Transcript Sampling (Ch 7)

Sampling
Neuman and Robson Ch. 7
Qualitative and Quantitative
Sampling
Introduction

Qualitative vs. Quantitative Sampling

Non-Random Sampling



Random sampling



Non-probability
Not representative of population
Probability
Representative of population
The sampling distribution

Used in probability sample to allow us to generalize from
sample to population
Non-Probability Samples

Haphazard, convenience or accidental



Quota



Choose any convenient cases
Highly distorted
Establish categories of cases
Choose fixed number in each category
Purposive (judgmental)


Use expert judgment to pick cases
Used for exploratory or field research
Non-Probability (cont.)

Snowball



Network or chain referral
Use of sociograms to represent
Other types

Deviant case


Choose cases for difference from dominant pattern
Sequential

Select cases until all possible information obtained
Probability Sampling

Used for quantitative research

Representative of population

Can generalize from sample to population
through use of sampling distribution
Logic Behind Probability
Sampling

Problem:
The populations
we wish to study
are almost always
so large that we
are unable to
gather information
from every case.




















Logic (cont.)
























 


Solution:
We choose a sample
-- a carefully chosen
subset of the
population – and use
information gathered
from the cases in the
sample to generalize
to the population.
Terminology



Statistics are
mathematical
characteristics of
samples.
Parameters are
mathematical
characteristics of
populations.
Statistics are used to
estimate parameters.
PARAMETER
STATISTIC
Probability Samples:

Must be representative of the population.


Representative: The sample has the same
characteristics as the population.
How can we ensure samples are
representative?

Samples drawn according to the rule of
EPSEM (every case in the population has the
same chance of being selected for the
sample) are likely to be representative.
The Sampling Distribution




We can use the sampling distribution to
calculate our population parameter based on
our sample statistic.
The single most important concept in
inferential statistics.
Definition: The distribution of a statistic for
all possible samples of a given size (N).
The sampling distribution is a theoretical
concept.
The Sampling Distribution


Every application of
inferential statistics
involves 3 different
distributions.
Information from the
sample is linked to the
population via the
sampling distribution.
Population
Sampling Distribution
Sample
The Sampling Distribution:
Properties
1. Normal in shape.
2. Has a mean equal to the population mean.
μx=μ
3. Has a standard deviation (standard error)
equal to the population standard deviation
divided by the square root of N.
σx= σ/√N
First Theorem

Tells us the shape of the sampling distribution
and defines its mean and standard deviation.

If we begin with a trait that is normally distributed
across a population (IQ, height) and take an
infinite number of equally sized random samples
from that population, the sampling distribution of
sample means will be normal.
Central Limit Theorem

For any trait or variable, even those that are not
normally distributed in the population, as sample
size grows larger, the sampling distribution of
sample means will become normal in shape.

Note: The Census is a sample of the entire
population
Simple Random Sampling (SRS)

Sampling frame and elements

Selection techniques


Table of random numbers
Other types of samples are variants of the
simple random sample
Other Probability Samples

Systematic Random Sampling

Stratified Random Sampling

Cluster Sampling

Random Route Sampling
Other Strategies and Issues
Related to Random Sampling

Random Digit Dialing (RDD)

Hidden Populations

Sampling Error and Bias

Sample Size