1 / 11

BHS 204-01 Methods in Behavioral Sciences I

BHS 204-01 Methods in Behavioral Sciences I. May 9, 2003 Chapter 6 and 7 (Ray) Control: The Keystone of the Experimental Method. Sources of Variance. Systematic variation – differences related to the experimental manipulation.

claudiaeaves
Download Presentation

BHS 204-01 Methods in Behavioral Sciences I

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BHS 204-01Methods in Behavioral Sciences I May 9, 2003 Chapter 6 and 7 (Ray) Control: The Keystone of the Experimental Method

  2. Sources of Variance • Systematic variation – differences related to the experimental manipulation. • Can also be differences related to uncontrolled variables (confounds) or systematic bias (e.g. faulty equipment or procedures). • Chance variation – nonsystematic differences. • Cannot be attributed to any factor. • Also called “error”.

  3. F-Ratio • A comparison of the differences between groups with the differences within groups. • Between-group variance = treatment effect + chance variance. • Within-group variance = chance variance. • If there is a treatment effect, then the between-group variance should be greater than the within-group variance.

  4. Testing the Null Hypothesis • Between-group variance (treatment effect) must be greater than within-group variance (chance variation), F > 1.0. • How much greater? • Normal curve shows that 2 SD, p <.05 is likely to be a meaningful difference. • The p value is a compromise between the likelihood of accepting a false finding and the likelihood of not accepting a true hypothesis.

  5. Box 6.1. (p. 135)Type I and Type II Errors.

  6. Types of Errors • Type I error – likelihood of rejecting the null when it is true and accepting the alternative when it is false (making a false claim). • This is the p value -- .05 is probability of making a Type I error. • Type II error – likelihood of accepting null when it is false and rejecting the alternative when it is true. • Probability is b, the power of a statistic is 1-b.

  7. Reporting the F-Ratio • ANOVA is used to calculate the F-Ratio. • Example: • The experimental group showed significantly greater weight gain (M = 55) compared to the control group (M = 21), F(1, 12) = 4.75, p=.05. • Give the degrees of freedom for the numerator and denominator.

  8. When to Use ANOVA • When there are two or more independent groups. • When the population is likely to be normally distributed. • When variance is similar within the groups compared. • When group sizes (N’s) are close to equal.

  9. Threats to Internal Validity • It is the experimenter’s job to eliminate as many threats to internal validity as possible. • Such threats constitute sources of systematic variance that can be confused with an effect, resulting in a Type I error. • Potential threats to validity must be evaluated in the Discussion section of the research report.

  10. Two Ways of Achieving Control • Participant assignment and selection: • Random sampling. • Random assignment to conditions. • Experimental design: • Add a control group. • Include a baseline measurement before the treatment (pretest). • Treat subjects consistently across all groups. • Four-group design tests for effects of the testing.

  11. Figure 7.3. (p. 159)The four conceptual steps in experimentation.

More Related