Related Experiment Video
Updated: Feb 25, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Analysis on the Association Among Stunting, Wasting and Underweight in Malawi: An Application of a Log-Linear Model
Alfred Ngwira1, Eddons C S Munthali1, Kondwani D Vwalika2
1Department of Basic Sciences, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi.
Abstract:
Childhood undernutrition is an important public health problem. Many studies have investigated the factors of childhood undernutrition, but not the association between the undernutrition indicators. This study aimed at investigating the association between the childhood undernutrition indicators. A loglinear model of cell counts of a three way table of stunting, wasting, and underweight was fitted based on the 2010 Malawi demographic health survey data. Interaction terms in the model depicted deviations from independence. A multiple correspondence analysis of undernutrition indicators was also plotted to have a visual impression of association of the undernutrition variables. A loglinear model showed that underweight was associated with both stunting (P<0.001), and wasting (P<0.001). There was no association between stunting and wasting (P=1). Furthermore there was no three way association of stunting, wasting and underweight (P=1). Lack of three way interaction of stunting, wasting and underweight means that childhood undernutrition multidimensional nature is still valid, and no each indicator can represent the other.
Related Concept Videos
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
Comparing the Survival Analysis of Two or More Groups
One-Way ANOVA: Unequal Sample Sizes
The Mantel-Cox Log-Rank Test
Friedman Two-way Analysis of Variance by Ranks
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

