# SAMPLING PRINCIPLES Research Methods University of Massachusetts at

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SAMPLING PRINCIPLES Research Methods University of Massachusetts at Boston © 2011 William Holmes 1

WHAT IS A SAMPLE? • 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 2

WHY DO YOU NEED A SAMPLE? • 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 3

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 nonprobability. 4

TYPES OF SAMPLES • Non-probability Sample— haphazard, convenient • Probability Sample— systematic • Fraudulent Sample— deliberately biased 5

WHAT ARE PROBABILITY SAMPLES? • Follows standard procedure for everyone in population • Chance of selection using procedure is known • Unintended, random bias is possible 6

TYPES OF PROBABILITY SAMPLES • • Simple Random Sample Systematic Sample Cluster Sample Stratified Sample 7

WHAT ARE NONPROBABILITY SAMPLES? • Uses Non-standardized (Variable) procedures • Chance of selection is unknown • Unintended, systematic bias may creep in 8

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 9

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. 10

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 weirdness 11

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 criteria 12

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. 13

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^ Rdm mean age=20. 3 7 33 2 7 4 Rdm mean sex=0. 67 8 20 1 7 4^ Syst mean age=24. 4 9 21 2 5 5 Syst mean sex=0. 60 10 24 2 6# 5^ Random Number Criteria: select persons with even random numbers Systematic Sample start: person number 2 *from random number table. #Selected for random sample. ^Selected for systematic sample. 14

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