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### 54 Cards in this Set

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 nominal definition and examples categories no rank no order examples, gender, eye color, limited to frequency of occurence (aka frequency data) ordinal categorical, ranked, more or less of something; likert scale, can only describe in terms of more or less cannot apply math prniciples interval ordered and equal can add or subtract; zero is arbitrary but does not imply absence of (e.g. temp) ratio ordered equal intervals absolute zero ex. height weight reaction time mesokurtic normal curve leptokurtic tall and peaked platokurtic flat or plateau-like central limit theorem three principles 1. sampling distribution of means increasingly approaches normal as sample size increases regardless of the shape of the population distribution 2. mean of sampling distribution equals population mean 3. SD of sample equals SD of population from which you drew your sample why/when do a one or two tailed test 1. one tail when you expect the results will show a specific direction 2. two tail when predicted direction of results are uncertain alpha- definition and alternatve namesq rejection region region of unlikely values, critical region Type I error rejecting a trye null hypothesis- probability is always equal to alpha Type II error probability of retaining a false null hypothesis;;probability is equal to beta power ability to reject null how to increase condience reduce alpha to increase power 1.increase alpha 2.increase sample size 3.use a reliable DV measure 4.one tail test chi square test nonparametric nominal variable used when analyzing frequency of a category use multiple chi square if more than one variable when to use parametric tests interval/ratio when to use nonparametric tests nominal/ordinal t-test for single sample/group aka student t test used to compare treatment group to population mean t-test for correlated samples comparing subjects to other ss or even themselves. pre-test, post-test t-test for independent samples have a tx group and control group and comparing them ANOVA when to use and why use for two or more group means use to manage experiment wise error rate manages possibility of Type I error one way ANOVA one IV 2+ Ind Grps 1 DV 2 way (factorial) ANOVA 2+ IV's 2+ Ind Grps 1 DV MANOVA 1+ IVs 2+ Dvs use instead of 2 ANOVAs to control for experiment-wise error rates ANCOVA analysis of covariance ANOVA plus regression analysis reduces within group vriability considered more powerful test randomized block ANOVA blocks extraneous variables and use as aonther IV used to study unique effects on the DV split plot/mixed ANOVA -used with mixed design study study mixed design of a within and between groups variable e.g. two between groups (tx vs control) and one within gps trend analysis IV describe in terms of shape and form four outcomes: linear, cubic, quadratic, quartic why use post hoc use after conducting planned comparisons Scheffe least vulnerable to Type I more vulnerable to Type II Tukey least vulnerable to Type I most vulnerable to Type II fisher most vulnerable to type I error least vulnerable to type II f score msb/msw in correlation ______ is to IV as _____ is to DV predictor, criterion Pearson r when and under what assumptions select whenever u have two continuous variables Assumptions 1. linearity 2.unrestricted range 3. homoscedasticity what is assumption of linearity mean relative to pearson r should be able to draw a straight line; if not linear would underestimate degree of association between the two variables assumption of unrestricted range relative to pearson r must be an unrestricted range of scores along x and y axes consequence would underestimate relationship assumption of homoscedasticity range of y scores should be ablout the same at all values of x range- if not homoscedastic, would not represent full range of scores between x and y spearman rho select with two rank-ordered variables (e.g. rank in high school and rank in SAT biserial one continuous one artifically dichotomous variable point biserial one continuous varialb ena done true dichotomy (e.g.scores and gender eta two continuous nonlinear relationship variables (on test, level of anxiety and performance on a memory test regression (aka prediction) analysis used to predict a score on some criterion based uopn obtained score on a predictor e.g sat score, college GPA multivariate techniques for prediction assesses degree of association between 3 or more varialbes using to make predictions multiple regression two predictors one or more criterion ex. SAT V, SAT M---college GPA canonical correlations 2+ predictors 2+ continuous criterion assertive+years experience---predict job rating yearly sales discriminant analysis 1 discrete criterion battery of tests---choose college major stepwise multiple regression manages for error, determining the fewest number of variables to determine. multicolinearity observed in stepwise multiple regression, if two variables are highly correlated remove one as they are redundant and increase chance for error (jewish mother taking two temps) Path analysis one way, consider relationships in observed (measured) variables LISREL one or two way paths, relatioship between observed and latent variables looking at main effects examine marginal means effects of each IV without considering effects of other IVs interaction effects look for the cross, if no cross no interaction interaction occurs when effect of one IV are different at different levels of the other IV