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60 Cards in this Set

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  • 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 decision-making accuracy
p-value
in item response theory
characteristics of each item are described with an item response curve

p-value: 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
KR-20
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, Spearman-Brown is needed to correct for the decreased number of items
measures of inter-rater 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 criterion-related validity coefficient would increase if both the predictor (test) & criterion (outcome) were perfectly reliable
Split plot ANOVA
used with mixed design of within- & between-subjects 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
Multi-trait Multi-method 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.