SAMPLING PRINCIPLES

Research Methods

University of Massachusetts at Boston

©2011William Holmes

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• Part of a whole. The larger whole is a population. The subgroup is the sample.

• Some selected by scientific procedures

• Some selected by haphazard procedures

• Some selected with deliberate bias

WHAT IS A SAMPLE?

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• To make generalizations about a population.

• Populations are expensive to get.

• Populations are difficult to obtain.

• A good sample is better than a poor population

WHY DO YOU NEED A SAMPLE?

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HOW DO YOU GET A GOOD SAMPLE?

• Fit the sampling procedure to the population, the resources, and the moral and legal constraints.

• Choose the most scientific procedure feasible.

• Choose the largest sample possible.• Choose probability samples over non-

probability.4

TYPES OF SAMPLES

• Non-probability Sample—haphazard, convenient

• Probability Sample—systematic

• Fraudulent Sample—deliberately biased

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WHAT ARE PROBABILITY SAMPLES?

• Follows standard procedure for everyone in population

• Chance of selection using procedure is known

• Unintended, random bias is possible

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TYPES OF PROBABILITY SAMPLES

• Simple Random Sample

• Systematic Sample

• Cluster Sample

• Stratified Sample

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WHAT ARE NONPROBABILITY SAMPLES?

• Uses Non-standardized (Variable) procedures

• Chance of selection is unknown

• Unintended, systematic bias may creep in

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TYPES OF NON-PROBABILITY SAMPLES

• Convenience Sample—not deliberately biased

• Purposive Sample—chosen to be similar to a population, according to the chooser

• Quota Sample—chosen to be similar to a population, according to known characteristics

• Snowball Sample—using referrals from known members of a population

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FRAUDULENT SAMPLES

• Artificially constructed to show a characteristic or a relationship

• Violates norms of science and research

• Selects cases to prove a point

• Concerned with non-scientific ends—money, promotion, ideology.

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HOW DO YOU TELL IF YOU’VE GOT A GOOD SAMPLE?

• Check for scientific procedures

• Check for ethical and legal requirements

• Compare with known population characteristics

• Look for weirdness11

SELECTING A RANDOM SAMPLE

• 1. Define population

• 2. Get list of random numbers or choose a random process

• 3. Make a decision rule to select cases

• 4. Assign random numbers

• 5. Select persons who meet criteria12

SELECTING A SYSTEMATIC SAMPLE

• 1. Define population.• 2. Decide on sample size.• 3. Divide population into groups where the

number of groups equals the sample size.• 4. For first group, select one by simple random

sampling.• 5. Count down on list a number equal to the

group size.• 6. Select each person at end of count. Repeat.

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SAMPLING EXAMPLE

Person Age Gender Rdn Nbr* Grp

1 18 1 4# 1

2 25 1 3 1^

3 21 2 7 2

4 34 2 5 2^

5 22 1 1 3

6 19 1 2# 3^

7 33 2 7 4

8 20 1 7 4^

9 21 2 5 5

10 24 2 6# 5^

*from random number table. #Selected for random sample. ^Selected for systematic sample.

Random Number Criteria: select persons with even random numbers

Systematic Sample start: person number 2

Rdm mean age=20.3

Rdm mean sex=0.67

Syst mean age=24.4

Syst mean sex=0.60

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