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Descriptive Statistics by Study group (experimental condition) +----------------+--------------------+--------------------+--------------------+--------------------+------------------------------+ | |1 VUBook |2 CommercialBook |3 NoBookGiven |Combined | Test | | |(N=62) |(N=66) |(N=70) |(N=198) |Statistic | +----------------+--------------------+--------------------+--------------------+--------------------+------------------------------+ |awcess >= 8 | 63% ( 39)| 71% ( 47)| 63% ( 44)| 66% (130)|Chi-square=1.36 d.f.=2 P=0.508| +----------------+--------------------+--------------------+--------------------+--------------------+------------------------------+ |depress.increase| 19% ( 12)| 24% ( 16)| 23% ( 16)| 22% ( 44)|Chi-square=0.47 d.f.=2 P=0.792| +----------------+--------------------+--------------------+--------------------+--------------------+------------------------------+ |depress.consec | 37% ( 23)| 30% ( 20)| 26% ( 18)| 31% ( 61)|Chi-square=2.01 d.f.=2 P=0.366| +----------------+--------------------+--------------------+--------------------+--------------------+------------------------------+Before we had said to only look at threshold items on the aims page that deal with baseline (wave 1) data. Only one item on the aims only uses wave 1 data, so I went ahead and did the calculations for more until we have a chance to meet. I was going to grab some lunch now, but should we touch base sometime this afternoon about the threshold scores (or anything else)? 3/9/09

maritaland

> chisq.test(bigdat$marital[bigdat$awwave == 1], bigdat$studygrp[bigdat$awwave == 1])$expected bigdat$studygrp[bigdat$awwave == 1] bigdat$marital[bigdat$awwave == 1] 1 VUBook 2 CommercialBook 3 NoBookGiven Single 48.467005 51.593909 53.939086 Married 10.385787 11.055838 11.558376 Other 3.147208 3.350254 3.502538 Warning message: In chisq.test(bigdat$marital[bigdat$awwave == 1], bigdat$studygrp[bigdat$awwave == : Chi-squared approximation may be incorrect

Descriptive Statistics by Study group (experimental condition) +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | |N |1 VUBook |2 CommercialBook |3 NoBookGiven | Test | | | |(N=62) |(N=66) |(N=70) |Statistic | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ |income : 1 Less than $8,000|120| 29% (13)| 29% (10)| 15% ( 6)|Chi-square=7.2 d.f.=2 P=0.027| +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 2 $8,000 - $12,000 | | 22% (10)| 15% ( 5)| 20% ( 8)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 3 $12,001 - $16,000 | | 11% ( 5)| 18% ( 6)| 2% ( 1)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 4 $16,001 - $21,000 | | 4% ( 2)| 15% ( 5)| 12% ( 5)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 5 $21,001 - $26,000 | | 13% ( 6)| 6% ( 2)| 12% ( 5)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 6 $26,001 - $30,000 | | 4% ( 2)| 3% ( 1)| 5% ( 2)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 7 $30,001 - $40,000 | | 4% ( 2)| 9% ( 3)| 10% ( 4)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | 8 $40,001 - $50,000 | | 7% ( 3)| 0% ( 0)| 7% ( 3)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ | over $50,000 | | 4% ( 2)| 6% ( 2)| 17% ( 7)| | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+ |as.numeric(income) |120| 1/2/5 | 1/3/4 | 2/5/7 | F=3.6 d.f.=2,117 P=0.031 | +---------------------------+---+-------------------+-------------------+-------------------+-----------------------------+Did we group "other" for awbq09 as "not working"? Maybe we should make them working since maternity leave would imply they have a job.

ANOVA (Row Mean Scores) Statistic The ANOVA statistic can be used only when the column variable Y lies on an ordinal (or interval) scale so that the mean score of Y is meaningful. For the ANOVA statistic, the mean score is computed for each row of the table, and the alternative hypothesis is that, for at least one stratum, the mean scores of the R rows are unequal. In other words, the statistic is sensitive to location differences among the R distributions of Y. The matrix of column scores C_h has dimension 1 ×C, the column scores are determined by the SCORES= option. The matrix of row scores R_h has dimension (R-1) ×R and is created internally by PROC FREQ as R_h = [ I_{R-1} , -J_{R-1} ] where I_{R-1} is an identity matrix of rank R-1, and J_{R-1} is an (R-1) ×1 vector of ones. This matrix has the effect of forming R-1 independent contrasts of the R mean scores. When there is only one stratum, this CMH statistic is essentially an analysis of variance (ANOVA) statistic in the sense that it is a function of the variance ratio F statistic that would be obtained from a one-way ANOVA on the dependent variable Y. If nonparametric scores are specified in this case, then the ANOVA statistic is a Kruskal-Wallis test. If there is more than one stratum, then this CMH statistic corresponds to a stratum-adjusted ANOVA or Kruskal-Wallis test. In the special case where there is one subject per row and one subject per column in the contingency table of each stratum, this CMH statistic is identical to Friedman's chi-square. See Example 2.8 for an illustration.

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Topic revision: r14 - 09 Mar 2009, BenSaville

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