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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.

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Agenda

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  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

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