Advancements of phonetics in the 21st century: Quantitative data analysis

Most heart failure patients have multiple comorbidities. This study aims to test the moderating effect of comorbidity on the relationship between self-efficacy and self-care in adults with heart failure. Secondary analysis of four mixed methods studies (n = 114) was done. Self-care and self-efficacy were measured using the Self-Care of Heart Failure Index. Comorbidity was measured with the Charlson Comorbidity Index. Parametric statistics were used to examine the relationships among self-efficacy, self-care, and the moderating influence of comorbidity. Qualitative data yielded themes about self-efficacy in self-care and explained the influence of comorbidity on self-care. Most (79%) reported two or more comorbidities. There was a significant relationship between self-care and the number of comorbidities (r = -.25; p = .03). There were significant differences in self-care by comorbidity level (self-care maintenance, F[1, 112], 5.96, p = .019, and self-care management, F[1, 72], 4.66, p = .034). Using moderator analysis of the effect of comorbidity on self-efficacy and self-care, a significant effect was found only in self-care maintenance among those who had moderate levels of comorbidity (b = .620, p = .022, F(change) df[6,48], 5.61, p = .022). In the qualitative data, self-efficacy emerged as an important variable influencing self-care by shaping how individuals prioritized and integrated multiple and often competing self-care instructions. Comorbidity influences the relationship between self-efficacy and self-care maintenance, but only when levels of comorbidity are moderately high. Methods of improving self-efficacy may improve self-care in those with multiple comorbidities.

In the study, which was carried out to compare the perception of organizational power distance of the academic staff working at the university with the social networks they established with their colleagues at the university, the quantitative and qualitative data collection and analysis processes were carried out simultaneously using the Convergent Parallel Design, one of the mixed methods research designs, and the results of the data analysis were integrated. This research compares the organizational power distance perceptions of the faculty members working at A University, a public university in Turkey, with the social networks they have established with their colleagues at the university. In the quantitative dimension of the research carried out with the mixed method, the research population consists of 1848 academic staff working at A University, one of the public universities in Turkey, in the 2020-2021 academic year. The research sample consists of 319 academic staff. 385 academic staff from 30 academic units, 14 faculties, and 16 colleges/vocational schools, were included in the research. In the qualitative dimension of the research, 27 of 34 academic staff working in the C Department of the B Faculty of the A University were included in the study group. Convergent Parallel Design was used in the research; within this context, the quantitative and qualitative data collection and analysis processes were carried out simultaneously and the data analysis results were integrated. The quantitative data were analyzed with the SPSS 21 program, the participants' views on organizational power distance were analyzed with the MAXQDA 2022 program, and social network analysis data were analyzed with the UCINET 6.0 program. According to the research findings' conclusion, the academic staff's general social network tendencies in the quantitative dimension were high, and the participants had the highest perception of "liking to connect." It was determined that the academic staff's general organizational power distance perceptions were at a moderate level, and the participants had the highest perception of "acquiescence of power." Another conclusion was that the social network tendencies of academic staff did not differ according to the academic title variable. However, the organizational power distance differed significantly in favor of research assistants. It was revealed that there was no statistically significant relationship between the general social network tendencies of the academic staff and their perception of organizational power distance. In the qualitative dimension of the study, it was observed that the participants expressed their opinions on organizational power distance mostly in terms of accepting power and least in terms of consenting to power. Social network analysis revealed that the professional network had a denser structure than the friendship network, but the friendship network had more structured and stronger ties. In social networks, professors and research assistants were found to be at the center of the network. The study observed that the results of quantitative and qualitative data analysis confirmed each other at many points. This study is expected to contribute to the literature, policymakers in higher education management, university senior management, academics, and researchers.

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3) integration and discussion of results. Furthermore, we illustrated the application of this framework by examining the relationships between learning process metrics and academic performance in the subject of Computer Programming coupled with content analysis of the responses to a students’ perception questionnaire of their learning experiences in this subject. Background: There is a prevalence of quantitative research designs in learning analytics, which limits the understanding of students’ learning processes. This is due to the abundance and ease of collection of quantitative data in virtual environments and learning management systems compared to qualitative data. Methodology: This study uses a mixed-methods, non-experimental, research design. The quantitative phase of the framework aims to analyze the data to identify behaviors, trends, and relationships between measures using correlation or regression analysis. On the other hand, the qualitative phase of the framework focuses on conducting a content analysis of the qualitative data. This framework was applied to historical quantitative and qualitative data from students’ use of an automated feedback and evaluation platform for programming exercises in a programming course at the National University of Colombia during 2019 and 2020. The research question of this study is: How can mixed-methods research applied to learning analytics generate a better understanding of the relationships between the variables generated throughout the learning process and the academic performance of students in the subject of Computer Programming? Contribution: The main contribution of this work is the proposal of a mixed-methods learning analytics framework applicable to computer programming courses, which allows for complementing, corroborating, or refuting quantitatively evidenced results with qualitative data and generating hypotheses about possible causes or explanations for student behavior. In addition, the results provide a better understanding of the learning processes in the Computer Programming course at the National University of Colombia. Findings: A framework based on sequential explanatory mixed-methods design in the field of learning analytics has been proposed to improve the models used to support the success of the learning process and the learner. The answer to the research question posed corresponds to that the mixed methods effectively complement quantitative and qualitative data. From the analysis of the data of the application of the framework, it appears that the qualitative data, representing the perceptions of the students, generally supported and extended the quantitative data. The consistency between the two phases allowed us to generate hypotheses about the possible causes of student behavior and provide a better understanding of the learning processes in the course. Recommendations for Practitioners: We suggest implementing the proposed mixed-methods learning analytics framework in various educational contexts and populations. By doing so, practitioners can gather more diverse data and insights, which can lead to a better understanding of learning processes in different settings and with different groups of learners. Recommendation for Researchers: Researchers can use the proposed approach in their learning analytics projects, usually based exclusively on quantitative data analysis, to complement their results, find explanations for their students’ behaviors, and understand learning processes in depth thanks to the information provided by the complementary analysis of qualitative data. Impact on Society: The prevalence of exclusively quantitative research designs in learning analytics can limit our understanding of students’ learning processes. Instead, the mixed-methods approach we propose suggests a more comprehensive approach to learning analytics that includes qualitative data, which can provide deeper insight into students’ learning experiences and processes. Ultimately, this can lead to more effective interventions and improvements in teaching and learning practices. Future Research: Potential lines of research to continue the work on mixed-method learning analytics methodology include the following: first, implementing the framework on a different population sample, such as students from other universities or other knowledge areas; second, using techniques to correct unbalanced data sets in learning analytics studies; third, analyzing student interactions with the automated grading platform and their academic activities in relation with their activity grades; last, using the findings to design interventions that positively impact academic performance and evaluating the impact statistically through experimental study designs. In the context of introductory programming education, AI/large language models have the potential to revolutionize teaching by enhancing the learning experience, providing personalized support, and enabling more efficient assessment and feedback mechanisms. Future research in this area is to implement the proposed framework on data from an introductory programming course using these models.

Chapter 22 Quantitation of Protein–Protein Interactions: Confocal FRET Microscopy

The purpose of this study is to describe the process and learning outcomes through singing methods that can improve children's counting skills. The subjects of this action research were 13 children. This research method is an action research that refers to the Kemmis Mc Taggart action research model which consists of the stages of planning, action, and observation, and reflection. This research consisted of 2 cycles, where each cycle consisted of 8 meetings. Data analysis techniques used in this study are quantitative and qualitative data analysis techniques. Quantitative data analysis with descriptive statistics that compares the results obtained from the pre cycle, the first cycle, to the second cycle. Qualitative data analysis by analyzing data from the results of field notes, interview notes, and documentation notes with the stages of data reduction, data display, and conclusions. The results of this study indicate an increase in the ability to counting through singing in which the average value in the pre cycle of 2.76 increased in the first cycle to be 3.38, and in the second cycle to be 5.07.

The main aim of this article is to discuss the factors a researcher should take into account when selecting the appropriate research design or method (i.e. qualitative, quantitative, and mixed-methods). The article also discusses sample size determination and sampling procedures in qualitative and quantitative research. Further, the paper has provided an examination of qualitative and quantitative data collection and analysis methods. The study used online desk research to collect data from online public access scholarly databases such as Google Scholar, ResearchGate, Academia, and many more freely accessible electronic books in research methods. Search terms that were used included, among many, the following: qualitative research, quantitative research, mixed-methods, qualitative and quantitative data collection, qualitative and quantitative data analysis; descriptive and inferential statistics. A wealth of relevant and timely scholarly literature was downloaded, read, and used to write this paper, and draw the conclusion provided. Keywords: Qualitative Research; Quantitative Research; Mixed-Methods Research; Qualitative Data Collection Techniques; Quantitative Data Collection Techniques; Qualitative Data Analysis Techniques; Quantitative Analysis Techniques; Descriptive Statistics; Inferential Statistics. DOI: 10.7176/JMCR/87-06 Publication date: November 30 th 2022

The chapter presents general aspects of quantitative data analysis as they relate to information sciences. The chapter is based on a literature review. It begins with explaining the meaning of data and quantitative data. Kinds of quantitative data are presented. The meaning of data analysis and the reasons for data analysis are also discussed. Reasons for quantitative data analysis are also discussed. The ‘what' and ‘why' of statistics in general and for information science researchers in particular is also presented. The chapter also presents the main issues of quantitative data analysis. Steps in quantitative data analysis are also presented. Preparation of quantitative data analysis is followed by a presentation on quantitative data analysis methods. The chapter highlights the popular quantitative data analysis software. A brief presentation on how quantitative data are presented and interpreted is given. The chapter ends with a discussion on the advantages and disadvantages of quantitative data analysis.

21st-century leaders need strong leadership skills to effectively lead schools. They must use and implement 21st-century skills for long term change. The study aimed to identify the practices of secondary school leaders of Rawalpindi city about [recommended] 21st century 4 Cs leadership skills (Competence, Character, Compassion & Courage) during the pandemic of Covid-19. This was exploratory research following the quantitative research approach. One hundred and nine school leaders were selected through a simple random sampling technique to complete an adapted survey questionnaire. Descriptive statistics were used for analyzing and reporting the findings. Quantitative data analysis indicates that school leaders are using 21st-century leadership skills during Covid-19, but the level of use is dissimilar to the recommended 21st century 4 Cs leadership skills. It was identified that school leaders use relationship (mean= 4.21), Accountability (mean= 4.45), and Self-Belief (mean= 4.37) skills more than other sub-skills in their leadership practices. The findings recommend that policymakers and professional development organizations should plan workshops on these recommended leadership skills for school leaders so they can perform well under situations like Covid-19.

This study investigated the effectiveness of the Statistical Toolbox for Android (STA) in enhancing senior high school students’ quantitative data analysis and interpretation skills, compared with the traditional use of calculators. Employing a two-group pre-test–post-test quasi-experimental design, the study involved two groups of students: one utilizing the STA application and the other using calculators. Instruction focused on core statistical procedures, namely the one-sample t-test, t-test for independent samples, one-way ANOVA, and Pearson’s product-moment correlation. Pre-test results indicated that both groups initially demonstrated only beginning-level proficiency in quantitative analysis and interpretation. Following the intervention, post-test results revealed that all participants reached the proficient level. Statistical analysis showed a significant difference in favor of the calculator group in terms of quantitative data analysis skills, suggesting a higher computational efficiency with traditional methods. Conversely, the STA group obtained slightly higher mean scores in interpretation skills, although the difference was not statistically significant, indicating that both approaches were equally effective in fostering interpretation abilities. The findings suggest that while both calculators and STA contribute to skill development, calculators remain more effective for enhancing computational proficiency. Nevertheless, STA and similar mobile applications offer unique pedagogical value by facilitating engagement with complex statistical procedures and promoting digital literacy—skills increasingly vital in modern education. The results underscore the importance of aligning digital tool integration with specific learning objectives, and they highlight the need for further research into context-specific applications of mobile statistical tools in classroom settings.

Quantitative and qualitative data analysis are often undertaken as separate enterprises, as they emerge from differing philosophies of science and methodologies for data collection, management and analysis. Quantitative data analysis is sometimes seen in philosophic and methodologic conflict with a naturalistic, human science perspective of science. Researchers interested in data from both realms often rely on triangulation procedures, in which each is considered from its representative lens. Results are then projected out into a common area where data are melded and discussed. The purpose of this paper is to introduce the meta-matrix as a tool for triangulation in nursing research and to demonstrate its usefulness in an exemplar case. The exploratory nature of a recent study led to the decision to manage triangulation using an emerging methodology, thereby allowing consideration of all data simultaneously through the use of a meta-matrix. Discussion of the meta-matrix as a method is presented. Use of the meta-matrix facilitated data analysis and allowed pattern recognition across data sets. Discovery of several unexpected relationships deepened understanding of the results and assisted in identifying questions for further research. The meta-matrix method provides a useful alternative approach for secondary-level data analysis in mixed-methods research.

Study object included leaves of deciduous trees in the Krasnodar Territory at different stages of senescing visually manifested in their color alteration. Study subject was the optical characteristics of light diffused reflection from green, yellow-green and yellow leaves of deciduous trees in the Krasnodar Territory during the autumn season. Work objective lies in identifying the possibility to establish differences between green leaves of deciduous trees, and yellow-green and yellow leaves of deciduous trees using the terrain multispectral and hyperspectral sounding methods, as well as collecting information on spectral characteristics of the diffused light being reflected from various biological objects. Results of quantitative and qualitative analysis of data obtained through the diffused light reflectance spectroscopy from leaves of deciduous trees are presented. Narrow-band vegetation indices mNDVI705, mSR705, CRI1, SIPI and PSRI were used in quantitative analysis of data on the diffused light reflection spectra obtained from green, yellow-green and yellow leaves of deciduous trees. It was revealed that the use of narrow-band vegetation indices in the remote sensing algorithms using multi- and hyperspectral cameras makes it possible to rather accurately distinguish leaves at different stages of senescing. Optical characteristics of diffused light reflection from green, yellow-green and yellow leaves of deciduous trees, which are typical species of trees in urban and rural plantings in the Krasnodar Territory, are described for the first time

This study investigated the effects of two types of bilingual programs (two-way and transitional) on the academic performance and attitudes of fifth-grade students who entered kindergarten or first grade with different levels of English proficiency. A mixed methods design with both quantitative and qualitative data collection and analyses phases was employed. Quantitative data analyses indicated no significant differences in standardized measures of English achievement, although significant differences were found in other measures, including measures of oral language acquisition in English, Spanish-reading ability, students' attitudes, and perceived levels of proficiency in English and Spanish. Qualitative data analysis indicated that the students in two-way bilingual education programs were more likely to express positive attitudes towards bilingualism. Based on the mixed data, it is concluded that despite some similarity in the effects, each of the bilingual programs also has unique effects. Policy decisions should be made on the basis of relative importance, value, and the costs of these unique advantages and disadvantages.

This study aims to determine the secondary school students' conditions and needs about reading and reading comprehension skills. According to this aim, the research process was structured as a case study that identifies reading and reading comprehension problems. Therefore, the research process was prepared according to the holistic-single case design of the case study. The research question is focused on: What are the secondary school students' needs regarding the problems they face in reading and reading comprehension processes? In this context, qualitative and quantitative data were collected through questionnaires, observation forms and semi-structured interviews to determine the students’ needs and to make analyzes via these data. Quantitative data were collected from 307 secondary school students. Then, qualitative data were obtained from the participants who were identified as homogeneous sampling in this group. The data collected during eight weeks were analyzed by the quantitative and qualitative data analysis methods. According to the results of the analysis, it was found out that the students were affected by the factors that affect reading comprehension, physical factors that affect reading, reading difficulties and reading and reading comprehension problems. According to these findings, it was determined that the students have commonly the reading problems such as not caring about reading comprehension, lack of expectation against reading, get bored from the reading process, tiredness from reading, distraction, skipping reading, making habit of reading problems, not knowing punctuation, mixing letters. Based on these problems, students' needs for reading and reading comprehension were determined. To meet students' needs, suggestions were given on how reading and reading comprehension education to be designed. Article visualizations:

BackgroundMany community-based HIV research studies incorporate principles of greater involvement and meaningful engagement of people living with HIV (GIPA/MEPA) by training people with HIV as peer researchers. Unfortunately, there are still some aspects of research (e.g., quantitative data analysis and interpretation) where many projects fall short in realizing GIPA/MEPA principles. To address these gaps, we developed an eight-week training course that aimed to build the capacity of peer researchers around the understanding and interpretation of quantitative data and incorporating lived experience to increase the impact of the knowledge transfer and exchange phase of a study.MethodsPeer researchers (n = 8) participated from British Columbia, Alberta, and Ontario and lessons learned from the training were implemented throughout the dissemination of research findings from the People Living with HIV Stigma Index study. This paper presents the curriculum and main training components, course evaluation results, and challenges and lessons learned. The manuscript was created in collaboration with and includes the perspectives of both the peer researchers involved in the training, as well the course facilitators.ResultsThroughout the course, peer researchers’ self-assessed knowledge and understanding of quantitative research and data storytelling improved and, through interactive activities and practice, they gained the confidence to deliver a full research presentation. This improved their understanding of research findings, which was beneficial for discussing results with community partners and study participants. The peer researchers also agreed that learning about integrating lived experience with quantitative data has helped them to make research findings more relatable and convey key messages in a more meaningful way.ConclusionsOur training curriculum provides a template for research teams to build capacity in areas of research where peer researchers and community members are less often engaged. In doing so, we continue to uphold the principles of GIPA/MEPA and enhance the translation of research knowledge in communities most greatly affected.