Lecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments)
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1 ecture 4: Chapter 3, Section 4 Designing Studies (Focus on Experiments) Definitions Randomization Control Blind Experiment Pitfalls Specific Experimental Designs Cengage earning Elementary Statistics: ooking at the Big Picture 1
2 ooking Back: Review 4 Stages of Statistics Data Production Displaying and Summarizing Probability Statistical Inference Elementary Statistics: ooking at the Big Picture 4.2
3 ooking Back: Review 2 Types of Study Design Observational study: record variables values as they naturally occur Drawback: confounding variables due to selfassignment to explanatory values Example: Men who drink beer are more prone to lung cancer than those who drink red wine (what is the confounding variable here?) Experiment: researchers control values of explanatory variable If well-designed, provides more convincing evidence of causation. Elementary Statistics: ooking at the Big Picture 4.3
4 Definitions Factor: an explanatory variable in an experiment. Treatment: value of explanatory variable imposed by researchers in an experiment. A control group (individuals receiving no treatment or base-line treatment) may be included for comparison. If individuals are human, we call them subjects. Elementary Statistics: ooking at the Big Picture 4.4
5 Example: Randomized Controlled Experiment Background: To test if sugar causes hyperactivity, researchers randomly assign some children to low and others to high levels of sugar consumption Population Question: Why are random assignments best? Response: Randomization helps rule out Sample H Elementary Statistics: ooking at the Big Picture 4.6 H H
6 Experiment vs. Observational Study In an experiment, researchers decide who has low sugar intake () and who has high (H). Population Sample H Sugar intake has not yet been determined. Researchers assign sugar intake or H. H H Elementary Statistics: ooking at the Big Picture 4.7
7 Experiment vs. Observational Study In observational study, individuals have already chosen low () or high (H) sugar intake. Population H H H H H Sample H H H Researchers make no changes to sugar intake. Elementary Statistics: ooking at the Big Picture 4.9
8 Example: Randomize at 1st or 2nd Stage? Background: Consider two selection issues in our sugar-hyperactivity experiment: What individuals are included in the study? Who consumes low and high amounts of sugar? Question: At which stage is randomization important? Response: 1st stage: Individuals studied (Otherwise may be an issue.) 2nd stage:assignment to sugar ( or H) Volunteering which treatment to get is Elementary Statistics: ooking at the Big Picture 4.11
9 Must an experiment have a control group? Recall our definition: Experiment: researchers manipulate explanatory variable, observe response Thus, experiment may have no control group if all subjects must be treated if simulated treatment is risky if the experiment is poorly designed As long as researchers have taken control of the explanatory variable, it is an experiment. Elementary Statistics: ooking at the Big Picture 4.12
10 Definitions: Three Meanings of Control We control for a confounding variable in an observational study by separating it out. Researchers control who gets what treatment in an experiment by making the assignment themselves, ideally at random. The control group in an experiment consists of individuals who do not receive a treatment per se, or who are assigned a baseline value of the explanatory variable. Elementary Statistics: ooking at the Big Picture 4.13
11 Double-blind Experiments Two pitfalls may prevent us from drawing a conclusion of causation when results of an experiment show a relationship between the so-called explanatory and response variables. If subjects are aware of treatment assignment If researchers are aware of treatment assignment Elementary Statistics: ooking at the Big Picture 4.14
12 Definitions The placebo effect is when subjects respond to the idea of treatment, not the treatment itself. A placebo is a dummy treatment. A blind subject is unaware of which treatment he/she is receiving. The experimenter effect is biased assessment of (or attempt to influence) response due to knowledge of treatment assignment. A blind experimenter is unaware of which treatment a subject has received. Elementary Statistics: ooking at the Big Picture 4.15
13 Example: Subjects Not Blind Background: Suppose after children are randomly assigned to consume either low or high amounts of sugar, researchers find proportion hyperactive is greater for those who consumed higher amounts. Question: Can we conclude sugar causes hyperactivity? Response: Improvement: Elementary Statistics: ooking at the Big Picture 4.17
14 Example: Experimenters Not Blind Background: Suppose after children are randomly assigned to diets sweetened either artificially or with sugar, researchers find proportion hyperactive is greater for those who consumed sugar. Question: Can we conclude sugar causes hyperactivity? Response: More problematic if responses are assessed Improvement: Elementary Statistics: ooking at the Big Picture 4.19
15 Best Evidence of Causation In general, conclusions of causation are most convincing if a relationship has been established in a randomized controlled double-blind experiment. A Closer ook: In the original studies reporting a relationship between sugar and hyperactivity, conducted in the 1970 s, experimenters may have been aware of the children s diet when they assessed behavior (randomized controlled single-blind). Many studies since then have failed to establish a relationship. Elementary Statistics: ooking at the Big Picture 4.20
16 Other Pitfalls in Experimentation ack of realism (lack of ecological validity) Hawthorne effect (people s performance is improved due to awareness of being observed) Non-compliance Treatments unethical Treatments impractical/impossible to impose Elementary Statistics: ooking at the Big Picture 4.21
17 Example: Hawthorne Effect, ack of Realism Background: Suppose researchers want to determine if TV makes people snack more. While study participants are presumably waiting to be interviewed, half are assigned to a room with a TV on (and snacks), the other half to a room with no TV (and snacks). See if those in the room with TV consume more snacks. Question: If participants in the room with TV snack more, can we conclude that, in general, people snack more when they watch TV? Response: No: (TV & snacking habits different in contrived setting); (if people suspect they re observed). Elementary Statistics: ooking at the Big Picture 4.23
18 Example: Non-Compliance in Experiment Background: To test if sugar causes hyperactivity, researchers randomly assign 50 children to low and 50 to high levels of sugar consumption; 20 drop out of each group. For remaining children (30 in each group), suppose proportion hyperactive is substantially greater in the high-sugar group. Question: Can we conclude sugar causes hyperactivity? Response: makes treatment and control groups different in ways that may affect response. Elementary Statistics: ooking at the Big Picture 4.25
19 Example: Another Flawed Experiment Background: To test if stuttering is a learned (rather than inborn) trait, a researcher in Iowa in 1939 randomly assigned subjects to Control: 11 orphans in ordinary speech therapy Treatment: 11 orphans badgered and interrupted in sessions with speech therapist Of the 11 in treatment group, 8 became stutterers. Question: What s wrong with this experiment? Response: Elementary Statistics: ooking at the Big Picture 4.27
20 Examples: Treatments Impossible/Impractical Taller men get married sooner, promoted quicker, and earn higher wages There is a link between obesity and low socio-economic status in women Height is impossible to control. Weight is difficult to control. Socio-economic status is too costly to control. Elementary Statistics: ooking at the Big Picture 4.28
21 Modifications to randomized experiment Blocking: Divide first into groups of individuals who are similar with respect to an outside variable that may be important in relationship studied. Paired design: Randomly assign one of each pair to receive treatment, the other control. (Before-and-after is a common paired design.) ooking Back: blocking is to experimentation as stratification is to sampling. Elementary Statistics: ooking at the Big Picture 4.29
22 Example: Blocked Experiment Background: Study tested theory that use of stronger sunscreen causes more time in sun. Before vacation, 40+ students given weak sunscreen, 40+ given strong. Students recorded time spent in sun each day. Question: How to incorporate blocking, if researchers suspect location plays a role in relationship between type of sunscreen and amount of time spent in sun? Response: Elementary Statistics: ooking at the Big Picture 4.31
23 Example: Paired Experiment Background: Study tested theory that use of stronger sunscreen causes more time in sun. Before vacation, 40+ students given weak sunscreen, 40+ given strong. Students recorded time spent in sun each day. Question: How to incorporate paired design, if researchers suspect location plays a role in relationship between type of sunscreen and amount of time spent in sun? Response: Elementary Statistics: ooking at the Big Picture 4.33
24 Advantage of Paired Design The paired design helps to ensure that treatment and control groups are as similar as possible in all other respects, so that if their responses differ, we have evidence that the treatment is responsible. Discussion Question: Why do not just twins, but also researchers, flock to the annual festival in Twinsburg, Ohio? Elementary Statistics: ooking at the Big Picture 4.34
25 Example: Combining Paired and Two-Sample Designs Background: Studies often randomly assign one group to a placebo and the other to a drug. Responses to the variable of interest are assessed before and after a period of time, then compared to see benefits or side effects. Question: What aspect of the design is two-sample, and what aspect is paired? Response: two-sample: paired: Elementary Statistics: ooking at the Big Picture 4.37
26 ecture Summary (Experiments) Definitions Randomization; 2 stages of selection Control group Blind study design Subjects blind to avoid placebo effect Researchers blind to avoid experimenter effect Other pitfalls of experiments: lack of realism, Hawthorne effect, non-compliance, unethical or impractical treatment Specific experimental designs Blocked Paired or two-sample Elementary Statistics: ooking at the Big Picture 4.38
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