1. Chapters 12 & 13 - Sampling & Designs of Surveys Experiments Objective: To understand the various types of experimental designs and techniques

2. Warm-up Various claims are often made for surveys. Why is each of the following claims incorrect?  Stopping college students on their way out of the campus cafeteria is a good way to sample if we want to know about the quality of the food there.  We drew a sample of 100 from 3000 students in a school. To get the same level of precision for a town of 30,000 residents, we’ll need a sample of 1000.  A poll taken at a statistics support website garnered 12,357 responses. The majority said they enjoy doing statistics homework. With a sample size that large, we can be pretty sure that most Statistics students feel this way.  In the last example, the true percentage of all Statistics students who enjoy homework is called a “population statistic.”

3. 4 Ways to Collect Data  Observational study – observe and measure specific characteristics, but we don’t attempt to modify the subject being studied  Experiment – a treatment is applied to observe its effect on the subjects  Simulation – mathematical or physical model used to reproduce a situation  Survey – investigation of characteristics of a population

4. 4 Ways to Collect Data Examples  A study of the effect of changing flight patterns on the number of airplane accidents  A study of the effect of eating oatmeal on lowering blood pressure  A study showing how fourth grade students solve a puzzle  A study of US residents’ approval rating of the US president

5. Basic Steps to Designing Experiments  Identify the objective  Collect sample data  Use a random procedure that avoids bias  Analyze the data and form conclusions

6. Ways to Control Treatments  Placebo – a faux treatment that looks like the real treatment (i.e. sugar pill)  Placebo Effect – occurs when an untreated subject incorrectly believes that he/she is receiving a treatment and reports an improvement in symptoms.

7. Ways to Control Treatments (continued)  Blinding – a technique in which the subject doesn’t know whether he/she is receiving a treatment or a placebo.  This is used so we can determine if the treatment effect is significantly greater than the placebo effect  Single-Blind – the researcher knew which subject received which treatment, but the subjects did not know  Double-Blind – neither the researcher nor the subjects know who received a placebo or treatment

8. Ways to Control Treatments (continued)  Block – a group of subjects (or experimental units) that are similar to test the effectiveness of one or more treatments  The groups need to be similar in the ways that might affect the outcome.  Randomized design – this is a way to assign subjects to blocks through random selection  Randomly assigning treatments or placebos to groups  Controlled design – experimental units are carefully chosen so that the subject in each block are similar in the ways that are important.  Testing the use of a nicotine patch on groups of similar age and gender. A heavy smoker in her 40’s gets the treatment, and a similar heavy smoker in her 40’s gets a placebo.

9. Ways to Control Treatments (continued)  Confounding – occurs in an experiment when the effects from two or more variables cannot be distinguished from each other  Example – A professor in Vermont experiments with a new attendance policy (your course average drops one letter grade for each class cut), but an exceptionally mild winter moderates the discomforts that have reduce attendance in the past. If attendance improves, we can’t tell whether it was because of the new attendance policy or due to weather conditions.

10. Ways to Control Treatments (continued)  Sample Size  Make sure your sample is large enough, however, an extremely large sample is not necessarily a good sample.  Make sure the sample is large enough to see the true nature of the effects  Replication  Replication helps to confirm results by repeating the experiment

11. Ways to Control Treatments (continued)  Randomization  Collect data in an appropriate way, otherwise your data are useless.  Random Sample – members of the population are selected in a way that each has an equal chance of being selected

12. Sampling Techniques  To select a sample at random, we first need to define where the sample will come from. sampling frame  The sampling frame is a list of individuals from which the sample is drawn.  Once we have our sampling frame, the easiest way to choose an SRS is to assign a random number to each individual in the sampling frame.

13. Sampling Techniques Simple Random Sample (SRS) – n subjects are selected in a way that every possible sample of size n has the same chance of being chosen. Steps in simple random sampling 1. Identify and define the population. 2. Determine the sample size. 3. List all members of the population. 4. Assign each member of the population a consecutive number from zero to the desired sample size (i.e. 00 to 35 – each member needs to have a number with the same number of digits). 5. Select an arbitrary starting number from the random number table. 6. Look for the subject who was assigned that number. If there is a subject with that assigned number, they are in the sample. 7. Look at the next number in the random number table and repeat steps 6 and 7 until the appropriate number of participants has been selected.

14. SRS Example:  Randomly assign 5 members to participate in Ms. Halliday’s experiment using Line 21 of Appendix G: KesleyJakeSean JohnAnneDerek JoeDanAlyssa SarahKatieTara TandiHallieKaren BellaCoreyJim

15. Sampling Techniques  Systematic Sampling – randomly select a starting point through a random # generator, see calculator or software, and take every kth subject of the population  Steps in systematic sampling 1. Identify and define the population. 2. Determine the sample size. 3. List all members of the population. 4. Determine K by dividing the number of members in the population by the desired sample size. 5. Choose a random starting point in the population list. 6. Starting at that point in the population, select every Kth name on the list until the desired sample size is met. 7. If the end of the list is reached before the desired sample size is drawn, go to the top of the list and continue.

16. Sampling Techniques  Stratified Sampling – we subdivide the population into at least two different subgroups (or strata) that share the same characteristics (such as age or gender), then draw a sample from each stratum  Steps in stratified random sampling 1. Identify and define the population. 2. Determine the sample size. 3. Identify variable and strata for which equal representation is desired. 4. Classify all members of the population as a member of one strata. 5. Choose the desired number of subjects from each strata using the simple random sampling technique. Day 2

17. Stratified Example: A math club has 30 students and 10 faculty members. The students are: AbelEllisHuber MirandaCarsonGhosh ReinmanJimenezChen MoskowitzSantosGriswold DavidJonesNeyman DemingHeinKim FisherThompsonHernandez PearlUtzHolland PotterVeraniShaw O’Brien Klotz Liu The faculty members are: KraulandKarkariaGraham SemegaWalkerKeffalas MagillMagnaniHalliday Kotula The club can send 4 students and 2 faculty members to the convention. Use a random # generator.

18. Sampling Techniques  Cluster Sampling – first divide the population area into sections (or clusters), then randomly select some of those clusters, and then choose ALL members from those selected clusters.  Steps in cluster sampling 1. Identify and define the population. 2. Determine the sample size. 3. Identify and define a cluster (neighborhood, classroom, city block) 4. List all clusters. 5. Estimate the average number of population members per cluster. 6. Determine the number of clusters needed. 7. Choose the desired number of clusters using the simple random sampling technique. 8. All population members in the included clusters are part of the sample.

19. Sampling Techniques  Convenience Sampling – a researcher chooses a sample that is convenient or easy for them to access.  Convenience sampling is a BAD sampling technique, as the sample is rarely representative of the population of interest.  Sadly, it is commonly used among inexperienced researchers.  Example – Ms. Halliday is conducting research at Pitt. She needs a sample of students and chooses all of her CHS Statistics students to participate in her study.

20. Sampling Techniques Examples:  You select a class at random and question each student in the class.  You divide the student population with respect to majors and randomly select and question some students in each major.  You question every 20th student you see in the hall.  You assign each student a number and generate random numbers. You then question each student whose number is randomly selected.

21. Multistage Sampling  Sometimes we use a variety of sampling methods together.  Sampling schemes that combine several methods are called multistage samples.  EXAMPLE: Most surveys conducted by professional polling organizations use some combination of stratified and cluster sampling as well as simple random sampling.

22. Sampling Error  Sampling Error – the difference between a sample result and the true population result; such as an error results from chance sample fluctuations  Non-Sampling Error – occurs when the sample data are incorrectly collected, recorded, or analyzed (such as selecting a biased sample, using a defective measurement instrument, or copying the data incorrectly)

23. The Valid Survey  It isn’t sufficient to just draw a sample and start asking questions. A valid survey yields the information we are seeking about the population we are interested in. Before you set out to survey, ask yourself:  What do I want to know?  Am I asking the right respondents?  Am I asking the right questions?  What would I do with the answers if I had them; would they address the things I want to know?

24. The Valid Survey (cont.) These questions may sound obvious, but there are a number of pitfalls to avoid.  Know what you want to know.  Understand what you hope to learn and from whom you hope to learn it.  Use the right frame. sampling frame  Be sure you have a suitable sampling frame.  Tune your instrument.  The survey instrument itself can be the source of errors - too long yields less responses.

25. The Valid Survey (cont.)  Ask specific rather than general questions.  Ask for quantitative results when possible.  Be careful in phrasing questions.  A respondent may not understand the question or may understand the question differently than the way the researcher intended it.  Does family mean immediate or extended?  Even subtle differences in phrasing can make a difference.  See example on page 281 about 9/11

26. The Valid Survey (cont.)  Be careful in phrasing answers.  It’s often a better idea to offer choices rather than inviting a free response.  Think about how difficult it would be to organize the various responses!

27. The Valid Survey (cont.) The best way to protect a survey from unanticipated measurement errors is to perform a pilot survey. pilot A pilot is a trial run of a survey you eventually plan to give to a larger group.

28. Bad Sampling!  Sample Badly with Volunteers: voluntary response sample  In a voluntary response sample, a large group of individuals is invited to respond, and all who do respond are counted.  Voluntary response samples are almost always biased  Voluntary response samples are almost always biased, and so conclusions drawn from them are almost always wrong.  Voluntary response samples are often biased toward those with strong opinions or those who are strongly motivated. voluntary response bias  Since the sample is not representative, the resulting voluntary response bias invalidates the survey.

29. Bad Sampling! (cont.)  Sample Badly, but Conveniently: convenience sampling  In convenience sampling, we simply include the individuals who are convenient.  Unfortunately, this group may not be representative of the population.  Sample from a Bad Sampling Frame:  An SRS from an incomplete sampling frame introduces bias because the individuals included may differ from the ones not in the frame.  In telephone surveys, people who have only cell phones or internet phones are often missing from the sampling frame.  Undercoverage  Undercoverage : undercoverage  Many of these bad survey designs suffer from undercoverage, in which some portion of the population is not sampled at all or has a smaller representation in the sample than it has in the population.  Undercoverage can arise for a number of reasons, but it’s always a potential source of bias.

30. Bad Sampling! (cont.)  Watch out for nonrespondents. nonresponse bias  A common and serious potential source of bias for most surveys is nonresponse bias.  No survey succeeds in getting responses from everyone.  The problem is that those who don’t respond may differ from those who do.  And they may differ on just the variables we care about.  Work hard to avoid influencing responses.  Response bias  Response bias refers to anything in the survey design that influences the responses.  For example, the wording of a question can influence the responses:

31. Book Assignment  Day 1  Chapter 13: pp. 312-316  # 1, 2, 4  #5-20 –answer ONLY question “a” for each with either of these responses: experiment, observational study  Day 2  Chapter 12: pp. 288-291  # 1, 3-6, 8, 9, 15-17, 23, 25 Please check your answers on the solutions manual posted online.