ANALYSIS OF VARIANCE WITH RANDOM SAMPLE SIZES

1981 ◽  
Author(s):  
ΠΑΝΑΓΗΣ ΜΟΣΧΟΠΟΥΛΟΣ
Keyword(s):  
Random Sample ◽  
Analysis Of Variance ◽  
Sample Sizes

Influence of Clothing and Physical Attractiveness in Person Perception

1976 ◽  
Vol 42 (3) ◽  
pp. 775-780 ◽  
Author(s):  
J. Paull Nielsen ◽  
Anne Kernaleguen
Keyword(s):  
University Students ◽  
Random Sample ◽  
Analysis Of Variance ◽  
Social Desirability ◽  
Physical Attractiveness ◽  
Person Perception ◽  
Latin Square ◽  
Facial Attractiveness ◽  
Semantic Differential ◽  
Female Stimulus

Using a semantic differential to assess person perception, a non-random sample of 30 female university students recorded their impressions of a number of pictures of female stimulus persons. The pictures represented a systematic pairing of faces of varying levels of attractiveness, and clothed bodies of varying levels of attractiveness. The results of a 3 × 3 Latin square analysis of variance showed facial attractiveness to be a significant factor in the perception of physical attractiveness of the total unit, social and professional happiness, and social desirability. Attractiveness of the clothed body exerted a significant effect in the perception of bourgeois orientation. Pearson productmoment correlation results indicated that as level of perceived physical attractiveness increased, so did the perception of social and professional happiness and social desirability.

scholarly journals ANOVA with random sample sizes: An application to a Brazilian database on cancer registries

2013 ◽  
Author(s):  
Célia Nunes ◽  
Gilberto Capistrano ◽  
Dário Ferreira ◽  
Sandra S. Ferreira
Keyword(s):  
Random Sample ◽  
Cancer Registries ◽  
Sample Sizes

Empirical Formulae for Estimating Appropriate Sample Sizes for Analysis of Variance Designs

1968 ◽  
Vol 27 (2) ◽  
pp. 363-367 ◽  
Author(s):  
John E. Overall ◽  
Sudhir N. Dalal
Keyword(s):  
Sample Size ◽  
Analysis Of Variance ◽  
Error Variance ◽  
Sampling Unit ◽  
Sample Sizes ◽  
Constant Power ◽  
Treatment Difference ◽  
High Side ◽  
Power Curves ◽  
Size Estimates

Simple empirical formulae are presented for estimating appropriate sample size for simple randomized analysis of variance designs involving 2, 3, 4 or 5 treatments. In order to use these formulae one must specify the magnitude of a meaningful treatment difference and must have an estimate of the error variance. Sample size estimates derived from the simple formulae have been found to differ from values obtained using constant power curves by no more than one sampling unit on the low side and no more than two sampling units on the high side.

The influence of education and income on responses to the QuickDASH questionnaire

2014 ◽  
Vol 40 (4) ◽  
pp. 401-405 ◽  
Author(s):  
V Finsen
Keyword(s):  
Socioeconomic Factors ◽  
Random Sample ◽  
Analysis Of Variance ◽  
Income Level ◽  
Level Of Evidence ◽  
Men And Women ◽  
Level Of Education ◽  
The Mean

We studied the influence of levels of income and education on QuickDASH scores. The scores were collected in a random sample of 1376 residents of Norway. The level of income was divided into four bands and level of education into five bands. The mean QuickDASH score for both men and women fell with every increase in education and income level. For women the mean score was 30 for those with the shortest education and 9 for those with the longest ( p < 0.001). The corresponding figures for men were 19 and 7 ( p < 0.01). The women with the lowest level of income had a mean score of 23, compared with 8 for women with the highest income level ( p < 0.001). For men the corresponding mean scores were 20 and 5 ( p < 0.001). Analysis of variance showed that age alone accounted for 16% of the variability of the scores among women and 7% among men. When levels of education and income were added to the analysis, these three factors accounted for 21% of the variability among women and 13% among men. We conclude that socioeconomic factors significantly influence QuickDASH scores. Level of evidence: 3

An Analysis of the Competencies of Sports and Athletic Club Managers

1987 ◽  
Vol 1 (2) ◽  
pp. 116-128 ◽  
Author(s):  
Keith W. Lambrecht
Keyword(s):  
Factor Analysis ◽  
Hypothesis Testing ◽  
Random Sample ◽  
Analysis Of Variance ◽  
Organizational Size ◽  
Group Iii ◽  
Group I ◽  
Group Ii ◽  
Study Manager ◽  
Athletic Club

The focus of this study was to identify the competencies needed to manage athletic clubs and to determine if differences exist in required competencies regarding organizational size. A random sample of 264 managers participated in the study; there were 83 in Group I, 95 in Group II, and 86 in Group III. A one-way analysis of variance was employed for hypothesis testing, Tukey’s ω method was utilized for comparing rejected hypotheses, and factor analysis was used for clustering competency areas. Based on the findings of this study, manager competencies have been identified and a difference does exist in managing varying sizes of athletic clubs.

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