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# Chapter Fifteen - PowerPoint PPT Presentation

Chapter Fifteen. Sampling and Sample Size. Sampling. A sample represents a microcosm of the population you wish to study

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### Chapter Fifteen

Sampling and Sample Size

• A sample represents a microcosm of the population you wish to study

• If the sample is representative of the population from which it is drawn, the researcher can have confidence in concluding that the results are generalizable to the entire population studied

• Save time & money and yet get an accurate description of a population

• Poorly selected samples may misrepresent the population

• Literary Digest example Landon vs Roosevelt. George Gallop establishes his name by indicating reservations

• Population: the entire group one wishes to describe; it could be the student body at St. FXU, the province, the state, the country

• Sampling frame: the list from which a sample is selected

• Sample: those units (individuals) selected for a study

• Response rate: percentage of successfully contacted respondents who participate

• Simple random sample: each unit in the population has a equal chance of being selected.

• Process:

• number units

• table of random numbers or computer (SPSS) will do selection

• replacement units selected using the same process

• Systematic Sample: here the process is to give everyone an equal chance but process a little different.

• Process

• list, map, diagram as appropriate

• divide sample required into number on list to determine skip interval or sample interval

• random numbers used to begin randomly then every kth number selected

• Stratified Sample: sometimes to ensure an adequate representation of sub-groups, we use stratified samples, which provide random samples within sub-groups. For example:

• study of nursing graduates from different classes

• members of early, middle, late adolescent age group

• Stratified Sampling proceed by:

• determine sample size needed for sub-groups

• obtain list for each sub-group

• using either simple random or systematic sampling select respondents

• Note that within SPSS it is possible to weight cases to return the sample so it can represent the larger population

• Multi-Stage Area Sample: these are used when doing large populations such as states, provinces, or a whole country

• identify primary sampling units: select sample

• identify sub-units within selected units (city blocks, square kilometers etc.)

• identify households within sub-units: select sample

• within household select respondents

• Non-probability samples do not provide an equal or a known chance of being selected

• Quota Sample: the parallel here is the stratified sample; a quota sample requires that a certain number be selected in each category--usually done on a first-come first included basis. Sampling stops when enough are included in each category

• Convenience Sampling: purely convenience used to choose participants. Examples include using all those in attendance at a meeting/class; interviewing people in a mall clinic or doctor’s office

• Snowball Sampling: also known as “referral sampling”.

• Used on hard to locategroups that one cannot obtain a list of the individuals who possess the attributes or phenomenon you wish to study; e.g. blind, those with some sort of disability, “closet” homosexuals, etc

• Purposive sampling: uses the researcher’s knowledge of the population to hand pick the cases to be included

• common in qualitative studies

• useful when you are interested in understanding the experiences of certain segments of a population

• limitation is inability to assess representativeness of participants in relation to the population

• Expert Sampling: a type of purposive sampling using the Delphi technique

• Researcher handpicks a group of participants because of their expertise in the study phenomenon

• A means to achieve experts’ consensus on an issue

• Interested in samples of participants who can share their interpretation of the experience with others

• Goal is understanding the meaning of the participants’ experience

• Typically not interested in generalizing their results

• Typically do not use probability sampling

• Decide on confidence level--usually 95% level selected; this means that you will be 95% confident that the sample will be within a given range; 19 out of 20 times sample will be within  a given range

• Choose major variable and key on that

• Determine precision needed: how precise do you need the estimate to be?

• Compute sample size:

2

Reqd. Sample = Confidence limit * sd pop

Accuracy

• Are there sufficient cases?

• Adjust Sample for Time and Cost factors

• Sample size and accuracy: to double accuracy you quadruple sample size

• Power is the ability to detect real differences among variables

• Power consists of 4 elements: alpha or significance level, sample size, effect size, power

• If any 3 are known the fourth can be found using the power analysis formula

• Alpha refers to the probability of making a Type I error (.05 or .01)

• Beta refers to the probability of making a Type II error

• Power of a statistical test = (1-beta) = 1-.20=.80 The standard for power is .80

• Effect Size is the strength of the relationship among the study variables

• Literature Review (meta-analysis)

• Pilot Study

• Dummy Table Analysis

• Estimate on the Basis of Clinical Experience or Previous Research

• Estimate the population effect size

• Review the literature to see if prior studies report the effect of the intervention

• Consult a table of sample size requirements in a statistic text to determine the # of participants per group for various effect sizes, powers & alpha or significant levels

• If no previous research is available estimate the effect size based on experience, intuitive knowledge, & literature

By Convention Effect Size in a 2-group Test of Means

• .20 for small effects

• .50 for medium effects

• .80 for large effects

• Most nursing studies can not expect effect sizes in excess of .50

• .20 to .40 effect size is a realistic expectation for nursing studies

• Sample size & accuracy - to double accuracy sample size must be quadrupled

• Sample size & confidence limits - to move from 95% to 99% multiply sample size by 1.73

• Impact of refusals

• Confirming representativeness