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Lecture 10 - T-Test

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University of Ottawa
Political Science
Ivan Katchanovski

Feb. 25, 2014 T-Test Independent Samples T-Test o Used to test research hypothesis • Example: Men and women in Canada have different levels of confidence in political parties. o Determines if differences between means of two independent groups/sub-samples are significant o Independent groups/subsamples: if one member/case in a group does not influence place of another member/case in that group or another group • Example: male and female respondents are independent groups/subsamples Dependent Variable o In theory: Interval/ratio o In practice: ordinal variables can also be used if they assumed to be interval/ratio • Need to specify explicitly/check this assumption • Recode values if needed Independent Variable o Nominal o Two categories/values • Male and female o Nominal variables with more than 2 categories: • Can be recoded into 2 categories for t-test • Or used in ANOVAtest Independent Samples T-Test • Assumptions • T-Test uses Student t-distribution/t-statistics • Dependent variable is normally distributed in both independent variable levels o Each group/category of the independent variable: at least 30 members/cases o Sample drawn from populations with equal variances on the dependent variable Criteria of Statistical Significance • Statistically significant if less than one of the conventional levels of statistical significance • Use equal variances is assumed p(obtained) values for T-Test • Levene’s test for equality of variances is statistically non-significant Criteria of Statistical Significance • Statistical significant is less than one of the conventional levels of statistical significance • Conventional levels of statistical significance:  Less than .001: Probability that a t
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