1 / 28

Experimental Design Sections 1.2 & 1.3

Experimental Design Sections 1.2 & 1.3. Section 1.2 - Random Samples. Samples are used to gain an understanding of “Total Population” Def: Simple Random Sample (SRS) - is a subset of the population that is selected in a way where each member had an equal chance of being chosen.

wilmer
Télécharger la présentation

Experimental Design Sections 1.2 & 1.3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Experimental DesignSections 1.2 & 1.3

  2. Section 1.2 - Random Samples • Samples are used to gain an understanding of “Total Population” • Def: Simple Random Sample (SRS) - is a subset of the population that is selected in a way where each member had an equal chance of being chosen. • What does it mean to be “random”?

  3. Using the Random Number Table (Appendix 1) • Gives a starting point for you to find “matches” or select individuals. Steps: 1. number all members of population sequentially. 2. determine starting point on random # table (given). 3. looking at correct number of digits, find matches which will produce the sample.

  4. Example 1 - SRS Pick an SRS of size 4 from the class starting on the 7th row, 3rd block • Label students 01-25 • Looking at 2 digits at a time, start on the 7th row, 3rd block to locate matches 7th row: 82739 5789020807 47511 81676 55300 94383 14893 8th row: 60940 72024 17868 24943 61790 90656 87964 18883

  5. Example 2 - SRS Example 3 - SRS Pick an SRS of size 30 from a population of 500 cars starting on the 11th row, 1st block. List only the first 5 matches. Answer: 092,041,271,238,276 Pick 150 students from the Univ. of Florida which has a population of 50,000 students starting on the 1st row, 7th block. List the first 5 matches. Answer: 42544, 47150, 01927, 27754, 42648

  6. Other types of Sampling (Summary on page 17) • Stratified - draw a certain number of individuals from a population after it is divided up into smaller groups (strata) Lynchburg College Students Fr. So. Jr. Sr. Sample (10 from each strata)

  7. Other types of Sampling (Summary on page 17) • Systematic – arrange individuals in some order, then select every kth element. Ex. Elementary school – count up by 3’s: 1-2-3, 1-2-3, 1-2-3, … to form 3 groups. • Downfall: if a machine produces something and you check every 16th element, but there is a mistake in every 17th element, big problem.

  8. Other types of Sampling (Summary on page 17) • Cluster – divide up demographic area into sections, select a few sections, and then sample every individual in that section. Ex. Large city school children: select 5 schools (clusters) and then sample all kids from those 5 schools. • Sample is compose of 5 schools rather than an SRS of 2500 students.

  9. Other types of Sampling (Summary on page 17) • Multistage – start with a large group to sample and then break down group based on certain factors with final stage consisting of clusters. Ex. 60,000 households → then by race → smaller groups (age, income, etc.) → clusters → interviews/surveys

  10. Other types of Sampling (Summary on page 17) • Convenience – use results/data that is available, some information is better than no information. Ex. Ask your friends to complete a survey on nuclear reactors, stratification layers in lakes, or equilibrium points of a specific parasite that infect dogs.

  11. Types of errors • Sampling errors: difference between measurements from a sample compared to what the population data should actually be. • Nonsampling errors: result of poor sample design, sloppy data collection, faulty machines, bias, undercoverage, etc.

  12. Section 1.3 – Intro. To Experimental Design • Guidelines for a Study • Identify individuals of interest • Specify variables to be studied • Sample or population? Size? • Create data collection plan and obtain permissions. • Collect data • Analyze data using statistics • Conclusions and concerns

  13. Good Practices / Terminology • Treatment – Specific condition administered to subjects (diet pill, music, etc.) • Placebo – “dummy” treatment given to a test group. Has no actual effect on subjects. Placebo Effect – subject receives no actual treatment, but thinks he/she is receiving treatment and responds favorably. • Blind study – subjects do not know which treatment they are receiving. Double Blind – neither subjects nor persons performing study know treatment groups.

  14. Good Practices / Terminology • Control Group – treatment group who is given a placebo. Should not show any changes, but if there is change, results could be used to account for any lurking or confounding variables. Lurking Variable – variable that isn’t studied, but may have an influence on other variables in the study. ex. Diet pills and weight loss: Lurking variable could be exercise or nutrition. Confounding Variable – Two variables whose effects can’t be distinguished from each other. ex. Study involving GPA: difficulty of courses, IQ, and available study time are all confounding variables.

  15. Good Practices / Terminology • Matched Pairs Design Test the time it takes for two groups to complete a maze with the treatment being a certain type of music. with musicwithout music Trial 1: Group AGroup B Trial 2: Group BGroup A • Compare results and make conclusions using data about each group with both treatments.

  16. Good Practices / Terminology • Replication – Perform an experiment on many individuals to reduce the possibility of error or a result occurring by chance. • Census – data from the entire population is used • Sample – data from part of the population is used • Bias – Results skewed in some way due to personal opinion / favoritism.

  17. Usefulness of Data • Situation 1 A uniformed police officer interviews a group of 20 college freshman. The officer asks each one his or her name and then if he or she has used an illegal drug in the last month. • In fear of getting in trouble – students may not answer truthfully or refuse to participate.

  18. Usefulness of Data • Situation 2 Jessica saw some data showing that cities with more low-income hosing have more homeless people. Does building low-income hosing cause homelessness? • Lurking / Confounding Variables such as the size of the city.

  19. Usefulness of Data • Situation 3 A survey about food in the café was conducted by placing forms for students to pick up as you got your card scanned. A drop box was then placed in the foyer outside the café. • Voluntary response likely produced negative comments • Nonresponse by losing document before it was turned in • Fill out form before you ate, not accurate account of the quality of the food

  20. Usefulness of Data • Situation 4 Extensive studies on coronary problems were conducted using men over the age of 50. • Results may not help out other age groups or the female gender

  21. Types of Studies Observational Experiment Researcher actually does something (treatment) to the subject being studied Ex. Subjects were assigned to two groups. The first group was given an herbal supplement and the other a placebo. After 6 months the red blood cell counts were compared. • Simply observe subjects without any influence on the variable being studied Ex. A researcher stood by a busy intersection to see if the color of the car someone drove related to running red lights.

  22. Types of Experimental Designs • Completely Randomized – random process (ie. random number table) is used to assign each individual to one of the treatment groups. Ex. Laser heart treatment on 300 individuals with heart pain problems. Group 1, 150 patients, laser treatment Patients w/heart pain problems Compare pain relief Random Assignments Group 2, 150 patients, no treatment

  23. Types of Experimental Designs • Randomized Block Experiment – sort individuals into blocks and then use random process to assign individuals in the block to one of the treatments. Def: block – group of individuals that share a common feature that may affect the treatment.

  24. Patients w/heart pain problems Men Women Random Assignments Random Assignments Group 1, laser treatment Group 2, no treatment Group 1, laser treatment Group 2, no treatment Compare pain relief Compare pain relief

  25. Data Collection • By means of surveys Sampling, census, observation, or experiments. Not just questionnaires. • Methods most commonly used • Surveys – quick and effective • Observational study – less permission needed and don’t have to bother anyone to obtain data • Experiments – time consuming, but yield the most meaningful and valid results.

  26. Problems with Data Collection Surveys • Nonresponse / Small sample size • Truthfulness • Faulty recall – forgot details of an event • Hidden bias – wording leads subjects to respond a certain way • Vague wording – words with different meanings to different persons are used (often, seldom, occasionally) • Interview influence – tone of voice, body language, attire, etc. influence results • Voluntary response – Individuals with strong feelings about a subject are more likely to respond in a positive way, which is not reflective about the entire population.

  27. Problems with conclusions when using a sample • Inference about a population • Results of a sample may not reflect the entire population correctly • Large sample sizes give more accurate results

  28. Homework • Section 1.2 Pg. 18 #’s: 6a, 7 starting at line 7, 8 starting at line 8, 9 starting at line 9, 10 starting at line 10, 13 starting at line 13. • Section 1.3 Pg. 28 #’s: 5,7a,9 & Pg. 32 #’s: 3a,6,7

More Related