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Significance Testing

Significance Testing. 10/15/2013. Readings. Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp. 58-76) Chapter 5 Making Controlled Comparisons (Pollock) Chapter 4 Making Comparisons (Pollock Workbook) . Homework and Exams. Homework 3 due today

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Significance Testing

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  1. Significance Testing 10/15/2013

  2. Readings • Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (Pollock) (pp. 58-76) • Chapter 5 Making Controlled Comparisons (Pollock) • Chapter 4 Making Comparisons (Pollock Workbook)

  3. Homework and Exams • Homework 3 due today • Exam 2 on Thursday

  4. Opportunities to discuss course content

  5. Office Hours For the Week • When • Wednesday 10-12 • Thursday 8-12 • And by appointment

  6. Course Learning Objectives • Students will learn the basics of research design and be able to critically analyze the advantages and disadvantages of different types of design.  • Students will achieve competency in conducting statistical data analysis using the SPSS software program.

  7. Bivariate Data Analysis CROSS-TABULATIONS and Compare Means

  8. Running a Test • Select and Open a Dataset in SPSS • Run either • A cross tab with column %’s (two categorical variables) • A compare means test (involves a categorical and continuous variable)

  9. What are Cross Tabs? • a simple and effective way to measure relationships between two variables. • also called contingency tables- because it helps us look at whether the value of one variable is "contingent" upon that of another

  10. When To Use Compare Means? • A way to compare ratio variables by controlling for an ordinal or nominal variable • One ordinal vs. a ratio or interval • One nominal vs. a ratio or interval • This shows the average of each category

  11. Running Cross Tabs • Select, Analyze • Descriptive Statistics • Cross Tabulations

  12. Running Cross-Tabs We have to use the measures available • Dependent variable is usually the row • Independent variable is usually the column.

  13. Lets Add Some Percent's Click on Cells Cell Display

  14. In SPSS • Open the States.SAV • Analyze • Compare Means • Means

  15. Where the Stuff Goes • Your categorical variable goes in the independent List • Your continuous variable goes in the Dependent List

  16. Hypothesis Testing

  17. Why Hypothesis Testing • To determine whether a relationship exists between two variablesand did not arise by chance. (Statistical Significance) • To measure the strength of the relationship between an independent and a dependent variable? (association)

  18. What is Statistical Significance? • The ability to say that that an observed relationship is not happening by chance. It is not causality • It doesn't mean the finding is important or that it has any real world application (beware of large samples) • Practical significance is often more important

  19. Determining Statistical Significance • Establishing parameters or “confidence intervals” • Are we confident that our relationship is not happening by chance? • We want to be rigorous (we usually use the 95% confidence interval any one remember why)

  20. How do we establish confidence • Establishing a “p” value or alpha value • This is the amount of error we are willing to accept and still say a relationship exists

  21. P-values or Alpha levels • p<.05 (95% confidence level) - There is less than a 5% chance that we will be wrong. • p<.01. (99% confidence level) 1% chance of being wrong • p<.001 (99.9 confidence level) 1 in 1000 chance of being wrong

  22. Problems of the Alpha level (p-value) • Setting it too high (e.g. .10) • Setting it too low (.001) • We have to remember our concepts and our units of analysis

  23. You should always use the 95% Confidence interval (p<.05) unless there is a good reason not to.

  24. Stating hypotheses

  25. Testing a hypothesis • Before we can test it, we have to state it • The Null Hypothesis- There is no relationship between my independent and dependent variable • The Alternate Hypothesis • We are testing for Significance: We are trying to disprove the null hypothesis and find it false!

  26. About the Null

  27. The Alternate Hypothesis • Also called the research hypothesis • State it clearly • State an expected direction

  28. After testing, the Null is either • True-no relationship between the groups, in which case the alternate hypothesis is false---- Nothing is going on (except by chance)! • False- there is a relationship and the alternative hypothesis is correct-- something is going on (statistically)!

  29. It seems pretty obvious whether or not you have a statistically significant relationship, but we can often goof things up.

  30. Decision types and errors

  31. Keep or Reject the Null?

  32. Errors and Decisions

  33. A Type I Error • Type I Error- the incorrect or mistaken rejection of a true null hypothesis (a false alarm)

  34. A Type II error • A Type II Error- accepting a null-hypothesis when it should have been rejected. (denial)

  35. Type I and II (Climate Change)

  36. You do not want to make either error

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