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Published byFerdinand Glenn Modified over 9 years ago
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Making Comparisons All hypothesis testing follows a common logic of comparison Null hypothesis and alternative hypothesis – mutually exclusive – exhaustive Experimental design and control group “Republicans have higher income than Democrats”?
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Methods of Making Comparisons Independent Variable Categorical measures (nominal or ordinal) Continuous measures (interval or ratio) Dependent Variable Categorical measures (nominal or ordinal) Cross-Tabulation & (Chapter 7) Chi- square (Chapter 10) Logistic Regression Continuous measures (interval or ratio) Compare Means & (Chapter 9) Dummy Variables (Chapter 8) Correlation & Linear Regression
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Cross-tabulation Relationship between two (or more) variables – Joint frequency distribution – Contingency table Observations should be independent of each other – One person’s response should tell us nothing about another person’s response Mutually exclusive and exhaustive categories
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Cross-tabulation If the null hypothesis is true, the independent variable has no effect on the dependent variable The expected frequency for each cell MaleFemaleTotal Pro-??20 Anti-??80 Total50 100
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Expected Frequency of Each Cell Expected frequency in the ith row and the jth column ……… (E ij ) Total counts in the ith row ……… (T i ) Total counts in the jth column ……… (T j ) Total counts in the table ……… (N)
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Inferences about Sample Means Hypothesis testing is an inferential process Using limited information to reach a general conclusion Observable evidence from the sample data Unobservable fact about the population Formulate a specific, testable research hypothesis about the population
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Null Hypothesis no effect, no difference, no change, no relationship, no pattern, no … any pattern in the sample data is due to random sampling error
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Errors in Hypothesis Testing Type I Error – A researcher finds evidence for a significant result when, in fact, there is no effect (no relationship) in the population. – The researcher has, by chance, selected an extreme sample that appears to show the existence of an effect when there is none. – The p-value identifies the probability of a Type I error.
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