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Data Collection

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Data Collection

Sampling

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

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

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

- 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.

- -A single member of the target population.

-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

- -Subpopulations of the target population

- -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.

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

- Sampling procedures
using other than random procedures.

- CONVENIENCE SAMPLING
- PURPOSIVE SAMPLING
- QUOTA SAMPLING

- Involves the use of the most convenient and readily available subjects for the sample.
- CMan on the street interviews
- CTeacher uses students
- CVolunteers

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

- 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.

- 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

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

- 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

- Assumption:
- judgemental errors will tend to balance out.
- Risk of conscious bias greatly multiplied
- Should be avoided if the population is heterogeneous.

- 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)

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.

- 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.

- Multistage sampling process
- Used when target population is very large
- Results in more sampling error
- Statistical analysis more complicated

- Selection of every Kth case from a list of possible subjects.
- ( K represents any number)

- N Determined by:
- COHEN’S POWER ANALYSIS
Determine “effect size of treatment”

Use in power analysis formula

Achieves the least measurement error

The bigger the better

Ccost and convenience

C10% minimum for descriptive studies

C15 subjects/group for experiments

C5 for each cell in factorial