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

Data Collection

Sampling


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


Data collection

  • 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

    • CTeacher uses students

    • CVolunteers


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.


Data collection

  • 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


Data collection

  • THE BASIS OF THE CHARACTERISTICS CHOSEN SHOULD REFLECT IMPORTANT DIFFERENCES IN THE DEPENDENT VARIABLE

    • Cage

    • Cgender

    • Cethnicity

    • Csocioeconomic status

    • Ceducation

    • Cmedical diagnosis

    • Coccupation


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

    • Cselects “typical” persons

    • Cselects 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

Cidentify population

Cestablish sampling frame

Cnumber elements in sampling frame consecutively

Crandomly select from list


Data collection

  • 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

Ccost and convenience

C10% minimum for descriptive studies

C15 subjects/group for experiments

C5 for each cell in factorial


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