Data Collection

Sampling

Target Population

The group of people to whom the researcher wishes to generalize the results of the study

Accessible Population

-The smaller portion of the target population to whom the researcher actually has access

Target

Accessible

Sample

Sample

-The group of people who supply data for the study (Study group)

Sampling

the process of selecting a portion of the target population (sample) in such a way that the individuals chosen represent, as nearly as possible, the characteristics of the target population.

Sampling Unit

-A single member of the target population.

Sampling Bias

-An overrepresentation or underrepresentation of some characteristic in the sample relative to the target population

Unconscious

Conscious

The extent to which bias is a concern is a function of the homogeneity or heterogeneity of the target population.

When a variation (relevant to the research question) occurs in a population, then it must occur in the sample

Strata

-Subpopulations of the target population

Sampling error

-the fluctuation of a statistic from one sample to another drawn from the same population. (Can be estimated with probability sampling) Note: the larger the sample, the less sampling error.

Probability Sampling

-Sampling procedures use some form of randomization to select samples from the population.

Non Probability Sampling

Sampling procedures

using other than random procedures.

NON PROBABILITY SAMPLING CONVENIENCE SAMPLING

PURPOSIVE SAMPLING

QUOTA SAMPLING

Convenience Sampling(Accidental Sampling) Involves the use of the most

convenient and readily available subjects for the sample.– Man on the street interviews– Teacher uses students– Volunteers

Convenience/accidental sampling

Problem: Sample bias because of “self selection”--available subjects may be highly atypical of the population with regard to critical variables.

SNOWBALL SAMPLING”

Variation of above, used when subjects are hard to find. One subject recommends another. Even more prone to bias.

Convenience sampling is the most widely used yet weakest form of sampling. There is no way to evaluate all of the biases that may be operating.

QUOTA SAMPLING

Researcher uses some knowledge of the population to build some representativeness into the sampling plan

divides population into different strata and samples from each of them

USUALLY BETTER THAN JUST CONVENIENCE

THE BASIS OF THE CHARACTERISTICS CHOSEN SHOULD REFLECT IMPORTANT DIFFERENCES IN THE DEPENDENT VARIABLE– age– gender– ethnicity– socioeconomic status– education– medical diagnosis– occupation

Quota Sampling

Problem: you cant always determine which characteristics in the sample are going to be reflected in the dependent variable

PURPOSIVE SAMPLING“Judgmental Sampling” PROCEEDS ON THE BELIEF THAT

THE RESEARCHER KNOWS ENOUGH ABOUT THE POPULATION AND ITS ELEMENT TO HANDPICK THE SAMPLE– selects “typical” persons– selects widest variety

Purposive or Judgemental Sampling Assumption: judgemental errors will tend to balance

out.

Risk of conscious bias greatly multiplied Should be avoided if the population is

heterogeneous.

PROBABILITY SAMPLING

SIMPLE RANDOM STRATIFIED RANDOM CLUSTER

The probability of any member of the target population being included in the sample can be calculated.

SYSTEMATIC SAMPLING(Can be either probability or non probability)

SIMPLE RANDOM SAMPLING

identify population

establish sampling frame

number elements in sampling frame consecutively

randomly select from list

Random sampling does not guarantee representativeness, it does guarantee that difference between the sample and the population are purely a function of chance.

STRATIFIED RANDOM SAMPLE

The population is divided into two or more strata by relevant characteristics and subjects are randomly chosen from these strata

Slightly better than simple random, especially if the sample is not very large.

CLUSTER SAMPLING

Multistage sampling process Used when target population is very

large

Results in more sampling error

Statistical analysis more complicated

SYSTEMATIC SAMPLING

Selection of every Kth case from a list of possible subjects.

( K represents any number)

SAMPLE SIZE

N Determined by: COHEN’S POWER ANALYSIS

Determine “effect size of treatment”

Use in power analysis formula

Achieves the least measurement error

N DETERMINED BY CONVENTION

The bigger the better

cost and convenience

10% minimum for descriptive studies

15 subjects/group for experiments

5 for each cell in factorial