Sampling

Sampling Methodology

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Sampling Methodology

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Sample

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Population

I don’t have the time or money to talk to everyone!

Sampling is the process of collecting data from the population. By analyzing the selected samples, you can draw a conclusion about the characteristics of the full population. When it is not feasible to study the full population due to time and cost constraints, sampling a subset of the population provides a representative sample of the full population.

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<< click to reveal biases in sampling >>

Biases can occur in various ways during the sampling process which leads to a distorted representation of the population. In a biased sample, one or more parts of the population are favored based on certain characteristics or groups resulting in either over representing or under representing compared to their actual occurrence.

Selection

Non-response

Voluntary Response

Convenience Sampling

Survivorship

Convenience sampling bias occurs when individuals or elements in the population are chosen based on their ease of access or availability rather than through a random selection process. In this example, only the closest individuals were chosen.

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Convenience Sampling

I love the color red!

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Selection bias occurs when certain segments of the population are deliberately included or excluded based on specific characteristics. It can also arise when the selection process is influenced by factors related to the research question or outcome.

Selection

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Hmm. Who should I interview?

So annoying!

I hate red

Non-response bias occurs when the individuals who choose not to participate in a study have different characterists or opinions than those who do participate. It can lead to an under-representation of certain viewpoints or groups in the sample, and it affects the generalizability of the findings.

I would never wear red

I’m out!

I do not agree with guys in red jackets

Waste of time

I love to participate

I do agree with guys in red jackets

I need to be part of this

Voluntary response bias occurs when individuals self-select to participate in a study or survey where participation is voluntary and based on personal interest or motivation. The sample may not represent the entire population because individuals with strong opinions or experiences are more likely to participate.

I got some quick responses

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Successful

I am studying the success factors of entrepreneurs

Survivorship bias occurs when only a specific subset of a population is included in the sample because they have survived or persisted through a certain event or condition. The interviewer chooses only successful entrepreneurs and ignores those who failed which will skew the results.

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I don’t have the time or money to talk to everyone, but I don't want a biased sample!

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A good sample is one that is unbiased—it is representative of the entire population. Unbiased sampling methodologies minimize or eliminate biases in the selection process.

<< click to reveal unbiased sampling methods >>

Multistage Sampling

Simple Random Sampling

Stratified Sampling

Probability Proportional to Size

Systemic Sampling

Multistage sampling uses a combination of two or more SRSs. The interviewer will go through different stages to get his sample. In this example, stage one is randomly selecting which group is chosen. Then another SRS results in the individuals in the sample.

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Group 1

Group 2

Group 3

Stage 1: Randomly select a group

Stage 2: Use simple random sampling to select the sample

Multistage Sampling

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Stratified sampling divides the population into homogeneous subgroups, or strata, based on specific characteristics such as age, gender, or location. Individuals are then randomly selected from each stratus in proportion to their representation in the population.

The interviewer uses a random number table to make selections. If an individual's number matches the number that appears in the box, he is part of the sample. If the number exceeds the pool of numbers assigned or is a duplicate, it is ignored, and the process continues.

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Simple Random Sampling (SRS) provides an equal chance of being selected for every individual in the population. Random number generators or assigning numbers to the population and selecting based on those numbers are both techniques to achieve randomization.

Systemic Sampling

The randomly selected starting point is 32.

Systemic sampling uses a randomly selected starting point, and then selects every nth individual from the population. The interval (n) is determined by dividing the population size by the desired sample size. This method ensures an equal probability of selection for each individual while maintaining simplicity in the sampling process.

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Since the researcher needs a sample size of 8, every 5th individual gets selected.

Probability proportional to sizing is used when the population elements have different sizes (or weights). In this example, the interviewer selects individuals from each group in proportion to the group’s size. This approach ensures that larger groups are represented in proportion to smaller groups.

Probability Proportional to Size

9 in group; need 3

12 in group; need 4

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15 in group; need 5