1. LIS 397.1 Introduction to Research in Library and Information Science Summer, 2003 Day 6 and 7

2. More 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.”

3. Today: Research 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.”

4. Science . . .
. . . Is a cumulative affair. Current research builds on previous research.
Scientific Method:
Empirical (acquires new knowledge via direct observation and experimentation)
Systematic, controlled observations.
Unbiased, objective.
Operational definitions.
Valid, reliable, testable, critical, skeptical.

5. CONTROL
. . . 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.

6. More 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).

7. Key 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.”

8. Operational 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: “Pretty women.”
Good” “Women rated ‘pretty’ by at least 50% of the sample.”

9. Validity and Reliability
Validity: the “truthfulness” of a measure. Are you really measuring what you claim to measure? “The validity of a measure is supported to 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.

10. Theory 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.

11. Goals 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

12. Understanding
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.

13. Confounding
When two potentially effective IVs are allowed to covary simultaneously.
Poor control!

14. Intervening 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.

15. A 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

16. Populations and Samples
Population: the set of all cases of interest
Sample: Subset of all the population that we choose to study.
Population
Parameters
Sample
Statistics

17. Ch. 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:

18. Ch. 7 – Independent Groups Design
Description and Prediction are crucial to the scientific study of behavior, but they’re not sufficient for understanding the causes. We need to know WHY.
Best way to answer this question is with the experimental method.
“The special strength of the experimental method is that it is especially effective for establishing cause-and-effect relationships.”

19. Good Paragraph
P. 196, para. 2 – Discusses how experimental methods and descriptive methods aren’t all THAT different – well, they’re different, but related. And often used together.

20. Good page – P. 197 Why we conduct experiments
If results of an experiment (a well-run experiment!) are consistent with theory, we say we’ve supported the theory. (NOT that it is “right.”)
Otherwise, we modify the theory.
Testing hypotheses and revising theories based on the outcomes of experiments – the long process of science.

21. Logic of Experimental Research
Researchers manipulate an independent variable in an experiment to observe the effect on behavior, as assessed by the dependent variable.

22. Independent Groups Design
Each group represents a different condition as defined by the independent variable.

23. Random . . . Random Selection vs. Random Assignment
Random Selection = every member of the population has an equal chance of being selected for the sample.
Random Assignment = every member of the sample has an equal chance of being placed in the experimental group or the control group.
Random assignment allows for individual differences among test participants to be averaged out.

24. Let’s step back a minute
An experiment is personkind’s way of asking nature a question.
I want to know if one variable (factor, event, thing) has an effect on another variable – does the IV influence the DV?
I manipulate some variables (IVs), control other variables, and count on random selection to wash out the effects of all the rest of the variables.

25. Block Randomization Another way to wash-out error variance.
Assign subjects to blocks of subjects, and have whole blocks see certain conditions.
(Very squirrelly description in the book.)

26. Challenges to Internal Validity
Testing intact groups. (Why is the group a group? Might be some systematic differences.)
Extraneous variables. (Balance ‘em.) (E.g., experimenter).
Subject loss
Mechanical loss, OK.
Select loss, not OK.
Demand characteristics (cues and other info participants pick up on) – use a placebo, and double-blind procedure
Experimenter effects – use double-blind procedure

27. Role of Data Analysis in Exps.
Primary goal of data analysis is to determine if our observations support a claim about behavior. Is that difference really different?
We want to draw conclusions about populations, not just the sample.
Two ways – stat and replication.

28. Two methods of making inferences
Null hypothesis testing
Assume IV has no effect on DV; differences we obtain are just by chance (error variance)
If the difference is unlikely enough to happen by chance (and “enough” tends to be p < .05), then we say there’s a true difference.
Confidence intervals
We compute a confidence interval for the “true” population mean, from sample data. (95% level, usually.)
If two groups’ confidence intervals don’t overlap, we say (we INFER) there’s a true difference.

29. What data can’t tell us
Proper use of inferential statistics is NOT the whole answer.
Scientist could have done a trivial experiment.
Also, study could have been confounded.
Also, could by chance find this difference. (Type I and Type II errors – hit this for real in week 5.)

30. This is HUGE.
When we get a NONsignificant difference, or when the confidence intervals DO overlap, we do NOT say that we ACCEPT the null hypothesis.
Hinton, p. 37 – “On this evidence I accept the null hypothesis and say that we have not found evidence to support Peter’s view of hothousing.”
We just cannot reject it at this time.
We have insufficient evidence to infer an effect of the IV on the DV.

31. Notice
Many things influence how easy or hard it is to discover a difference.
How big the real difference is.
How much variability there is in the population distribution(s).
How much error variance there is.
Let’s talk about variance.

32. Sources of variance Systematic vs. Error
Real differences
Error variance
What would happen to the standard deviation if our measurement apparatus was a little inconsistent?
There are OTHER sources of error variance, and the whole point of experimental design is to try to minimize ‘em.
Get this: The more error variance, the harder for real differences to “shine through.”

33. One way to reduce the error variance
Matched groups design
If there’s some variable that you think MIGHT cause some variance,
Pre-test subjects on some matching test that equates the groups on a dimension that is relevant to the outcome of the experiment. (Must have a good matching test.)
Then assign matched groups. This way the groups will be similar on this one important variable.
STILL use random assignment WITHIN the groups.
Good when there are a small number of possible test subjects.

34. Another design Natural Groups design
Based on subject (or individual differences) variables.
Selected, not manipulated.
Remember: This will give us description, and prediction, but not understanding (cause and effect).

35. We’ve been talking about . . .
Making two groups comparable, so that the ONLY systematic difference is the IV.
CONTROL some variables.
Match on some.
Use random selection to wash out the effects of the others.
What would be the best possible match for one subject, or one group of subjects?

36. Themselves!
When each test subject is his/her own control, then that’s called a
Repeated measures design, or a
Within-subjects design.
(And the random groups design is called a “between subjects” design.)

37. Repeated Measures
If each subject serves as his/her own control, then we don’t have to worry about individual differences, across experimental and control conditions.
EXCEPT for newly introduced sources of variance – order effects:
Practice effects
Fatigue effects

38. Counterbalancing ABBA Used to overcome order effects.
Assumes practice/fatigue effects are linear.
Some incomplete counterbalancing ideas are offered in the text.

39. Which method when?
Some questions DO lend themselves to repeated measures (within-subjects) design
Can people read faster in condition A or condition B?
Is memorability improved if words are grouped in this way or that?
Some questions do NOT lend themselves to repeated measures design
Do these instructions help people solve a particular puzzle?
Does this drug reduce cholesterol?

40. Hinton typo
P. 62, para. 1: “. . . population standard deviation, µ, divided by ”

41. Midterm Emphasize De-emphasize Totally ignore for now
How to lie with statistics – concepts
To know a fly – concepts
SZ&Z – Ch. 1, 2, 7, 8
Hinton – Ch. 1, 2, 3, 4, 5
De-emphasize
SZ&Z – Ch. 3
Other readings
Totally ignore for now
SZ&Z – Ch. 14
Hinton – Ch. 6, 7, 8

42. Some questions we’d like to ask Nature
What do principals really know about a school librarian’s job?
Is there a relationship between housing situation and career success?
Have the summers in Austin really gotten hotter?
What percentage of students read books they check out?
Why do cats get in front of you as you walk?
How many times must a student be exposed to a database before he/she can retrieve results?

43. Homework
Study.
Midterm Thursday.
See you tomorrow.