A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

In hepatitis delta virus (HDV) infection, which is an important etiological cause of chronic liver disease, the relationship between serum quantitative HBsAg level and fibrosis and histological activity was investigated. Between 2014 and 2020, 98 patients with chronic HDV infection (53 noncirrhotic, 45 cirrhotic) participated in this prospectively designed study. Quantitative HBsAg levels of the patients were measured and their relationship with the stage of chronic liver disease was compared with histological activity index (HAI), fibrosis score and HDV RNA, model for end-stage liver disease score and other biochemical parameters. All patients were infected with genotype 1 (100%). HBeAg was positive in 8 (8.1%) of the patients. A correlation was found between quantitative HBsAg level and HDV RNA level in patients with both cirrhotic (r = 0.568; P < 0.001) and noncirrhotic (r = 0.644; P < 0.001) HDV infection. Alanine transaminase (P = 0.001; r = 0.495) and aspartate transaminase (P = 0.001; r = 0.511) levels correlated with quantitative HBsAg levels, more prominently in noncirrhotic patients. There was a correlation between quantitative HBsAg level and histological activity index (HAI) in patients with noncirrhotic HDV infection (P < 0.001; r = 0.664). In receiver operating characteristic analysis, both quantitative HBsAg (for cutoff: 1000; sensitivity 76%; specificity 17%; P = 0.335) and HDV RNA (for cutoff: 100000; sensitivity 2%; specificity 98%; P = 0.096) were not predictive markers for cirrhosis. Quantitative HBsAg level can be evaluated as an indicator of viral replication and histological activity in patients with chronic delta hepatitis without cirrhosis. We think that quantitative HBsAg level will be useful in the management of chronic HDV infection, especially in noncirrhotic patients.

Historic and current hepatitis B viral DNA and quantitative HBsAg level are not associated with cirrhosis in non-Asian women with chronic hepatitis B

This article uses neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques such as technical analysis, fundamental analysis, and regression compared with neural network performance. Proposed intelligent stock market prediction system is based on the quantitative and qualitative factors. Three feedforward neural models can be used to analyze these factors. Input data to the neural network proposed are quantitative factors. Input data to the neural network proposed for qualitative factors can be factors related to the political effect considered. Third neural network consists of decision integration in which input data will be the outputs of above-mentioned neural networks. This facilitates to make right decision whether stock market is influenced by quantitative or qualitative factors

Purpose This chapter aims to explicate the use of computer software for qualitative data analysis. Drawing on both a review of relevant literature and a reflexive commentary on an ongoing ethnography, this chapter argues that the use of computer software for qualitative data analysis facilitates rigour and reliability in research, whilst also contributing to wider debates regarding the distinctions made between different research paradigms. Design/methodology/approach The chapter is divided into two sections. In the first, a review of literature pertaining to the use of computer software for qualitative data analysis is reported. The key themes to emerge from this review are then explored in the second section, which consists of a reflexive commentary on the use of computer software for qualitative data analysis within an ongoing three-year Canadian/UK research project. Findings The chapter concludes firstly by foregrounding the methodological benefits of using computer software for qualitative data analysis, and secondly by commenting on wider debates relating to the historical distinctions between quantitative and qualitative research paradigms. Practical implications The chapter suggests that the uptake of computer software for qualitative data analysis should be considered as an integral element of the research design process. Originality/value The originality of this chapter rests in its focus on methodology rather than method, on a reflexive discussion of the place of computer software within the research process rather than a technical description of how software should be used. This chapter is of value not only to researchers who are using or considering using software for their research, but also to researchers who are engaged in wider methodological discussions relating to qualitative and quantitative research paradigms, and to research quality and generalisability.

This paper presents a qualitative and quantitative research to explore and understand the agents that determine the level of productiveness and the importance of qualitative factors. A questionnaire was conducted to estimate the outcome of the qualitative agents or factors in improving the productivity of microtunneling projects. Information was collected from 26 competent professionals in microtunneling projects for almost 20years through an expert's opinion, using a semi-structured questionnaire. The Analytical Hierarchy Process and the clustering analysis technique have been used to account for the twenty qualitative agents or factors concerned in developing a proposed assessment model. The present research has developed an assessment model that stems from the result of qualitative factors on microtunneling to investigate the effect induced by the qualitative agents on productiveness. The assessment model is thereafter confirmed, showing an analytical outcome of 79.85% moderately justified percent. This study proved that the qualitative factors affecting the productivity of microtunneling projects can be assessed through the productivity index concept. This paper helps the industry parties to understand the factors influencing the success of microtunneling projects as well as, development of an assessment model to calculate productivity. Most of the past research on productivity models are based on quantitative factors only. This paper presents qualitative research to explore and understand the factors that affect productivity and the importance of qualitative factors. The developed assessment model presented is the first in connecting theory, analytical methods, and experts' opinions to help practitioners to understand and assess the productivity of microtunneling projects.

The purpose of the study is to form an economic and mathematical instrument for assessing the role of quantitative and qualitative factors in changing the output of dairy products. Objectives: to determine the main theoretical provisions for calculating the indicator of dairy production depending on the resources used; to present a formalized expression for calculating quantitative factors of dairy output growth; to substantiate methodological tools for calculating qualitative, intensive factors of dairy output growth. The object of research is the scientific and technological development of the dairy subcomplex. The economic-mathematical and economic-statistical research methods were used. It has been established that the scientific and technological development of the dairy subcomplex is expressed, first of all, in an increase in the values of qualitative factors, the formal expression of which may be the values of labor productivity, capital productivity, yield or productivity; In periods with a constant level of technical development, quantitative factors receive priority - the number of workers, the cost of basic funds, the acreage, and the number of cows. A toolkit has been developed to assess the cumulative impact of both quantitative and qualitative factors on the resulting indicator of milk production, which will make it possible to reasonably assess the scientific and technological development of the dairy subcomplex. The qualitative factors include the productivity of the workers of the dairy subcomplex, the return on capital and the productivity of cows. Possible combinations of the influence of factors on the resulting indicator of milk production are determined. The key features of changes in influencing variables during periods with a constant level of technical development and in the context of the emergence of new, innovative technological solutions are characterized. Possible directions for further research to assess the scientific and technological de-velopment of the dairy-food subcomplex are outlined.

Exact [formula omitted]-optimal designs for a second-order response surface model on a circle with qualitative factors

The multi-criteria facilities layout problem can be formulated as a quadratic assignment model that can handle multiple qualitative and quantitative factors in the objective function. Some studies have shown that the techniques and tools of facilities layout problems can equally be applied for the layout design of user interface components in human-computer interface. This paper presents an alternate approach, which handles multiple qualitative and quantitative factors in a different manner separately in the objective function to obtain the initial layouts. The proposed approach also consists of a layout procedure, in which the pair of facilities with the least composite criterion value has been selected to be placed far apart in the layout to generate an initial layout in the construction procedure. The results of the proposed approach are compared with that of an existing approach which handles a number of qualitative and quantitative factors in the same manner as in the objective function to obtain the initial layouts for the example task of the user interface components layout problem under consideration.

Modeling experiments with qualitative and quantitative factors is an important issue in computer modeling. We propose a framework for building Gaussian process models that incorporate both types of factors. The key to the development of these new models is an approach for constructing correlation functions with qualitative and quantitative factors. An iterative estimation procedure is developed for the proposed models. Modern optimization techniques are used in the estimation to ensure the validity of the constructed correlation functions. The proposed method is illustrated with an example involving a known function and a real example for modeling the thermal distribution of a data center.

Fractional factorial designs are widely used in screening experiments. They are often chosen by the minimum aberration criterion, which regards factor levels as symbols. For designs with quantitative factors, however, permuting the levels for one or more factors could alter their geometrical structures and statistical properties. We provide a justification of the minimum β-aberration criterion for quantitative factors and study level permutations for regular fractional factorial designs in order to improve their efficiency for screening quantitative factors. We show how regular designs can be linearly permuted to reduce contamination of nonnegligible interactions on the estimation of linear effects without increasing the run size. We further show that such linear permutations are unique under the minimum β-aberration criterion and the best level permutations can be determined without an exhaustive search. We establish additional theoretical results for three-level designs and obtain the best level permutations for regular designs with 27 and 81 runs. We illustrate the practical benefits of level permutation with an antiviral drug combination experiment.

: This thesis analyzes the effect of quantitative and qualitative factors used in the admissions process at the U.S. Naval Academy in determining the performance of candidates admitted. In determining student performance, graduation, Order of Merit (OOM), cumulative academic Quality Point Ratio (QPR), cumulative military QPR, and striper selection are used as performance outcome measures. The data is from Naval Academy graduation year groups 1995 through 2001. The analysis separates the Naval Academy's Whole Person Multiple into quantitative and qualitative inputs. The Candidate Multiple (CM) is the quantitative input to the admissions process derived from a statistics-based scoring model anchored in proven high school performance measures such as the SAT and high school GPA. The Recommendations of the Admissions Board (RAB) are the qualitative input, which awards points for subjective traits not captured in the CM or that are from various other subjective measures, such as student interviews and essays. This research highlights the properties of the two admissions factors and their estimated impact on student performance. The results show that student performance increased as CM and RAB increased, revealing the importance of a combined quantitative and qualitative admissions process, and emphasizing qualitative input as the value added factor that provides an increased predictability of student success. (26 tables, 22 figures, 26 refs.)

Risk-Value-Cost-based Optimization of Global Value-adding Structures

PurposeThe purpose of this study is to investigate the decision-making process of librarians at the University of Western Ontario who attempted to cancel the Wiley Big Deal. The aim of the study is to reveal the underlying factors that affected their decision-making process. By understanding the decision-making process of librarians, it may be possible to devise a system that takes into consideration not only quantitative factors but also the subjective or qualitative factors that impact librarians’ decisions and thus make it easier to cancel these Big Deals.Design/methodology/approachThe study involved administering an online survey to 25 librarians involved in the cancellation project. Follow-up interviews were conducted with 13 of these librarians to understand at a deeper and more nuanced level the factors that influenced their decisions.FindingsThe main finding was that the librarians who participated in the study could be divided into two groups – a data-driven criteria group and a subjective criteria group – based on their ranking of the factors used to make their cancellation decisions. Most librarians interviewed used a mixture of quantitative factors and qualitative factors when making their cancellation decisions. The authors found that those participants who had greater professional experience and a closer relationship with the faculties in their subject areas had more difficulty in cancelling journals. Very few librarians relied on quantitative data alone.Originality/valueThis study is one of few that have examined the subjective factors that influence librarians’ decisions regarding cancellation of Big Deals. It has implications regarding the movement towards centralized collection management and reliance on quantitative data alone when making collection decisions.

We propose a flexible yet computationally efficient approach for building Gaussian process models for computer experiments with both qualitative and quantitative factors. This approach uses the hypersphere parameterization to model the correlations of the qualitative factors, thus avoiding the need of directly solving optimization problems with positive definite constraints. The effectiveness of the proposed method is successfully illustrated by several examples.

The multifactor facilities layout problem is one of the important cases of quadratic assignment problem (QAP), which deals with the problem of optimum assignment of n facilities to n locations and optimizing multifactor cost objective. The authors present an alternate mathematical model for QAP that combines multiple qualitative and quantitative factors separately, treating them in a different manner for the facilities layout problem. The composite factor is obtained by combining the weighted combined qualitative and quantitative factors. The distances between the locations weigh the composite factor. The objective function is to minimize the total weighted workflow distance between the facilities. The results of the proposed model are compared with that of an existing model that handles multiple qualitative and quantitative factors in the same manner.