Nonprobability Sample Designs. 1. Convenience samples 2. purposive or judgmental samples 3. snowball samples 4.quota samples

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1 Nonprobability Sample Designs 1. Convenience samples 2. purposive or judgmental samples 3. snowball samples 4.quota samples

2 Unrepresentative sample Some characteristics are overrepresented or underrepresented

3 Typical Problems in sampling frames 1. Incomplete frames-units are missing from list 2. cluster of elements-listed in clusters rather than individually-city blocks 3. blank foreign elements-listing is outdated

4 True Experiments must have at least 3 things 1. An experimental and control group 2. variation in the independent variable before assessment of change in the dependent variable(treatment) 3. random assignment to two groups

5 The Classic Experimental Design Experimental group, control group randomization pretest posttest

6 Target population A set of elements larger than or different from the population sampled and to which the researcher would like to generalize study findings.

7 Systematic sampling Select every kth element in a population,where k is determined by dividing the population sixe by the desired sample size. Select a random number between 0 and k and picking that element in the population,systematically pick every kth element

8 Survey Sampling Sampling designed to produce information about particular characteristics of a finite population.

9 Stratified samples Done by dividing the population into groups(strata) that are homogeneous on one or more traits,then sampling from each of these groups

10 Stratified Proportionate sample The number of elements selected from each stratum is proportional to that stratum s representation in the population The same number of sampling units from each stratum or a uniform sampling fraction (n/n)

11 Stratified Disproportionate sample Chosen to yield numbers in a stratum to allow intensive analysis of that particular stratum Variable sampling fractions,total number in each stratum is different,population parameters have to be weighted by the number of each stratum

12 Standard error Allows the researcher to determine the probability that a given sample estimate is close to the actual population value. S.E.=standard error,the distribution of all samples about the mean of the samples is S.E.Calculate standard deviation and estimate the S. E.

13 Simple random sampling Numbering all population elements,then selecting enough random numbers to complete a sample of the desired size.it is simple but inconvenient with large populations

14 Scale Type of composite measure composed of several items that have logical or empirical structure among them Take account of differing intensity of indicators e.g. Likert scale, Guttman scale

15 Sampling Theory Major objective is to provide accurate estimates of unknown parameters in population from sample statistics Population=parameter sample=statistic

16 Sampling Frame A list of all elements or other units containing the elements in a population

17 Sampling Error -contd The larger the sampling error,the less representative the sample.

18 Sampling Error Any difference between the characteristics of a sample and the characteristics of a population

19 Sampling distribution When an infinite number of independently selected sample values such as the means are placed in a distribution,the distribution is called the sampling distribution Its standard deviation is the standard error

20 Sample generalizability Refers to the ability to generalize from a sample,or subset of a larger population to that population itself.

21 Sample A subset of a population that is used to study the population as a whole. Subset=sample

22 Representative sample A sample that looks like the population from which it was selected in all respects that are potentially relevant to the study.

23 Random selection procedures Ensure that every sampling unit of the population has an equal and known probability of being included in the sample,the probability is n/n n=sample, N=population

24 Random Selection Each element has an equal chance of selection independent of any other event in the selection process

25 Quota sample Select respondents such that quotas of various types of people are filled in proportion to their prevalence in the population

26 Quasi-experimental design Subjects are not randomly assigned to to the experimental and control or comparison group

27 Purposive or judgmental sample Select a sample that, in their subjective judgment,is representative of the population

28 Procedures of Control 1. Randomization or random assignment-removes bias from the assignment process by relying on chance-flipping coin or random number table assures that case has an equal probability of being assigned to either group 2. matching- or pairwise matching,for each case in experimental group, another one with identical characteristics is selected for the control group

29 Probability vs. Nonprobability Sampling Probability sample allows estimates to population from sample Nonprobability sample-list of sample population is unavailable-e;g, illegal residents, drug addicts

30 Probability Sample Designs 1. random sample 2. systematic samples 3. stratified samplesproportionate, disproportionate 4. cluster samples 5. multistage samples

31 pretests Measures the dependent variables prior to the experimental intervention,they provide a direct measure of how much the experimental and comparison groups changed over time,tests effects of intervention

32 PPS-probability proportionate to size Type of multistage cluster sample in which clusters are selected,not with equal probabilities(epsem) but with probabilities proportionate to their sizes

33 Population-finite or infinite Finite population-contains a countable number of sampling units Infinite population-consists of an endless number of sampling units,an unlimited number of coin tosses

34 Population The entire set of individuals or other entities to which study findings are to be generalized Whole=population

35 Weighting Assigning different weights to cases that were selected into a sample with different probabilities of selection.,each case given weight equal to the inverse of its probability of selection