Psych Prep Key Terms- Stats

Front 1

IV v. DV

Back 1

IV: form the basis of the groups to be compared, can be manipulated or non-manipulated

DV: outcome measure

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true experimental v. quasi-experimental v. observational

Back 2

true experimental: at least one IV is manipulated and subjects are randomly assigned

quasi-experimental: at least one IV is manipulated but there is non-random assignment

observational: no intervention or manipulation

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group v. single subject design

Back 3

group (nomothetic): can be b/w groups when comparing independent groups, or with/in subjects when comparing correlated or related groups (e.g., matched subjects or repeated measurement)

single subject (idiographic): one or very few subjects are studied and are measured many times during baseline and treatment (e.g., AB, ABAB, multiple baseline)

Front 4

AB v. ABAB v. multiple baseline v. simultaneous treatment v. changing criterion

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AB: baseline condition followed by tx condition

ABAB: baseline, tx, baseline, tx

multiple baseline: tx is applied sequentially or consecutively across subjects, situations, or bx's

simultaneous treatment: two or more tx's alternated during tx phase

changing criterion: attempt to change bx in increments to match a changing criterion

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simple random sampling v. stratified random sampling v. proportional sampling v. cluster sampling v. systematic sampling

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simple random sampling: every member of the population has equal chance

stratified random sampling: population is divided into strata and then a random sample of equal size selected from each stratum

proportional sampling: individuals are randomly selected in proportion to their representation in the general population

cluster sampling: random selection of naturally occuring clusters

systematic sampling: selecting every kth element after a random start

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interval recording v. event sampling

Back 6

interval recording: also called time sampling, a type of bx measurement that involves breaking the period of interest into samller periods of time and observing whether the bx occurs the entire interval (whole-interval) or at all (momentary)

event sampling: tallying the number of times the target bx occured

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threats to internal validity

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hx: specific incidents that intervene b/w measuring points

maturation: time passing

testing: familiarity w/ testing

instrumentation: changes in observers or calibration of equipment

statistical regression: tendency for extreme scores to become less extreme

selection: non-random assignment

attrition: differential loss of subjects

diffusion: when the non-tx group gets some tx

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threats to construct validity

Back 8

attention and contact with clients: difficult to tell whether the change is due to the technique or contact w/ the therapist

experimenter expectancies (Rosenthal effect): cues or clues transmitted to subjects by experimenter

demand characteristics: factors in the procedures that suggest how the subject should behave

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threats to external validity

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sample characteristics: differences b/w the sample and the population

stimulus characteristics: artificiality of research arrangements

contextual characteristics: the conditions in which the intervention is imbedded such as reactivity which involves the subjects behaving in a certain way b/w they are subjects (e.g., Hawthorne effect)

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threats to statistical conclusion validity

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low power: a diminished ability to find significant results (e.g., small sample size, inadequate interventions)

unreliability of measures

variability in procedures: inconsistency in tx procedures

subject heterogeneity

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nominal v. ordinal v. interval v. ratio

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nominal: categories with no inherent order

ordinal: ordered categories

interval: equal intervals b/w scores, no absolute zero

ratio: equal intervals and an absolute zero

Front 12

descriptive v. inferential statistics

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descriptive: description of data

inferential: used to make inferences about the general population

Front 13

mean v. mode v. median

Back 13

measures of central tendency

mean: average

mode: most frequent score

median: 50th percentile

Front 14

standard deviation v. range v. variance

Back 14

measures of variability

standard deviation: average deviation from the mean

range: the difference b/w the highest and lowest value

variance: standard deviation squared

Front 15

criterion-referenced v. norm referenced scores

Back 15

criterion-referenced: demonstrates how a person scored relative to a particular criterion (e.g., percentage correct)

norm-referenced: demonstrates how a person scored relative to a group (e.g., percentagile rank)

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z scores

Back 16

a standard score that corresponds directly to standard deviation with a distribution identical to the raw score distribution

Front 17

standard error of the mean and central limit theorum

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SEM: if one were to plot the means of an infinate number of equal sized samples, the deviation in the means would be error

central limit theorum: if one were to plot the means of an infinite number of equal sized samples, a normally distributed distribution of means will result

Front 18

null hypothesis v. alternative hypothesis

Back 18

null hypothesis: states that there are no differences b/w groups

alternative hypothesis: states that there are differences b/w groups

Front 19

rejection v. retention region

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rejection region: region at the tail end of the curve, if the mean falls in this region, the researcher must reject the null b/c it is unlikely that this mean was obtained by chance

retention region: the null must be retained if the mean falls in this region

Front 20

alpha v. beta

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alpha: the size of the rejection region, directly corresponds to the likelyhood of making a Type I error

beta: the probability of making a Type II error (Power = 1 - beta)

Front 21

Type I v. Type II error

Back 21

Type I: the null is mistakenly rejected

Type II: the null is mistakenly retained

Front 22

power

Back 22

1 - beta, the ability to correctly reject the null, increased when sample size is large, the magnitude of the intervention is large, random error is small, the statistical test is parametric, and the test is one-tailed

Front 23

t-test v. ANOVA

Back 23

t-test: interval or ratio data are collected for one or two groups of subjects

ANOVA: interval or ratio data are collected for more than two groups of subjects

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one-way ANOVA v. factorial ANOVA

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one-way ANOVA: interval or ratio data are collected for more than two groups of subjects with only 1 IV

factorial ANOVA:

Front 25

split plot ANOVA v. randomized block ANOVA v. repeated measures ANOVA

Back 25

split plot ANOVA:
randomized block ANOVA:
repeated measures ANOVA:

Front 26

MANOVA v. ANCOVA

Back 26

MANOVA: more than one DV

ANCOVA:

Front 27

main v. interaction effects

Back 27

main effects: the effect of a single IV

interaction effects: the effect of an interaction b/w the two IVs

Front 28

trend analysis

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when the IV is quantitative, the outcome frequently is nonlinear, trend analysis describes the trends of the data or the ups and downs

Front 29

bivariate v. multivariate correlation

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bivariate correlation: looks at the relationship b/w two variables where neither is an IV is the truest sense, x predicts y

multivariate correlation: involves several predictors(IVs) and one or more criterions (DVs)

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least squares criterion

Back 30

A

Front 31

Pearson r v. eta v. biserial correlation

Back 31

Pearson r: a measure of how well a linear equation describes the relationship b/w X and Y when both X and Y are interval or ratio

eta: used to calculate the correlation b/w x and y when it is thought that they have a curvilinear relationship

biserial correlation: a measure of how well a linear equation describes the relationship b/w X and Y when X is interval or ratio and Y is dichotomous

Front 32

zero order v. partial v. semipartial correlation

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zero order: analyzes the relationship b/w X and Y when there are no extraneous variables affecting the relationship

partial: analyzes the relationship b/w X and Y with the effect of a third variable being removed

semipartial correlation: analyzes the relationship b/w X and Y with the effect of a third variable being removed from only one of the origonal variables

Front 33

multiple R v. canonical v. discriminant v. loglinear correlation

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multiple R: a correlation b/w two or more IVs and one DV where Y is always interval or ratio and at least one X is interval or ratio, squaring multiple R generates the coefficient of determination

canonical R: a correlation between two or more IVs and two or more DVs

discriminant: two or more predictors and one criterion which is nominal

loglinear: used to predict a categorical criterion based on categorical predictors

Front 34

multicollinearity

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a problem that occurs in a multiple regression equation when the predictors are highly correlated with one another and therefore essentially redundant

Front 35

correlation v. regression

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correlation: measures the relationship b/w two variables where neither is an IV in the truest sense

regression: describes the relationship b/w two variables where neither is an IV in the truest sense by creating the line of best fit

Front 36

path analysis v. LISREL

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path analysis: applies multiple regression techniques to testing a model that specifies causal links among variables

LISREL: enables researchers to make inferences about causation, can be used to test many different causal pathways involving multiple predictors and criterion variables

Front 37

orthogonal v. oblique

Back 37

concepts related to factor analysis

orthogonal: perpendicular, results in no correlation

oblique: non-perpendicular, results in correlation b/w factors

Front 38

factor analysis v. cluster analysis

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factor analysis: extracts significant factors (dimensions) from the data

cluster analysis: statistically looking for naturally occuring subgroups in data collected on a variety of dependent variables

Front 39

reliability v. validity

Back 39

reliability: amount of consistency, repeatability, and dependability in scores obtained on a given test

validity: meaningfulness, usefulness, or accuracy

Front 40

test-retest v. parallel form v. internal consistency v. interrater reliability

Back 40

test-retest: correlating pairs of scores from the same sample of people who are administered the identical test at 2 points in time

parallel form: correlating the scores obtained by the same group of people on 2 roughly equivalent but not identical forms of the same test administered at 2 different points in time

internal consistency: consistency of scores w/in the test, includes split-half and kuder-richardson or chronback's alpha (correlation of each item w/ every other item)

interrater reliability: degree of agreement b/w 2 or more scorers when a test is subjectively scored

Front 41

Spearman-Brown prophecy formula

Back 41

when calculating split-half reliability, use the Spearman-Brown to determine how much more reliable the test would be if it were longer

Front 42

split-half v. coefficient alpha and Kuder-Richardson

Back 42

types of internal consistency reliability

split-half: calculated by splitting the test in half and then correlating the scores obtained on each half by each person

coefficient alpha and Kuder-Richardson: analyze the correlation of each item with every other item in the test

Front 43

standard error of measurement

Back 43

the standard deviation of a theoretically normal distribution of test scores obtained by one individual on equivalent tests

Front 44

calculating confidence intervals

Back 44

the standard error of measurement is added and subtracted from the actual score once for 68% confidence, twice for 95% confidence, and three times for 99% confidence

Front 45

content v. criterion-related v. construct validity

Back 45

content: how adequately a test samples a particular content area

criterion-related: how adequately a test score can be used to infer, predict, or estimate criterion outcome (concurrent or predictive)

construct validity: how adequately a new test measures a construct or trait (convergent or divergent)

Front 46

concurrent v. predictive validity

Back 46

concurrent validity: the predictor and criterion are measured and correlated at about the same time

predictive validity: there is a delay b/w the measurement of the predictor and the criterion

Front 47

standard error of estimate

Back 47

the standard deviation of a theoretically normal distribution of criterion scores obtained by one person measured repeatedly, maximum value is the SD of the criterion

Front 48

Taylor-Russell tables

Back 48

numerically describe the amount of improvement that occurs in selection decisions when a predictor test is introduced

base rate: the rate of selecting successful employees w/o using a predictor test

selection ratio: the proportion of available openings to available applicants

incremental validity: the amount of improvement in success rate that results from using a predictor test

Front 49

false positives v. true positives v. false negatives v. true negatives

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false positives: those incorrectly identified as possessing what is being measured

true positives: those correctly identified as possessing what is being measured

false negatives: those incorrectly identified as not possessing what is being measured

true negatives: those correctly identified as not possessing what is being measured

Front 50

multi-trait multi-method matrix

Back 50

a table with information about convergent and divergent validity, both of which are necessary to establish construct validity

Front 51

convergent v. divergent (discriminant) validity

Back 51

convergent validity: the correlation of scores on the new test with other available measures of the same trait, must be moderate to high

divergent (discriminant) validity: the correlation of scores on the new test with scores on another test that measures a different trait or construct, should be low

Front 52

classical test theory v. item response theory (item characteristics curve)

Back 52

classical test theory: any obtained score is a combination of truth and error (reliablity concept)

item response theory (item characteristics curve): used to calculate to what extent a specific item on a test correlations with an underlying contstruct (validity concept)