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Issues and strategies of quantitative analysis
Published in John A. Bilorusky, Principles and Methods of Transformative Action Research, 2021
Throughout this chapter, I’d like for us to keep in mind that our decisions are always made by us, not by numbers, for we always choose how to use numbers, and what meaning and importance to give to numbers. In order to discuss better the importance of qualitatively understanding quantitative analyses, I wish to describe, and review, widely practiced quantitative approaches, discuss the reasoning and assumptions behind these approaches, and note their uses and limitations.
Risk Characterization, Communication, and Perception
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
Qualitative discussion provides the framework for the quantitative analysis, but it goes beyond that. It must state the limitations of the quantitative analysis, clearly setting forth underlying assumptions and simplifications and noting key factors affecting risk or its valuation that cannot be quantified.
How Will the Data be Analyzed?
Published in Trena M. Paulus, Alyssa Friend Wise, Looking for Insight, Transformation, and Learning in Online Talk, 2019
In this chapter we will provide guidance around how to select which quantitative analysis methods are appropriate given the intended outcomes of the study and the research design. Regardless of this choice, issues of reliability and validity remain paramount. Specific evidence that can be generated to support claims of reliability of the analysis and the validity of inferences are discussed throughout the chapter. Tools that can be used to support each method of analysis are also introduced.
Bridging the gap: Identifying diverse stakeholder needs and barriers to accessing evidence and resources for children’s pain
Published in Canadian Journal of Pain, 2022
Nicole E. MacKenzie, Christine T. Chambers, Jennifer A. Parker, Erin Aubrey, Isabel Jordan, Dawn P. Richards, Justina Marianayagam, Samina Ali, Fiona Campbell, G. Allen Finley, Emily Gruenwoldt, Bonnie Stevens, Jennifer Stinson, Kathryn A. Birnie
Quantitative analyses were conducted to address both study objectives. To address objective 1, descriptive statistics were conducted to characterize general stakeholder needs, barriers, and accessibility of evidence-based resources. To address objective 2, a series of one-way analyses of variance were conducted to examine differences between the three stakeholder groups (i.e., knowledge users, patients/caregivers/family members, and researchers) in frequencies with which barriers are encountered when accessing evidence-based resources, as well as ratings of accessibility of evidence-based resources. Also related to objective 2, a series of chi-square tests was conducted to examine differences between stakeholder groups in terms of types of barriers encountered when accessing evidence-based resources as well as stakeholder needs to ensure uptake of evidence-based resources.
Mathematical and computational modeling for the determination of optical parameters of breast cancer cell
Published in Electromagnetic Biology and Medicine, 2021
Shadeeb Hossain, Shamera Hossain
The magnitude obtained in Figure 1 for both malignant and healthy cell satiates the Snell’s relation to calculate refractive index. The critical angle measurement can be used as a sensing application for real-time determination of biochemical properties of the tissue. A shift towards a lower critical angle can be an indicator for changes in the molecular arrangement of tissue and effectively used as a non-invasive procedure for malignant tissue identification. The theoretical model can be used in conjunction with experimental data for future analysis. The higher sodium ion concentration and water content in malignant cell contributes to the variation in biochemical properties of tissue. Any site recognized for a shift in its refractive properties than its vicinity commensurate with alteration in tissue organization and higher anisotropic property (Wang et al. 1995). The quantitative analysis has limited validity as it does not incorporate several factors including: (i) the site of analysis, (ii) nature of tissue and (iii) stage of lesion. However, advancement in artificial intelligence and machine learning with collaboration of data analysis provides prospect for earlier and effective diagnosis.
Selecting the proper Tau-U measure for single-case experimental designs: Development and application of a decision flowchart
Published in Evidence-Based Communication Assessment and Intervention, 2021
Joelle Fingerhut, Xinyun Xu, Mariola Moeyaert
A variety of different analysis techniques have been developed to evaluate intervention effects using SCED data. Visual analysis has historically been used to determine intervention effectiveness in SCED research (Horner et al., 2012). However, within the past few decades there has been an increased interest in using quantitative analyses (Fingerhut et al., 2021). One possible reason for this is due to criticisms of visual inspection. Reliance on visual analysis alone may lead to Type II errors, as visual analysis is less sensitive to small data changes (McClain et al., 2014). Other research has found visual analysis to lack interrater agreement (Brossart et al., 2006; Ninci et al., 2015; Wolfe et al., 2016). Another reason for the increase in popularity of quantitative analyses is that it better allows for results to be aggregated; for example, the average estimated intervention effect across participants (and even studies) can be calculated. This aggregation of results permits more generalized conclusions about intervention effectiveness; the quantification of the effect aids in summarizing findings and allows for easier dissemination of findings (Shadish, 2014). Furthermore, legislation such as the Every Student Succeeds Act (2015) calls for the use of evidence-based practices in the classroom, and consequently there has been a greater need for researchers to quantitatively represent, summarize, and interpret results from SCEDs (Solomon et al., 2015). As a result, there has been an increased need for quantitative procedures to evaluate SCED data.