Data Collection

# Data Collection

## Data Collection

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##### Presentation Transcript

1. Data Collection Sampling

2. Target Population The group of people to whom the researcher wishes to generalize the results of the study

3. Accessible Population • -The smaller portion of the target population to whom the researcher actually has access

4. Sample • -The group of people who supply data for the study (Study group)

5. 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.

6. Sampling Unit • -A single member of the target population.

7. Sampling Bias -An overrepresentation or underrepresentation of some characteristic in the sample relative to the target population Unconscious Conscious

8. 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

9. Strata • -Subpopulations of the target population

10. 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.

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

12. Non Probability Sampling • Sampling procedures using other than random procedures.

13. NON PROBABILITY SAMPLING • CONVENIENCE SAMPLING • PURPOSIVE SAMPLING • QUOTA SAMPLING

14. 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

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

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

17. 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.

18. 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

19. 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

20. Quota Sampling • Problem: you cant always determine which characteristics in the sample are going to be reflected in the dependent variable

21. 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

22. 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.

23. 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)

24. SIMPLE RANDOM SAMPLING C identify population C establish sampling frame C number elements in sampling frame consecutively C randomly select from list

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

26. 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.

27. CLUSTER SAMPLING • Multistage sampling process • Used when target population is very large • Results in more sampling error • Statistical analysis more complicated

28. SYSTEMATIC SAMPLING • Selection of every Kth case from a list of possible subjects. • ( K represents any number)

29. SAMPLE SIZE • N Determined by: • COHEN’S POWER ANALYSIS Determine “effect size of treatment” Use in power analysis formula Achieves the least measurement error

30. 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