experimental sampling

7/28/2019 Experimental Sampling

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CHAPTER 3 EXPERIMENTAL AND SAMPLING DESIGNOverview of how to answer a research question:

1. Pick a specific question you want to answer.2. Decide on your population.3. Select a sampling design and gather your sample. The choices for sampling designs for thisclass are:

voluntary response (the only one not random, not the best)

simple random sample

stratified random sample

multistage sample

catch-and-release sample

4. Decide whether to conduct an observational study or an experiment. If observational study,just statethe sampling design. If experiment, the choices for experimental designs for this class are:

completely randomized design

block design

matched pairs

5. Choose your response variable and your explanatory variables. Decide on your treatments(forexperiments).

6. Collect the data.7.Analyze your data using either exploratory data analysis (looking for trends/relationships inthe actualdata) or formal statistical inference (answering statistical questions with a known degree ofconfidence).

8. State your conclusions.What can go wrong?

Bias (response bias, nonresponse, undercoverage)

Variability

Poor experimental design (not using a control, not randomizing, not replicating)

Other (poor choice of sampling design, date of survey)Think about why your variables are related. Causation is not the same thing as association! Istherelationship between your variables based on:

Causation

Confounding

Common responsePrinciples of Ethical ExperimentsPlanned studies should be reviewed by a board to protect subjects from harm.All subjects must give their informed consent before data are collected.

All individual data must be kept confidential. Only summaries can be made public.(Anonymity isnot the same as confidentiality.)

2VOCABULARYPopulation: The entire group of units orindividuals about which we desire information.Sample: The part of the population selected tobe measured or observed in order to gather data


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for analysis.Census: An attempt to contact every individualin the entire population.Response Variable: Variable we are interested in studying.Unit: An individual person, animal or object upon which the response variable is measured.Units are

called individuals when they refer to people.EXAMPLE

A forester is interested in determining the total number of trees that are planted on tree farms inMontana.The forester believes the number of trees varies with the size of the tree farm. He divides allsuch farmsinto four classes depending on their size. From each class, he selects a sample of 15 farms. Hecounts andrecords the total number of trees for each of the selected tree farms.Unit:Population:Sample:

Response variable:MAJOR IDEAWe are interested in one or more variables associated with a population of units. Because it isimpossibleor too expensive to measure the variables of interest on all the units in the population, we onlymeasure thevariables on a subset or a sample of units. We use the sample to draw conclusions about thepopulation.To be useful, however, the sample must represent the population.SAMPLING DESIGN

Anectdotal evidence is information based on haphazardly selected individual cases which oftencome to our

attention because they are striking in some way.Voluntary Response Sample: A sample which consists of people who choose themselves byresponding toa general appeal. (Also called non-random or convenience sampling.)Random or Probability-Based Sampling: A sample that is selected in such a way that each unitin thepopulation has a non-zero chance of being chosen. (SRS, Stratified Random Sample,Multistage Sample,Capture-Recapture)

3Types of Random Sampling DesignsSimple Random Sample (SRS) of size n: A sample that is selected from the population in such

a way thatevery set of n units has an equal chance of being the selected sample. (We use SPSS or othercomputerpackage to select the sample.)Stratified Random Sample: The population is first divided into groups of similar units. A SRS isthenselected from each of the groups.Multistage sample: A sample in which successively smaller groups within the population areselected in


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stages. It is typically used when our population is so large that it is difficult or impossible to get alist of allof the units in the population. Here you start by splitting the population into groups and randomlyselectinga number of the groups. You can split these selected groups again using another variable.Finally, when

you have the number of units down to a manageable size, you select a SRS of units from eachof thegroups.Capture-Recapture Sample: A type of repeated SRS sampling that biologists use to estimatethe size ofanimal populations. This type of sample may also be used by the government when estimatingthe numberof households in an area. Take a SRS from the population and label them (or tag them). Latertake a newSRS and find the percent of this sample that were in the original sample. Assume the proportiontagged inthe second sample is equivalent to the proportion of the population who were tagged in the

original sample.The population size can thus be estimated.

4In order to make sure that a random sample is really random, you should use either a Table ofRandomDigits or a statistical computer application like SPSS to select the sample. You will learn how touse SPSSto take a random sample in lab on Friday.BIAS IN SAMPLINGSampling Bias occurs when the sample systematically favors certain parts of the populationover others.Types of Sampling Bias

Undercoverage Bias: This can occur when the sample systematically excludes a portion of thepopulation.If the excluded portion systematically differs with respect to the response from those units thatare availablefor sampling, sampling bias will be introduced into the study.Example If you wanted to take a survey of people in Lafayette, could you use the phone booktoselect your sample? Why not?Nonresponse Bias: This can occur when a selected unit either cannot be contacted or refuses tocooperate.If the non-respondent systematically differs with respect to the variable of interest compared tothose who

do respond, then sampling bias will be introduced into the study.Example If you were able to find phone numbers for everyone in your sample group, whatmighthappen if you called each one and asked if they would take part in your survey?Response Bias: When the behavior of the respondent or the interviewer changes the sampleresult such thatthe results do not agree with the true population value. Examples of this include the respondentlying, poor


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interviewing techniques, wording of questions, race or sex of interviewer influencing respondent,etc.Example where respondents might lie:Example where the wording of the question might be a problem:Example where race/sex of interviewer might result in biased answers:Example where the interviewer might influence the respondent:

EXPERIMENT OR OBSERVATIONAL STUDYObservational Study: observe units or individuals and measure variables of interest but

experimental sampling

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