can access and study

Sampling

• Systematic Process: Methodically selecting the group to be studied
• Reflects the characteristics of the bigger population in a manageable way

Sample Size

• 95% Distribution: Ideally, the sample size should accurately represent 95% of the population.
• High Probability: Ensuring a sample accurately represents the broader population.

Sampling Techniques

• Probability Sampling: Choosing participants at random
  • Random Selection: Participants have an equal chance of being in the sample
  • Purposive or Controlled Selection: Participants are carefully chosen to represent specific characteristics
• Non-Probability Sampling: Participants are selected based on specific characteristics or traits
  • Examples: Quota sampling, Voluntary sampling, Purposive sampling, Snowball sampling

Literature Review

• Purpose: Reviewing relevant previous research to inform current research
• Significance: Understands how the current research relates to previous studies in the field

Formulas

• Sample Size Calculation: Established methods (like Slovin's formula) for determining a suitable sample size.
• Power Analysis: Using established principals to make sure that the study will return results, and a particular method used to test the reliability of the study's results.

Statistical Techniques

• Statistical Power: Measuring the study's ability to detect an effect (i.e., relationship)
• Effect Size: Assessing the strength or magnitude of an observed relationship
• Hypothesis Testing: Testing if there's a meaningful relationship between an independent and a dependent variable in the given context

Stratified Sampling

• Group participants based on shared traits to ensure a proportionate representation of those traits in the sample
• Groups would need to have similar/similarly described characteristics

Cluster Sampling

• Divide respondents into groups (clusters) using characteristics
• Can select entire clusters or random subsets from clusters
• Used when studying large areas or specific groups

Simple Random Sampling

• Every member of the population has an equal chance of being chosen for the sample
• A common and useful technique for selecting a sample at random from a population

Stage Sampling

• Involves selecting participants in stages
• Each stage may require a certain sample to be selected from the previous chosen group
• Useful when accessing very large populations or samples that may require different levels of data collection across groups

Non-Probability Sampling

• Characteristics: Deliberately choosing participants based on their characteristics or traits
• Some methods: Purposive sampling, Quota sampling, Voluntary sampling, Snowball sampling.