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. ▫Therefore, higher test stats mean stronger relationships

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)

HW#5 Part II Cont PEOPLE HELPFUL OR LOOKING OUT FOR SELVES * RS HIGHEST DEGREE Crosstabulation RS HIGHEST DEGREE Total LT HIGH SCHOOL HIGH SCHOOL JUNIOR COLLEGEBACHELORGRAD People Are… HELPFUL LOOKOUT FOR SELF DEPENDS Total Chi-Square Tests Valuedf Asymp. Sig. (2-sided) Pearson Chi-Square a a. 0 cells (.0%) have expected count less than 5. The minimum expected count is

Part II, #4 CONFIDENCE IN MAJOR COMPANIES * SATISFACTION WITH FINANCIAL SITUATION Crosstabulation SATISFACTION WITH FINANCIAL SITUATION Total SATMORE/LESSNOT CONFA GREAT DEALCount % within SATISFACTION WITH FINANCIAL SITUATION 19.9%16.9%14.6%17.2% ONLY SOMECount % within SATISFACTION WITH FINANCIAL SITUATION 64.6%63.0%58.3%62.3% HARDLY ANYCount % within SATISFACTION WITH FINANCIAL SITUATION 15.5%20.0%27.1%20.5% TotalCount % within SATISFACTION WITH FINANCIAL SITUATION 100.0%

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

Part IIICont. Strength ▫r =.133 (weak, positive relationship) ▫r 2 =.018  Age explains about 2% of the variation in the number of hours people watch TV (very weak).

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”

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.

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

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

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”

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

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

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