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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. Sample.

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target population
Target Population

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

accessible population
Accessible Population
  • -The smaller portion of the target population to whom the researcher actually has access
sample
Sample
  • -The group of people who supply data for the study (Study group)
sampling
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
Sampling Unit
  • -A single member of the target population.
sampling bias
Sampling Bias

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

Unconscious

Conscious

slide9
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
Strata
  • -Subpopulations of the target population
sampling error
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
Probability Sampling
  • -Sampling procedures use some form of randomization to select samples from the population.
non probability sampling
Non Probability Sampling
  • Sampling procedures

using other than random procedures.

non probability sampling1
NON PROBABILITY SAMPLING
  • CONVENIENCE SAMPLING
  • PURPOSIVE SAMPLING
  • QUOTA SAMPLING
convenience sampling accidental sampling
Convenience Sampling(Accidental Sampling)
  • Involves the use of the most convenient and readily available subjects for the sample.
    • CMan on the street interviews
    • C Teacher uses students
    • C Volunteers
convenience accidental sampling
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
SNOWBALL SAMPLING”
  • Variation of above, used when subjects are hard to find. One subject recommends another. Even more prone to bias.
slide18
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
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
slide20
THE BASIS OF THE CHARACTERISTICS CHOSEN SHOULD REFLECT IMPORTANT DIFFERENCES IN THE DEPENDENT VARIABLE
    • C age
    • C gender
    • C ethnicity
    • C socioeconomic status
    • C education
    • C medical diagnosis
    • C occupation
quota sampling1
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
PURPOSIVE SAMPLING“Judgmental Sampling”
  • PROCEEDS ON THE BELIEF THAT THE RESEARCHER KNOWS ENOUGH ABOUT THE POPULATION AND ITS ELEMENT TO HANDPICK THE SAMPLE
    • C selects “typical” persons
    • C selects widest variety
purposive or judgemental sampling
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 sampling1
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
SIMPLE RANDOM SAMPLING

C identify population

C establish sampling frame

C number elements in sampling frame consecutively

C randomly select from list

slide26
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
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
CLUSTER SAMPLING
  • Multistage sampling process
  • Used when target population is very large
  • Results in more sampling error
  • Statistical analysis more complicated
systematic sampling
SYSTEMATIC SAMPLING
  • Selection of every Kth case from a list of possible subjects.
  • ( K represents any number)
sample size
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
N DETERMINED BY CONVENTION

The bigger the better

C cost and convenience

C 10% minimum for descriptive studies

C 15 subjects/group for experiments

C 5 for each cell in factorial

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