Study your flashcards anywhere!
Download the official Cram app for free >
 Shuffle
Toggle OnToggle Off
 Alphabetize
Toggle OnToggle Off
 Front First
Toggle OnToggle Off
 Both Sides
Toggle OnToggle Off
 Read
Toggle OnToggle Off
How to study your flashcards.
Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key
Up/Down arrow keys: Flip the card between the front and back.down keyup key
H key: Show hint (3rd side).h key
A key: Read text to speech.a key
60 Cards in this Set
 Front
 Back
Mu

population mean


Sigma

population standard deviation


Discriminant Validity

when a test does NOT correlate significantly with measures of different constructs
evidence of a test's construct validity 

Cluster Sampling

identifying naturally occurring groups or clusters & then randomly selecting certain of these clusters
typically all subjects within selected clusters are sampled; but subjects may be randomly selected from the selected clusters 

Differential Validity

a test's validity coefficient for one group is different from its validity coefficient for another group


Congruent Valiidty

when a test correlates highly with an established test that measures the same trait


Bayes Theorem

theory re: statistical probability
describes the likelihood of certain occurences given the likelihood of other occurences 

Latin Square

most sophisticated counterbalancing design, controls for carryover effects when repeated measures are used


Solomon 4 Group Design

controls for effects of testing/practice
Group 1: pretest/tx/posttest Group 2: pretest/ posttest Group 3: tx/posttest Group 4: postest 

Item Response Theory (IRT)

aka latent trait theory; used to establish a uniform scale for individuals of varying ability with items of varying difficulty
used to calculate to what extent a specific item on a test correlates with an underlying construct subject's performance on a test represents degree to which subject has a latent trait can be used to compare a subjects's performance on 2 measures that are diferent in scoring or # of items 

Interval Sampling

behavioral sampling used when a behavior has no distinct beginning or end (record whether a behavior occurred during each of a series of time intervals)


Standard Error of Measurement

average amount of error in each data point of a certain variable
SEmeas = SD x square root of 1 rxx example: average amount of error in each person's IQ score 

Central Limit Theorem

derived from probability theory
states that the sampling distribution of the mean: 1. will approach a normal shape as sample size increases regardless of the shape of the population distribution of individual scores 2. has a mean equal to the population mean 3. has a SD equal to the population SD divided by the square root of the sample size 

Power

1  beta; the ability to reject a false Ho


Ways to increase power:

increasing alpha
increasing N increasing effect size (by strengthening the IV) minimizing error using a one tailed test using a parametric test 

Type II Error

retaining Ho when it is false
probability = beta more likely when alpha is low, when N is small, & when the IV isn't intense enough 

Biserial Correlation Coefficient

used when 1 variable is an artificial dichotomy (made from a continuous variable) & the other variable is continuous


Point Biserial Correlation

used when 1 variable is a true dichotomy & the other is continuous


Spearman Rho

used to measure association between measures expressed as ranks


summative evaluation

type of program evaluation
conducted after a program has been administered to determine if the program goals were achieved 

Formative Evaluation

type of program evaluation
conducted during the development of a program to determine how the program should be altered to make it more effective 

standard error of the mean

estimate of how much a sample mean can be expected to differ from the population mean as the result of sampling error
calculated by dividing the population SD by the square root of the sample size 

Incremental Validity

benefits that use of a test provides to decisionmaking accuracy


pvalue
in item response theory 
characteristics of each item are described with an item response curve
pvalue: probability of getting the item correct (# of examinees who answered an item correctly / total # of examinees) 

Coefficient of Determination

proportion of variance shared between 2 variables
formula for variability shared between 2 variables = correlation coefficient squared 

Eigenvalues

can be calculated for each component extracted in a principal components analysis
indicates the total amount of variability in a set of tests or other variables that is explained by an identified component or factor 

Trend Analysis

type of ANOVA used to assess linear & nonlinear trends when the IV is quantitative


Spearman Brown formula

used to estimate the effects of increasing or decreasing the length of a test on its reliability coefficient


KR20

Kuder Richardson Formula 20
a method for assessing internal consistency reliability when test items are scored dichotomously a higher score = a more homogeneous test 

Mediators

explain why there is a relation between the predictor & criterion
when controlled for, the correlation between the DV & IV goes down close to 0 

Moderators

variables that influence the strength of the relation between 2 other variables


Homoscedasticity

similar variability among groups or data
an assumption of parametric tests & bivariate correlation coefficients 

formula for the relation between validity & reliability

validity is less than or = square root of reliability
(reliability is a decimal, square root of a decimal is a larger number) a test with reliability of .25 could have a validity of up to .50 

Rosenthal Effect

self fulfilling prophecy; refers to the tendency of experimenters to inject their bias into the experiment so that it comes out fulfilling their hypotheses


Empirical Criterion Keying

items are chosen based on their ability to discriminate group membership
used in development of the original MMPI 

Cluster Analysis

gathering data on a number of DV's & statistically looking for naturally occurring subgroups without any prior hypotheses
used to identify homogeneous groups from a collection of observations 

ways to increase a test's reliability:

* more items on the test
* more homogeneous items * unrestricted range of scores (results from a more heterogeneous sample) * difficulty of guessing 

Construction of Confidence Intervals

99% = +/ 3 SEM's
95% = +/ 2 SEM's 68% = +/ 1 SEM 

ANOVA

used when there is 1 IV & 1 DV


ANCOVA

used to control for or partial out a confounding variable


Factorial ANOVA

used when there are 2 or more IV's


MANOVA

used when there is >1 DV
less powerful than running separate ANOVA's (i.e., it's easier to find significance with separate ANOVA's but also have a greater chance of Type I error) 

Kuder Richardson

measures internal consistency by analyzing all possible split halves of a test; split half reliability creates 2 shorter tests, therefore, SpearmanBrown is needed to correct for the decreased number of items


measures of interrater reliability

Pearson r, percentage agreement, Kappa, Yule's Y


Standard Error of the Estimate

measure of the accuracy of predictions made with a regression line:
SEest = SDy√1(rxy)2 ranges from 0 (no error) to SD of y (lots of error) 

Correction for attenuation formula

used to determine how much the criterionrelated validity coefficient would increase if both the predictor (test) & criterion (outcome) were perfectly reliable


Split plot ANOVA

used with mixed design of within & betweensubjects variables (e.g., time & treatment type)


Tetrachoric coefficient

measures asociation between 2 artificial dichotomous variables


Phi coefficient

measures association between 2 true dichotomous variables


Multiple correlation (Multiple R)

measures association between 2 or more predictors and 1 continuous criterion


Canonical correlation

measures association between 2 or more predictors and 2 or more criterions


Orthogonal

variables are not correlated


Oblique

variables are correlated


Orthogonal Rotation

in factor analysis, results in uncorrelated factors


Oblique Rotation

in factor analysis, results in correlated factors


F ratio

in ANOVA, equals the variance between groups (due to treatment + error) divided by the variance within groups (error)
* when groups are not different, the F ratio = 1, no significance * when an F ratio gets above 2, there is generally significance 

Eta

coefficient to use to measure a curvilinear relationship


Multitrait Multimethod Matrix

used to determine a test's construct validity (both divergent & convergent)


Communality

the total amount of variability accounted for in a test by the identified factors and is a reflection of the amount of variance that the test has in common with the other measures included in the factor analysis


Latent Class Analysis

used to identify the underlying latent structure of a set of observed data
Latent trait analysis (LTA) is also used to identify the underlying latent structure of a set of observed data. A primary difference between the two techniques is that, in LCA, the latent variable that determines the structure is nominal; while, in LTA, the latent variable is continuous. 