1 / 15

Agenda

Agenda. Review Homework 5 Review Statistical Control Do Homework 6 (In-class group style). HW#5 Part I. Do not calculate b/c not significant p>alpha If not significant, cannot examine association Test stats tell us how different our findings are from the “no relationship” null.

winter-marsh
Download Presentation

Agenda

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. Agenda Review Homework 5 Review Statistical Control Do Homework 6 (In-class group style)

  2. HW#5 Part I • Do not calculate b/c not significant p>alpha • If not significant, cannot examine association • Test stats tell us how different our findings are from the “no relationship” null. • Therefore, higher test stats mean stronger relationships

  3. HW#5 Part II • Advantage of V = bounded for all cell sizes, (0-1) which is not true for phi if greater than 2x2 table • Lambda is a PRE measure that tells you the exact % reduction in error predicting the DV that you get by knowing the IV. Phi or V just tell you relative strength on some scale (V = 0-1)

  4. HW#5 Part II Cont

  5. Part II, #4

  6. HW#5, Part III • Regression line • Vertical distances (deviations) of scatter from line are at minimum and add to zero • Passes through (mean x, mean y) • Indicates direction/strength of relationship between 2 IR variables • Used to predict scores of Y by knowing value of X. • r bounded (-1 to +1) and slope is not (influenced by how variables are measured) • scatter plot – inverse relationship

  7. Part III Cont. • Strength • r = .133 (weak, positive relationship) • r2 = .018 • Age explains about 2% of the variation in the number of hours people watch TV (very weak).

  8. 3 CRITERIA OF CAUSALITY • When the goal is to explain whether X causes Y the following 3 conditions must be met: • Association • X & Y vary together • Direction of influence • X caused Y and not vice versa • Elimination of plausible rival explanations • Evidence that variables other than X did not cause the observed change in Y • Synonymous with “CONTROL”

  9. CONTROL VIA EXPERIMENT • BASIC FEATURES OF THE EXPERIMENTAL DESIGN: 1. Subjects are assigned to one or the other group randomly 2. A manipulated independent variable • (Some offenders get real rehabilitation program, others get “motivational speaker”) 3. A measured dependent variable • (Arrest in 2 years after treatment) 4. Except for the experimental manipulation, the groups are treated exactly alike, to avoid introducing extraneous variables and their effects.

  10. Statistical Control • Process of introducing control variables into a bivariate relationship in order to better understand the relationship • Control variable – • a variable that is held constant in an attempt to understand better the relationship between 2 other variables • Zero order relationship • in the elaboration model, the original relationship between 2 nominal or ordinal variables, before the introduction of a third (control) variable • Partial relationships • the relationships found in the partial tables

  11. 3 Potential Relationships between x, y & z 1. Spuriousness • A relationship between X & Y is SPURIOUS when it is due to the influence of an extraneous variable (Z) 2. Intervening variables • Clarifying the process through which the original bivariate relationship functions • A variable that is influenced by an independent variable, and that in turn influences a dependent variable 3. Moderating or “interaction” effects • Occurs when the association between the IV and DV varies across categories of the control variable • One partial relationship can be stronger, the other weaker. AND/OR, • One partial relationship can be positive, the other negative

  12. Using Statistical Controls • Nominal-Ordinal level data • Elaboration of bivariate tables • In SPSS, add the “control” variable in the “layer” box • Interval-Ratio data • Elaboration of correlations (partial correlations) • In SPSS, select “partial correlations”

  13. SPSS DEMO – Table Elaboration • Initial hypothesis = political view is related to whether people report having a gun in the home • Polyview = ordinal • Owngun = nominal (yes/no) • Test statistic? • Measure of strength? • What happens when we “control” for gender? • Sex as “layer” • Look at chi-squared, and measures of association (if appropriate) for each sex seperately

  14. SPSS DEMO Partial Correlations • Using survey of Adderall use in UMD students • Do delinquent peer associations predict crime in the past year? • Could “low self control” be the reason these things are related (spuriousness)? • Initial look at bivariate (zero order) “r” • Partial “r” after controlling for self-control

  15. Final Homework • IN CLASS • EITHER TYPE ANSWERS AT LAB AND EMAIL TO ME OR WRITE NEATLY • DUE BY START OF CLASS THURSDAY

More Related