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Chi Square

Chi square test. A chi square test is an inferential statisticAnalyzes proportions/frequency dataUses proportions/distributions from a sample to test hypotheses about proportions/ distributions in the populationTwo testsChi square test for goodness of fitChi square test for independence. Exampl

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Chi Square

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    1. Chi Square

    2. Chi square test A chi square test is an inferential statistic Analyzes proportions/frequency data Uses proportions/distributions from a sample to test hypotheses about proportions/ distributions in the population Two tests Chi square test for goodness of fit Chi square test for independence

    3. Example littering (Cialdini et al., 1990). Research question: Does the amount of existing litter influence littering behavior? IV: Amount of existing litter Levels (conditions): 0 or 1 piece 2 or 4 pieces 8 or 16 pieces DV: Littering behavior (littered or didnt litter) What scale/level of measurement is this?

    4. Example littering (Cialdini et al., 1990).

    5. Descriptive statistics Descriptive statistics for nominal data Mode Frequencies/proportions

    6. Two chi square tests Goodness of fit One variable Determines how well the sample proportions match a pre-specified distribution Independence Two variables Determines whether there is a relationship between two variables

    7. Steps in hypothesis testing State the hypotheses null research Select an alpha level and determine the critical value Compute the test statistic Make a decision

    8. Test for goodness of fit Forms of the null hypothesis No preference There is no difference in proportions among the categories Participants do not prefer one category over another Example: Pepsi: 50%, Coke 50% No difference from a comparison population There is no difference between the sample distribution and a known (population) distribution Example: ND: 20% Bl, 75% Br, 5% R US: 20% Bl, 75% Br, 5% R

    9. Test for goodness of fit Null hypothesis Specifies a distribution of proportions Research hypothesis Specifies that the distribution will be different than that indicated in the null hypothesis

    10. Calculating the test statistic Observed frequencies the number of individuals from the sample who are classified in a particular category fo Expected frequencies the number of individuals from the sample who are expected to be classified in a particular category fe

    11. Calculating the test statistic

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