1995
DOI: 10.1002/sim.4780140810
|View full text |Cite
|
Sign up to set email alerts
|

The log transformation is special

Abstract: The logarithmic (log) transformation is a simple yet controversial step in the analysis of positive continuous data measured on an interval scale. Situations where a log transformation is indicated will be reviewed. This paper contends that the log transformation should not be classed with other transformations as it has particular advantages. Problems with using the data themselves to decide whether or not to transform will be discussed. It is recommended that log transformed analyses should frequently be pre… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
284
0
4

Year Published

1998
1998
2020
2020

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 423 publications
(288 citation statements)
references
References 13 publications
0
284
0
4
Order By: Relevance
“…Because all PA variables were positively skewed, logarithmic transformations (log10) were used to improve normality. 37 For step counts, average values of more than 20,000 steps/day were recorded as 20,000 to limit unrealistically high averages. 33 Because prior chi-square tests and independent sample t tests showed that the urban and rural samples were comparable on all sociodemographic characteristics except for educational level (pG 0.001) and working situation (pG0.01), three-way interactions between the physical environment (urban versus rural), psychosocial factors (modeling from family and friends, social support from family and friends, self-efficacy, perceived benefits ([five factors] and perceived barriers [five factors]), and SES (low versus high education) in relation to PA were investigated using ANOVA models.…”
Section: Resultsmentioning
confidence: 99%
“…Because all PA variables were positively skewed, logarithmic transformations (log10) were used to improve normality. 37 For step counts, average values of more than 20,000 steps/day were recorded as 20,000 to limit unrealistically high averages. 33 Because prior chi-square tests and independent sample t tests showed that the urban and rural samples were comparable on all sociodemographic characteristics except for educational level (pG 0.001) and working situation (pG0.01), three-way interactions between the physical environment (urban versus rural), psychosocial factors (modeling from family and friends, social support from family and friends, self-efficacy, perceived benefits ([five factors] and perceived barriers [five factors]), and SES (low versus high education) in relation to PA were investigated using ANOVA models.…”
Section: Resultsmentioning
confidence: 99%
“…Natural logarithmic transformation of all biomarkers and dietary data were performed to reduce the within-sample variability and to avoid inappropriate exclusion of outliers. (59) Therefore, the size of the regression coefficient represents the proportional change in BAP or uNTx/Cr associated with a unit change in the predictor variable. Adjusting for total serum cholesterol instead of serum triglycerides gave similar results to the unadjusted analysis; therefore, only serum triglyceride was used.…”
Section: Discussionmentioning
confidence: 99%
“…Dissociation of phasic and sustained fear L Miles et al (K2 ¼ 51.78) and phasic fear (K2 ¼ 45.53) data sets, and because Grubb's test identified several outliers, betweengroup differences were evaluated using distribution-free (non-parametric) Mann-Whitney or Kruskal-Wallis tests, and also, to establish statistical robustness, by using t-tests and ANOVA on log-transformed scores (see Keene (1995)). Follow-up comparisons were made using Dunn's (nonparametric) or Dunnett's t-test (parametric) for multiple comparisons with a control.…”
Section: Statistical Analysesmentioning
confidence: 99%