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52 Cards in this Set

  • Front
  • Back
IV v. DV
IV: form the basis of the groups to be compared, can be manipulated or non-manipulated

DV: outcome measure
true experimental v. quasi-experimental v. observational
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
group v. single subject design
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)
AB v. ABAB v. multiple baseline v. simultaneous treatment v. changing criterion
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
simple random sampling v. stratified random sampling v. proportional sampling v. cluster sampling v. systematic sampling
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
interval recording v. event sampling
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
threats to internal validity
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
threats to construct validity
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
threats to external validity
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)
threats to statistical conclusion validity
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
nominal v. ordinal v. interval v. ratio
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
descriptive v. inferential statistics
descriptive: description of data

inferential: used to make inferences about the general population
mean v. mode v. median
measures of central tendency

mean: average

mode: most frequent score

median: 50th percentile
standard deviation v. range v. variance
measures of variability

standard deviation: average deviation from the mean

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

variance: standard deviation squared
criterion-referenced v. norm referenced scores
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)
z scores
a standard score that corresponds directly to standard deviation with a distribution identical to the raw score distribution
standard error of the mean and central limit theorum
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
null hypothesis v. alternative hypothesis
null hypothesis: states that there are no differences b/w groups

alternative hypothesis: states that there are differences b/w groups
rejection v. retention region
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
alpha v. beta
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)
Type I v. Type II error
Type I: the null is mistakenly rejected

Type II: the null is mistakenly retained
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
t-test v. ANOVA
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
one-way ANOVA v. factorial ANOVA
one-way ANOVA: interval or ratio data are collected for more than two groups of subjects with only 1 IV

factorial ANOVA:
split plot ANOVA v. randomized block ANOVA v. repeated measures ANOVA
split plot ANOVA:
randomized block ANOVA:
repeated measures ANOVA:
MANOVA: more than one DV

main v. interaction effects
main effects: the effect of a single IV

interaction effects: the effect of an interaction b/w the two IVs
trend analysis
when the IV is quantitative, the outcome frequently is nonlinear, trend analysis describes the trends of the data or the ups and downs
bivariate v. multivariate correlation
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)
least squares criterion
Pearson r v. eta v. biserial correlation
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
zero order v. partial v. semipartial correlation
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
multiple R v. canonical v. discriminant v. loglinear correlation
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
a problem that occurs in a multiple regression equation when the predictors are highly correlated with one another and therefore essentially redundant
correlation v. regression
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
path analysis v. LISREL
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
orthogonal v. oblique
concepts related to factor analysis

orthogonal: perpendicular, results in no correlation

oblique: non-perpendicular, results in correlation b/w factors
factor analysis v. cluster analysis
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
reliability v. validity
reliability: amount of consistency, repeatability, and dependability in scores obtained on a given test

validity: meaningfulness, usefulness, or accuracy
test-retest v. parallel form v. internal consistency v. interrater reliability
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
Spearman-Brown prophecy formula
when calculating split-half reliability, use the Spearman-Brown to determine how much more reliable the test would be if it were longer
split-half v. coefficient alpha and Kuder-Richardson
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
standard error of measurement
the standard deviation of a theoretically normal distribution of test scores obtained by one individual on equivalent tests
calculating confidence intervals
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
content v. criterion-related v. construct validity
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)
concurrent v. predictive validity
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
standard error of estimate
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
Taylor-Russell tables
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
false positives v. true positives v. false negatives v. true negatives
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
multi-trait multi-method matrix
a table with information about convergent and divergent validity, both of which are necessary to establish construct validity
convergent v. divergent (discriminant) validity
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
classical test theory v. item response theory (item characteristics curve)
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)