Tests for Normality Versus Lognormality

1975 ◽  
Vol 4 (11) ◽  
pp. 1009-1019
Author(s):  
L. A. Klimko ◽  
C. E. Antle ◽  
A. Rademaker
Keyword(s):  
Tests For Normality

scholarly journals Turnover Intention Ditijau dari Stres Kerja pada Karyawan

2020 ◽  
Vol 14 (1) ◽  
pp. 11-20
Author(s):  
Devi Devi ◽  
Dewi Lutfah ◽  
Yeni Irawaty Sihotang ◽  
Rianda Elvinawaty
Keyword(s):  
Data Analysis ◽  
Correlation Coefficient ◽  
Work Stress ◽  
Turnover Intention ◽  
Positive Relationship ◽  
The Relationship ◽  
Tests For Normality

This study aims to determine the relationship between turnover intentionwith work stress. The hypothesis of this study states that there is a positive relationship between turnover intention and work stress, assuming that the higher the work stress is, the higher the turnover intention, and conversely the lower the work stress is, the lower the turnover intention will be. The subjects of this study were 182 employees of PT. INDAKO TRADING COY MEDAN. Datas were obtained from scales used to measure turnover intention and work stress. Calculations were performed by testing the analysis requirements (assumption) that consisted of tests for normality and linearity. The data were analyzed using Product Moment Correlation with SPSS 20 for Windows. The results of the data analysis showed that the correlation coefficient was 0.405 with a significance value of 0.000 (p <0.05). It showed that there is a positive relationship between turnover intention and work stress. The results of this study indicate that the contributions made by the variable of work stress on turnover intention was 16,4 percent, while the remaining 83,6 percent was influenced by other factors that were not examined. From these results, it is concluded that the hypothesis, which stated that there is a positive relationship between the turnover intention and work stress, is acceptable.

An examination of the quality of social and environmental disclosures by Nigerian oil companies

2016 ◽  
Vol 16 (2) ◽  
pp. 400-419 ◽  
Author(s):  
Odhiambo Odera ◽  
Albert Scott ◽  
Jeff Gow
Keyword(s):  
Large Scale ◽  
Health And Safety ◽  
Annual Reports ◽  
Content Type ◽  
Oil Companies ◽  
Environmental Disclosures ◽  
Oil Company ◽  
A Company ◽  
Tests For Normality

Purpose This study seeks to examine the quantity and quality of social and environmental disclosures (SEDs) of Nigerian oil companies. The study aims to analyse SED activities as reported by the oil companies in their annual reports. Design/methodology/approach The study analyses annual reports through content analysis. SED quantity is measured by alternative two units: number of sentences and number of pages. A two-point scale system to assess SED quality is used as follows: 1 = if SED is quantitative and reports specific activities of a company concerning its social and environmental responsibility; 0 = otherwise. Correlation analysis is performed among the different SED categories to identify the relationships among them. Kolmongrov–Smirnov and Shapiro–Wilk tests for normality are utilised. Findings SED activities are reported by most of the companies, and by quantity, employee information is found to be the most common type of disclosure. SED quantity and quality in the environment category is found to be overwhelmingly low despite the large-scale public concern expressed about the levels of the environmental degradation caused by oil company operations. Research limitations/implications The data collected for this study are based on one country, which controls diversity but limits the generalizability of the findings. The study is limited by the sample which includes mainly quoted companies, as they are believed to make improved disclosures because of their investor orientation and statutory obligations. Originality/value The study extends SED research by focusing on social disclosures such as employee-, community- and health- and safety-related disclosures. The study also investigates the motivations of SED providers and establishes a link between stakeholder demands/engagement and the level of disclosure.

Two Powerful Tests for Normality

2016 ◽  
Vol 3 (2) ◽  
pp. 225-234 ◽  
Author(s):  
Havva Alizadeh Noughabi
Keyword(s):  
Tests For Normality

Comparisons of tests for normality with a cautionary note

Biometrika ◽  
1974 ◽  
Vol 61 (1) ◽  
pp. 185-189 ◽  
Author(s):  
ALAN R. DYER
Keyword(s):  
Cautionary Note ◽  
Tests For Normality

scholarly journals Normalization of the Kolmogorov–Smirnov and Shapiro–Wilk tests of normality

2015 ◽  
Vol 52 (2) ◽  
pp. 85-93 ◽  
Author(s):  
Zofia Hanusz ◽  
Joanna Tarasińska
Keyword(s):  
Sample Size ◽  
P Values ◽  
Normalizing Constants ◽  
Kolmogorov Smirnov ◽  
Tests For Normality

Abstract Two very well-known tests for normality, the Kolmogorov-Smirnov and the Shapiro- Wilk tests, are considered. Both of them may be normalized using Johnson’s (1949) SB distribution. In this paper, functions for normalizing constants, dependent on the sample size, are given. These functions eliminate the need to use non-standard statistical tables with normalizing constants, and make it easy to obtain p-values for testing normality.

scholarly journals A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality

2007 ◽  
Vol 135 (3) ◽  
pp. 1151-1157 ◽  
Author(s):  
Dag J. Steinskog ◽  
Dag B. Tjøstheim ◽  
Nils G. Kvamstø
Keyword(s):  
Goodness Of Fit ◽  
Small Sample ◽  
Power Comparison ◽  
Goodness Of Fit Test ◽  
Kolmogorov Smirnov ◽  
The Mean ◽  
Small Sample Problem ◽  
Tests For Normality ◽  
Test For Normality ◽  
Smirnov Test

Abstract The Kolmogorov–Smirnov goodness-of-fit test is used in many applications for testing normality in climate research. This note shows that the test usually leads to systematic and drastic errors. When the mean and the standard deviation are estimated, it is much too conservative in the sense that its p values are strongly biased upward. One may think that this is a small sample problem, but it is not. There is a correction of the Kolmogorov–Smirnov test by Lilliefors, which is in fact sometimes confused with the original Kolmogorov–Smirnov test. Both the Jarque–Bera and the Shapiro–Wilk tests for normality are good alternatives to the Kolmogorov–Smirnov test. A power comparison of eight different tests has been undertaken, favoring the Jarque–Bera and the Shapiro–Wilk tests. The Jarque–Bera and the Kolmogorov–Smirnov tests are also applied to a monthly mean dataset of geopotential height at 500 hPa. The two tests give very different results and illustrate the danger of using the Kolmogorov–Smirnov test.

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