PSYC 2300 Lecture Notes - Lecture 5: Observational Error, Selection Bias, Sample Size Determination
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Collecting Data and Sampling
1. Introduction
a. General question about group of individuals
b. Known as a population (entire set of individuals of interest)
c. Actual research conducted with smaller group known as a sample
d. Representative: applicable to entire population
2. Sampling and Generalization
a. Generalization: applicability of results from sample to population depends on
representativeness of a sample
b. Biased sample: does’t reflect populatio
i. Selection bias: selection of sample is not truly random
1. Result of/leads to biased sample
3. Populations
a. Target population: population; entire set of things in a group defined by the
researcher
i. E.g. college students, children of immigrants
b. Accessible population: specific version of the population the researcher is able to
study
i. E.g. college students—students at liberal arts colleges
4. Types of sampling
a. Probability sampling: random procedures for producing one outcome from a set
of possible outcomes
i. Exact size of population is known and all individuals listed
ii. Each individual/unit must have a specific probability of being selected
iii. X/N
iv. Selection process must be unbiased—all individuals/units must have an
equal chance of being included
v. Simple random sampling (SRS)—relies on chance to produce a
representative sample
1. Ensures procedure is unbiased
2. Reduces systematic bias
3. 2 types
a. Selection with replacement—conserve n—return
individual to population
b. Selection without replacement—probability of selection
changes as individual is removed—n changes
vi. Stratified sampling—guarantees that each subgroup will be adequately
represented
1. Population divided into strata
2. Random sample drawn from each stratum
3. Represetative saple
4. Groups balanced by equal numbers
vii. Proportionate random sampling (PRS)—proportions of different groups in
the population are reflected in sample strata
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