Browsing by Author "Li, Xin"
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Item Detecting and correcting publication bias in meta-analysis(2009-12) Li, Xin; Borich, Gary D.; Beretvas, Susan NatashaPublication bias (PB) makes the resources for meta-analysis (M-A) unreliable in the sense of completion and accuracy, so to investigate, identify and correct PB is a very important issue in M-A. The current study proposed an empirical comparison in both detection and correcting PB, using a Monte Carlo study. Conditions to be manipulated include the number of primary studies, number of missing studies and true effect size. RANNOR in SAS will be used to generate normally distributed random variables and, for each condition, 10,000 M-As will be simulated. Type I error rates are to be calculated for the conditions with no PB and powers were estimated for the conditions with PB and adequate type I error control. Finally, a demonstration of how M-A can and should be used as a part of program evaluations was given.Item An evaluation of parameter estimation when using multilevel structural equation modeling for mediation analysis(2011-05) Li, Xin; Beretvas, Susan Natasha; Borich, Gary; Dodd, Barbara; Pituch, Keenan; Stapleton, Laura; Whittaker, TiffanyHandling of clustered or nested data structures requires the use of multilevel modeling techniques. One such multilevel modeling technique is multilevel structural equation modeling (MLSEM). While estimation of indirect effect parameters and standard errors based on the conventional multilevel model (MMM) has been assessed, this is not the case for the use of the MLSEM model for estimating indirect effects. This simulation study was designed to investigate the use of the MLSEM for estimating mediated effects for the “upper-level” mediation model as compared with the MMM. The following conditions were manipulated: number of clusters (G), within-cluster sample size (nj ), intra-class correlation, measurement error in the mediator, and the true value of the mediated effect derived from various patterns of true values for a and b. The generating model entailed an upper-level mediation model for a cluster-randomized trial that included a dichotomous level two independent variable, a cluster-level latent mediator and an individual-level latent dependent variable both with four indicators. Relative parameter and standard error bias, obtained using the MLSEM and the MMM were evaluated and compared. Percent coverage was calculated and compared when PRODCLIN was used to calculate the confidence interval estimates of the ab effect. Finally, Type I error rates for conditions when ab = 0 were assessed and compared. In addition, statistical power for detecting a truly non-zero mediated effect was tallied and compared across models. Results showed that use of the MMM provided inaccurate and misleading parameter and standard error estimates for the estimates of the mediated effect, especially when the true values of a, b and ab were not zero and the measurement error for M was large. However, the MLSEM estimates were also unacceptable in some of the conditions with small values for G and nj. Researchers are encouraged to use the MLSEM for assessing the multilevel mediated effects when either or both paths a and b are expected to be non-zero, if G is at least 40 and nj is also greater than 40. Results are presented and discussed along with implications for applied researchers intending to assess mediated effect with clustered data.Item Explicit two-source extractors and more(2016-05) Chattopadhyay, Eshan; Zuckerman, David I.; Gal, Anna; Li, Xin; Waters, BrentIn this thesis we study the problem of extracting almost truly random bits from imperfect sources of randomness. This is motivated by the wide use of randomness in computer science, and the fact that most accessible sources of randomness generate correlated bits, and at best contain some amount of entropy. We follow Chor and Goldreich [CG88] and Zuckerman [Z90], and model weak sources using min-entropy, where an (n,k)-source X is a distribution on n bits and takes any string x with probability at most 2^-k. It is known that it is impossible to extract random bits from a single (n,k)-source, and Chor and Goldreich [CG88] raised the question of extracting randomness from two such independent (n,k)-sources. Existentially, such 2-source randomness extractors exist for min-entropy k >=log n + O(1), but the best known construction prior to work in this thesis requires min-entropy k >=0.499 n [B2]. One of the main contributions of this thesis is an explicit 2-source extractor for min-entropy log^C n, for some constant C. Other results in this thesis include improved ways of extracting random bits from various other sources of randomness, as well as stronger notions of randomness extraction. Our results have applications in privacy amplification [BBR88,Mau92,BBCM95], which is a classical problem in information cryptography, and give protocols that achieve almost optimal parameters. Other applications include explicit constructions of non-malleable codes, which is a relaxation of the notion of error-detection codes and have applications in tamper-resilient cryptography [DPW10].Item Overexpression of higher plant nitrite reductase(Texas Tech University, 1999-05) Li, XinThe gene encoding the ferredoxin-dependent nitrite reductase of spinach chloroplasts (NiR; EC 1.7.7.1) has been isolated and sequenced (Back et ah, 1988). Although some information is available about the possible role of a few specific amino acids in the enzyme mechanism (Hirasawa et ah, 1994; Bellissimo et fl/.,1995; Dose et ah, 1997 and Dose, 1996), the absence of a 3-dimensional structure for the protein and the scarcity of data obtained with site-specific mutants have left considerable uncertainty about the enzyme mechanism. The work presented in this dissertation represents an attempt to address, in part, these issues by designing a system to over-express NiR in Escherichia coli and investigate the roles of the tryptophan residue at position 92 in the enzyme mechanism.