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INF 397C
Introduction to Research in Library and
Information Science
Spring, 2005

Day 4

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
1
3 things today                                                   i
1. Work the sample problems
2. z scores and “area under the curve”
3. Start to look at experimental design

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
2
z scores – table values                                                         i
• z = (X - µ)/σ
• It is often the case that we want to know
“What percentage of the scores are
above (or below) a certain other score”?
• Asked another way, “What is the area
under the curve, beyond a certain point”?
• THIS is why we calculate a z score, and
the way we do it is with the z table, on p.
306 of Hinton.

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
3
Z distribution                                                 i

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
4
z table practice                                                   i
1.      What percentage of scores fall above a z score of
1.0?
2.      What percentage of scores fall between the mean
and one standard deviation above the mean?
3.      What percentage of scores fall within two standard
deviations of the mean?
4.      My z score is .1. How many scores did I “beat”?
5.      My z score is .01. How many scores did I “beat”?
6.      My score was higher than only 3% of the class. (I
suck.) What was my z score.
7.      Oooh, get this. My score was higher than only 3%
of the class. The mean was 50 and the standard
deviation was 10. What was my raw score?

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
5
The Scientific Method                                                        i

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
6
More than anything else . . .                                                           i
• . . . scientists are skeptical.
• P. 28: 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
7
Research Methods                                                         i
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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Science . . .                                                i
• . . . 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
9
CONTROL                                                     i
• . . . 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
10
More control                                                  i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
11
Key Distinction                                                   i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
12
Operational Definitions                                                        i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
13
Validity and Reliability                                                      i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
14
Theory and Hypothesis                                                           i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
15
Goals of Scientific Method                                                           i
• Description
– Nomothetic approach – establish broad generalizations and
general laws that apply to a diverse population
– Versus idiographic approach – interested in the individual,
their 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.”)
• Understanding – con’t. on next page
• Creating change
– Applied research

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
16
Understanding                                                     i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
17
Confounding                                                    i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
18
Intervening Variables                                                        i
• Link the IV and the DV, and are used to
explain why they are connected.
• Here’s an interesting question: WHY did
the authors put this HERE in the
chapter?
– Because intervening variables are important
in theories.

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
19
A bit more about theories                                                          i
• Good theories provide “precision of
prediction”
• The “rule of parsimony” is followed
– The simplest alternative explanations are
accepted
• A good scientific theory passes the most
rigorous tests
• Testing will be more informative when
you try to DISPROVE (falsify) a theory

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
20
Populations and Samples                                                             i
• 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 | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
21
Ch. 3 -- Ethics                                                  i
• Read the chapter.
• Understand informed consent, p. 57 – a person’s
expressed willingness to participate in a research
project, based on a clear understanding of the nature
of the research, the consequences of declining, and
other factors that might influence the decision.
• Odd quote, p. 69 – Debriefing should be informal and
indirect.
• Know that UT has an IRB:
http://www.utexas.edu/research/rsc/humanresearch/

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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