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Final

Review_for_Final.docx

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School
Department
Psychology
Course
PSYC 305
Professor
All Professors
Semester
Spring

Description
Nonparametric Test1Parametric testaAssumptionsiNormal Distribution1In a normal Distribution mean and variance are called Parameter2Statistical test that assume a distribution and use parameter are called Parametric testiiEqual Variances homogenous variancesiiiEqual covariances homogenous covariances Repeated measures data2Nonparametric TestaDef Statistical test that do not assume a distribution or use parameter are called Nonparametric tests These tests can be use when the underlying parametric tests are questionable Nonnormal data Discrete DataiThis test can be use when1The distribution do not appear to be normally distributed2There is an extreme outlier influence the meanbClassification of the Nonparametric Tests The tests can be classify according to the following Criteria TABLEiThe level of measurement Nominal or OrdinaliiWhich information is used Frequency sign rankiiiIndependent or dependent samplesivThe number of samples to be compared K1 K2 K22cCHISQUAREtestiChiSquare is used for testing the INDEPENDENCE OF NOMINAL VARIABLES1Independence When variables are not associated when the score of one variable do not depend on the scores of the othersiiThe Data are arranged in the form of a table called Contingency tableiiiPRIOR ASSUMPTIONS REQUIREMENT1 Random Samples2 Independent observations3 A sufficiently large sample size is required at least 204 Average cell frequency should be5ivTestH Two nominally scaled variables are statistically independent no 0associationH Two variables are not independent association1 In nonparametric procedures the null hypothesis is formulated in a more general form because no parameter are specified to be comparedC1C2R1OO1112R2OO21222rcOErcrc2DFR1C1Er1c1rcETTRrCcrcTSamplerowtotalColumntotalErc2If thevalue is greater than the critical value for DF R1C1 at 005 we may reject the null hypothesis of independence
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