R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 1

i

INF 397CIntroduction 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 2

i3 things today

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 3

iz scores – table values

• 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 4

iZ distribution

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

iz table practice

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 6

iThe Scientific Method

R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 7

iMore than anything else . . .

• . . . 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 8

iResearch Methods

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 9

iScience . . .

• . . . 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 10

iCONTROL

• . . . 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 11

iMore control

• 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 12

iKey Distinction

• 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 13

iOperational Definitions

• 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 14

iValidity and Reliability

• 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 15

iTheory and Hypothesis

• 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 16

iGoals of Scientific Method

• 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 17

iUnderstanding

• 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 18

iConfounding

• 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 19

iIntervening Variables

• 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 20

iA bit more about theories

• 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 21

iPopulations and Samples

• 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 22

iCh. 3 -- Ethics

• 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/