A CURRENT EXAMINATION OF DIETARY INTAKES OF FIBER, CALCIUM, IRON, AND ZINC AND THEIR RELATIONSHIP TO BLOOD LEAD LEVELS IN U.S. CHILDREN AGED 1-5 YEARS

A CURRENT EXAMINATION OF DIETARY INTAKES OF FIBER, CALCIUM, IRON, AND ZINC AND THEIR RELATIONSHIP TO BLOOD LEAD LEVELS IN U.S. CHILDREN AGED 1-5 YEARS Stephanie Ann Melchert, M.S. University of Nebraska, 2010 Adviser: Kaye Stanek Krogstrand The effect of lead on the health and well-being of those exposed has been well documented and many efforts have been made to reduce exposure of lead to the United States population. Despite these efforts, many studies have documented cognitive impairments and behavioral problems in children with even low levels of lead in their blood. Previous studies have suggested that a proper diet may have a role in the prevention of elevated blood lead levels in children. The objective of this study was to determine if there was an inverse correlation of blood lead levels (BLL) in children to their dietary intakes of fiber, calcium, iron, and zinc considering low levels of lead exposure. This study examined 1019 children in the National Health and Nutrition Examination Survey (NHANES) conducted from 2005-2006. Data were analyzed using Spearman’s rank correlations to correlate continuous variables to BLL in children and independent samples t-tests were used to compare mean blood lead levels of categorical variables. Results indicate that BLL in children is significantly correlated with and weight, recumbent length/standing height, dietary fiber intake and continine, a marker of cigarette smoke exposure. BLL was not significantly correlated with calcium, iron, zinc, or vitamin C. A significant difference was found in the mean BLL of children who took supplements, lived in smoking homes, as well as those who lived in homes built before 1978. Overall, this study shows that children living in homes built before 1978 remain at greater risk for lead exposure, and adequate dietary fiber intake may provide benefits to children who are exposed to lead.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.