1. Data Collection & Sampling Dr. Guerette

2. Gathering Data Three ways a researcher collects data: Three ways a researcher collects data: By asking questions By asking questions By direct observation By direct observation By using written records By using written records The fundamental issue in data collection is its’ representativeness. The fundamental issue in data collection is its’ representativeness.

3. Logic of Probability Sampling Allows researchers to generalize to unobserved cases. Allows researchers to generalize to unobserved cases. Conscious and Unconscious Sampling Bias Conscious and Unconscious Sampling Bias Bias in this case means that selections are not typical or representative of the population from which they are drawn. This could be result of personal beliefs or many other reasons. Bias in this case means that selections are not typical or representative of the population from which they are drawn. This could be result of personal beliefs or many other reasons.

4. Logic of Probability Sampling Representativeness and Probability of Selection Representativeness and Probability of Selection All members of a population have an equal chance of being selected for a sample in probability selections. All members of a population have an equal chance of being selected for a sample in probability selections.

5. Probability Theory & Sampling Distribution Probability theory permits inferences about how sampled data are distributed around the value found in a larger population. Probability theory permits inferences about how sampled data are distributed around the value found in a larger population. Sample Element Sample Element The unit about which information is collected and it provides the basis of analysis. It is the grouping of study elements. The unit about which information is collected and it provides the basis of analysis. It is the grouping of study elements.

6. Probability Theory & Sampling Distribution Population parameter Population parameter The summary description of a given variable in the population. The summary description of a given variable in the population. Sample statistic Sample statistic The summary description of a given variable in the sample. The summary description of a given variable in the sample. Sampling distribution Sampling distribution The range of sample statistics that would be obtained when many samples are selected. The range of sample statistics that would be obtained when many samples are selected.

7. From Sampling Distribution to Parameter Estimate Sampling frame Sampling frame A list of elements in the population A list of elements in the population Binomial variable Binomial variable A variable that has only two values A variable that has only two values Estimating sampling error Estimating sampling error When independent random samples are selected from a population and sample statistics are calculated from those samples they will be distributed around the population parameter in a known way. When independent random samples are selected from a population and sample statistics are calculated from those samples they will be distributed around the population parameter in a known way.

8. From Sampling Distribution to Parameter Estimate Standard error Standard error Is the way of estimating how closely the sample statistics are clustered around the true value. Is the way of estimating how closely the sample statistics are clustered around the true value. Confidence levels and Confidence Intervals Confidence levels and Confidence Intervals Use probability theory to indicate sample estimates that fall within one, two or three standard errors of the parameter. Use probability theory to indicate sample estimates that fall within one, two or three standard errors of the parameter.

10. Standard Error Diagram Source: Jeremy Kemp (2005)

11. SE = √[p(1-p)/n] Or SE = √[p x q/n]

12. In Class Exercise – Probability Sampling You have selected a probability sample and want to determine the sampling error in order to estimate the population parameter. In your sample you compute that 70 percent of your sample opposes establishing a hurricane relief fund derived from Miami-Dade tax dollars, while 30 percent favor such a fund. You had a sample of 400 (N = 400). You have selected a probability sample and want to determine the sampling error in order to estimate the population parameter. In your sample you compute that 70 percent of your sample opposes establishing a hurricane relief fund derived from Miami-Dade tax dollars, while 30 percent favor such a fund. You had a sample of 400 (N = 400). Report your confidence level and confidence interval. Report your confidence level and confidence interval.

13. Probability Sampling Simple random sampling Simple random sampling All elements in the population have an equal chance of being selected for the sample. All elements in the population have an equal chance of being selected for the sample. Systematic sampling Systematic sampling Drawing a sample of every Nth element in the population. Drawing a sample of every Nth element in the population. Stratified sampling Stratified sampling Based upon prior knowledge of a population, a sample is drawn that will offer a greater degree of representativeness. Based upon prior knowledge of a population, a sample is drawn that will offer a greater degree of representativeness.

14. Probability Sampling Disproportionate Stratified Sampling Disproportionate Stratified Sampling Specifically produce samples that are not representative of a population on some variable. Specifically produce samples that are not representative of a population on some variable. Multistage Cluster Sampling Multistage Cluster Sampling Used when populations cannot easily be listed for sampling purposes. Generally involves geographic dispersion. While this technique increases efficiency it decreases accuracy. Used when populations cannot easily be listed for sampling purposes. Generally involves geographic dispersion. While this technique increases efficiency it decreases accuracy.

15. Probability Sampling Multistage Cluster Sampling with Stratification Multistage Cluster Sampling with Stratification Used to increase the homogeneity of the sample. Used to increase the homogeneity of the sample.

16. Non-Probability Sampling Used when the likelihood of any given element will be selected is not known (e.g. when random probability sampling is not possible). Used when the likelihood of any given element will be selected is not known (e.g. when random probability sampling is not possible). Purposive or Judgemental Sampling Purposive or Judgemental Sampling Selection based upon prior knowledge of the population. Selection based upon prior knowledge of the population. Quota sampling Quota sampling Uses a matrix or table to describe the characteristics of the population and the sample is drawn to reflect the cells of the matrix. Uses a matrix or table to describe the characteristics of the population and the sample is drawn to reflect the cells of the matrix.

17. Non-Probability Sampling Reliance on available subjects Reliance on available subjects Using people that are readily available seldom produces data that have great value. Using people that are readily available seldom produces data that have great value. Snowball Sampling Snowball Sampling Begins by identifying a single member of a population and then having that subject identify others like him/her. Begins by identifying a single member of a population and then having that subject identify others like him/her.