the F-test is significant and the researcher wants to know which means are significant).
Many computer packages also include tests of a posterior) differences (i.e. The mean BMIs for the four groups are shown in Table 2 together with the analysis of variance (ANOVA). In Bangladesh it is usual to grade people's educational attainment into four levels, no education (coded as 0 here), primary (1), secondary (2) and tertiary (3). It is frequently reported that BMI varies between people with different educational levels, where the educational level is taken as a proxy for a combination of knowledge of health matters and socio-economic status. The calculations of both one- and two-tailed l-tests are identical the only difference is in the interpretation of the probability tables.Ĭontinuous dependent variable and an independent variable with 3 or more categories (one-way analysis of variance) If, however, some previous study had shown a significantly reduced BMI in mothers whose child had died the hypothesis would have been the alternative one (H1) and a one-tailed t-test would have been used. In these analyses a two-tailed t-test was used because the null hypothesis (Ho) was that there was no difference between means. The results show that there is a highly significant difference in means mothers whose child died have, on average, a lower mean BMI. Since there was no difference in sample variances a pooled t-test statistic was calculated. The comparison of mean BMIs of mothers by birth outcome is presented in Table 1. SPSS/PC+) provide both the pooled and separate variance t-tests. If the F-test shows significant heterogeneity a separ ate variance t-test is used and most computer-based statistical packages (e.g. The simple t-test assumes non-significant differences in sample variances and a test for homogeneity of variances (F-test) is usually performed before going on to the l-test. does the infant die? Since there are only two categories (death or no child death) a simple t-test will suffice. One question of interest is whether there is any significant relationship between mothers' BMI and birth outcome, i.e. Analysis of variance Source of variationĬontinuous dependent variable and an independent variable with 2 categories (l-test and F-test) Analysis of variance of BMI by educational level and gravidity Mothers with antepartum haemorrhage, or undergoing miscarriage and abortion, multiple pregnancy, eclampsia or with gross fetal abnormalities were excluded.
#Anthropometrics graphical analysis full
The study was conducted in 10 medical centres in Bangladesh and all the women were full term.
To illustrate the types of tests which can be used, data from a large Bangladeshi survey of 4150 mother-child pairs in which mothers' anthropometric data were related to birth outcome have been used. *Denotes pairs of groups significantly different at the 0.050 level Multiple range test: Student-Newman-Keuls procedure One-way Analysis of Variance and a posterior) test For instance body mass index (BMI: kg/m 2) has been shown to show skewness in some populations because of the extended tail at the upper end of the distribution. If the distribution of an anthropometric character does show significant skewness then a simple logarithmic (either log 10 or loge) transformation will probably normalize the distribution. Nevertheless significant skewness and/or kurtosis may occur with large samples even though the magnitude of the effect (s) is very small. Since skewness is more constraining than kurtosis the Cox test is preferable.
For example the Kolmorogov-Smirnoff test examines the cumulative distribution, which conflates skewness and kurtosis, while the Cox test determines the extent of skewness and kurtosis separately. There are a number of statistical tests available for testing 'normality' and the researcher may well get different results depending on which test is used. As the distribution becomes more skewed, the difference between mean and median increases. For normal distributions the mean and median are numerically identical. An easy way of seeing whether the distribution is skewed is to compare the values of the mean and median. Anthropometric characters tend to be continuous and many tests are constructed on the assumption that the data approximate to a normal distribution.
0 kommentar(er)
