Multivariate Analysis with Parametric Statistics

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Presentation transcript:

Multivariate Analysis with Parametric Statistics Review: Central Limit Theorem what percentage of the time would we make an error in referring to the population? with one statistic with two statistics

Comparing Two Statistics

Alternative (something is going on) Hypotheses: Null (nothing is going on) H0 : x̄1 = x̄2 Alternative (something is going on) HA : x̄1 ≠ x̄2 OR HA : x̄1 < x̄2 OR HA : x̄1 > x̄2

Inference testing = Convoluted Language: You can “fail to reject the null hypothesis” or You can “reject the null hypothesis” margin of error (related to p level)

Risks of Inferential Testing Type 1 Error the error is in rejecting a true null hypothesis a false alarm - an alarm without a fire alpha (p) level of .05 makes it harder to reject null hypothesis thus harder to make this mistake Type 2 Error accepting a false null hypothesis failing to reject the null hypothesis when the Ha is in fact true something there – you are missing it okay to do

Confidence intervals My mother understood me: X = 5.54, s = 1.680 My father understood me: X = 5.18, s = 1.916 95% Confidence interval for mother: [5.32, 5.76] 95% Confidence interval for father: [4.91, 5.45] Overlap means?

T-Test, or Student’s T tests the difference of two means -- one nominal (2-value) variable -- one interval (or ordinal) variable, usually the DV examples: gender and years of education gender and occupational prestige

Formula for t statistic See Salkind, p. 174

Running the t-test Compare means - does it pass the eyeball test? Construct confidence intervals around the means (optional) is there overlap in the two in estimation of the true population? t-test under “compare means,” “independent samples t-test” two-tailed test is default in SPSS