# Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. Gathering Useful Data Chapter 3.

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Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. Gathering Useful Data Chapter 3

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 2 Principal Idea: The knowledge of how the data were generated is one of the key ingredients for translating data intelligently.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 3 3.1Description or Decision? Using Data Wisely Descriptive Statistics: using numerical and graphical summaries to characterize a data set. Inferential Statistics: using sample information to make conclusions about a broader range of individuals than just those observed.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 4 The Fundamental Rule for Using Data for Inference Available data can be used to make inferences about a much larger group if the data can be considered to be representative with regard to the question(s) of interest.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 5 Example 3.1 Do First Ladies Represent Other Women? Past First Ladies are not likely to be representative of other American women, nor even future First Ladies, on the question of age at death, since medical, social, and political conditions keep changing in ways that may affect their health.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 6 Example 3.2 Do Penn State Students Represent Other College Students? If question of interest = average handspan of females in college age range => Yes If question of interest = how fast ever driven a car => No, since Penn State in rural area with open spaces, country roads, little traffic.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 7 Populations, Samples, and Simple Random Samples Population: the larger group of units about which inferences are to be made. Sample: the smaller group of units actually measured. Simple Random Sample: every conceivable group of units of the required size from the population has the same chance to be the selected sample. Helps ensure sample data will be representative of the population, but can be difficult to obtain.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 8 3.2Speaking the Language of Research Studies Observational Study: Researchers observe or question participants about opinions, behaviors, or outcomes. Participants not asked to do anything differently. Two special cases: sample surveys and case-control studies.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 9 Experiment: Researchers manipulate something and measure the effect of the manipulation on some outcome of interest. Randomized experiments: participants are randomly assigned to participate in one condition (called treatment) or another. Sometimes cannot conduct experiment due to practical/ethical issues.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 10 Who is Measured: Units, Subjects, Participants Unit: a single individual or object being measured. If an experiment, then called an experimental unit. When units are people, often called subjects or participants.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 11 Roles Played by Variables – Measured or Not Explanatory variable (or independent variable) is one that may explain or may cause differences in a response variable (or outcome or dependent variable). A confounding variable is a variable that affects the response variable and also is related to the explanatory variable. A potential confounding variable not measured in the study is called a lurking variable.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 12 Example 3.3 What Confounding Variables Lurk behind Lower Blood Pressure? Recall Case Study 1.5: people who attended church regularly had lower blood pressure than those who stayed home. Possible confounding variables: Amount of social support Health status Age Attitude toward life

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 13 Example 3.4 The Fewer the Pages, the More Valuable the Book? Data on number of pages and price of 15 books (ordered by number of pages). Do prices increase? No, many books with fewer pages are more expensive.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 14 Example 3.4 The Fewer the Pages, the More Valuable the Book? Confounding Variable = Type of book (hardcover versus paperback). For each type of book, price does tend to increase with number of pages, especially for technical books. Type of book affects price and is related to number of pages.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 15 Case Study 3.1 Lead Exposure and Bad Teeth Observational study involving 24,901 children. Explanatory variable = level of lead exposure. Response variable = extent child has missing/decayed teeth. Possible confounding variables = income level, diet, time since last dental visit. Lurking variables = amount of fluoride in water, health care Children exposed to lead are more likely to suffer tooth decay … USA Today

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 16 3.3Designing a Good Experiment Randomized experiments: often allow us to determine cause and effect. Random assignment: to make the groups approximately equal in all respects except for the explanatory variable.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 17 Who Participates in Randomized Experiments? Participants in randomized experiments are often volunteers. Remember Fundamental Rule: Available data can be used to make inferences about a much larger group if the data can be considered to be representative with regard to the question(s) of interest.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 18 Randomization: The Crucial Element Randomizing the Type of Treatment: Randomly assigning the treatments to the experimental units keeps the researchers from making assignments favorable to their hypotheses and also helps protect against hidden or unknown biases. Randomizing the Order of Treatments: If all treatments are applied to each unit, randomization should be used to determine the order in which they are applied.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 19 Case Study 3.2 Kids and Weight Lifting Randomized Experiment involving 43 young volunteers. Three groups: 1 = heavy load 2 = moderate load 3 = control group Is weight training good for children? If so, is it better to lift heavy weights for few repetitions or moderate weights more times? Leg extension strength significantly increased in both exercise groups compared with that in the control subjects. Faigenbaum et al., 1999, p. e5

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 20 Control Groups, Placebos, and Blinding Control Groups: Treated identically in all respects except they dont receive the active treatment. Sometimes they receive a dummy treatment or a standard/existing treatment. Placebo: Looks like real drug but has no active ingredient. Placebo effect = people respond to placebos. Blinding: Single-blind = participants do not know which treatment they have received. Double-blind = neither participant nor researcher making measurements knows who had which treatment. Double Dummy: Each group given two treatments… Group 1 = real treatment 1 and placebo treatment 2 Group 2 = placebo treatment 1 and real treatment 2

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 21 Pairing and Blocking Matched-Pair Designs Use either two matched individuals or same individual receives each of two treatments. Special case of a block design. Important to randomize order of two treatments and use blinding if possible. Block Designs Experimental units divided into homogeneous groups called blocks, each treatment randomly assigned to one or more units in each block. If blocks = individuals and units = repeated time periods in which receive varying treatments; called repeated-measures designs.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 22 Case Study 3.3 Quitting Smoking with Nicotine Patches Double-blind, Placebo-controlled Randomized Experiment 240 smokers recruited (volunteers) Randomized to 22-mg nicotine patch or placebo (controlled) patch for 8 weeks. Double-blind: neither the participants nor the nurses taking the measurements knew who had received the active nicotine patches. After the eight-week period of patch use, almost half (46%) of the nicotine group had quit smoking, while only one-fifth (20%) of the placebo group had.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 23 3.4Designing a Good Observational Study Disadvantage: more difficult to try to establish causal links. Advantage: more likely to measure participants in their natural setting.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 24 Types of Observational Studies Retrospective: Participants are asked to recall past events. Prospective: Participants are followed into the future and events are recorded. Case-Control Studies: Cases who have a particular attribute or condition are compared to controls who do not to see how they differ on an explanatory variable of interest. Advantages: Efficiency and Reduction of Potential Confounding Variables through careful choice of controls.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 25 Case Study 3.4 Baldness and Heart Attacks Case-control study cases = men admitted to hospital with heart attack controls = men admitted for other reasons. Explanatory variable: heart attack status (yes or no) Response variable: degree of baldness Men with typical male pattern baldness … are anywhere from 30 to 300 percent more likely to suffer a heart attack than men with little or no hair loss at all. Newsweek, March 9, 1993, p. 62

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 26 3.5Difficulties and Disasters in Experiments and Observational Studies Confounding Variables and the Implication of Causation in Observational Studies Big misinterpretation = reporting cause-and-effect relationship based on an observational study. No way to separate the role of confounding variables from the role of explanatory variables in producing the outcome variable if randomization is not used. Extending Results Inappropriately Many studies use convenience samples or volunteers. Need to assess if the results can be extended to any larger group for the question of interest.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 27 3.5Difficulties and Disasters in Experiments and Observational Studies Interacting Variables A second variable can interact with the explanatory variable in its relationship with the outcome variable. Results should be reported taking the interaction into account. Example: Interaction in Case Study 3.3 The difference between the nicotine and placebo patches is greater when there are no smokers in the home than when there are smokers in the home.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 28 3.5Difficulties and Disasters in Experiments and Observational Studies Hawthorne and Experimenter Bias Hawthorne effect = participants in an experiment respond differently than they otherwise would, just because they are in the experiment. Many treatments have higher success rate in clinical trials than in actual practice. Experimenter effects = recording data to match desired outcome, treating subjects differently, etc. Most overcome by blinding and control groups.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 29 3.5Difficulties and Disasters in Experiments and Observational Studies Ecological Validity and Generalizability When variables have been removed from their natural setting and are measured in the laboratory or in some other artificial setting, the results may not reflect the impact of the variable in the real world. Example 3.7 Real Smokers with a Desire to Quit Case Study 3.3: Ensured ecological validity and generalizability by using participants around the country of wide range of ages, and recorded many other variables and checked that they were not related to the patch assignment or the response variable.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. 30 3.5Difficulties and Disasters in Experiments and Observational Studies Relying on Memory or Secondhand Sources Can be a problem in retrospective observational studies. Try to use authoritative sources such as medical records rather than rely on memory. If possible, use prospective observational studies.

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