Historically, a variety of parametric and nonparametric methods were employed to determine z-score values, but such models did not allow the calculation of percentiles or equivalent z-scores for other than the selected smoothed percentiles. As an alternative, but less widely used, linear regression models have been proposed to estimate z-scores and to identify implausible z-score values. For values in the obesity range, BMI z-scores have been found unsatisfactory because the statistical method used to construct the growth charts compresses the z-score scale. These charts are based on the National Health and Nutrition Examination Survey data from the 1960s through the 1980s to determine the distribution of height, weight and BMI in children, which varies by age and sex. In 2002, the CDC published growth charts for several anthropometric measurements. ![]() Z-score values below -3 indicate severe wasting and stunting. Similarly, moderate wasting (low weight-for-height (WFH)), stunting (low height-for-age (HFA)) are defined as z-score between -3 and -2 SD. Moderate malnutrition is defined as a weight-for-age (WFA) between -3 and -2 SD below the mean of the WHO child growth standards. WHO proposes the calculation of z-scores for the analysis and interpretation of anthropometric values either for population-based and individual assessment, and suggests z-scores as a sex-independent variable that can be grouped by combining sex and age groups. Z-score charts (also known as centile growth charts) are used in paediatric growth follow-up and to compare anthropometrical variables to detect the presence of malnutrition or disease. Z-score equal to 0 means an average value, while a z-score of +1 means the value is one SD above the mean value of the population. For a normal distribution, a z-score represents the distance in SDs of a given value to the mean value of the distribution. Although curve-fitting may be imprecise, normal distributions are the most popular because they are scalable to the mean and standard deviation (SD). Īnthropometric measurements may have different distributions for different populations. Using the most precise methods to calculate z-score is important because of the risk of misclassification and its additional consequences. 2.The use of z-scores in medicine and paediatrics is widespread to accurately assess growth through anthropometric measurements such as height, weight and Body Mass Index (BMI). It wasn't even in the top 10% of scores in the class, even though at first sight we may have expected it to be. However, the key finding is that Sarah's score was not one of the best marks. Hence, 24.86% of the scores (0.2486 x 100 = 24.86%) were lower than Sarah's, but above the mean score. We can also see how well she performed relative to the mean score by subtracting her score from the mean (0.5 - 0.2514 = 0.2486). ![]() Going back to our question, "How well did Sarah perform in her English Literature coursework compared to the other 50 students?", clearly we can see that Sarah did better than a large proportion of students, with 74.86% of the class scoring lower than her (100% - 25.14% = 74.86%). In other words, around 25% of the class got a better mark than Sarah (roughly 13 students since there is no such thing as part of a student!). If we look at this as a percentage, we simply times the score by 100 hence 0.2514 x 100 = 25.14%. This means that the probability of a score being greater than 0.67 is 0.2514. Therefore, we start with the y-axis, finding 0.6, and then move along the x-axis until we find 0.07, before finally reading off the appropriate number in this case, 0.2514. The y-axis in the table highlights the first two digits of our z-score and the x-axis the second decimal place. To use the table, which is easier than it might look at first sight, we start with our z-score, 0.67 (if our z-score had more than two decimal places, for example, ours was 0.6667, we would round it up or down accordingly hence, 0.6667 would become 0.67). This table helps us to identify the probability that a score is greater or less than our z-score score.
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