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Q4_W3 PR 2 Reviewer

Sampling and Probability Sampling

  • Probability sampling: Each member of the population has a non-zero chance of being selected, with a known probability.

  • Non-probability sampling: Not every member has a known or equal chance of being selected.

Sampling Definition

  • Sampling involves selecting a subset (called a sample) from a population to make statistical inferences.

Population

  • Population is the entire group that you want to generalize about.

Sample

  • A sample is the actual units chosen from the population for analysis.

Stages in the Sampling Process

  1. Defining the target population.

  2. Choosing a sampling frame.

  3. Choosing a sample using a defined sampling technique (Bhattacherjee, 2012).

Target Population Definition

  • The target population can be a person, group, organization, country, object, or any entity for which inferences are drawn.

Sampling Frame Definition

  • A sampling frame is a list from which the sample is drawn, containing the accessible section of the target population.

Choosing a Sample

  • Probability sampling ensures that every unit in the population has a defined chance of being selected.

Sampling Techniques

Cluster Sampling

  • Population divided into clusters (e.g., geographical areas).

  • A random sample of clusters is selected, and all individuals within those clusters are surveyed.

  • Useful for large, spread-out populations.

Simple Random Sampling

  • The simplest and most generalizable probability sampling technique.

  • Allows all possible subsets of a population to have equal chances of selection.

  • Involves randomly selecting respondents from the sampling frame (e.g., fishbowl method).

Stratified Random Sampling

  • The sampling frame is divided into homogeneous non-overlapping subgroups (strata).

  • A simple random sample is drawn from each subgroup, allowing for equal representation

Systematic Sampling

  • The sampling frame is ordered and elements are selected at regular intervals (e.g., every 10th person).

Examples of Sampling Techniques

Systematic Sampling Example

  • With a list of 500 households, select every 10th household after randomly choosing a starting point.

Cluster Sampling Example

  • Survey residents in 5 randomly selected neighborhoods from 20.

Simple Random Sampling Example

  • Use a generator to pick 100 unique numbers from a list of 1,000 employees, ensuring equal selection chances.

Stratified Random Sampling Example

  • Survey 100 students from each year of a university, ensuring representation from freshmen, sophomores, juniors, and seniors.

Non-Probability Sampling Techniques

Convenience Sampling

  • Samples based on the availability of members (also known as grab sampling).

Purposive Sampling

  • Samples chosen based on study goals or specific traits desired by the researcher.

Quota Sampling

  • Samples reflect the proportion of groups within the population.

Snowball Sampling

  • Existing participants recruit new members for the study.

Slovin's Formula

  • Used to calculate the minimum sample size for estimating a statistic with a specific margin of error.

  • Formula: n = N / (1 + Ne²)

    • n = Sample size needed

    • N = Population size

    • e = Acceptable margin of error

Example Calculation

  • For a population of 1,000 with a 5% margin of error:

    • n = 1000 / (1 + (1000)*(0.05²)) = 286