gathering chapter 3 useful data - york university...“men with typical male pattern baldness …...

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.


3.1 Description 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.


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.


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.


Example 3.2 Do Penn State StudentsRepresent 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.


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.


3.2 Speaking the Languageof 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.


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.


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.


Roles Played by Variables – Measured or NotExplanatory 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.


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 supportHealth statusAgeAttitude toward life

Sample test question:Smoking and Health

In 1964, the Public Health Service of the United States studied the effects of smoking on health in a sample of 42,00 households. For men and for women in each age group, they found that those who had never smoked were on average somewhat healthier than the current smokers, but the current smokers were on average much healthier than the former smokers.

Why did they study men and women and the different age groups separately?

The lesson seems to be that you shouldn't start smoking, but once you've started, don't stop. Comment.


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.


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.


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


3.3 Designing 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.


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.


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.


Case Study 3.2 Kids and Weight Lifting

Randomized Experiment involving 43 young volunteers.

Three groups: 1 = heavy load2 = 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


Control Groups, Placebos, and BlindingControl Groups:

Treated identically in all respects except they don’t 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 2Group 2 = placebo treatment 1 and real treatment 2


Pairing and BlockingMatched-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 DesignsExperimental 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.


Case Study 3.3 Quitting Smoking with Nicotine Patches

Double-blind, Placebo-controlledRandomized 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.”


3.4 Designing a Good Observational Study

Disadvantage: more difficult to try to establish causal links.

Advantage: more likely to measure participants in their natural setting.


Types of Observational StudiesRetrospective:

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”.


Case Study 3.4 Baldness and Heart Attacks

Case-control study cases = men admitted to hospital with heart attackcontrols = 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


3.5 Difficulties and Disasters in Experiments and Observational Studies

Confounding Variables and the Implication of Causation in Observational StudiesBig 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 InappropriatelyMany studies use convenience samples or volunteers. Need to assess if the results can be extended to any larger group for the question of interest.



Interacting VariablesA 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 3.3The 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.



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.


3.5 Difficulties and Disasters in Experiments and Observational StudiesEcological Validity and GeneralizabilityWhen 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 QuitCase 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.



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.

gathering chapter 3 useful data - york university...“men with typical male pattern baldness …...

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