Let G be a graph with vertex set V and no isolated vertices. A subset S ⊆ V is a semipaired dominating set of G if every vertex in V \ S is adjacent to a vertex in S and S can be partitioned into two element subsets such that the vertices in each subset are at most distance two apart. We present a method of building trees having a unique minimum semipaired dominating set.

Let G be a graph with vertex set V and no isolated vertices. A subset S ⊆ V is a semipaired dominating set of G if every vertex in V \ S is adjacent to a vertex in S and S can be partitioned into two element subsets such that the vertices in each subset are at most distance two apart. The semipaired domination number γ pr2 (G) is the minimum cardinality of a semipaired dominating set of G. We show that if G is a connected graph G of order n ≥ 3, then γ pr2 (G) ≤ 32 n, and we characterize the extremal graphs achieving equality in the bound.

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Let G be a graph with vertex set V. A subset S ? V is a semipaired dominating set of G if every vertex in V \ S is adjacent to a vertex in S and S can be partitioned into two element subsets such that the vertices in each subset are at most distance two apart. The semipaired domination number is the minimum cardinality of a semipaired dominating set of G. We characterize the trees having a unique minimum semipaired dominating set. We also determine an upper bound on the semipaired domination number of these trees and characterize the trees attaining this bound.

<p> Different instructions for associating concrete nouns were given in a paired-associate learning situation. Material was varied to produce different types of interference against which to evaluate the effectiveness of the different instructions.</p> <p> The major findings were: (1) Specific instructions for association produced fewer errors than no specific instructions on the first list a subject learned. (2) After the first list, only the instructions which asked the learner to produce a logical scene from the nouns produced fewer errors than non-specific instructions. (3) Specific instructions did not interact with material.</p> <p> Little evidence was found that the significant instructional effects were due to the specific characteristics of the learners' mediations. These effects were more plausibly attributed to such general mechanisms as either giving the mediations during learning or having learning time occupied with irrelevant activity.</p> / Thesis / Master of Arts (MA)

Metamemory effects under retroactive interference (RI) were tested using a modified RI paradigm in order to determine whether relatedness of word pairs impacts metamemory predictions and to investigate the bases of these predictions. Conflicting findings from prior research suggested that the relatedness of materials used to test RI could produce disparate effects; some studies showed association between memory and metamemory but other studies showed dissociation between the two. The experiments consisted of four phases: original and interpolated study, prediction, and test; participants were tested on the words encountered at original study. By comparing predictions to recall, we determined that memory and metamemory were dissociated under RI regardless of cue-target relatedness and regardless of the relatedness of targets from original to interpolated study. Additionally, the findings support the use of the accessibility heuristic to make metamemory predictions under RI. The results support a separate systems viewpoint.

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The purpose of this investigation was to examine the effects of collaborative learning strategies on formulating solutions to linear programming word problems that were designed to incorporate problem-solving skills. Forty-six students majoring in business at a small southwest college in Virginia participated in the study. After an instruction session, a study period, and a question and answer discussion, participants completed the test instrument based upon random assignment to three treatment groups. These included individual study with individual test taking (control), paired study with individual test taking, and paired study with paired test taking. All participants returned in 17 days to complete a posttest individually having received no further instruction in linear programming theory. The following null hypothesis was examined: No differences in treatment means measuring problem-solving abilities would be found based on students' test and posttest scores using two treatment groups of collaborative study, with collaborative or individual test taking, and a control group of individual study with individual test taking. After satisfying the assumptions of no difference in ability in the treatment groups, establishing significant influence of ability on test score and posttest score variables, and establishing homogeneity of regression, an analysis of covariance (ANCOVA) was used to test the null hypothesis. The null hypothesis was rejected. Treatment had a significant effect on the variance for the test score variable, F = 3.92, p < .05, and for the posttest score variable, F = 4.44, p < .05. Newman-Keuls post hoc test showed significant differences in the adjusted means of the test score variable between the individual study with individual test taking group (72.22) and the paired study with paired test taking group (87.86). For the posttest score variable, the Newman-Keuls post hoc test revealed significant differences between the adjusted means of the individual study with individual test taking group (36.25) and the paired study with individual test taking group (59.20), and between the adjusted means of the individual study with individual test taking group (36.25) and the paired study with paired test taking group (55.77). Implications of findings and recommendations for further research were discussed. / Ph. D.

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In this thesis, we focus on the the comparison of treatments with ordered categorical responses. The three cases of treatment comparisons will all be studied. The main objective of this thesis is to develop more effective comparison methods for treatments with ordinal responses and to address some important issues involved in different comparison problems. Our major statistical approach is to consider ordinal responses as manifestations of some underlying continuous random variables. / The comparison of treatments to detect possible treatment effects is a very important topic in statistical research. It has been drawing significant interests from both academicians and practitioners. Important research work on treatment comparisons dates back several decades. For treatment comparisons, the following three cases are very common: the comparison of two independent treatments; the comparison of treatments with repeated measurements; and the multiple comparison of several treatments. For different cases, the involved research issues are usually different. In many fields of study, the level of measurement for responses of the treatments is ordinal. Many examples can be found in areas such as biostatistics, psychology, sociology, and market research, where the ordered categorical variables play an important role. / This thesis consists of three main parts. In the first part, we consider the modeling of treatments with longitudinal ordinal responses by a latent growth curve. On the basis of such a latent growth curve, we achieve a comprehensive flexible model with straightforward interpretations and a variety of applications including treatment comparison, the analysis of covariates, and equivalence test of treatments. In the second part, we consider the comparison of several treatments with a control for ordinal responses. By considering the ordinal responses as manifestations of some underlying normal random variables, a latent normal distribution model is utilized and the corresponding parameter estimation method is proposed. Further, we also derive testing procedures that compare several treatments with a control under an analytical framework. Both single-step and stepwise procedures are introduced, and these procedures are compared in terms of average power based on a simulation study. In the last part of this thesis, we establish a unified framework for treatment comparisons with ordinal responses, which allows various treatment comparison methods be comprehended using a unified perspective. The latent variable model is also utilized, but the underlying random variables are allowed to have any member of the location-scale distribution family. This latent variable model under such a specification of underlying distributions subsumes many existing models in the literature. A two-step procedure to identify the model and produce the parameter estimates is proposed. Based on this procedure, many important statistical inferences can be conveniently conducted. Furthermore, the sample size determination method based on the latent variable method is also proposed. The proposed latent variable method is compared with the existing methods in terms of power and sample size. / Lu, Tongyu. / Adviser: Wai-Yin Poon. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 94-101). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.