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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
                                                                                                  8
                                     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
                                                                                                  22

				
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