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William Wundt (271)
 1879
 founded first psychology laboratory

Hermann Ebbinghaus (271)
showed that higher mental processes could be studied using experimental methodology

Oswald Kulpe (271)
 disagreed with Wundt fundamentally  Wundt believed that whenever you thought of something an image of that thing formed in your mind (there could no thought without a mental image)
 performed experiments to prove that there could be imageless thoguht

James McKeen Cattell (272)
studied under Wundt; introduced mental testing to the U.S.

Binet (272)
of BinetSimon
 in 1905, collaborated with Simon to publish the first intelligence test
 introduced the concept of "mental age" or the age level at whic a person functions intellectually, regardless of his actual chronological age

BinetSimon test (272)
 first intelligence test
 use to assess the intelligence of French school children to ascertain which children were too mentally retarded to benefit from ordinary schooling

William Stern (272)
developed an equation to compare mental age to chronological age (which came to be known as the intelligence quotient, or IQ)

Lewis Terman (272)
in 1916, he revised the BinetSimon test for use in the U.S.  it was called the StanfordBinet Intelligence test

hypothesis (272)
 the first setp in research design involves indentifying a problem to study and forming this
 tentative and testable explanation of the relationship between two or more variables

variable (272)
characteristic or property that varies in amount or kind, and can be measured

operational definitions (272)
how the researcher plans to define the varilables in the experiment so that the variables are measurable

independent variable (273)
variable whose effect is being studied and is the variable that the experimenter manipulates

dependent variable (273)
the response that is expected to vary with differences in the independent variable

3 types of research types (273)
 true experiments
 quasiexperiments
 correlational

corellational study (275)
does not alter the independent variable (IV)

true experiment (274)
researcher uses random assignment and manipulates the IV

quasiexperimental design (274)
 does not use random assignment
 lacks sufficient control over the variables, and therefore, definitive statements on causal factors cannot be made

naturalistic observation (274)
 researcher does not intervene at all in what is being studied, but observes what occurs naturally
 AKA: field study

sample (275)
subset of the population

random selection (275)
each member of the population has an equal chance of being selected for the sample

stratified random sampling (275)
assures that each subgroup of the population is randomly sampled in proportion to its size

representative sample (276)
miniature version of the population

3 types of research designs (276)
 betweensubjects
 matchedsubjects
 withinsubjects (AKA repeated measures)

betweensubjects design (276)
each subject is exposed to only one level of each indpendent variable

matchedsubjects design (276)
 experimenter matches subjects on the basis of a varilable that he/she wants to control (if he/she wants to control for intelligence  he will pick the top 2 most intelligent, split them up, and split the pairs in this fashion until all subjects are assigned to groups)
 pairing ensures that both groups are approximately equal on the matching variable

withinsubjects design (277)
 each subject is exposed to all the conditions (give all subjects all levels of IV)
 this allows the researcher to separate the effects of individual differences of a variable from the effects of the IV
 problem: people may just do better on the second test because they are more familiar with the test format  or they may do worse on the second test because of boredom

what is counterbalancing and why do we use it? (278)
 half of the subjects are assigned to group 1 and then group 2; the other half are assigned to group 2 and then 1
 this is done because of the problems with withinsubjects design (people may just do better on the second test because they are familiar with format or they may do worse on the second test because of boredom)
 all subjects will experience both levels in different orders

confounding principles (278)
unintended independent variables

control group design (279)
treating both groups equally in all respects except that the control group gets no treatment; the experimental group receives treatment

nonequivalent group design (279)
the control group is not necessarily similar to the experimental group since the researcher does not use random assignment

experimenter bias (279)
due to his/her expectations the experimenter might inadvertently treat groups of subjects differently, experimenter might let his/her expectations affect how the results are interpreted

doubleblinding (279)
 one way to control for experimenter bias
 neither the researcher who interacts with the subject nor the subjects themselves know which groups received the IV or which level of the IV

singlebind experimenter (279)
subjects do not know whether they are in the treatment or control group

demand characteristics (279)
refer to any cues that suggest to subjects what the researcher expects from them  if subjects have an idea what the researcher expectes, they will perform as expected

placebo effect (280)
 a kind of demand characteristic
 when people are given a drug, they usually expect that the drug will be effective

Hawthorne effect (280)
tendency of people to behave differently if they know they are being observed

External validity (280)
how generalizable the results of an experiment are

how can you control for experimenter bias? (280)
doubleblinding

how can you control for placebo effect? (280)
control groups

how can you control for Hawthorne effect?
control groups

how can you control for demand characteristics? (280)
deception?

2 basic types of statistics (280)

inferential statistics (280, 288)
 generalize beyond actual observations
 allow us to use a relatively small batch of actual observations to make conclusions about the entire population of interest

descriptive statistics (280)
organizing, describing, quantifying, and summarizing a collection of actual observation

frequency distribution (281)
graphic represenations of how often each value occurs

measures of central tendency (281)

mode (281)
 value of the most frequent observation in a set of scores
 if all values occur with equal frequency, there is no _____

bimodal (281)
when the data has two modes

median (282)
 the middle value when observations are ordered from least to most or from most to least
 not the halfway point of numerical values
 number in the middle of the ranking

mean (282)
the numerical halfway point between the highest score and the lowest score, the arithmetic average

outlier (282)
 extreme scores
 the mean is the measure of central tendency that is most sensitive to these

measures of variablity (282)
 range
 standard deviation
 variance
 AKA dispersion of scores
 range (282)simply t

range (282)
simply the smallest number in the distribution subtracted from the largest number

standard deviation (282)
 provides a measure of the typical distance of scores from the mean
 square root of variance

variance (282)
 standard deviation ^{2}
 description of how much each score varies from the mean

normal distribution (283)
symmetrical bellshaped curve

percentile (283)
percentage of scores that fall at or below a particular score

percentages of the normal distribution (283)
 between 1 and +1 SD  68% (34% between 1 and 0; and between 0 and +1)
 between 2 and +2 SDs 96% (14% between 2 and 1; and between +1 and +2)
 100% between 3 and +3 SDs (2% from 2 to 3; and between +2 and +3)

zscore (284)
 another way of calculating how many standard deviations above or below the mean your score is
 formula = score  mean / SD

if you converted every score in a distribution to a zscore........(285)
 if you have a distribution of zscores and calculate the mean and standard distribution, the mean of the distribution of zscores will always be zero andthe standard deviation will always be 1
 this is true regardless of the mean and the standard deviation of the original distribution

tscores (285)
tscore distribution has a mean of 50 and a standard deviation of 10

correlation coefficients (286)
 a descriptive statistic that measures to what extent, if any, two variables are related
 two variables are related if knowing the value of one variable helps you predict the value of the other variable
 this helps us understand the relationship and degree of association between two variables
 allows us to mathematically specify how well we can predict the value of the second variable given the corresponding value of the first variable
 can only be between 1.00 and +1.00

positive correlation (286)
a change in value of one of the variables tends to be associated with a change in the same direction of the value of the other variable

negative correlation (286)
change in the value of one of the variables tends to be associated with a change in the opposite direction of the other variable

scatterplot (287)
graphical representation of correlational data

factor analysis (288)
attempts to account forthe interrelationships found among various variables by seeing how groups of variables "hang together"

significance test (289)
 a tool researchers use to draw conclusions about populations based upon research conducted on samples
 helps the researcher decide whether the research hypothesis or the null hypothesis is true

null hypothesis (289)
the population mean is the same as the sample mean

statistically significant (290)
if we reject the null hypothesis, the observed difference is ______

criterion of significance (290)
 the researcher decide what probability represents statistical significance before collecting data by establishing this...
 by convention, psychologists usually use 5% (most are willing to reject the nulll hypothesis only if they are very sure that observed differences are not due solely to chance)
 AKA alpha level

Type I error (291)
 rejecting the null hypothesis when it is true (there is no difference, but the researcher thinks there is a difference)
 significant results are due to chance

Type II error (291)
 accepting the null hypothesis when it is false (thinking there is not a difference when there is)
 AKA Beta

types of significance tests (292)
 ttest
 ANOVA
 chisquare test

ttests (292)
used to compare the means of two groups

ANOVA (292)
used to compare the means of more than two groups

chisquare (292)
tests the equality of two frequencies or proportions

metaanalysis (294)
 statistical procedure that can be used to make conclusions on the basis of data from different studies
 can be used to combine the results of these studies and come up with a more general conclusion

normreferenced testing (295)
 assessing an individual's performance in terms of how that individual performs in comparion to others
 one problem: the population to whom the tests will be administered can, and often does, change

domainreferenced testing (295)
 AKA criterionreferenced testing
 concerned with the question of what the test tasker knows about a specified content domain

reliability (295)
 consistency with which a test measures whatever it is that the test measures.
 if it is high, the test measures are dependable, reproducible, and consistent
 standard error

standard error of measurement (295)
 an index of how much, on average, we expect a person's observed score to vary from the score the person is capable of receiving based on actual ability
 the smaller this is, the better

how do we assess reliability? (296)
 testretest
 alternateform
 splithalf

testretest method (296)
 same test is administered to the same group of people twice
 estimates the interindividual stability of test scores over time

alternateform method (296)
 tests reliability
 examinees are given two different forms of a test that are taken at two different times

splithalf reliability (296)
 test takers take only one test, but that one test is divided into equal halves
 scores on one half are correlated with the scores on the other half

validity (296)
concerned with the extent to which a test actually measures what it purports to measure

types of validity (296)
 content
 face
 criterion
 cross validation
 construct  AKA convergent
 discriminant
 predictive

content validity (296)
test's coverage fo the particular skill or knowledge area that it is supposed to measure

face validity (296)
whether or not the test items appear to measure what they are supposed to measure

criterion validity (297)
how well the test can predict an individual's performance on an established test of the same skill or knowledge area

cross validation (297)
testing the criterion validity of a test on a second sample, after you demonstrated validity on an initial sample

construct validity (297)
 how well performance on the tests fits into the theoretical framework related to what it is you want the test to measure
 AKA convergent validity
 in order to show a test has this , researchers also have to show that performance on the test is not correlated with other variables that the theory predicts that test performance should not be related to (discriminat validity)

discriminant validity (297)
 in order to show a test has this , researchers also have to show that
 performance on the test is not correlated with other variables that the
 theory predicts that test performance should not be related to.

