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INF 397C
Introduction to Research in Library and
Information Science
Fall, 2009
Day 4
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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4 things today
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1. I’ll work the answers to the practice
questions.
2. We’ll talk about graphs and tables.
3. I will spend a few minutes introducing
the scientific method and experimental
design--to provide some context for . . .
4. Dr. Mary Lynn Rice Lively on qualitative
methods.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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1 – Practice problems
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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2 - Graphs
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• Graphs/tables/charts do a good job
(done well) of depicting all the data.
• But they cannot be manipulated
mathematically.
• Plus it can be ROUGH when you have
LOTS of data.
• Let’s look at your examples.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Your Charts/Graphs/Tables
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Some rules . . .
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• . . . For building graphs/tables/charts:
– Label axes.
– Divide up the axes evenly.
– Indicate when there’s a break in the rhythm!
– Keep the “aspect ratio” reasonable.
– Histogram, bar chart, line graph, pie chart,
stacked bar chart, which when?
– Keep the user in mind.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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3 - The Scientific Method
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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More than anything else . . .
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• . . . scientists are skeptical.
• P. 15 – “Scientific skepticism is a gullible
public’s defense against charlatans and
others who would sell them ineffective
medicines and cures, impossible
schemes to get rich, and supernatural
explanations for natural phenomena.”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Research Methods
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S, Z, & Z, Chapters 1, 2, 3, 7, 8
Researchers are . . .
- like detectives – gather evidence, develop a
theory.
- Like judges – decide if evidence meets
scientific standards.
- Like juries – decide if evidence is “beyond a
reasonable doubt.”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Science . . .
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• . . . Is a cumulative affair. Current
research builds on previous research.
• The Scientific Method:
– is Empirical (acquires new knowledge via
direct observation and experimentation)
– entails Systematic, controlled observations.
– is unbiased, objective.
– entails operational definitions.
– is valid, reliable, testable, critical, skeptical.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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CONTROL
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• . . . is the essential ingredient of science,
distinguishing it from nonscientific
procedures.
• The scientist, the experimenter,
manipulates the Independent Variable
(IV – “treatment” – at least two levels –
“experimental and control conditions”)
and controls other variables.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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More control
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• After manipulating the IV (because the
experimenter is independent – he/she
decides what to do) . . .
• He/she measures the effect on the
Dependent Variable (what is measured –
it depends on the IV).
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Key Distinction
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• IV vs. Individual Differences variable
• The scientist MANIPULATES an IV, but
SELECTS an Individual Differences
variable (or “subject” variable).
• Can’t manipulate a subject variable.
– “Select a sample. Have half of ‘em get a
divorce.”
• Consider an Individual Difference, or
Subject Variable, as a TYPE of IV.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Operational Definitions
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• Explains a concept solely in terms of the
operations used to produce and measure it.
–
–
–
–
–
–
Bad: “Smart people.”
Good: “People with an IQ over 120.”
Bad: “People with long index fingers.”
Good: “People with index fingers at least 7.2 cm.”
Bad: Ugly guys.
Good: “Guys rated as ‘ugly’ by at least 50% of the
respondents.”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Validity and Reliability
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• Validity: the “truthfulness” of a measure. Are
you really measuring what you claim to
measure? “The validity of a measure . . . the
extent that people do as well on it as they do
on independent measures that are presumed
to measure the same concept.”
• Reliability: a measure’s consistency.
• A measure can be reliable without being valid,
but not vice versa.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Theory and Hypothesis
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• Theory: a logically organized set of
propositions (claims, statements, assertions)
that serves to define events (concepts),
describe relationships among these events,
and explain their occurrence.
– Theories organize our knowledge and guide our
research
• Hypothesis: A tentative explanation.
– A scientific hypothesis is TESTABLE.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Goals of Scientific Method
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• Description
– Nomothetic approach – establish broad generalizations and
general laws that apply to a diverse population
– Versus idiographic approach – interested in the individual,
his/her uniqueness (e.g., case studies)
• Prediction
– Correlational study – when scores on one variable can be
used to predict scores on a second variable. (Doesn’t
necessarily tell you “why.”)
• Explanation – con’t. on next page
• Application
– Applied research
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Explanation
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• Three important conditions for making a
causal inference:
– Covariation of events. (IV changes, and the
DV changes.)
– A time-order relationship. (First the scientist
changes the IV – then there’s a change in
the DV.)
– The elimination of plausible alternative
causes.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Confounding
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• When two potentially effective IVs are allowed to
covary simultaneously.
– Poor control!
• Remember week 1 – Men, overall, did a better job of
remembering the 12 “random” letters. But the men
had received a different “clue” (“Maybe they’re the
months of the year.”)
• So GENDER (what type of IV? A SUBJECT variable,
or indiv. differences variable) was CONFOUNDED with
“type of clue” (an IV).
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Populations and Samples
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• Population: the set of all cases of
interest
• Sample: Subset of all the population that
we choose to study.
Population
Sample
Parameters
Statistics
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Some (as yet untested) online
practice problems
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• http://webster.edu/~woolflm/zscores.html
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Homework
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• None.
• No new readings!
• Catch up!
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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