Stat Terms

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Inferential Statistics

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Is basically concerned with making conclusions and predictions about the population based on the examined samples.

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Survey Sampling or Sampling

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Refers to the process of choosing a sample of elements from a total population of elements.

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Probability Sampling and Non-Probability Sampling

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Two broad categories of sampling

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Population

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Is a finite set of objects, events, or individuals with specified characteristics needed in an investigation.

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“N”

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Population size is denoted by

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Finite Population

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Is one which consists of finite or fixed number of objects

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Infinite Population

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Population that has no limit

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Sample

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A subset of population denoted by “n”

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Parameter

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Is a measurable characteristic of a population

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Statistic

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A measurable characteristic of a sample

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Sampling Method

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Is a procedure for selecting sample elements from a population

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Sampling Distribution

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A probability distribution of statistics. Refers to the mean valuesof every possible samples that can be obtained from the population

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Sampling with replacement

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When a population element can be selected more than one time

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Sampling without replacement

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Population element can be selected only once

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Standard Error

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Refers to the standard deviation of the sampling distribution. Hence, the standard error of the mean is the standard deviation of the sampling distribution of the mean

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Accuracy

Precision

Margin of Error

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Quality of Survey Results

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Accuracy

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Refers to the closeness of the parameter of a sample statistics to a population

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Precision

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Refers to the closeness of the estimates and the different samples.

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Margin of Error

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The maximum expected difference between the true population parameter and a sample estimate of that parameter is expressed by the margin of error

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Sampling Method

Estimator

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A sample design can be described by two factors:

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Sampling Method

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Refers to the process of selecting a part from a given whole.

Random Sampling

Stratified Sampling

Cluster Sampling

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Estimator

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Refers to the process of calculating sampling statistics.

Survey Objectives and Survey Resources 2 factors where the best sample design depends

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1. Economy or reduced cost relative to doing a complete enumeration of the population

2. Timeliness

3. Provides greater scope and coverage

4. May generate more accurate results

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Advantages of Sampling over Population

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A. Simple Random Sampling

B. Stratified Sampling

C. Cluster Sampling

D. Multistage Sampling

E. Systematic Random Sampling

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Probability Sampling Methods

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Simple Random Sampling

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A process for sampling from a population in which the selection of a sample unit is based on chance and every element of the population has a known nonzero probability of being selected.

Fishbowl Sampling

Using Table of Random Numbers

Electronic Drawing of Lots

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Stratified Sampling

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When the population can be divided into several strata or groups based on some characteristics

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Cluster Sampling

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It is when every number of the population is assigned to one and only one group

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Multistage Sampling

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In this sampling, we select a sample by using combinations of different sampling methods.

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Systematic Random Sampling

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In this sampling, a list of every member of the population can be created

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1. Derive objective measures of sampling errors measurability

2. Subject data to statistical sign

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Advantages of Probability Sampling

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Units selected may be far apart, then it may be more costly to implement.

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Disadvantages of Probability Sampling

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Non Probability Sampling

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We cannot specify the probability that each element of the population will be included in the sample or we cannot be sure that each population has a nonzero chance of being chosen as a sample

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A. Voluntary Sampling

B. Convenience Sampling

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2 main types of Non Probability Sampling

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Voluntary Sampling

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Usually done in television or radio programs asking people to participate

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Convenience Sampling

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Is done by considering those people around as the qualified samples.

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Bias Survey Sampling

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Refers to the tendency of a sample statistic to systematically overestimate or underestimate a population parameter

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A. Under Coverage

B. Non Response Bias

C. Voluntary Response Bias

D. Response Bias

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4 bias survey sampling

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Under Coverage

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Occurs when some members of the population are inadequately represented in the sample.

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Non Response Bias

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Is the bias that results when there is incomplete information about the respondents because the individuals chosen for the sample are unwilling or unable to participate in the survey.

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Voluntary Response Bias

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Occurs when sample members are self selected volunteers, as in voluntary samples.

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Response Biasy

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It refers to the bias that results from problems in the measurement process.

Leading Questions

Social Desirability

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The sampler has more control in ensuring that the sampling units are closer to one another, then it may be cheaper to implement

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Advantage of Non Probability Sampling

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1. One Cannot Derive objective measures of sampling error unless very strong assumptions are made.

2. One cannot subject data to statistical rigor.

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Disadvantage of Non-Probability Sampling

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Sampling Error

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A survey produces a sample statistic, which is used to estimate a population parameter.

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1. Measurability

2. Efficiency

3. Simplicity

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Criteria for good Survey Practice

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Measurability

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Possible to provide estimates having the needed accuracy. It must be possible to measure the accuracy on the basis of the survey

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Efficiency

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Striking a measurable balance between accuracy and cash

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Simplicity

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The survey can be carried out in a way faithful to the design relative notion.

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1. List the research goals

2. Identify potential sampling methods that might effectively achieve those goals.

3. Test the ability of each method to achieve each goal.

4. Choose the method that does the best job of achieving the goals.

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Strategies to Identify the Best Sampling Method

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- The population consists of a finite N objects

- The sample consists of n objects

- All possible samples of n objects are equally likely to occur.

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Properties of Simple Random Sampling

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Precision

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Measure the extent to which estimates are close to one another.

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Accuracy

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Measures the extent to which estimates are close to the true value of the parameter being estimated.