i INF 397C Introduction to Research in Library and Information Science Fall, 2003 Day 5 R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 1 The Scientific Method R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 2 i • 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.” R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 3 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 4 Science . . . i • . . . 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 5 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 6 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 7 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.” R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 8 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 9 Validity and Reliability i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 10 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 11 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 12 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 13 Confounding i • When two potentially effective IVs are allowed to covary simultaneously. • Poor control! R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 14 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 15 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 16 Populations and Samples i • Population: the set of all cases of interest • Sample: Subset of all the population that we choose to study. Population Parameters Sample Statistics R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 17 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 18 Ch. 7 – Independent Groups Design i • 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.” R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 19 Good Paragraph i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 20 Good page – P. 197 i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 21 Logic of Experimental Research i • Researchers manipulate an independent variable in an experiment to observe the effect on behavior, as assessed by the dependent variable. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 22 Independent Groups Design i • Each group represents a different condition as defined by the independent variable. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 23 Random . . . i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 24 Let’s step back a minute i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 25 Block Randomization i • 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.) R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 26 Challenges to Internal Validity i • 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 doubleblind procedure • Experimenter effects – use double-blind procedure R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 27 Role of Data Analysis in Exps. i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 28 Two methods of making inferences i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 29 What data can’t tell us i • 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.) R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 30 This is HUGE. i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 31 Notice i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 32 Sources of variance i • 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.” R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 33 One way to reduce the error variance i • 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. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 34 Another design i • 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). R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 35 We’ve been talking about . . . i • 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? R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 36 Themselves! i • 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.) R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 37 Repeated Measures i • 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 R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 38 Counterbalancing i • ABBA • Used to overcome order effects. • Assumes practice/fatigue effects are linear. • Some incomplete counterbalancing ideas are offered in the text. R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 39 Which method when? i • 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? R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 40 Hinton typo i • P. 62, para. 1: “. . . population standard deviation, µ, divided by . . . .” R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 41 Some questions we’d like to ask Nature R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu i 42 Midterm i • Emphasize – – – – 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 R. G. Bias | School of Information | SZB 562BB | Phone: 512 471 7046 | rbias@ischool.utexas.edu 43