Data collection
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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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Data Collection

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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

  • -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.

    • CMan on the street interviews

    • CTeacher uses students

    • CVolunteers


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

    • Cage

    • Cgender

    • Cethnicity

    • Csocioeconomic status

    • Ceducation

    • Cmedical diagnosis

    • Coccupation


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

    • Cselects “typical” persons

    • Cselects 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

Cidentify population

Cestablish sampling frame

Cnumber elements in sampling frame consecutively

Crandomly 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

Ccost and convenience

C10% minimum for descriptive studies

C15 subjects/group for experiments

C5 for each cell in factorial


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