Sampling.ppt-2

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Presentation Transcript

Why Sample?:

Why Sample?

Why Sample?:

Why Sample? Samples are taken to obtain information about populations. Sample estimators are computed to estimate parameters of the the population from which the sample was drawn.

Advantages:

Advantages Complete enumeration of all sample units in the entire universe is often unnecessary to obtain reasonably accurate results.

Advantages:

Advantages An examination of the entire population is often too costly, too time-consuming, and impractical (if not impossible).

Advantages:

Advantages In the case of destructive testing, the sample elements or units must be destroyed or must be consumed to obtain necessary measurements.

Sampling…:

Sampling… The process of selecting a number of individuals for a study in such a way that the individuals represent the larger group from which they were selected

Slide 7:

Sample … …the representatives selected for a study whose characteristics exemplify the larger group from which they were selected

Slide 8:

Population … …the larger group from which individuals are selected to participate in a study

The purpose for sampling…:

The purpose for sampling… To gather data about the population in order to make an inference that can be generalized to the population

The sampling process…:

The sampling process… POPULATION SAMPLE INFERENCE

Regarding the sample…:

Regarding the sample… POPULATION (N) SAMPLE (n) IS THE SAMPLE REPRESENTATIVE?

Regarding the inference…:

Regarding the inference… POPULATION (N) SAMPLE (n) INFERENCE IS THE INFERENCE GENERALIZABLE?

Mistakes to be conscious of...:

Mistakes to be conscious of... 2. Sampling bias …which threaten to render a study’s findings invalid 1. Sampling error

Slide 14:

Sampling error … …the chance and random variation in variables that occurs when any sample is selected from the population …sampling error is to be expected

Slide 15:

…to avoid sampling error, a census of the entire population must be taken …to control for sampling error, researchers use various sampling methods

Slide 16:

Sampling bias … …nonrandom differences, generally the fault of the researcher, which cause the sample is over-represent individuals or groups within the population and which lead to invalid findings …sources of sampling bias include the use of volunteers and available groups

Steps in sampling...:

Steps in sampling... 3. Determine sample size (n) 4. Control for bias and error 5. Select sample 1. Define population (N) to be sampled 2. Deciding sampling frame

1. Define population to be sampled...:

1. Define population to be sampled... Identify the group of interest and its characteristics to which the findings of the study will be generalized …called the “ target ” population (the ideal selection) …oftentimes the “ accessible ” or “ available ” population must be used (the realistic selection)

Slide 19:

A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be selected. Difficult to get an accurate list. Sample frame error occurs when certain elements of the population are accidentally omitted or not included on the list. 2. Deciding about the sampling frame

3. Determine the sample size...:

3. Determine the sample size... The size of the sample influences both the representativeness of the sample and the statistical analysis of the data …larger samples are more likely to detect a difference between different groups …smaller samples are more likely not to be representative

Rules of thumb for determining the sample size...:

Rules of thumb for determining the sample size... 2. For smaller samples (N ‹ 100), there is little point in sampling. Survey the entire population. 1. The larger the population size, the smaller the percentage of the population required to get a representative sample

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4. If the population size is around 1500, 20% should be sampled . 3. If the population size is around 500 (give or take 100), 50% should be sampled. 5. Beyond a certain point (N = 5000), the population size is almost irrelevant and a sample size of 400 may be adequate.

4. Control for sampling bias and error...:

4. Control for sampling bias and error... Be aware of the sources of sampling bias and identify how to avoid it Decide whether the bias is so severe that the results of the study will be seriously affected In the final report, document awareness of bias, rationale for proceeding, and potential effects

Sampling Methods:

Sampling Methods Probability sampling Nonprobability sampling

Types of Sampling Methods:

Types of Sampling Methods Probability Simple random sampling Systematic random sampling Stratified random sampling Cluster sampling Nonprobability Convenience sampling Judgment sampling Quota sampling Snowball sampling Quantitative research – Probability Qualitative research – Nonprobability

Simple Random Sampling:

Simple Random Sampling Simple random sampling is a method of probability sampling in which every unit has an equal nonzero chance of being selected

Systematic Random Sampling:

Systematic Random Sampling Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the sample is selected according to position using a skip interval

Steps in Drawing a Systematic Random Sample:

Steps in Drawing a Systematic Random Sample 1: Obtain a list of units that contains an acceptable frame of the target population 2: Determine the number of units in the list and the desired sample size 3: Compute the skip interval 4: Determine a random start point 5: Beginning at the start point, select the units by choosing each unit that corresponds to the skip interval

Stratified Random Sampling:

Stratified Random Sampling Stratified random sampling is a method of probability sampling in which the population is divided into different subgroups and samples are selected from each

Steps in Drawing a Stratified Random Sample:

Steps in Drawing a Stratified Random Sample 1: Divide the target population into homogeneous subgroups or strata 2: Draw random samples fro each stratum 3: Combine the samples from each stratum into a single sample of the target population

Slide 31:

Cluster sampling - the subjects are selected in groups or clusters rather than randomly E.g., interviewing McDonald’s employees Clusters would be every employee at a particular store. One-stage cluster sampling – sample all members of the cluster Two-stage cluster sampling – random sampling within the clusters

Slide 33:

Types of nonprobability sampling Convenience sampling (haphazard, accidental) – sample whoever is available. Used by both quantitative and qualitative researchers Problems no representativeness It is haphazard, can be very biased Not random (avoid using word)

Slide 34:

Judgement sampling - Use judgment and deliberate effort to pick individuals who meet a specific criteria. Especially good for exploratory or field research. Appropriate for at least 3 situations. 1. select cases that are especially informative. E.g., college coaches and championships 2. desired population for the study is rare or very difficult to locate. E.g., prostitutes 3. case studies analysis – find important individuals and study them in depth.

Slide 35:

Quota sampling - quotas for certain types of people or organizations are selected as the sample Interviewers are required to find cases with particular characteristics E.g., certain number of Hispanics, teenagers, etc. Like nonrandom version of stratified Pros – better than convenience; introduce some diversity Cons – Theoretical quotas must be accurate to be useful. It is nonrandom sampling

Slide 36:

Snowball sampling – an individual or group of individuals are sampled. They provide other sources to be sampled. Sampling snowballs into a large selection. aka. Chain sampling Useful for hard to identify groups. E.g., study of criminal organizations May lead to biased sample

Nonprobability Sampling Methods:

Nonprobability Sampling Methods Convenience sampling relies upon convenience and access Judgment sampling relies upon belief that participants fit characteristics Quota sampling emphasizes representation of specific characteristics Snowball sampling relies upon respondent referrals of others with like characteristics

Factors to Consider in Sample Design:

Factors to Consider in Sample Design Research objectives Degree of accuracy Resources Time frame Knowledge of target population Research scope Statistical analysis needs

Determining Sample Size:

How many completed questionnaires do we need to have a representative sample? Generally the larger the better, but that takes more time and money. Answer depends on: How different or dispersed the population is. Desired level of confidence. Desired degree of accuracy. Determining Sample Size