• Shuffle
Toggle On
Toggle Off
• Alphabetize
Toggle On
Toggle Off
• Front First
Toggle On
Toggle Off
• Both Sides
Toggle On
Toggle Off
Toggle On
Toggle Off
Front

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

Play button

Play button

Progress

1/60

Click to flip

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 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.