Validity

Reliability

Kinds of error in Operational Definitions:

Systematic Error

Random Error

if random error in operational definitions is controlled, the measure (exam/survey) will be more reliable.

μ = POPULATION MEAN

x̄ or σ = SAMPLE MEAN

N = POPULATION

n = SAMPLE

Population

Sample

Parameter

Statistic

Descriptive Statistics:

[mean] statistical measure that takes one score that represents the information as a whole

Inferential Statistics:

techniques that allow us to study a sample then generalize it to the population

—can help us generalize about the populations from which samples were selected

—can tell us how likely it is that the differences between samples reflect real differences between populations

Non Probability Sampling (cannot calculate sampling error, try to avoid)

chose who is available [doesn't necessarily represent population you are talking about]
- unqualified volunteer sample
- purposive sample [survey those who relate to survey]
- quota sampling
- snowball sample [survey those who know people who have taken it]

Probability Sampling (can calculate sampling error)

random sampling: everyone has an equal chance of being selected to respond
- systematic random sampling
- stratified sampling
- multistage sampling

Central Limit Theorem

the average/sum of a large bunch of measurements follows a normal bell curve even if the individual measurements do not

applies to distribution of sample means for ANY population

don't need large sample size before distribution is almost perfect (n=30)

can tell how much sampling error we have based on the relationship between samples and the normal curve

Week 1: Research V