Chapter%204:%20Gathering%20Data

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Chapter 4Gathering Data

• Section 4.1
• Should We Experiment or Should We Merely Observe?

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Learning Objectives

1. Population versus Sample
2. Types of Studies Experimental and Observational
3. Comparing Experimental and Observational Studies

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Learning Objective 1Population and Sample

• Population all the subjects of interest
• We use statistics to learn about the population,
  the entire group of interest
• Sample subset of the population
• Data is collected for the sample because we
  cannot typically measure all subjects in the
  population

Population
Sample
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Learning Objective 2Type of Study
Observational Study

• In an observational study, the researcher
  observes values of the response variable and
  explanatory variables for the sampled subjects,
  without anything being done to the subjects (such
  as imposing a treatment)

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Learning Objective 2Observational Study
Sample Survey

• A sample survey selects a sample of people from a
  population and interviews them to collect data.
• A sample survey is a type of observational study.
• A census is a survey that attempts to count the
  number of people in the population and to measure
  certain characteristics about them

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Learning Objective 2Type of Study Experiment

• A researcher conducts an experiment by assigning
  subjects to certain experimental conditions and
  then observing outcomes on the response variable
  
• The experimental conditions, which correspond to
  assigned values of the explanatory variable, are
  called treatments

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Learning Objective 2Example

• Headline Student Drug Testing Not Effective in
  Reducing Drug Use
• Facts about the study
• 76,000 students nationwide
• Schools selected for the study included schools
  that tested for drugs and schools that did not
  test for drugs
• Each student filled out a questionnaire asking
  about his/her drug use

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Learning Objective 2Example

• Conclusion Drug use was similar in schools that
  tested for drugs and schools that did not test
  for drugs

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Learning Objective 2Example

• This study was an observational study.
• In order for it to be an experiment, the
  researcher would had to have assigned each school
  to use or not use drug testing rather than
  leaving this decision to the school.

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Experimental Design????

• Observational Studies????
• An observational study observes individuals and
  measures variables of interest but does not
  attempt to influence the responses.
• Designed experiments????
• An experiment deliberately imposes some treatment
  on individuals in order to observe their
  responses.

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Learning Objective 3Comparing Experiments and
Observational Studies

• An experiment reduces the potential for lurking
  variables to affect the result. Thus, an
  experiment gives the researcher more control over
  outside influences.
• Only an experiment can establish cause and
  effect. Observational studies can not.
• Experiments are not always possible due to
  ethical reasons, time considerations and other
  factors.

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Example 3.2

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• ?????????????????,?????????????
• ????????????????
• ???????????????

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Example

• ???????????????
• ?????????????????????????????????
• ???????????
• ??????????????????????

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Do smaller classes benefit students?

• ????????,????????????
• ????????????,?????????
• ????????
• ???STAR program
• 6385 ????regular class (22-25) with one
  teacher, regular class with a teacher and a
  full-time teachers aid, small class (13-17)

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Where can we find observational data?
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http//webapp.icpsr.umich.edu/GSS/
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http//www.dgbas.gov.tw/
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http//www.dgbas.gov.tw/dgbas03/bs8/others.htm
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http//srda.sinica.edu.tw/
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Sampling

• Sampling??
• GSS???????3000?
• ????????2000?
• Census??
• ????
• ????
• ??????

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(No Transcript)
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Sampling

• A sample may be more accurate than a
  census??????????,??
• Accuracy and precision??????????????
• Census of a large population increase the
  likelihood of nonsampling errors because of the
  increased volume of work. ???????????90??????????
  
• EXgtBureau of the Census uses samples to check the
  accuracy of the U.S. Census.
• Speed of response???????????
• Cost
• Destructive sampling ?????

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Sampling

• ????????????????????,????????????(sampling
  design)????

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Chapter 4Gathering Data

• Section 4.2
• What are Good Ways and Poor Ways to Sample?

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Learning Objectives

1. Sampling Frame Sampling Design
2. Simple Random Sample (SRS)
3. Random number table
4. Margin of Error
5. Convenience Samples
6. Types of Bias in Sample Surveys
7. Key Parts of a Sample Survey

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Learning Objective 1Sampling Frame Sampling
Design

• The sampling frame is the list of subjects in the
  population from which the sample is taken,
  ideally it lists the entire population of
  interest
• The sampling design determines how the sample is
  selected. Ideally, it should give each subject
  an equal chance of being selected to be in the
  sample

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Learning Objective 2Simple Random Sampling, SRS

• Random Sampling is the best way of obtaining a
  sample that is representative of the population
• A simple random sample of n subjects from a
  population is one in which each possible sample
  of that size has the same chance of being selected

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Learning Objective 2SRS Example

• Two club officers are to be chosen for a New
  Orleans trip
• There are 5 officers President, Vice-President,
  Secretary, Treasurer and Activity Coordinator
• The 10 possible samples are
• (P,V) (P,S) (P,T) (P,A) (V,S)
• (V,T) (V,A) (S,T) (S,A) (T,A)
• For a SRS, each of the ten possible samples has
  an equal chance of being selected. Thus, each
  sample has a 1 in 10 chance of being selected and
  each officer has a 1 in 4 chance of being
  selected.

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Learning Objective 3SRS Table of Random Numbers
Table of Random Numbers

• Table E on pg. A6 of text

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Leaning Objective 3Using Random Numbers to
select a SRS

• To select a simple random sample
• Number the subjects in the sampling frame using
  numbers of the same length (number of digits)
• Select numbers of that length from a table of
  random numbers or using a random number generator
• Include in the sample those subjects having
  numbers equal to the random numbers selected

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Learning Objective 3Choosing a simple random
sample

• We need to select a random sample of 5 from a
  class of 20 students.
• List and number all members of the population,
  which is the class of 20.
• The number 20 is two-digits long.
• Parse the list of random digits into numbers that
  are two digits long. Here we chose to start with
  line 103, for no particular reason.

22 36 84 65 73 25 59 58 53 93 30 99 58 91 98 27
98 25 34 02
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22 36 84 65 73 25 59 58 53 93 30 99 58 91 98 27
98 25 34 02
24 13 04 83 60 22 52 79 72 65 76 39 36 48 09 15
17 92 48 30
1 Alison 2 Amy 3 Brigitte 4 Darwin 5 Emily 6
Fernando 7 George 8 Harry 9 Henry 10 John 11
Kate 12 Max 13 Moe 14 Nancy 15 Ned 16 Paul 17
Ramon 18 Rupert 19 Tom 20 Victoria

1. Choose a random sample of size 5 by reading
   through the list of two-digit random numbers,
   starting with line 2 and on.
2. The first five random numbers matching numbers
   assigned to people make the SRS.

The first individual selected is Amy, number 02.
Thats it from line 2. Move to line 3 Then Moe
(13), Darwin, (04), Henry (09), and Net (15)

• Remember that 1 is 01, 2 is 02, etc.
• If you were to hit 09 again before getting five
  people, dont sample Ramon twiceyou just keep
  going.

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Learning Objective 4Margin of Error

• Sample surveys are commonly used to estimate
  population percentages
• These estimates include a margin of error which
  tells us how well the sample estimate predicts
  the population percentage
• When a SRS of n subjects is used, the margin of
  error is approximately

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Learning Objective 4Example Margin of Error

• A survey result states The margin of error is
  plus or minus 3 percentage points
• This means It is very likely that the reported
  sample percentage is no more than 3 lower or 3
  higher than the population percentage

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Sampling Design
Sample designs

• Nonprobability samples
• Voluntary Response Sample
• Convenience
• Judgment
• Quota
• Snowball

• Probability samples
• Simple random
• Systematic
• Stratified
• Proportionate
• Disproportionate
• Cluster
• Multistage

There are no appropriate statistical techniques
for measuring random sampling error from a
non-probability sample. Thus projecting the data
beyond the sample is statistical inappropriate.
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Comparisons of sampling techniques
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Learning Objective 5Convenience Samples Poor
Ways to Sample

• Convenience Sample a type of survey sample that
  is easy to obtain
• Unlikely to be representative of the population
• Often severe biases result from such a sample
• Results apply ONLY to the observed subjects

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Convenience Sampling

• ???? (haphazard or accidental sampling), relying
  on available subjects
• EXgt man-on-the-street interviews, talk to friend
  about their political sentiment
• EXgt professor uses students as sample
• EXgt every tenth student entering the university
  library.
• EXgt Survey over sea Chinese for international
  marketing?

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Convenience Sampling

• Advantages Very low cost, extensively used, No
  need for list of population.
• It is justified only if the researcher wants to
  study the characteristics of people passing the
  sampling point at specified times or if less
  risky sampling methods are not feasible.

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Convenience Sampling

• Problems
• (1) no way of knowing if those included are
  representative.
• (2) Variability and bias of estimates cannot be
  measured or controlled.
• (3) Projecting the results beyond the specific
  sample is inappropriate.
• Should be use only for exploratory design to
  generate ideas and insights.
• you should alert readers to the risks associated
  with this method.

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Learning Objective 5Convenience Samples Poor
Ways to Sample

• Volunteer Sample most common form of
  convenience sample
• Subjects volunteer for the sample
• Volunteers do not tend to be representative of
  the entire population

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• ???call in??????????????????,?????????

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Judgment Samples (Purposive Samples)????

• hand-picked sample elements, believed to be
  representative of the population of interest
• EXgt a fashion manufacturer regularly selects a
  sample of key accounts that it believes are
  capable of providing the information to predict
  what will sell in the fall.
• EXgt Dow Jones industrial average select 30
  blue-chip stocks out of 1,800 stocks. Highly
  correlated with other NYSE indicators on the
  daily percentages of price changes
• EXgtRepresentative communities in U.S.
  presidential election.
• EXgt CPI????????

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Snowball sample????

• Locate an initial set of respondents. These
  individual are then used as informants to
  identify others with the desired characteristics.
  
• Appropriate when the members of a special
  population are difficult to locate.
  ????????????????

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Snowball sample????

• EXgt survey users of an unusual product a study
  among deaf for product that would allow deaf
  people to communicate over telephone.
• EXgt ??????(????),homeless, gangsters, migrant
  workers, undocumented immigrants.
• EXgt network study,????(HIV)
• Bias a person who is known to someone has a
  higher probability of being similar to the first
  person.

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Quota samples????

• by selecting sample elements in such a way that
  the proportion of the sample elements possessing
  a certain characteristics is approximately the
  same as the proportion with the characteristics
  in the population.
• Establishing a characteristics matrix What
  proportion of the target population is male and
  female? what proportions of each gender fall
  various age categories, educational level, ethnic
  groups,etc.
• Once such a matrix has been created and a
  relative proportion assigned to each cell in the
  matrix, you collect data from people having all
  the characteristics of a given cell.
• All the persons in a given cell are then assigned
  a weight appropriate to their portion of the
  total population.

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Quota samples????

• Problems
• The sample could be far off with respect to other
  important characteristics.
• The quota frame must be accurate, and it is often
  difficult to get up-to-date information for this
  purpose.

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Quota samples????

• Biases may exist in the selection of sample
  elements within a given cell. The interviewer has
  a quota to achieve. The actual choice of elements
  left to the discretion of the individual field
  worker. Interviewers are prone to follow certain
  practices

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Quota samples????

• those who are similar to the interviewers are
  more likely to be interviewed,
• toward the accessible (first floor, airline
  terminals, business district, college campus),
• toward household with children, exclude working
  people,
• against workers in manufacturing (service and
  administrative),
• against extreme of income (EXgt "mansions" were
  skipped because the interviewer did not feel
  comfortable knocking on doors that were answered
  by servants. ),
• against the less educated, against low-status
  individuals

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Learning Objective 6Types of Bias in Sample
Surveys

• Bias Tendency to systematically favor certain
  parts of the population over others
• Sampling Bias bias resulting from the sampling
  method such as using nonrandom samples or having
  undercoverage
• Nonresponse bias occurs when some sampled
  subjects cannot be reached or refuse to
  participate or fail to answer some questions
• Response bias occurs when the subject gives an
  incorrect response or the question is misleading
• A Large Sample Does Not Guarantee An Unbiased
  Sample!

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Error in survey research
Systematic (nonsampling) error
Random sampling error
Respondent error
Administrative error
Data processing error
Response bias
Nonresponse error
Sample selection error
Deliberate falsification Unconscious
misrepresentation
Self-selection bias
Interviewer cheating
Interviewer error
Acquiescence bias
Extremity bias
Interviewer bias
Auspices bias
Social desirability bias
Contamination by others
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Learning Objective 7Key Parts of a Sample Survey

• Identify the population of all subjects of
  interest
• Construct a sampling frame which attempts to list
  all subjects in the population
• Use a random sampling design to select n subjects
  from the sampling frame
• Be cautious of sampling bias due to nonrandom
  samples
• We can make inferences about the population of
  interest when sample surveys that use random
  sampling are employed.

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Chapter 4Gathering Data

• Section 4.3
• What Are Good Ways and Poor Ways to Experiment?

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Learning Objectives

1. Identify the elements of an experiment
2. Experiments
3. 3 Components of a good experiment
4. Blinding the Study
5. Define Statistical Significance
6. Generalizing Results of the Study

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Experimental Design????

• A Designed Experiment
• Folic Acid (??) and Birth Defects
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  ???????,?????????????????

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Experimental Design????

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Principles of Experimental Design

• Treatment group ????????
• Control group ???(???)????????
• Experimental units subjects ???,?????
• Response variable ????(????)???????????,????????
• Factor ????????????????????,???????
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Learning Objective 1Elements of an Experiment

• Experimental units the subjects of an
  experiment the entities that we measure in an
  experiment
• Treatment A specific experimental condition
  imposed on the subjects of the study the
  treatments correspond to assigned values of the
  explanatory variable
• Explanatory variable Defines the groups to be
  compared with respect to values on the response
  variable
• Response variable The outcome measured on the
  subjects to reveal the effect of the
  treatment(s).

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Learning Objective 2Experiments

• An experiment deliberately imposes treatments on
  the experimental units in order to observe their
  responses.
• The goal of an experiment is to compare the
  effect ofthe treatment on the response.
• Experiments that are randomized occur when the
  subjects are randomly assigned to the treatments
  randomization helps to eliminate the effects of
  lurking variables

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Learning Objective 33 Components of a Good
Experiment

• Control/Comparison group allows the researcher
  to analyze the effectiveness of the primary
  treatment
• Randomization eliminates possible researcher
  bias, balances the comparison groups on known as
  well as on lurking variables
• Replication allows us to attribute observed
  effects to the treatments rather than ordinary
  variability

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Learning Objective 3Principle 1 Control or
Comparison Group

• A placebo is a dummy treatment, i.e. sugar pill.
  Many subjects respond favorable to any treatment,
  even a placebo.
• A control group typically receives a placebo. A
  control group allows us the analyze the
  effectiveness of the primary treatment.
• A control group need not receive a placebo.
  Clinical trials often compare a new treatment for
  a medical condition, not with a placebo, but with
  a treatment that is already on the market.

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Learning Objective 3Principle 1 Control or
Comparison Group

• Experiments should compare treatments rather than
  attempt to assess the effect of a single
  treatment in isolation
• Is the treatment group better, worse, or no
  different than the control group?
• Example 400 volunteers are asked to quit
  smoking and each start taking an antidepressant.
  In 1 year, how many have relapsed? Without a
  control group (individuals who are not on the
  antidepressant), it is not possible to gauge the
  effectiveness of the antidepressant.

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Learning Objective 3Placebo effect

• Placebo effect (power of suggestion) The placebo
  effect is an improvement in health due not to
  any treatment but only to the patients belief
  that he or she will improve.

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Learning Objective 3Principle 2 Randomization

• To have confidence in our results we should
  randomly assign subjects to the treatments. In
  doing so, we
• Eliminate bias that may result from the
  researcher assigning the subjects
• Balance the groups on variables known to affect
  the response
• Balance the groups on lurking variables that may
  be unknown to the researcher

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Learning Objective 3Principle 3 Replication

• Replication is the process of assigning several
  experimental units to each treatment
• The difference due to ordinary variation is
  smaller with larger samples
• We have more confidence that the sample results
  reflect a true difference due to treatments when
  the sample size is large
• Since it is always possible that the observed
  effects were due to chance alone, replicating the
  experiment also builds confidence in our
  conclusions

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Learning Objective 4Blinding the Experiment

• Ideally, subjects are unaware, or blind, to the
  treatment they are receiving
• If an experiment is conducted in such a way that
  neither the subjects nor the investigators
  working with them know which treatment each
  subject is receiving, then the experiment is
  double-blinded
• A double-blinded experiment controls response
  bias from the respondent and experimenter

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Double-blind

• ???? (double blindness)
• ????????????????? (treatment)
• ??????(?????)????
• ????????? (hidden bias)
• ????????????????????

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Learning Objective 5Define Statistical
Significance

• If an experiment (or other study) finds a
  difference in two (or more) groups, is this
  difference really important?
• If the observed difference is larger than what
  would be expected just by chance, then it is
  labeled statistically significant.
• Rather than relying solely on the label of
  statistical significance, also look at the actual
  results to determine if they are practically
  significant.

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Learning Objective 6Generalizing Results

• Recall that the goal of experimentation is to
  analyze the association between the treatment and
  the response for the population, not just the
  sample
• However, care should be taken to generalize the
  results of a study only to the population that is
  represented by the study.

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Milgram ???????

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• Experimental units subjects???
• Response variable ????????
• Factor ?????
• Level ???
• Control ???

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Chapter 4Gathering Data

• Section 4.4
• What are Other Ways to Conduct Experimental and
  Observational Studies

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Learning Objectives

• Sample Surveys Other Random Sampling Designs
• Types of Observational Studies Prospective and
  Retrospective
• Multifactor Experiment
• Matched pairs design
• Randomized block design

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Learning Objective 1Sample Surveys Random
Sampling Designs

• It is not always possible to conduct an
  experiment so it is necessary to have well
  designed, informative studies that are not
  experimental, e.g., sample surveys that use
  randomization
• Simple Random Sampling
• Cluster Sampling
• Stratified Random Sampling

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Learning Objective 1Sample Surveys Cluster
Random Sample

• Cluster Random Sample
• Steps
• Divide the population into a large number of
  clusters, such as city blocks
• Select a simple random sample of the clusters
• Use the subjects in those clusters as the sample

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Learning Objective 1Sample Surveys Cluster
Random Sample

• Cluster Random Sample
• Preferable when
• A reliable sampling frame is unavailable
• The cost of selecting a SRS is excessive
• Disadvantage
• Usually need a larger sample size than with a SRS
  in order to achieve a particular margin of error

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Learning Objective 1Sample Surveys Stratified
Random Sample

• Stratified Random Sample
• Steps
• Divide the population into separate groups,
  called strata
• Select a simple random sample from each strata
• Combine the samples from all strata to form
  complete sample

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Learning Objective 1Sample Surveys Stratified
Random Sample

• Stratified Random Sample
• Advantage is that you can include in your sample
  enough subjects in each stratum you want to
  evaluate
• Disadvantage is that you must have a sampling
  frame and know the stratum into which each
  subject belongs

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Learning Objective 1Stratified Random Sample -
Example

• Suppose a university has the following student
  demographics
• Undergraduate Graduate First Professional
  Special
• 55 20
  5 20

In order to insure proper coverage of each
demographic, a stratified random sample of 100
students could be chosen as follows select a
SRS of 55 undergraduates, a SRS of 20 graduates,
a SRS of 5 first professional students, and a SRS
of 20 special students combine these 100
students.
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Learning Objective 1Comparing Random Sampling
Methods
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Learning Objective 2Types of Observational
Studies

• An observational study can yield useful
  information when an experiment is not practical.
• Types of observational studies
• Sample Survey attempts to take a cross section
  of a population at the current time
• Retrospective study looks into the past
• Prospective study follows its subjects into the
  future
• Causation can never be definitively established
  with an observational study, but well designed
  studies can provide supporting evidence for the
  researchers beliefs

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Learning Objective 2Retrospective Case-Control
Study

• A case-control study is a retrospective
  observational study in which subjects who have a
  response outcome of interest (the cases) and
  subjects who have the other response outcome (the
  controls) are compared on an explanatory variable

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Learning Objective 2Example Case-Control Study

• Response outcome of interest Lung cancer
• The cases have lung cancer
• The controls did not have lung cancer
• The two groups were compared on the explanatory
  variable smoker/nonsmoker

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Learning Objective 2Example Prospective Study

• Nurses Health Study
• Began in 1976 with 121,700 female nurses aged 30
  to 55 questionnaires are filled out every two
  years
• Purpose was to explore the relationships among
  diet, hormonal factors, smoking habits and
  exercise habits and the risk of coronary heart
  disease, pulmonary disease and stroke
• Nurses are followed into the future to determine
  whether they eventually develop an outcome such
  as lung cancer and whether certain explanatory
  variables are associated with it

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Learning Objective 3Multifactor Experiments

• A Multifactor experiment uses a single experiment
  to analyze the effects of two or more explanatory
  variables on the response
• Categorical explanatory variables in an
  experiment are often called factors
• We are often able to learn more from a
  multifactor experiment than from separate
  one-factor experiments since the response may
  vary for different factor combinations

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Learning Objective 3Example Multifactor
experiment

• Examine the effectiveness of both Zyban and
  nicotine patches on quitting smoking
• Two factor experiment
• 4 treatments

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Learning Objective 3Example Multifactor
experiment

• subjects a certain number of undergraduate
  students
• all subjects viewed a 40-minute television
  program that included ads for a digital camera
• some subjects saw a 30-second commercial others
  saw a 90-second version
• same commercial was shown either 1, 3, or 5 times
  during the program
• there were two factors length of the commercial
  (2 values), and number of repetitions (3 values)

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Learning Objective 3Example Multifactor
experiment

• the 6 combinations of one value of each factor
  form six treatments

Factor B Repetitions Factor B Repetitions Factor B Repetitions
1 time 3 times 5 times
Factor A Length 30 seconds 1 2 3
Factor A Length 90 seconds 4 5 6

• after viewing, all subjects answered questions
  about recall of the ad, their attitude toward
  the commercial, and their intention to purchase
  the product these were the response variables.

102
Learning Objective 4Matched Pairs Design

• In a matched pairs design, the subjects receiving
  the two treatments are somehow matched (same
  person, husband/wife, two plots in the same
  field, etc.)
• In a crossover design, the same individual is
  used for the two treatments
• Randomly
• assign the two treatments to the two matched
  subjects, or
• randomize the order of applying the treatments in
  a crossover design
• The number of replicates equals the number of
  pairs
• Helps to reduce effects of lurking variables

103
Learning Objective 5Randomized Block Design

• A block is a set of experimental units that are
  matched with respect to one or more
  characteristics
• A Randomized Block Design, RBD, is when the
  random assignment of experimental units to
  treatments is carried out separately within each
  block

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Learning Objective 5Example Randomized Block
Design

• Block gender 3 treatments 3 types of
  therapy
• The men (as well as the women) are randomly
  assigned to the
• 3 treatments differences can be compared with
  respect to
• gender as well as therapy type

105
Learning Objective 5Randomized Block Design

• RBD eliminates variability in the response due to
  the blocking variable allows for better
  comparisons to be made among the treatments of
  interest
• A matched pairs design is a special case of a RBD
  with two observations in each block

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Statistical Designs

• Completely Randomized Design All the
  experimental units are assigned randomly among
  all the treatments.
• Randomized Block Design the experimental units
  are assigned randomly among all the treatments
  separately within each block. blocks are
  another form of control.

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Completely Randomized Design
108
Randomized Block Design
109
Page 28, Figure 1.5
????????????????????
Completely Randomized Design
110
Page 28, Figure 1.6
Randomized Block Design
111
???????
History??
O1 O2??,?X?,??????????O2

• O1 X1 O2
• History -- Specific events in the external
  environment occurring between the first and
  second measurements that are beyond the control
  of the experimenter and that affect the validity
  of an experiment.
• EX) Changes (departmental reorganization, a
  strike or large layoff, change in the economic
  climate) during the course of an OB field
  experiment.
• EX) Cohort effect --a change in the dependent
  variable because members of one experimental
  condition experienced historical situations
  different from those of members of other
  experimental conditions.

112
???????
Maturation????

• O1 ? O2
• An effect on the results of a research experiment
  caused by changes in the experimental subjects
  over time.
• ??????????? -- Fatigue, bored, sleepy, ageetc.
• EXgt ?????????????????? ??????

113
Pretesting and Postesting
Experimental Group
Control Group
Measure dependent variable
Compare same?
Measure dependent variable

• Differences noted between the first and last
  measurements on the dependent variable are then
  attributed to the independent variable.

Administer experimental stimulus
Remeasure dependent variable
Compare Different?
Remeasure dependent variable
114
???????
Testing??????

• The effect of pretesting in a before-and-after
  study may sensitize the subjects when taking a
  test for the second time, thus affecting the
  validity of an experiment.
• EX) students taking achievement and intelligence
  tests for the second time.
• Pretesting may increase awareness of socially
  approved answers, it may increase attention to
  experimental conditions, or it may make the
  subject more conscious of the dimensions of a
  problem.
• EXgt drug intervention , attitude changes

115
Pretesting and Postesting

• the subjects might respond differently to the
  questionnaires the second time even if their
  attitudes remained unchanged.
• The very act of studying something may change it.

116
???????
Instrumentation????????

• An effect on the results of an experiment caused
  by a change in the wording of questions, a change
  in interviewers, or other changes in procedures
  to measure the dependent variable.
• Problem of reliability
• EXgt ???????????????(????????)

117
???????
Selection bias????

• Inappropriate randomization--A sample bias
  resulting in differential selection of
  respondents for the comparison groups.
• ????????????
• EX). ?????????? ,????

118
???????
Experimental mortality????

• Sample attrition that occurs when some subjects
  withdraw from an experiment before it is
  completed, thus affecting the validity of the
  experiment.
• ????????? Problem of drop-out
• EXgt drug-intervention, who drop out from the
  intervention?
• EXgt a short film teach people how to reduce their
  attitudes toward homosexual.
• EXgt???????????????????

119
???????
Diffusion or imitation of treatment????"??"

• Without exposure to the treatment, control group
  were accidentally "contaminated". Experimental
  group might diffuse the information to the
  control group???????
• EXgt?????????

120
???????
Compensation???? Compensatory rivalry???? Demorali
zation????

• EXgt???????????
• EXgt??????????
• EXgt??????????

121
Time Series Experiment-the observation windows
Positive impact -- raised the firms market share
Market Share
Positive impact -- it halted a decline in market
share
No long-run impact
No impact -- the market share growth remained
steady
No impact -- fluctuation after X was introduced.
O1 O2 O3 O4 O5 O6 O7 O8 Time

• The impact of a package change, X, on the firms
  market share.