Nominal Type Research Articles

Use of Comparative Symbols Based on Data Type to Formulate a Questionnaire

When it comes to using the unit of a number, some researchers can-not distinguish when to use the comparison symbol while designing a questionnaire. This study will discuss how the use of comparative symbols by the type of data in the questionnaire questionsthrough literature studies. It can be concluded that the use of comparison symbols in the questionnaire options cannot be done when the nom-inal data type is used. The results of this study can be implemented for designing a research instrument that uses a comparison symbol on the question options.

Variation in differential object marking: On some differences between Spanish and Romanian

Abstract Although differential object marking (DOM) has been studied from a multitude of perspectives, research into the types of variation it allows in closely related languages is still needed. This article examines DOM from the point of view of (micro)variation, focusing on two genetically related languages, namely, standard Spanish and Romanian. Both identities and points of divergence are discussed in detail and argued to result from a morphosyntactic parameter in the realm of differential marking. Following López [López, Luis. 2012. Indefinite Objects Scrambling, Choice Functions and Differential Marking. Cambridge, MA: MIT Press], the Spanish special marker signals certain types of nominals which undergo short scrambling in order to have Case licensed. In Romanian, however, DOM is equated with an additional operation beyond the valuation of Case.

The processing of process and result deverbal nominals in Serbian and English

Process and result deverbal nominals are two types of nouns derived from related verbs. These two types of deverbal nominals exhibit different behavior in a number of aspects. The aim of this study was to test the differences of process and result deverbal nominals, in both Serbian and English, with respect to their cognitive processing. Two self-paced reading experiments were conducted. Experiment 1 was conducted in Serbian, with target constructions, process and result deverbal nominals (e.g., drhtaj/drhtanje [EN trembling]), embedded in the sentence contexts, whereas Experiment 2 dealt with the equivalent constructions in English. Data were analyzed with the Generalized Additive Mixed Models ? GAMMs (Wood, 2006, 2011) measuring reading times (RTs) at the word level (deverbal nouns) and the sentence level (the whole sentence, including the deverbal nominal) in both languages. The final results in general suggested that result deverbal nominals were processed faster than process deverbal nominals. It was assumed that these differences were obtained because process deverbal nominals are syntactically more complex than result deverbal nominals.

Nominal types in Gitksan split-absolutive agreement

This paper presents a study of a split absolutive-nominative agreement pattern in Gitksan (Tsimshianic) which co-occurs with ergative agreement. The split is conditioned on the basis of nominal type: alongside ergative agreement, a second type of agreement targets absolutives (S, O) when the subject is a participant or third-singular pronoun, or nominatives (S, A) when the subject is a full DP or third-plural pronoun. This results in what appears to be construction-dependent variation in the applicability of the Activity Condition, as some transitive subjects receive both ergative and nominative agreement. It is proposed that DP arguments and third-plural pronouns are able to receive multiple instances of agreement by virtue of bearing D-features; in contrast to the situation with typical pronouns, these D-features remain active for agreement even if prior ergative agreement has already deactivated the argument’s ϕ-features. Both ergative and nominative agreement may target a single argument because different groups of features are targeted during the two operations. The D-feature property is ultimately linked to a structural DP/non-DP distinction amongst arguments, providing some insight into the role of D in pronominal systems. This analysis, which crucially relies on an approach to activity that explicitly allows incremental deactivation of an argument, accounts for the empirical facts in Gitksan more readily than a purely morphological approach or an approach that considers distinctions between ϕ-features alone.

Design and analysis of three-arm parallel cluster randomized trials with small numbers of clusters.

In this article, we review and evaluate a number of methods used in the design and analysis of small three-arm parallel cluster randomized trials. We conduct a simulation-based study to evaluate restricted randomization methods including covariate-constrained randomization and a novel method for matched-group cluster randomization. We also evaluate the appropriate modelling of the data and small sample inferential methods for a variety of treatment effects relevant to three-arm trials. Our results indicate that small-sample corrections are required for high (0.05) but not low (0.001) values of the intraclass correlation coefficient and their performance can depend on trial design, number of clusters, and the nature of the hypothesis being tested. The Satterthwaite correction generally performed best at an ICC of 0.05 with a nominal type I error rate for single-period trials, and in trials with repeated measures type I error rates were between 0.04 and 0.06. Restricted randomization methods produce little benefit in trials with repeated measures but in trials with single post-intervention design can provide relatively large gains in power when compared to the most unbalanced possible allocations. Matched-group randomization improves power but is not as effective as covariate-constrained randomization. For model-based analysis, adjusting for fewer covariates than were used in a restricted randomization process under any design can produce non-nominal type I error rates and reductions in power. Where comparisons to two-arm cluster trials are possible, the performance of the methods is qualitatively very similar.

Testing small study effects in multivariate meta-analysis.

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the CochraneDatabase.

Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.

Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Information fraction estimation based on the number of events within the standard treatment regimen.

For a Phase III randomized trial that compares survival outcomes between an experimental treatment versus a standard therapy, interim monitoring analysis is used to potentially terminate the study early based on efficacy. To preserve the nominal TypeI error rate, alpha spending methods and information fractions are used to compute appropriate rejection boundaries in studies with planned interim analyses. For a one-sided trial design applied to a scenario in which the experimental therapy is superior to the standard therapy, interim monitoring should provide the opportunity to stop the trial prior to full follow-up and conclude that the experimental therapy is superior. This paper proposes a method called total control only (TCO) for estimating the information fraction based on the number of events within the standard treatment regimen. Based on theoretical derivations and simulation studies, for a maximum duration superiority design, the TCO method is not influenced by departure from the designed hazard ratio, is sensitive to detecting treatment differences, and preserves the TypeI error rate compared to information fraction estimation methods that are based on total observed events. The TCO method is simple to apply, provides unbiased estimates of the information fraction, and does not rely on statistical assumptions that are impossible to verify at the design stage. For these reasons, the TCO method is a good approach when designing a maximum duration superiority trial with planned interim monitoringanalyses.

Homogeneity testing under finite location‐scale mixtures

The testing problem for the order of finite mixture models has a long history and remains an active research topic. Since Ghosh & Sen (1985) revealed the hard‐to‐manage asymptotic properties of the likelihood ratio test, many successful alternative approaches have been developed. The most successful attempts include the modified likelihood ratio test and the EM‐test, which lead to neat solutions for finite mixtures of univariate normal distributions, finite mixtures of single‐parameter distributions, and several mixture‐like models. The problem remains challenging, and there is still no generic solution for location‐scale mixtures. In this article, we provide an EM‐test solution for homogeneity for finite mixtures of location‐scale family distributions. This EM‐test has nonstandard limiting distributions, but we are able to find the critical values numerically. We use computer experiments to obtain appropriate values for the tuning parameters. A simulation study shows that the fine‐tuned EM‐test has close to nominal type I errors and very good power properties. Two application examples are included to demonstrate the performance of the EM‐test.

Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations.

Stepped wedge cluster trials are an increasingly popular alternative to traditional parallel cluster randomized trials. Such trials often utilize a small number of clusters and numerous time intervals, and these components must be considered when choosing an analysis method. A generalized linear mixed model containing a random intercept and fixed time and intervention covariates is the most common analysis approach. However, the sole use of a random intercept applies a constant intraclass correlation coefficient structure, which is an assumption that is likely to be violated given stepped wedge trials (SWTs) have multiple time intervals. Alternatively, generalized estimating equations (GEE) are robust to the misspecification of the working correlation structure, although it has been shown that small-sample adjustments to standard error estimates and the use of appropriate degrees of freedom are required to maintain the validity of inference when the number of clusters is small. In this article, we show, using an extensive simulation study based on a motivating example and a more general design, the use of GEE can maintain the validity of inference in small-sample SWTs with binary outcomes. Furthermore, we show which combinations of bias corrections to standard error estimates and degrees of freedom work best in terms of attaining nominal type I error rates.

The impact of misclassification on covariate-adaptive randomized clinical trials.

Covariate-adaptive randomization (CAR) is widely used in clinical trials to balance treatment allocation over covariates. Over the past decade, significant progress has been made on the theoretical properties of covariate-adaptive design and associated inference. However, most results are established under the assumption that the covariates are correctly measured. In practice, measurement error is inevitable, resulting in misclassification for discrete covariates. When covariate misclassification is present in a clinical trial conducted using CAR, the impact is twofold: it impairs the intended covariate balance, and raises concerns over the validity of test procedures. In this paper, we consider the impact of misclassification on covariate-adaptive randomized trials from the perspectives of both design and inference. We derive the asymptotic normality, and thereby the convergence rate, of the imbalance of the true covariates for a general family of covariate-adaptive randomization methods, and show that a superior covariate balance can still be attained compared to complete randomization. We also show that the two sample t-test is conservative, with a reduced Type I error, but that this can be corrected using a bootstrap method. Moreover, if the misclassified covariates are adjusted in the model used for analysis, the test maintains its nominal Type I error, with an increased power. Our results support the use of covariate-adaptive randomization in clinical trials, even when the covariates are subject tomisclassification.

Statistical analysis of longitudinal data on tumour growth in mice experiments

We consider mice experiments where tumour cells are injected so that a tumour starts to grow. When the tumour reaches a certain volume, mice are randomized into treatment groups. Tumour volume is measured repeatedly until the mouse dies or is sacrificed. Tumour growth rates are compared between groups. We propose and evaluate linear regression for analysis accounting for the correlation among repeated measurements per mouse. More specifically, we examined five models with three different variance-covariance structures in order to recommend the least complex method for small to moderate sample sizes encountered in animal experiments. We performed a simulation study based on data from three previous experiments to investigate the properties of estimates of the difference between treatment groups. Models were estimated via marginal modelling using generalized least squares and restricted maximum likelihood estimation. A model with an autoregressive (AR-1) covariance structure was efficient and unbiased retaining nominal coverage and type I error when the AR-1 variance-covariance matrix correctly specified the association between repeated measurements. When the variance-covariance was misspecified, that model was still unbiased but the type I error and the coverage rates were affected depending on the degree of misspecification. A linear regression model with an autoregressive (AR-1) covariance structure is an adequate model to analyse experiments that compare tumour growth rates between treatment groups.

A Randomised Control Trial and Comparative Analysis of Multi-Dimensional Learning Tools in Anatomy

This article presents the results of a study that examined students’ ability to retain what they have learned in an anatomy course after thirty days via using various learning tools for twenty minutes. Fifty-two second-year medical students were randomly assigned to three learning tools: text-only, three-dimension visualisation in a two-dimensional screen (3DM), or mixed reality (MR). An anatomy test lasting for twenty minutes measuring spatial and nominal knowledge was taken immediately after the learning intervention and another thirty days later. Psychometric tests were also used to measure participants’ memory, reasoning and concentration abilities. Additionally, electroencephalogram data was captured to measure the participants’ awakeness during the learning session. Results of this study showed that the MR group performed poorly in the nominal questions compared to the other groups; however, the MR group demonstrated higher retention in both the nominal and spatial type information for at least a month compared to the other groups. Furthermore, participants in the 3DM and MR groups reported increased engagement. The results of this study suggest that three-dimensional visualiser tools are likely to enhance learning in anatomy education. However, the study itself has several limitations; some include limited sample size and various threats to internal validity.

Triple Social Nutrition Construction

Introduction. The purpose of the paper is analysis of the humanity perception of material objects that are used as food, from the point of view of sociology, and analysis of material relations of entry-level (people and material objects recognized as the food), in which the social construction and impact of social engineering to limit the actions of people in public life. The novelty of the author’s approach is to allocate reverse the effects of nutrition on human behavior as a factor of social process.Methodology and sources. In this paper, for the analysis of social practices of nutrition as a social process used to conceptual design of “social construction” from the point of view of the sociology of things the sociology of knowledge and sociology of nutrition. The analysis of the authors’ works that address issues of social construction (Durkheim, Latour, Berger, Luckmann, Schütz, etc.).Results and discussion. According to the study author proposes a classification, according to which the social construction of power is divided into three types (levels). Nominative type is elementary awareness of the substances. Measurement type is further systematization of knowledge about food and the actions relating thereto (the distinction between substances on the basis of edibility, the establishment of various foundations products or their social attributes, defining the main ways of making food). Institutional type is determining in what form to carry out actions with the products and everything associated with them (the emergence of order meals and nutrition, the overcoming of primitive naturalism in power).The hypothesis is expressed and investigated that each level of the design is conditioned by the social and structural environment interaction of the actors.Conclusion. It is stated that in the modern system of nominative power, and measuring the types of institutional design are in a state of complex interdependence, since over time a system of knowledge, constructed by the forerunners, turned into a “cash knowledge” with the result that subsequent generations gradually ceased to distinguish between the complexity of multilevel social constructions of reality.Formulated thesis is that the analysis of the social construction of reality should help to better understand the social nature of food, in particular, to answer the question: how do food products are social constructions, as they are created by our consciousness under the influence of the existing “cash” system of knowledge as constructed, their properties (their tastes), which, as it turns out, are not so much biological, but also socio-cultural properties.

On nominal tense

Abstract Nordlinger & Sadler’s (2004. Nominal tense in crosslinguistic perspective. Language 80. 776–806) seminal work fostered an intense debate on the semantics of nominal tense systems, with the side effect of widening the typological coverage of this grammatical feature. This paper aims at contributing to the ongoing debate. In contrast with work by Tonhauser, who excluded ‘tense’ as a semantic component of the Paraguayan Guaraní nominal tense system, the paper claims that all TAM dimensions are involved – temporality, aspect, modality – with different proportions in the individual markers. Most importantly, it claims that nominal tense does not presuppose a semantics of its own, other than the one needed for verbal tenses. Moreover, the paper presents evidence that the semantic component of aspect, besides being necessarily activated in any nominal tense marker, is also directly conveyed by some of them, which can legitimately be called ‘nominal aspect’ markers. This integrates Nordlinger & Sadler’s (2004) survey, in which aspect was notably absent. In addition, the paper points out possible cases of nominal actionality (a.k.a. Aktionsart). Finally, the paper suggests that the pervasive presence of aspect (and also, but rarely, actionality) among nominal tense markers finds interesting parallels in some types of deverbal nominalizations, although these belong in another grammatical drawer.

Group sequential comparison of positive predictive value curves for correlated biomarker data

Clinical studies of predictive diagnostic tests consider the evaluation of a single test and comparison of two tests regarding their predictive accuracy of disease status. The positive predictive value (PPV) curve is used for assessing the probability of predicting the disease given a positive test result. The sequential property of one PPV curve had been studied. However, in later stages of diagnostic test development, it is more interesting to compare predictive accuracy of two tests. In this article, we propose a group sequential test for the comparison of PPV curves for paired designs when both diagnostic tests are applied to the same subject. We first derive asymptotic properties of the sequential differences of two correlated empirical PPV curves under the common case-control sampling. We then apply these results to develop a group sequential test procedure. The asymptotic results are also critical for deriving both the optimal sample size ratio and minimal required sample sizes for the proposed procedure. Our simulation studies show that the proposed sequential testing maintains the nominal type I error rate in finite samples. The proposed design is illustrated in a hypothetical lung cancer predictive trial and in a cancer diagnostic trial.

Detección de valores atípicos con técnicas de minería de datos y métodos estadísticos

La detección de valores atípicos en el campo de la minería de datos (DM) y el descubrimiento de conocimiento a partir de datos (KDD) es de gran interés en áreas que requieren sistemas de soporte a la toma de decisiones, como, por ejemplo, en el área financiera, en donde mediante DM se pueden detectar fraudes financieros o encontrar errores producidos por los usuarios. Entonces, es esencial, evaluar la veracidad de la información, a través de métodos de detección de comportamientos inusuales en los datos. Este artículo propone un método para detectar valores que se consideran valores atípicos en una base de datos de datos de tipo nominal. El método implementa un algoritmo global de “k” vecinos más cercanos, un algoritmo de agrupamiento denominado k-means y un método estadístico denominado chi-cuadrado. La aplicación de estas técnicas ha sido implementada sobre una base de datos de clientes que han solicitado un crédito financiero. El experimento se realizó sobre un conjunto de datos con 1180 tuplas, en donde, deliberadamente se introdujeron valores atípicos. Los resultados demostraron que el método propuesto es capaz de detectar todos los valores atípicos introducidos.

On comparative study of deverbal nominalizations denoting process and result in Lithuanian and English

Although the Lithuanian and English languages are bound within the family of IndoEuropean languages, the typological differences between the two languages lie in the system of inflectional and derivational morphology. The paper analyses the concept of nominalization and discusses the deverbal process and result nominalizations in Lithuanian and English. For the comparative qualitative and quantitative analysis, 965 equivalents of deverbal nouns have been selected from the “Parallel Corpus”. Out of them, 802 examples belong to the category of deverbal process nouns, whilst the category of deverbal result nouns includes 163 examples. From the point of view of morphology, in both languages nominalization is a word-formation process by which a noun is derived from a verb, adjective or another noun, or even other parts of speech, usually through suffixation and by adding the ending in the Lithuanian language. Two types of nominalization can be found across languages: lexical and syntactic. Lexical nominalization refers to the formation of deverbal nouns or nominal words derived from the verb or a nominal word, and syntactic nominalization refers to turning a clause into a noun phrase. In summary, the investigation of the derivational affixes of deverbal nouns in Lithuanian and their equivalents in English has revealed the following differences: in Lithuanian, the deverbal nominalizations – deverbal process nouns and deverbal result nouns – can be formed with 132 suffixes and 5 endings, whilst in English – with 10 suffixes and by employing the derivational strategy of conversion. Also, the analysis of the empirical material revealed that the suffix -imas/-ymas in Lithuanian prevails in forming deverbal process nouns (they make 73 per cent of all deverbal process nouns), while the suffix -inys is the most prolific in forming deverbal result nouns (they make 38 per cent of all deverbal result nouns). The English equivalents usually have the suffix -ion/-tion/-sion/-ation, quite many derivatives have the suffix -ing. It should be noted that deverbal nominalizations in the Lithuanian language often correlate with abstract and concrete nouns (non-derivatives) in the English language: 23 per cent of all derivatives in Lithuanian have more than one equivalent (derivative or non-derivative) in English.

Using the concept of instance typicality in instance-based learning environments involving nominal attributes

Instance-Based Learning (IBL) is a machine learning research area with focus on supervised algorithms that use the given training set as the expression of the learned concept. Usually the training instances in the set are described by vectors of attribute values and an associated class. The general ization process conducted by an instance-based algorithm happens during the classification phase, when a class should be assigned to a new instance of unknown class. Attributes that describe instances can be of different types, depending on the values they represent and, usually, can be of discrete or continuous type. A subtype of the discrete type is known as nominal. An attribute of nominal type usually represents categories and there is no order among its possible values. This paper proposes and investigates an alternative strategy for dealing with nominal attributes during the classification phase of the well-known instance-based algorithm NN (Nearest Neighbor). The proposed strategy is based on the concept of typicality of an instance, which can be taken into account as a possible tiebreaker, in situations where the new instance to be classified is equidistant from more than one nearest neighbor. Experiments using the proposed strategy and the default random strategy used by the conventional NN show that a strategy based on the concept of instance typicality can be a convenient choice to improve accuracy, when data instances have nominal attributes among the attributes that describe them.

TESTING THE POPULATION INVERSE-COEFFICIENTS OF VARIATION AND ITS APPLICATION

In this paper, some tests are introduced and compared for testing the equality of inverse-coefficients of variation. Monte-Carlo simulation method is used for comparisons. In this simulation study, various simulation scenarios were designed with different population numbers (k = 3, 6), sample sizes, parameter values and type I error rates (alpha = 0.01, 0.05). The tests were compared in terms of type I error rate and power in these scenarios. When the sample sizes are small, the D and WT tests showed good results in terms of type I error, but the LR and ST tests did not give good results. As the sample sizes increased, the experimental type I error rates of the LR and ST tests converged to the nominal type I error and all tests showed good results in general. While the sample sizes were equal, it was found that the LR test was the most powerful test and the ST test sometimes yielded good results. For these sample sizes, the D test yielded the worst results. When the sample sizes are different, the LR and D tests are powerful than the other tests, and the ST test is the worst test in terms of power. As expected, as the sample sizes and nominal type I error rate increased, the powers of the tests also increased. In addition, an application for the tests was made on real data. It was seen that the results of this application and simulation study coincide.