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<ul><li> Slide 1 </li> <li> SELECTING A SAMPLE </li> <li> Slide 2 </li> <li> To Define sampling in both: QUALITATIVE RESEARCH &amp; QUANTITATIVE RESEARCH </li> <li> Slide 3 </li> <li> Slide 4 </li> <li> A sample is a selected group (that when properly selected) provides information the same as the population. The representation of the information from the sample group is intended to be the same as the population. </li> <li> Slide 5 </li> <li> The entire group of interest which the researcher would like to get their study results from. A population may be of any size, and usually begins with the word ALL </li> <li> Slide 6 </li> <li> PROBABILITY IS EQUALIZED ERROR &amp; BIAS ARE MINIMALIZED </li> <li> Slide 7 </li> <li> Why do a sample, and not a whole population? </li> <li> Slide 8 </li> <li> Slide 9 </li> <li> Everyone in the population has an equal chance of selection for the sample. The researcher has no control over the selection. The selection of an individual does not effect the selection of any other individual (independent) You should have at least 30 samples. The sample size should be less than 10% of the entire sample. </li> <li> Slide 10 </li> <li> Random Numbers </li> <li> Slide 11 </li> <li> The sample size formula for the infinite population is given as : </li> <li> Slide 12 </li> <li> Slide 13 </li> <li> Slide 14 </li> <li> Here, SS = Sample size. Z = Given z value p = Percentage of population C = Confidence level Pop = Population </li> <li> Slide 15 </li> <li> Stratified RS is a way to guarantee representation of relevant subgroups within a sample. Population are subdivided into subgroups (strata) on a certain variable. From each group proportional or equal numbers of subjects are selected randomly to form a sample </li> <li> Slide 16 </li> <li> 1. Identify and define the population. 2. Determine the desired sample size. 3. Identify the variables and subgroups (strata). 4. Classify all members of the population into subgroups. 5. Randomly select an equal or proportional number of individuals from each subgroup (using table of random numbers). </li> <li> Slide 17 </li> <li> Slide 18 </li> <li> intact groups (clusters), not GROUPS are randomly selected. All the individuals of the selected clusters are included. May be the only feasible method of selecting a sample when the researcher is unable to obtain a list of all members of an intended population. </li> <li> Slide 19 </li> <li> 1.Identify and define the population. 2.Determine the desired sample size. 3.Identify and define a logical cluster. 4.List all clusters. 5.Estimate the average number of population members per cluster. 6.Divide the sample size by the estimated size of cluster to determine the number of clusters. 7.Randomly select the needed number of clusters. 8.Include all population members in each selected cluster. </li> <li> Slide 20 </li> <li> Selecting every Kth individual from the list of the population. K = Number of Individuals on the list/Number of individuals desired for the sample All members dont have an independent chance of selection. It is considered random sampling if the list of the population is randomly ordered. Process may cause certain subgroups of the population to be excluded from the sample * NOT USED VERY OFTEN </li> <li> Slide 21 </li> <li> 1. Identify and define the population. 2.Determine the desired sample size. 3.Obtain a list of the population. 4.Determine K by dividing the size of the population by the desired sample size. 5.Start at some random place in the population list. 6.Take ever K th individual on the list. 7.If the end of the list is reached before the desired sample is reached, go back to the top of the list. </li> <li> Slide 22 </li> <li> AKA: non-probability sampling. Independent or biased free selection of the individuals will not happen. Useful when the population cant be described. </li> <li> Slide 23 </li> <li> AKA accidental Sampling or haphazard sampling. Sample includes available individuals; whoever is available Volunteers Pre-existing groups Difficult to describe the population from which the sample was drawn and to whom results can be generalized </li> <li> Slide 24 </li> <li> AKA judgment sampling. Selection based on the researchers experience and knowledge of the individuals being sampled. Researcher select the criteria to select the individuals. Main weakness is the imperfections in the researchers criteria. </li> <li> Slide 25 </li> <li> Process based on required, exact numbers, or quotas of individuals or groups with varying characteristics. Mostly used in wide-scale survey research when listing all members of the population is not possible. Data obtained from easily accessible individuals. People who are less accessible are underrepresented due to their own unavailability. </li> <li> Slide 26 </li> <li> Slide 27 </li> <li> In qualitative research the sampling is mainly purposive. Selecting process designed to select a small number of individuals that will be good key informants. QUALITY instead of Quantity The researcher first identifies the potential participants of the research. Participants are selected on some criteria according to their knowledge, experience, characteristics and willingness </li> <li> Slide 28 </li> <li> Slide 29 </li> <li> Slide 30 </li> <li> Slide 31 </li> <li> Slide 32 </li> <li> Slide 33 </li> <li> Slide 34 </li> <li> Slide 35 </li> <li> Slide 36 </li> <li> Slide 37 </li> <li> Slide 38 </li> </ul>