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DESCRIPTIVE STATISTICS - Coggle Diagram

- - - - Discrete variables result from a countable number of possible values. (ex, number of children in a family, number customers waiting to be served, etc.)
      - Continuous variables can have infinitely many possible values that can be associated with points on a continuous scale in such a way that there are no gaps or interruptions. (ex, the amount of milk a cow can produce per day, etc.)
- - - - A parameter is estimated by computing a similar characteristic of the sample. That is called a statistic.
  - - - Research problem
        
        Specify population
        
        Choose a sampling method
        and collect data
        
        Explore data
        
        Use inferential statistics
        to draw conclusions
        
        Are conclusions reliable?
        
        2 more items...
    - - Sample can save money and time
      - Sample can broaden the scope of the study. More detailed information can be gathered by taking a sample than from the whole population.
      - Sampling is deemed to be appropriate, it must be decided how to select a sample.
      - Sample will be employed to draw conclusions about the entire population, it is crucial that the sample is representative, it should reflect as closely as possible the relevant parameter of the population under consideration.
    - - Random sampling (probability sample), every unit of the population has the same probability of being selected into the sample. Random sampling implies that chance enters into the process of selection.
        
        simple random sampling
        
        Each unit of the frame is numbered from 1 to N (where N is the size of the population). Next, a random number generator (or a table of random numbers, which is an outdated technique) is used to select n items into the sample.
        
        stratified random sampling
        
        Population is divided into non-overlapping subpopulations called strata. The researchers then extracts a sample from each of the subpopulations. Using stratified random sampling is that it has the potential for reducing sampling error.
        
        The potential to match the sample closely to the population is greater than it is with simple random sampling because of portions of the total sample are taken from different population subgroups.
        
        However, stratified random sampling is generally more costly than simple random sampling because each unit of the population must be assigned to a stratum before the random selection process begins.
        
        Strata selection is usually based on available information. Information may been obtained from previous censuses or surveys. Stratification benefits increase as the strata differ more. Internally, a stratum should be relatively homogeneous; externally, strata should contrast with each other.
        
        systematic random sampling
        
        systematic sampling is not done in an attempt to reduce sampling error. Rather, systematic sampling is used because of its convenience and relative ease of administration. With systematic sampling, every kth item is selected to produce a sample of size n from a population of size N. The value of k, sometimes called the sampling cycle, can be determined using k = N/n.
        
        if k is not an integer value, then a whole-number value should be used.
        
        for example, a sample of (n) 20 students from the (N) 659 high school students using systematic sampling. this would be: k = 659/20 ~ 32.
        
        Besides convenience, systematic sampling has other advantages. Because systematic sampling is evenly distributed across the population, a knowledgeable person can easily determine whether a sampling plan has been followed in a study.
      - Non-random sampling, not every unit of the population has the same probability of being selected into the sample.
        
        Sampling techniques that does not involve a random selection process are called non-random sampling techniques. Because chance is not used to select items for the samples, these techniques are non-probability techniques and are not desirable for use in gathering data to be analyzed by standard methods of inferential statistics.
        
        Convenience Sampling , elements for the sample are selected for the convenience of the researcher. Choose elements that are readily available.
        
        For example, a convenience sample of homes for door-to-door interviews might include houses where people are at home, houses with no dogs, houses near the street, first-floor apartments, and houses with friendly people.
        
        in contrast, a random sample would require the researcher to gather data only from houses and apartments that have been selected randomly.
        
        Quota Sampling , appears to be similar to stratified random sampling. However, instead of selecting a sample from each stratum, we use a non-random sampling method to gather data from one stratum until the desire quota of samples is filled.
        
        Quotas are described by setting the sizes of the samples to be obtained from the subgroups. A quota is based on the proportions of the subclasses in the population.
        
        For example, a company is test marketing a new soft drink and is interested in how different age groups react to it. The researchers go to a shopping mall and interview shoppers aged 16-20, for example, until enough responses are obtained to fill the quota. In quota sampling, an interviewer would begin by asking a few filter questions; if the respondents represents a subclass whose quota has been filled, the interviewer would stop the interview.
        
        quota sampling can be useful if no previous information is available for the population.
        
        quota sampling is less expensive than most random sampling techniques because it is essentially a technique of convenience. Another advantage of quota sampling is the speed of data gathering, we do not need to call back or send out a second questionnaire if we do not receive a responses; we just move on to the next element.
        
        the problem with quota sampling is that it is a non-random sampling technique. Some researchers believe that a solution to this issue can be achieved if the quota is filled by randomly selecting elements and discarding those not from a stratum. This way quota sampling is essentially a version of stratified random sampling. The object is to gain the benefits of stratification without a high costs of stratification. However, it remains a non-probability sampling method.