Presentation is loading. Please wait.

Presentation is loading. Please wait.

Applied Statistics Using SAS and SPSS Topic: Inferences for Different Types of Studies By Prof Kelly Fan, Cal State Univ, East Bay.

Similar presentations


Presentation on theme: "Applied Statistics Using SAS and SPSS Topic: Inferences for Different Types of Studies By Prof Kelly Fan, Cal State Univ, East Bay."— Presentation transcript:

1

2 Applied Statistics Using SAS and SPSS Topic: Inferences for Different Types of Studies By Prof Kelly Fan, Cal State Univ, East Bay

3 I want to slim down. Should I do exercise or limit my fat intake?

4 Research studies Observational studies Sample surveysCase control studies Randomized experiments

5 Inferences and Conclusions Cause and effect Relationship Restriction on the application of conclusions: retrospective or prospective?

6 Designing a Good Experiment Randomizing the condition assigned to a unit  the type of treatment  the order of treatments Adding control groups  placebo; standard treatment; both Preventing bias: blinding  double blind; single blind Reducing the variability/ Increasing the accuracy  blocking

7 Designing a Good Observational Study Case-control studies  can reduce the variability

8 Representative Sample Simple random sample: Each group of units of the required size from the population has the same chance to be the selected sample.

9 Random Assignment of Treatments Goal: randomly select 4 units to each of 3 treatments Algorithm: 1.Generate 12 random numbers from Unif[0,1] and use them to label the 12 units. 2.Sort the 12 random numbers and assign the units labeled the first 4 numbers to treatment 1, the 2 nd 4 to treatment 2, and the 3 rd 4 to treatment 3.

10 Random Assignment of Treatments By SAS: See Pages 188, 189 of SAS textbook By Minitab: 1.Create one column for subj 1,2,…,12 and another one for treatment 1,1,1,1,2,2,2,2,3,3,3,3. 2.Create a column of 12 random numbers from Unif[0,1]: Calc>>Random Data>>Uniform 3.Sort only columns of subj and random numbers by the column of random numbers: Data>>Sort

11 More Sampling Methods Determine when samples of convenience are acceptable Describe stratified sampling, cluster sampling, systematic sampling, and voluntary response sampling

12 DETERMINE WHEN SAMPLES OF CONVENIENCE ARE ACCEPTABLE Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

13 Samples of Convenience In some cases, it is difficult or impossible to draw a sample in a truly random way. In these cases, the best one can do is to sample items by some convenient method. A sample of convenience is a sample that is not drawn by a well-defined random method. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

14 Example A construction engineer has just received a shipment of1000 concrete blocks. The blocks have been delivered in a large pile. The engineer wishes to investigate the crushing strength of the blocks by measuring the strengths in a sample of 10 blocks. Explain why it might be difficult to draw a simple random sample of blocks. Solution: To draw a simple random sample would require removing blocks from the center and bottom of the pile. One way to draw a sample of convenience would be to simply take10 blocks off the top of the pile. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

15 Problems with Sample of Convenience The problem with samples of convenience is that they may differ systematically in some way from the population. If it is reasonable to believe that no important systematic difference exists, then it is acceptable to treat the sample of convenience as if it were a simple random sample. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

16 Describe stratified sampling, cluster sampling, systematic sampling, and voluntary response sampling MORE SAMPLING METHODS Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

17 Stratified Random Sampling In stratified random sampling, the population is divided up into groups, called strata, then a simple random sample is drawn from each stratum. Stratified sampling is useful when the strata differ from one another, but the individuals within a stratum tend to be alike. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

18 Example – Stratified Random Sampling A company has 800 full-time and 200 part-time employees. To draw a sample of 100 employees, a simple random sample of 80 full-time employees is selected and a simple random sample of 20 part-time employees is selected. GROUP 1 Full-time Employees GROUP 2 Part-time Employees Stratified Random Sample of 100 Choose simple random sample of 80 full-time employees Choose simple random sample of 20 part-time employees Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

19 Cluster Sampling In cluster sampling, items are drawn from the population in groups, or clusters. Cluster sampling is useful when the population is too large and spread out for simple random sampling to be feasible. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

20 Example To estimate the unemployment rate, a government agency draws a simple random sample of households in a county. Someone visits each household and asks how many adults live in the household, and how many of them are unemployed. What are the clusters? Why is this a cluster sample? Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

21 Solution Cluster Sample of Individuals Interview every individual in each household Household Random sample of households is taken Household The clusters are the groups of adults in each of the households in the county. This a cluster sample because a simple random sample of clusters is selected, and every individual in each selected cluster is part of the sample. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

22 Systematic Sampling In systematic sampling, items are ordered and every k th item is chosen to be included in the sample. Systematic sampling is sometimes used to sample products as they come off an assembly line, in order to check that they meet quality standards. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

23 Example Automobiles are coming off an assembly line. It is decided to draw a systematic sample for a detailed check of the steering system. The starting point will be the third car, then every fifth car after that will be sampled. Which cars will be sampled? Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

24 Solution 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1516 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The sample will consist of cars numbered 3, 8, 13, 18, 23, 25, and so on. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

25 Voluntary Response Sampling Voluntary response samples are often used by the media to try to engage the audience. For example, a radio announcer will invite people to call the station to say what they think. Voluntary response samples are never reliable for the following reasons: People who volunteer an opinion tend to have stronger opinions than is typical of the population. People with negative opinions are often more likely to volunteer their response. Copyright ©2014 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.


Download ppt "Applied Statistics Using SAS and SPSS Topic: Inferences for Different Types of Studies By Prof Kelly Fan, Cal State Univ, East Bay."

Similar presentations


Ads by Google