compute.es (version 0.2-5)

a.pes: One or Two-tailed p-value from ANCOVA to Effect Size

Description

Converts a one or two-tailed p-value from ANCOVA to an effect size of $d$ (mean difference), $g$ (unbiased estimate of $d$), $r$ (correlation coefficient), $z^{\prime }$ (Fisher's $z$), and log odds ratio. The variances, confidence intervals and p-values of these estimates are also computed, along with NNT (number needed to treat), U3 (Cohen's $U_{(}3)$ overlapping proportions of distributions), CLES (Common Language Effect Size) and Cliff's Delta.

Usage

a.pes(p, n.1, n.2, R, q, tail = "two", 
      level = 95, cer = 0.2, dig = 2, verbose = TRUE, id=NULL, data=NULL)

Arguments

p

One- or two-tailed p-value.

n.1

Treatment group sample size.

n.2

Comparison group sample size.

R

Covariate outcome correlation or multiple correlation.

q

number of covariates.

tail

One or two-tailed p-value. The argument is scalar only--it can only take on a single value of 'one' or 'two'. Default is two.

level

Confidence level. Default is 95%.

cer

Control group Event Rate (e.g., proportion of cases showing recovery). Default is 0.2 (=20% of cases showing recovery). CER is used exclusively for NNT output. This argument can be ignored if input is not a mean difference effect size. Note: NNT output (described below) will NOT be meaningful if based on anything other than input from mean difference effect sizes (i.e., input of Cohen's d, Hedges' g will produce meaningful output, while correlation coefficient input will NOT produce meaningful NNT output).

dig

Number of digits to display. Default is 2 digits.

verbose

Print output from scalar values? If yes, then verbose=TRUE; otherwise, verbose=FALSE. Default is TRUE.

id

Study identifier. Default is NULL, assuming a scalar is used as input. If input is a vector dataset (i.e., data.frame, with multiple values to be computed), enter the name of the study identifier here.

data

name of data.frame. Default is NULL, assuming a scalar is used as input. If input is a vector dataset (i.e., data.frame, with multiple values to be computed), enter the name of the data.frame here.

Value

d

Standardized mean difference ($d$).

var.d

Variance of $d$.

l.d

lower confidence limits for $d$.

u.d

upper confidence limits for $d$.

U3.d

Cohen's $U_{(}3)$, for $d$.

cl.d

Common Language Effect Size for $d$.

cliffs.d

Cliff's Delta for $d$.

p.d

p-value for $d$.

g

Unbiased estimate of $d$.

var.g

Variance of $g$.

l.g

lower confidence limits for $g$.

u.g

upper confidence limits for $g$.

U3.g

Cohen's $U_{(}3)$, for $g$.

cl.g

Common Language Effect Size for $g$.

p.g

p-value for $g$.

r

Correlation coefficient.

var.r

Variance of $r$.

l.r

lower confidence limits for $r$.

u.r

upper confidence limits for $r$.

p.r

p-value for $r$.

z

Fisher's z ($z^{\prime }$).

var.z

Variance of $z^{\prime }$.

l.z

lower confidence limits for $z^{\prime }$.

u.z

upper confidence limits for $z^{\prime }$.

p.z

p-value for $z^{\prime }$.

OR

Odds ratio.

l.or

lower confidence limits for $OR$.

u.or

upper confidence limits for $OR$.

p.or

p-value for $OR$.

lOR

Log odds ratio.

var.lor

Variance of log odds ratio.

l.lor

lower confidence limits for $lOR$.

u.lor

upper confidence limits for $lOR$.

p.lor

p-value for $lOR$.

N.total

Total sample size.

NNT

Number needed to treat.

References

Borenstein (2009). Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta analysis (pp. 279-293). New York: Russell Sage Foundation.

Cohen, J. (1988). Statistical power for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Furukawa, T. A., & Leucht, S. (2011). How to obtain NNT from Cohen's d: comparison of two methods. PloS one, 6(4), e19070.

McGraw, K. O. & Wong, S. P. (1992). A common language effect size statistic. Psychological Bulletin, 111, 361-365.

Valentine, J. C. & Cooper, H. (2003). Effect size substantive interpretation guidelines: Issues in the interpretation of effect sizes. Washington, DC: What Works Clearinghouse.

See Also

pes

Examples

# NOT RUN {
# CALCULATE SEVERAL EFFECT SIZES BASED ON P-VALUE FROM ANCOVA STATISTIC: 

a.pes(.3, 30, 30, .2, 3)
# }