Chapter 6 Flashcards
In all forms of research, it would be ideal to test the entire population, but in most cases the population just too large that it is impossible to include every individual
Sampling
What is the reason why most researchers rely on sampling techniques
In all forms of research, it would be ideal to test the entire population, but in most cases, the population is just too large that it is impossible to include every individual. This is the reason why most researchers rely on sampling techniques
a non-probaility sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher
convenience sampling
What are some strengths of convenience sampling
fast, inexpensive, easy,
In pilot studies this allows the researcher to obtain basic data and tends regarding this study without the complications of using a randomized sample.
the strength of convenience sampling
is useful in documenting that a particular quality of a substance or phenomenon
the strength of convenience sampling
such studies are also very useful for detecting relationships among different phenomena
the strength of convenience sampling
Name a weakness of convenience sampling
sampling bias; the sample is not representative of the entire population
systematic bias stems from
constant difference between the results from a sample and the theoretical results from the entire population. Weakness of convenience sampling
refers to a constant difference between the results from a sample and the theoretical results from the entire population
systematic bias stems from sampling bias which is a weakness of convenience sampling
refers to a constant difference between the results tom the sample and the theoretical results from the entire population
systematic bias stems from sampling bias which is a weakness of convenience sampling
Limitation in the ability to generalize leading to problems with external validity
non-probability sampling/ convenience sampling weakness
Name the types of nonprobabilty sampling
convenience sampling and quota sampling
a type of non-probaility sample in which the researcher selects people according to some fixed quota.
quota sampling
what are the limitations of quota sampling
first, researcher might inaccurately determine the appropriate representation of the characteristic in question
second, the selection of sample elements within a given category of the quota frame may be biased even though is proportion of the population is accurately estimated
A quota sample of 32 adults and children in a street scene select 10 people for a sample
there would be 4 adult males, 4 adult females, 1 male child and 1 female child
get all possible cases that fit particular criteria, using various methods
purposive or judgmental sampling (non probability sampling)
get cases using referrals from one or a few cases, and then referrals from those case, and so forth
snowballing sampling (non probability sampling)
get cases that substantially differ from the dominant pattern ( a special type of purposive sample)
deviant case (non probability sampling)
get cases until there is no additional information or new characteristics (often used with other sampling methods)
sequential (non probability sampling)
In quantitative studies the primary purpose of sampling
is to create a representative sample that closely reproduces features of interest in a larger collection of cases
a selected small collection of cases or units
sample
a sample that closely reproduces features of interest in a larger collection of cases called
population
by sampling correctly you will be able to
generalize accuracy to the entire population
To create representative sampling in quantitative research
you need to use very precise sampling procedures
Procedures that rely on the mathematics of probability are called
probability sampling
Get any cases in any manner that is convenient
Convenience sampling
What’s the great thing about probability samples in a quantitative research
Their efficiency ( they save a great deal of time and cost for the accuracy they deliver.
Give an example of how probability samples in quantitative research is Efficiency.
A properly conducted probability sample may cost 1/1000 the cost and time of gathering information on an entire population yet yield virtually identical results. For example, if you want to learn about the 18 million people in the .u.s. With diabetes. From a well designed probability sample of 1,800 you can take what you learned and generalize it to the 18 million people.
Sample that can be highly accurate
Probability samples
For large-population, a well-designed, carefully executed______ sample can be equally if not more accurate than trying to teach every case in the population
A probability sample
What type of sampling permits accurate results using 10% of the population
Probability sampling
Sampling used by qualitative researchers
Nonprobability
Another name for no probability sampling
Nonrandom samples
Means they rarely determined the sample size in advance and have limited knowledge about the larger group or population from which the sample is taken
Nonprobability or nonrandom samples
What is sampling? (for quiz)
Sampling is the process of selecting units (e.g people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen
Define the concepts of Population in reference to sampling, (for quiz)
Population is the total of all the individuals who have certain characteristics and are of interest to a researcher. Community college students, race car drivers, teachers, college-level athletes, and disabled war veterans can be considered populations
What is the difference between a Parameter and a Statistic? (for quiz)
A parameter is a characteristic of a population while a statistic is a character of a sample
Why Do We Sample? (for quiz)
because it is often impossible to get responses from the entire population?
What is the difference between Probability and Nonprobability Sampling?
In probability sampling all persons have a chance of being selected, and results are likely to accurately reflect the entire poplulation
What is the difference between Probability and Nonprobability Sampling?
In probability sampling all persons have a chance of being selected, and results are likely to accurately reflect the entire population. Statistical analysis programs depend on probability sampling
Identify and Describe three types of Nonprobability Sampling
- convenience - purposive/judgmental
- quota - deviant case
- snowball - sequential
Why would you use Nonprobability Samples
convenience, easy, cheap
What is the difference between Probability and Nonprobability Sampling? (for quiz)
In probability sampling all persons have a chance of being selected, and results are likely to accurately reflect the entire population. Statistical analysis programs depend on probability sampling
Identify and Describe three types of Nonprobability Sampling (for quiz)
- convenience - purposive/judgmental
- quota - deviant case
- snowball - sequential
Why would you use Nonprobability Samples? (for quiz)
convenience, easy, cheap
What is a Probability Sample? (for quiz)
Any method of sampling that utilizes some form of random selection. In order to have random selection method, you must set up some process or procedure that assures that the different units of your population have equal probabilities of being chosen.
Why is the concept of random important to probability Sampling?
Eliminates systematic bias
Why is the concept of random important to probability Sampling? (for quiz)
- Eliminates systematic bias
- Statistical test used to analyze data are only valid with random samples
Define Sampling Distribution? (for quiz)
the probability distribution if a given statistic based on a random sample.
Define Central Limit Theorem? (for quiz)
states that, given certain conditions, the arithmetic mean of a succinctly large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance will be approximately normally distributed regardless of underlying distribution.
A population includes all of the elements from a set data
Populations
Consists of one or more o derivations from the population
A sample or sampling element
This is the proportion of elements in the population that are selected
Sampling ratio
One name for every two respondent in the class
Sampling ratio
Sample size/pop size
Sampling ratio
A list of all those within a population who can be sampled, and may include individuals, household or institutions.
Sampling frame
Characteristic of a population
Parameter
Define confidence interval
A type of interval
Estimate of a population parameter. It is an observed internal (i.e. It is calculated from the observation), in principle different from sample to sample, that frequently includes the parameter of interest if the experiment is repeated.
Any method of sampling that utilizes some form of random sampling. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen.
Probability Sampling
is one of the most popular types of probability sampling. In this technique, each member of the population has an equal chance of being selected as subject
Random sampling
The entire process of sampling is done in a single step with each subject selected independently of the other members of the population.
Random Sampling
Why Random Sampling?
Eliminates systematic bias and Statistical tests used to analyze data are only valid with random samples
