Research. remembering the great Lee Cronbach April 22 1916-October 1 2001 in 1992, Psychological Bulletin published a list of its 10 most cited articles.

research

remembering the greatLee Cronbach

April 22 1916-October 1 2001

• in 1992, Psychological Bulletin published a list of its 10 most cited articles. Lee Cronbach was first or only author on 4 of the 10

• the trick to doing research is to begin with the question and then to figure out the best way to answer that question.

• the mistake is to begin with the method and fit the question to the method.

an introduction to statistics (cont.)

• research using – measurement description – statistical analysis

critical for answering certain kinds of important questions

strengths of measurement description

• precise descriptions• often efficient—one can make confident

predictions based on relatively small samples—if samples good

• increasingly sophisticated ways of analyzing measurement data

• powerful stat packages now available for desktop computers, e.g, Systat, SPSS, SAS, Resampling Statistics

cautions• measure only what can be measured

– “to replace the unmeasureable with the unmeaningful is not progress” (Achen, 1977)

• value precision but realize that a precise description may not be an accurate one

• remember that scientific method (drawing inferences from observations) comprises many specific methods and that its strength does not come from any one method

my personal recommendations• whatever your Ph.D. Research

Specialization take at least one stat course

• whatever your methodological expertise, find people with similar interests but different methodological expertise and work with them—the best research often uses multiple methods

type I & type II error (revisited)• type I: accepting what is really

false (alpha error)• type II: rejecting as false what

should be accepted (beta error)• decreasing the probability of

one increases the probability of the other

when theory testing• be concerned about Type I errorwhen theory building• be concerned about Type II error

• pointless to talk about Type I and Type II error absent discussion of what is at stake if I am wrong

cost of type I error in theory testing

• dominant theory not challenged• knowledge production stopped

cost of type II error in theory building

• possibly important explanations etc ignored

• knowledge production stopped

this Type I/Type II error discussion one of the many contributions that Lee Cronbach made to doing research, and one of the many challenges he made to the accepted wisdom of the day.

KRATHWOHL

inferential statistics (ch 19)• inferential statistics allow one to infer

the characteristics of a population from a representative sample– from sample one can estimate

characteristics of population within a determined range with a given probability

– determine whether an effect beyond sampling and chance error exists in a study with a given probability

• parameters: refer to population• statistics: refer to sample• sampling distribution:

descriptive statistic calculated from repeated sampling

• confidence intervals: range that includes the population value with a given probability (based on standard error of measurement)

confidence level: • the probability that the interval will

contain the population value (conventionally 68, 95, and 99%, or 2 to 1, 19 to 1, 99 to 1 respectively, or +1 SE, + 1.96 SE, +2.58 SE respectively )

• the wider the interval the more certain it contains the population value (and the less valuable the information becomes)

hypothesis testing (traditionally takes form of rejecting the null hypothesis, i.e., that there is no effect beyond sampling and chance error)

alpha level: the risk the result is due to chance; set by the researcher in advance, traditionally .10, .05, .01, .001

p-level: the actual probability level found, which is then compared to the alpha level

two-tailed test: • non-directional, puts the alpha

level at both ends. Used when one does not expect results in one direction

one-tailed test: • directional, puts alpha level at one

end (determined by researcher). Increases probability of finding statistically significant result

common statistical testst test of difference between means• common and simple test for

differences between means of two groups

chi-square• common test for categorical data and

frequencies—are cell values different from what would be expected

ANOVA (analysis of variance)• commonly used in experimental

designs where two or more groups or multiple conditions are being compared (thus common in psychology and ed psych, and in educational research in general)

• powerful: more accurate measure of error variance, tests significance of each variable as well as combined effect, avoids inflation of probabilities problem

errors of inference• type I error: a concern when theory

testing (K, “when validating a finding”)

• type II error: a concern when theory building (K: “when exploring”)

statistical power: 1-beta • increasing statistical power:

– increase size of effect (stronger treatment)

– increase sample size– reduce variability

statistical & practical significance

• statistical: confidence at a given probability that the result is not due to chance

• practical: is the result important enough, big enough, feasible, affordable—all value judgments– if one apple a day keeps the

doctor away, but it takes three grapefruit, then…?

• no statistic or statistical test can make practical decision

• whether one risks being wrong cautiously (type I) or wrong incautiously (type II) cannot be decided absent cost and risk, what’s at stake, needs etc

• no statistical analysis better than the numbers (descriptions) fed into it: garbage in, garbage out

statistical significance refers only to samples from population

• it does not refer to size of effect—ceteris paribus larger effects are more likely to be statistically significant, but with large samples very small effects will be

• if you have the population, then any effects are real no matter what the size

no proof in science:• a statistically significant result

(assuming appropriate analysis etc) does not prove that the hypothesis is true, only that it has escaped disconfirmation

• the more often an hypothesis passes the test and the more demanding the tests it passes, then the more certain we can be that we know something—the more we have reduced uncertainty

other terms• parametric: assumes random

sampling, from distribution with known parameters, often normal distribution

• nonparametric: when data do not come from known distribution—often with nominal or ordinal data

• robust test: accurate even when assumptions violated

• effect size: too often ignored—journals now requiring estimates of effect size.

terms you should look up in Vogt• effect size• emic & etic• endogenous and exogenous

variables• face validity• file drawer problem• gambler’s fallacy• halo effect or bias• hold constant• independence• interaction effect

ethics

Sieber ch 6: Strategies for Assuring Confidentiality

6.1 Confidentiality refers to agreements with people about what can be done with data

• states steps will be taken to insure privacy

• states legal limitations to assurances of confidentiality

6.2 why an issue (be able to discuss the cases)

6.3 confidentiality or anonymity6.4 procedural approaches to assuring

confidentiality6.4.1 cross-sectional research

– anonymity– temporarily identified responses– separately identified responses

6.4.2 longitudinal data (requires links)– aliases

6.4.3 interfile linkage6.5 statistical strategies for assuring

confidentiality (coin flip example)6.6 certificates of confidentiality

– researchers do NOT have testimonial privilege unless they have certificate of confidentiality from Dept of Health and Human Services

6.7 confidentiality and consent:– consent statement must specify

promises of confidentiality researcher cannot make—be aware of state reporting laws, e.g., on child abuse

6.8 data sharing– when data shared publicly, all

identifiers must be removed and researcher must ensure no way to deduce identity

– techniques

simple statistical way to find out what people may not willing to admit

• ask people to flip coin– if head, answer “don’t know”– if tail and have done X, answer

“don’t know”– if tail and have not done X, answer

“no”• thus, no’s an estimate of half who have

not done x• thus, N minus twice the number of “no’s”

gives estimate of those who have done X

case 3• what did you learn from reading

this case?• how would your write this case

differently?• do you think that this case is

realistic?• what should our hero do?

writingwriting

general style rules and tips

use active voice• I interviewed the kids. (good)• The kids were interviewed. (bad)use first person to talk about

yourself• I interviewed the kids. (good)• The researcher interviewed the kids.

(bad)do not begin sentences with “there

is” or “it is” etc.• There were three kids who

answered… (bad)• Three kids answered the questions.

(good)

use who for people, that for things• I interviewed the kids, who all

agreed….(good)• I interviewed the teacher that was

in…. (bad)pronouns must refer to nouns• I entered the room and found the

kids running across the table tops and throwing erasers at each other. That made me nervous. (bad—not clear what made you nervous)

use comma to separate clauses in compound sentence joined by a conjunction (e.g., and, but) I interviewed the kids, but they did not appear comfortable.

introductory adjectival phrases must modify the subject

• Rushing into the room, the class had already begun. (bad)

• Rushing into the room, I discovered that the class had already begun. (good)

use “Harvard comma”• apples, pears, and bananas (good)• apples, pears and bananas (bad)find the right word• Mark Twain observed that the

difference between the right word and the almost right word is the difference between lightning and a lightning bug.

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