Between Groups & Within-Groups ANOVA

Transcription

1 Between Groups & Within-Groups ANOVA BG & WG ANOVA Partitioning Variation making F making effect sizes Things that influence F Confounding Inflated within-condition variability Integrating stats & methods ANOVA ANalysis Of VAriance Variance means variation Sum of Squares (SS) is the most common variation index SS stands for, Sum of squared deviations between each of a set of values and the mean of those values SS =? (value mean) 2 So, Analysis Of Variance translates to partitioning of SS In order to understand something about how ANOVA works we need to understand how BG and WG ANOVAs partition the SS differently and how F is constructed by each. Variance partitioning for a BG design Tx C Called error because we can t account for why the folks in a condition -- who were all treated the same have different scores. Mean Variation among all the participants represents variation due to treatment effects and individual differences Variation between the conditions represents variation due to treatment effects Variation among participants within each condition represents individual differences SS Total = SS Effect + SS Error 1

2 How a BG F is constructed Mean Square is the SS converted to a mean dividing it by the number of things SS Total = SS Effect + SS Error df effect = k - 1 represents # conditions in design = df error =?n - k represents # participants in study How a BG r is constructed r 2 = effect / (effect + error) conceptual formula = SS effect / ( SS effect + SS error ) definitional formula = F / (F + df error ) computational forumla = An Example Descriptives SCORE ANOVA SCORE Sum of Total N Mean Std. Deviation Between Groups Within Groups Total Squares df Mean Square F Sig SS total = SS effect + SS error = r 2 = SS effect / ( SS effect + SS error ) = / ( ) =.34 r 2 = F / (F + df error ) = / ( ) =.34 2

3 Variance partitioning for a WG design Tx C Mean Sum Dif Variation among participants estimable because S is a composite score (sum) Called error because we can t account for why folks who were in the same two conditions -- who were all treated the same two ways have different difference scores. Variation among participant s difference scores represents individual differences SS Total = SS Effect + SS Subj + SS Error How a WG F is constructed Mean Square is the SS converted to a mean dividing it by the number of things SS Total = SS Effect + SS Subj + SS Error = df effect = k - 1 represents # conditions in design df error = (k-1)*(n-1) represents # data points in study How a WG r is constructed r 2 = effect / (effect + error) conceptual formula = SS effect / ( SS effect + SS error ) definitional formula = F / (F + df error ) computational forumla = 3

4 An Example Descriptive Statistics Mean Std. Deviation N SCORE SCORE Don t ever do this with real data!!!!!! Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum Source of Squares df Mean Square F Sig. factor1 Sphericity Assumed Error(factor1) Sphericity Assumed Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept Error SS total = SS effect + SS subj + SS error = Professional statistician on a closed course. Do not try at home! r 2 = SS effect / ( SS effect + SS error ) = / ( ) =.68 r 2 = F / (F + df error ) = / ( ) =.68 What happened????? Same data. Same means & Std. Same total variance. Different F??? BG ANOVA SS Total = SS Effect + SS Error WG ANOVA SS Total = SS Effect + SS Subj + SS Error The variation that is called error for the BG ANOVA is divided between subject and error variation in the WG ANOVA. Thus, the WG F is based on a smaller error term than the BG F and so, the WG F is generally larger than the BG F. What happened????? Same data. Same means & Std. Same total variance. Different r??? r 2 = effect / (effect + error) conceptual formula = SS effect / ( SS effect + SS error ) definitional formula = F / (F + df error ) computational forumla The variation that is called error for the BG ANOVA is divided between subject and error variation in the WG ANOVA. Thus, the WG r is based on a smaller error term than the BG r and so, the WG r is generally larger than the BG r. 4

5 Both of these models assume there are no confounds, and that the individual differences are the only source of withincondition variability BG SS Total = SS Effect + SS Error WG SS Total = SS Effect +SS Subj +SS Error A more realistic model of F IndDif individual differences BG SS Total = SS Effect + SS confound + SS IndDif + SS wcvar WG SS Total = SS Effect + SS confound +SS Subj + SS IndDif + SS wcvar SS confound between condition variability caused by anything(s ) other than the IV (confounds) SS wcvar inflated within condition variability caused by anything(s ) other than natural population individual differences Imagine an educational study that compares the effects of two types of math instruction (IV) uponperformance (% - DV) Participants were randomly assigned to conditons, treated, then allowed to practice (Prac) as many problems as they wanted to before taking the DV-producing test IV compare Ss 5&2-7&4 Control Grp Prac DV Exper. Grp Prac DV S S S S S S S S Confounding due to Prac mean prac dif between cond WG variability inflated by Prac wg corrrelation or prac & DV Individual differences compare Ss 1&3, 5&7, 2&4, or 6&8 The problem is that the F-formula will Ignore the confounding caused by differential practice between the groups and attribute all BG variation to the type of instruction (IV) overestimating the effect Ignore the inflation in within-condition variation caused by differential practice within the groups and attribute all WG variation to individual differences overestimating the error As a result, the F & r values won t properly reflect the relationship between type of math instruction and performance we will make a statistical conclusion error! Our inability to procedurally control variables like this will lead us to statistical models that can statistically control them r = F / (F + df error ) 5

6 How research design impacts F integrating stats & methods! SS Total = SS Effect +SS confound +SS IndDif +SS wcvar SS Effect bigger manipulations produce larger mean difference between the conditions larger F SS confound between group differences other than the IV -- change mean difference changing F if the confound augments the IV F will be inflated if the confound counters the IV F will be underestimated SS IndDif more heterogeneous populations have larger withincondition differences smaller F SS wcvar within-group differences other than natural individual differences smaller F could be procedural differential treatment within-conditions could be sampling obtain a sample that is more heterogeneous than the target population 6