Lesson 2: Sample size & procedures

POPULATION

includes all of the members of a defined group that researchers are studying or collecting information on for data driven decisions. 

SAMPLE

is the part of the population, a slice or part of it, and all its characteristics. It is the subset of a population used to represent the entire group as a whole. 

SAMPLING ERRORS

- is the difference between what is present in a population and what is present in a sample.

sample size

selection pf respondents that represent total population

POPULATION SIZE

how many total people fit your demographic?

MARGIN OF ERROR (Confidence Interval)

how much error to allow?

CONFIDENCE LEVEL

how sure you can be? It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval.

STANDARD DEVIATION

how much variance do you expect in your responses?

slovin’s formula

n = N / (1+Ne^2)

what formula should you use in determining the correct sample size

SAMPLING

process or technique of choosing a subgroup from a population to participate in the study.

SAMPLING

It is the process of selecting a number of individuals for a study in such a way that the individuals selected represent the large group from which they were selected. 

• PROBABILITY SAMPLING PROCEDURES

• NON-PROBABILITY SAMPLING PROCEDURES

TWO MAJOR SAMPLING PROCEDURES

PROBABILITY SAMPLING PROCEDURES

everyone has a chance of being selected. This scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample.

• Simple random

• Systematic sampling

• Stratified

• Cluster

• Multi-stage

FIVE PROBABILITY SAMPLING PROCEDURES

SIMPLE RANDOM SAMPLING

every individual in the target population has an equal chance of being part of the sample.

• Obtain a complete list of the population

• Randomly select individuals from that list for the sample 

SIMPLE RANDOM SAMPLING requires two steps:

Random

is a technical term in social science that means that selection was made without aim, reason, or patterns.

SYSTEMATIC SAMPLING

often used in place of simple random sampling. The researcher selects every nth member after randomly selecting the first through nth element as the starting point.

STRATIFIED SAMPLING

the researcher divides the population into groups based on relevant characteristics and then selects participants within those groups.

STRATIFIED SAMPLING

 It is typically used when the researcher wants to ensure that specific subgroups of people are adequately represented within the sample.

CLUSTER SAMPLING

a group of population elements, constitutes the sampling unit, instead of a single element of the population.

1. saves time

2. saves money

3. provides more detailed data

4. may only require testing sample

5. reduces errors

why do we need to use sample size

• May not reflect the diversity of the community

• Other elements may share similar characteristics

• Provides redundant information from the others in the cluster

• Standard errors of the estimates are high 

disadvantages of cluster sampling

MULTI-STAGE SAMPLING

the sample is selected in multiple steps or stages.

NON-PROBABILITY SAMPLING PROCEDURE

used in some situations, where the population may not be well defined. In other situations, there may not be great interest in drawing inferences from the sample to the population.

• purposive sampling

• convenience sampling

• quota sampling

• snowball sampling

4types of NON-PROBABILITY SAMPLING PROCEDURE

Sampling

is a process or technique of choosing a sub-group from a population to participate in the study.

CONVENIENCE SAMPLING

sometimes known as opportunity, accidental, or haphazard sampling. It involves the sample being drawn from that part of the population which is close to hand, that is, a population which is readily available and convenient.

quota sampling

- the researcher selects people according to some fixed quota. That is, units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied.

1. Organize the sampling process into stages where the unit of analysis is systematically grouped.

2. Select a sampling technique for each stage . 3

. Systematically apply the sampling technique to each stage until the unit of analysis has been selected.

The steps in multi-stage sampling are as follows:

strata

Stratified random sampling, the population is divided into subpopulation called .

snowball sampling

a technique where the researcher identifies a key informant about a research of interest and then ask that respondent to refer or identify another respondent who can participate in the study.

Purposive sampling

sometimes called judgmental or subjective sampling employs a procedure in which samples are chosen for a special purpose. It may involve members of a limited group of population.

Quota Sampling

is gathering a representative sample from a group based on certain characteristics of the population chosen by the researcher. Usually the population is divided into specific groups. If the specific condition,