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32 Cards in this Set
- Front
- Back
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Voluntary Response Sample |
Sample made up of self selected response. People who volunteer usually have strong opinions Example: Radio Call in Show |
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Population
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the entire pool from which a statistical sample is drawn.
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Sample |
A subset of the population |
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Convience Sample |
Uses subjects that are readily available.
Advantage: Easy and less costly to collect Disadvantage: Not representative of the population Example: In order to get an idea of how students think of the new school policy, the principal stands outside the library and asks a few students their opinions. |
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SRS |
consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance of being the sample actually selected. This is often the best and most appropriate way to collect data for a sample.
Advantages – Easy to accomplish using a table of random digits; likely to produce samples that are good representatives of the population. Disadvantage – None (could be cost prohibited) Example: In order to determine how happy students are with their education at DHS, the principal assigns each student a number from 1 to 850 (the number of students at the school) and then uses a random number generator to choose 50 numbers between 1 and 850. He then surveys all the students with the chosen numbers. |
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Probability Sample |
a sampling technique wherein the samples are gathered in a process that gives all the individuals in the population equal chances of being selected.
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Stratified Random Sample |
Divide the population into groups of similar individuals (strata) then select an SRS within each strata. Combine the SRSs from each strata to form your full sample.
Advantage: Can produce more exact information (especially in large populations) by taking advantage of the fact that individuals in the same strata are similar to one another. Disadvantage: Not appropriate unless strata are easily defined.Example: In order to get a better idea of what MSHS athletes thought about homecoming last year, the director divides all MSHD athletes into the teams they play for, and then selects a random sample from each sports team. His full sample consists of aggregating the random samples form each team. |
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Under Coverage |
Occurs when some groups in the population are left out of the process of choosing a sample. Example: Because they are generally fearful of government intrusion, many immigrants from Latin America did not return their census questionnaire during the 1990 census.
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Non Response |
Occurs when an individual chosen for a sample can’t be contact or refuses to respond. Non-response is a big problem in mail surveys.
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Response Bias |
Caused by the behavior of the respondent or the interviewer
1) Sensitive questions 2) Socially acceptable answers 3) Telling the interviewer what he or she wants to hear. Ignorant people – People will give silly answers just so that they won’t appear like they know nothing about the subject. Lack of memory: giving a wrong answer simply because respondent doesn’t remember the correct answer. timing: When a survey is taken can have an impact on the answers. Phrasing of questions: Subtle differences in phrasing make large differences in the results. |
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Sampling Error |
The difference between a sample result and the true population result. This error results from chance variation.
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Systematic Random Sampling |
randomly select an arbitrary starting point, and then select every kth member of the population
Advantage: Every member has an equal probability of being selected Disadvantage: Not every sample of size n has an equal chance of being selected Example: HP Selects every 200th computer off the assembly line and inspects it for quality control. |
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Non-sampling error |
Occurs when the sample data are incorrectly collected, recorded or analyzed. Such an error results from an error other than chance sample fluctuations. Usually occurs when the sample is selected in a non-random fashion with obvious sources of bias.
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Experimental Units |
The things on which the experiment is done.
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Subjects |
When the experimental units are human beings
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Treatment |
A specific experimental condition applied to the units.
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Factor |
The explanatory variables in an experiment
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Level |
Each factor has two or more levels, i.e., different values of the factor.
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Completely Randomized Experiment |
Randomly assign subjects to the treatment or control group. This way any possible bias in the population should be evenly spread among the treatment and control groups. Sometimes instead of relying on randomization to make the groups as even as possible we actually force the groups to be similar.
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Statistically Significant |
The likelihood that a result or relationship is caused by something other than mere random chance.
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Replication |
In an experiment, replication refers to the practice of assigning each treatment to many experimental subjects. In general, the more subjects in each treatment condition, the lower the variability of the dependent measures
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Blind |
It is not just in receiving tablets that the “power of suggestion” plays an important role.It is usually best therefore if the subject does not know whether they are receiving the treatment or not. This practice is called Blinding.
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Double Blind |
Experiments in which both the subject and the administrator of the experiment do not know who receives the treatment
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Block Design |
This is an extension of the Matched Pair design to the case of three or more treatments (one may be the control). If there are 4 treatments and a control then there will be 5 blocks each one designed to be as similar as possible. 4 of the blocks will each receive one of the treatments and one block will be a control.
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Matched Pairs |
These are experimental designs in which either the same individual or two matched individuals are assigned to receive the treatment and the control. In the case where an individual receives both the treatment and the control, the order in which this happens should be random.
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Cluster Sampling
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Divide the population into sections (clusters) then randomly choose a few of those clusters, and select every member of the clusters chosen.
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Biased
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Samples that are systematically not representative of the desired population.
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How to design an Experiment
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1. Randomization
2. Control 3. Replication |
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Randomization
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the most important element of any experiment. It must be incorporated either in the selection process of experimental units and/or the distribution of experimental units into treatment and control groups.
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Control Group
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treated identically in all respects to the group receiving the treatment except that the members of the control group do not receive the treatment.
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Response variable
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measures an outcome of a study.
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Explanatory variable
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attempts to explain the observed outcomes.
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