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65 Cards in this Set
- Front
- Back
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Mu
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population mean
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Sigma
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population standard deviation
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Discriminant Validity
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when a test does NOT correlate significantly with measures of different constructs
evidence of a test's construct validity |
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Cluster Sampling
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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 |
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Differential Validity
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a test's validity coefficient for one group is different from its validity coefficient for another group
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Congruent Valiidty
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when a test correlates highly with an established test that measures the same trait
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Bayes Theorem
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theory re: statistical probability
describes the likelihood of certain occurences given the likelihood of other occurences: If cancer is related to age, information about age can be used to more accurately assess his or her chance of having cancer using Bayes' Theorem. |
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Latin Square
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most sophisticated counterbalancing design, controls for carryover effects when repeated measures are used
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Solomon 4 Group Design
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controls for effects of testing/practice
Group 1: pretest/tx/posttest Group 2: pretest/ posttest Group 3: tx/posttest Group 4: postest |
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Item Response Theory (IRT)
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aka latent trait theory; used to establish a uniform scale for individuals of varying ability with items of varying difficulty EX: GRE
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 |
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Interval Sampling
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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)
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Standard Error of Measurement
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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 |
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Central Limit Theorem
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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 |
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Power
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1 - beta; the ability to reject a false Ho
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Ways to increase power:
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increasing alpha
increasing N increasing effect size (by strengthening the IV) minimizing error using a one tailed test using a parametric test |
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Type II Error
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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 |
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Biserial Correlation Coefficient
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used when 1 variable is an artificial dichotomy (made from a continuous variable) & the other variable is continuous
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Point Biserial Correlation
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used when 1 variable is a true dichotomy & the other is continuous
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Spearman Rho
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used to measure association between measures expressed as ranks
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summative evaluation
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type of program evaluation
conducted after a program has been administered to determine if the program goals were achieved |
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Formative Evaluation
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type of program evaluation
conducted during the development of a program to determine how the program should be altered to make it more effective |
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standard error of the mean
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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 |
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Incremental Validity
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benefits that use of a test provides to decision-making accuracy
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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) |
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Coefficient of Determination
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proportion of variance shared between 2 variables
formula for variability shared between 2 variables = correlation coefficient squared It is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable. |
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Eigenvalues
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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 |
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Trend Analysis
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type of ANOVA used to assess linear & nonlinear trends when the IV is quantitative
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Spearman Brown formula
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used to estimate the effects of increasing or decreasing the length of a test on its reliability coefficient
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KR-20
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Kuder Richardson Formula 20
a method for assessing internal consistency reliability when test items are scored dichotomously a higher score = a more homogeneous test |
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Mediators
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explain why there is a relation between the predictor & criterion
when controlled for, the correlation between the DV & IV goes down close to 0 |
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Moderators
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variables that influence the strength of the relation between 2 other variables
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Homoscedasticity
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similar variability among groups or data
an assumption of parametric tests & bivariate correlation coefficients This assumption means that the variance around the regression line is the same for all values of the predictor variable (X). The plot shows a violation of this assumption. |
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formula for the relation between validity & reliability
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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 |
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Rosenthal Effect
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self fulfilling prophecy; refers to the tendency of experimenters to inject their bias into the experiment so that it comes out fulfilling their hypotheses
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Empirical Criterion Keying
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items are chosen based on their ability to discriminate group membership
used in development of the original MMPI |
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Cluster Analysis
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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 |
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ways to increase a test's reliability:
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* more items on the test
* more homogeneous items * unrestricted range of scores (results from a more heterogeneous sample) * difficulty of guessing |
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Construction of Confidence Intervals
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99% = +/- 3 SEM's
95% = +/- 2 SEM's 68% = +/- 1 SEM |
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ANOVA
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used when there is 1 IV & 1 DV
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ANCOVA
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used to control for or partial out a confounding variable
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Factorial ANOVA
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used when there are 2 or more IV's (side note: Use regression when you aren't sure whether the independent categorical variables have any effect at all. Use ANOVA when you want to see whether particular categories have different effects
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MANOVA
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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) |
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Kuder Richardson
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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
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measures of inter-rater reliability
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Pearson r, percentage agreement, Kappa, Yule's Y
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Standard Error of the Estimate
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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) |
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Correction for attenuation formula
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used to determine how much the criterion-related validity coefficient would increase if both the predictor (test) & criterion (outcome) were perfectly reliable
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Split plot ANOVA
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used with mixed design of within- & between-subjects variables (e.g., time & treatment type), usually used with repeated measures
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Tetrachoric coefficient
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measures asociation between 2 artificial dichotomous variables
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Phi coefficient
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measures association between 2 true dichotomous variables
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Multiple correlation (Multiple R)
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measures association between 2 or more predictors and 1 continuous criterion
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Canonical correlation
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extensionof multiple regression that is used when two or more continuous predictors areused to predict status on two or more continuous criteria.
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Orthogonal
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variables are not correlated
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Oblique
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variables are correlated
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Orthogonal Rotation
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in factor analysis, results in uncorrelated factors
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Oblique Rotation
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in factor analysis, results in correlated factors
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F ratio
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in ANOVA, equals the variance between groups (due to treatment + error) divided by the variance within groups (error)
* WhenHo is true, F=1; when Ho is false F>1. the larger the F ratio, the morelikely it is to be significant. Stat=F, DF=(C-1)(N-1) where C=number of levelsof IV and N=number of subjects. F cannot be 0 or less because it is a ratio F-value of between 0 and 1—that happens, of course, whenthe denominator (the within-group variance) is larger than the numerator (thebetween-group variance). |
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Eta
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a way to measures effect size in ANOVA
*coefficient to use to measure a curvilinear relationship |
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Multi-trait Multi-method Matrix
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used to determine a test's construct validity (both divergent & convergent)
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Communality
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communality is the extent to which an item correlates with all other items. Higher communalities are better. If communalities for a particular variable are low (between 0.0-0.4), then that variable may struggle to load significantly on any factor.
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Latent Class Analysis
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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. |
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Heteroscedasticity |
refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it |
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receiver operating curve I f you increase sensitivity then...? What is the x and y on an ROC What is the area under the ROC curve? |
Plots the true positive rate (sensitive) against the False positive rate (1-specificity) More False negatives, higher positive hit rate, decrease in specificity (ture negative rate) Specificity will decrease X=True positives y=False positives= the area under the ROC curve |
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Confusion matrix |
True condition True positive False positive (Type I error) Predicted False negative True Negative (Type II) |
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What are the DF for (paired/Correlated) dependent samples t-test. Example 10 astronaut's reaction time get measured one day at 75 cabin pressure and another and 95 cabin pressure? or weight before and after a workout plan comparing pre and post of the individual. Significant difference between husband and wife's income? |
n = number of pairs n-1 10-1=9 |
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DF for independent t-test Class A had 25 students with an average score of 70, standard deviation 15. Class B had 20 students with an average score of 74, standard deviation 25. Using alpha 0.05, did these two classes perform differently on the tests? |
n1+n2-2= n-2 or (n1-1)+ (n2-1)= n-2 n=number of subjects total df= 43 |
