1985
DOI: 10.1002/1097-4679(198503)41:2<285::aid-jclp2270410226>3.0.co;2-h
|View full text |Cite
|
Sign up to set email alerts
|

Effect size estimates in chemical aversion treatments of alcoholism

Abstract: This study found that aggregate studies on alcohol aversion therapy tended to support a moderate level of treatment impact that may have noteworthy practical import. Emetics appear to generate fairly consistent findings; a paralysis‐inducing chemical may produce variable results.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

1987
1987
2003
2003

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(2 citation statements)
references
References 8 publications
0
2
0
Order By: Relevance
“…This description led to Friedman's description of effect size measures for most common significance test statistics, including t, z, F, and chi-square (χ 2 ). Thurber (1985) and Good (1983) described some of these same statistics. Friedman (1968) also gave a generalized description of the correlational effect size measures derived from these test statistics: the square root of the test statistic value divided by that same value plus some measure of sample size, typically N or df.…”
Section: Different Effect Size Measures For Different Significance Tementioning
confidence: 95%
See 1 more Smart Citation
“…This description led to Friedman's description of effect size measures for most common significance test statistics, including t, z, F, and chi-square (χ 2 ). Thurber (1985) and Good (1983) described some of these same statistics. Friedman (1968) also gave a generalized description of the correlational effect size measures derived from these test statistics: the square root of the test statistic value divided by that same value plus some measure of sample size, typically N or df.…”
Section: Different Effect Size Measures For Different Significance Tementioning
confidence: 95%
“…Studies that fail to find statistically significant results are less likely to be published than studies that include statistically significant results (Schmidt & Hunter, 1995;B. Thompson, 1996;Thurber, 1985;Wampold et al, 1983;Woolley & Dawson, 1983). Any analysis of data without attempting to correct for this publication bias is likely to yield inflated levels of effect size.…”
Section: Applications Of Effect Sizementioning
confidence: 99%