Perceived Value and Purchase Intention Among Young Adults Perfume Consumers: A Mixed-Method Exploration

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

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.

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.

Pneumonia is part of Acute Respiratory Infections (ARI) that has high disease burden not only in the infants but also among Indonesian adult workforce. Following data from Indonesia medical trend by marsh mercer in 2023, Acute Respiratory Infections (ARI) and Pneumonia showed significant burden in the workplace and recognized as one of highest cases, medical claim, productivity loss and hospitalization. Despite the availability of the vaccine to prevent Pneumonia, the vaccination utilization remains very low in Indonesia workforce and adult in general (Utomo, 2023). This study is aiming to understand better what are the factors that associated with the workforce’ Pneumonia vaccination intention especially after COVID-19 pandemic. This study adopts (Ajzen, 1991) Theory of Planned Behavior (TPB) in explaining the variables (Attitude, Subjective Norm, Perceived Behavior Control and knowledge) that influencing the vaccination intention among Indonesian workforce. This study applies a convergent parallel study with quantitative data analysis and qualitative data analysis are performed parallelly, followed with data integration as summary. The quantitative data is collected through online survey with non-probability sampling & purposive method. Total 151 adult respondents provided their response. The qualitative data is collected through semi-structured interview with total 5 respondents of key identified stakeholders. This study utilized Partial Least Square Structural Equation Modelling (PLS SEM) as data analysis method for quantitative and Thematic Analysis for qualitative method. The results show that attitude, perceived behavior control and knowledge of Acute Respiratory Infections are positively influenced the Pneumonia vaccination intention while subjective norm is not. This study also found the underlying cause of low preventive measurement like vaccination are due to several reasons like the belief of curative is the basic medical needs in the workplace, providing vaccination coverage is part of talent retain/attract strategy, additional cost burden in providing vaccination coverage. Lower awareness on vaccination benefit in general, diseases burden & Prevention of Pneumonia & ARI are the key blockers of the lower vaccination acceptance as preventive measurement in the workplace setting in Indonesia. Lastly, Influenza Vaccination and mask wearing is the most common preventive measurement that embraced by the employer and the employee. This study contributes the application of Extended Theory Planned Behaviour towards vaccination intention among Indonesia workforce for Pneumonia Vaccine and provide extensive insights and recommendation to the multi stakeholders on the strategy to put prevention through vaccination to tackle the high burden of Acute Respiratory Infection Especially Pneumonia among Indonesia workforce.

Our purpose in this research study was to examine perceptions of food and nutrition educators (FNEs, i.e., Registered Dietitians Nutritionists and Family and Consumer Science teachers) and young adults (ages 18–25) on the status of cooking and food skills among young adults. Using a cross-sectional survey design, FNEs (<em>n</em> = 93) and college-attending young adults (<em>n</em> = 270) in the United States completed electronic surveys. The qualitative and quantitative data were collected and analyzed using a convergent mixed-methods approach. The qualitative data from open-ended survey responses collected from young adults and FNEs were analyzed using <em>coding reliability thematic analysis</em>. The quantitative data used Fisher’s Exact Test to compare the importance of food skills between FNEs and young adults. In the quantitative data, young adults and FNE reported the top five food skills essential for young adults with slight variations between the two groups. For young adults, “cooking meat and poultry” was their top skill while FNEs chose “reading and following a recipe” as the top food skill. Three themes were constructed through coding reliability thematic analysis: (1) definition of cooking, (2) perceptions of the purpose of cooking, and (3) young adults’ autonomy in cooking. We concluded that young adults understood the benefits of cooking and eating healthily, but many felt overwhelmed by it. While educational food skill interventions should focus on foundational skills, cooking should be taught with consideration of young adults in mind (e.g., time and money limitations) to make the skills practical and enduring.

This study examined turnover rates of teams implementing psychosocial evidence-based practices in public-sector mental health settings. It also explored the relationship between turnover and implementation outcomes in an effort to understand whether practitioner perspectives on turnover are related to implementation outcomes. Team turnover was measured for 42 implementing teams participating in a national demonstration project examining implementation of five evidence-based practices between 2002 and 2005. Regression techniques were used to analyze the effects of team turnover on penetration and fidelity. Qualitative data collected throughout the project were blended with the quantitative data to examine the significance of team turnover to those attempting to implement the practices. High team turnover was common (M+/-SD=81%+/-46%) and did not vary by practice. The 24-month turnover rate was inversely related to fidelity scores at 24 months (N=40, beta=-.005, p=.01). A negative trend was observed for penetration. Further analysis indicated that 71% of teams noted that turnover was a relevant factor in implementation. The behavioral health workforce remains in flux. High turnover most often had a negative impact on implementation, although some teams were able to use strategies to improve implementation through turnover. Implementation models must consider turbulent behavioral health workforce conditions.

Mixed methods research burst onto the scene around the beginning of the second millennium. After decades of intense dispute between those who preferred the qualitative perspective and their quantitative counterparts—with both sides having grown deeply entrenched in their respective views—a complementary approach promising the possibility of integration had finally been proposed. By that time, however, the vast majority of researchers had committed to one stance or the other; very few of us argued that the two approaches could be complementary. Since then, the number of publications, scientific meetings and other activities devoted to the mixed methods approach has increased exponentially throughout the world. For us, there are two definitions specially relevant. Teddie and Tashakkori (2010) defined mixed methods research as “research design using qualitative and quantitative data collection and analysis techniques in either parallel or sequential phases” (p. 11). And Johnson et al. (2007) say that “Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration” (p. 123). Moreover, Johnson et al. (2007) have listed and analyzed 19 definitions of mixed methods, and the authors that have worked on this topic as a part of a big community. The expansion of mixed methods in the scientific community has been expanding rapidly. At a substantive level, we are pleased to see that a growing number of fields are generating mixed methods research, and we are eager to assist in promoting this trend. However, the field has experienced some “growing pains”: a certain degree of heterogeneity in terms of approaches, differences of opinion regarding certain conceptualizations (for example, mixed methods vs. multi-methods), numerous design taxonomies, multiple ways of integrating qualitative and quantitative elements, and various positions on how best to overcome the enduring lack of symmetry between qualitative and quantitative aspects. The methodological and substantive spectrum is vast and broad, possibly because the mixed methods approach has become “obligatory” for much research, not only in psychology but in practically all branches of the social sciences. Our proposal for delineating between mixed methods and multimethods has been presented in a previous work (Anguera et al., 2018). We believe that a study will be multimethod when, driven by a common overall research goal, it uses a series of complementary methodologies, chosen according to a given criterion. According to our proposal, whether it has a predominantly qualitative or quantitative nature has no bearing on its consideration as a multimethod study. By contrast, the essence of mixed methods studies is that they contain qualitative and quantitative components that must be integrated to ensure the mixing of the information they carry. Combining and integrating quantitative and qualitative data in the same study, however, poses numerous challenges, and attempts have been made in recent years to untangle this Gordian knot, generating and developing strategies for successfully integrating qualitative and quantitative data. The aim of this Research Topic is to present a selection of studies whose methodological approaches include, as a central element, aspects related to the Gordian knot of mixed methods, that also incorporate secondary—but no less important—elements such as dataset transformation, analytical techniques and data integration, as well as studies in which systematic observation is used as a mixed method in itself. The Research Topic has promoted a transparent presentation of the mixed approach used to develop the conceptual, methodological or application-related contribution of each article. This transparency will enable other researchers to critically appraise and replicate the methods used. The 32 articles that make up the Research Topic Best Practice Approaches for Mixed Methods Research in Psychological Science, with contribution from 121 authors, are organized from a substantive point of view in different criteria, although each of the published articles could have been “classified” from several points of view.

The way educational institutions view certain technology has changed dramatically over the years, especially with the world lockdown in 2020. Understanding how digital technology is seen in an educational institution is the path to finding out how to improve and enhance the learning experience for students. A study by the OECD (Organisation for Economic Co-operation and Development) showed “digitalisation has been one of the main drivers of innovation in educational practices in the classroom in the past decade.” (Vlies, 2020)
 
 The study examined how different members of a New Zealand tertiary education institution perceive digital technology in their respective areas of study. It examined how technology is viewed by students from differing degrees and levels, as well as how these views differ within the undergraduate and postgraduate levels of study. The perception of educators was also examined to see how different departments view the tools they use in their respective programmes and how it differs from both past and present students. The methodology behind the research was using a mixed-method research approach to gain both qualitative and quantitative data. This method would allow for the Sequential Explanatory Strategy (Terrell, 2012) to interpret the study. The strategy is done via the collection and analysis of quantitative data followed by the collection and analysis of qualitative data. The quantitative data collection used an online survey to collect a large, population of anonymous participants.
 
 The online survey was conducted using the Qualtrics software and was administered amongst the population of the university. A particular focus was put on the undergraduate population, being the largest group of students, given their reliance on distance learning as a result of the lockdowns in New Zealand in 2020/21. The survey featured a multitude of questions to collect a mix of quantitative and qualitative data.
 
 The findings from the initial survey highlighted departments at the institution that have a stronger positive outlook towards using a higher amount digital technology in their curriculum. There was a subgroup that still preferred a more practical, face-to-face approach. In response to a question regarding whether digital technology adoption may be lagging in certain programmes/disciplines, the majority of participants gave the unknown response with the second-highest group saying it was likely. The main reason participants gave for the lag of adoption of digital technology was the underfunding of programmes, with some participants further suggesting a possible correlation between underfunding, understaffing and inadequate training. The latter coheres with the OECD study, with training being one of two key aspects of education policies: “First, teachers need sufficient training to deploy and teach about digital technologies. Second, countries need a standard for digital skills and literacy for students.” (Vlies, 2020)
 
 Ongoing follow-up interviews are currently being conducted to supplement the data from the online survey results. This study will be of interest to curriculum developers, decision-makers, policymakers, future students, educators, technologists, and other educational institution staff.

Implementing a chatbot on Facebook to reach and collect data from thousands of health care providers: PharmindBot as a case

Demand forecasting plays a very significant role in logistics activities due to efficiently guiding various activities and increasing the commercial competitive advantage in a steadily fluctuating business environment. Traditional methods generally employ statistical methods, such as exponential smoothing or auto-regressive integrated moving average methods, to forecast the product demands. However, most researches only consider quantitative data such as time series data. Despite the fact in the real world, there are diverse qualitative data have more effect on product demands. Hence, this study aims to propose different 3 models based on quantitative and qualitative data. Such models consist of model 1, used only qualitative data as input, model 2, used only quantitative data, and model 3, used both of qualitative and quantitative data. A TV demands data set provided by a well-known shopping mall in Thailand is adopted to verify the proposed model. According to the consideration of performance measurement, the finding indicates that model 3 is outstanding. It can be concluded that the qualitative data are very important to demand forecast.

There are many scenarios in which it is necessary to collect data from multiple sources in order to evaluate a system, including the collection of both quantitative data - from sensors and smart devices - and qualitative data - such as observations and interview results. However, there are currently very few systems that enable both of these data types to be combined in such a way that they can be analysed side-by-side. This paper describes an end-to-end system for the collection, analysis, storage and visualisation of qualitative and quantitative data, developed using the e-Science Central cloud analytics platform. We describe the experience of developing the system, based on a case study that involved collecting data about the built environment and its users. In this case study, data is collected from older adults living in residential care. Sensors were placed throughout the care home and smart devices were issued to the residents. This sensor data is uploaded to the analytics platform and the processed results are stored in a data warehouse, where it is integrated with qualitative data collected by healthcare and architecture researchers. Visualisations are also presented which were intended to allow the data to be explored and for potential correlations between the quantitative and qualitative data to be investigated.

BackgroundYoung people face many challenges when entering adult life. For young people with complex health needs the process of transitioning from child-centred health care to adult services adds additional challenges....

Background and Purpose: The prevalence of youth obesity has increased dramatically in the United States, becoming a severe concern in Hawai`i and disproportionally impacting Filipino youth. The main study objective was to describe the influence of parents and friends on adolescents’ dietary, physical activity, and sedentary behaviors. Methods: We collected quantitative and qualitative data from two classrooms in Hawai`i, from ethnic minority adolescents (N=42; 11th and 12th graders) and their parents (N=31). Participating adolescents were 86% female with a mean age of 16.5 + .6 years, and their parents were 77% female with a mean age of 45.9 + 6.9 years. The majority of participants described themselves as Filipino American. Self-report data were collected via adolescent surveys and follow-up group discussion, as well as individual adolescent-led parent interviews. Quantitative data were descriptive, and qualitative data were conceptualized into underlying themes based on the targeted health behavior and the source of influence (parents and friends). Results: The majority of students reported parents as the dominant influence on their dietary and sedentary behaviors; however, friends were reported as the principal influence on adolescents’ physical activity levels. Parents’ reported a strong dietary influence via home availability, but minimal influence on adolescents’ physical activity. Conclusion: Home food availability and reduced television time are prime targets for family-based interventions among ethnic minority Hawai`ian populations.

Knowledge of the genetic diversity of germplasm of breeding material is invaluable in crop improvement programmes. Frequently, qualitative and quantitative data are used separately to assess genetic diversity of crop genotypes. While assessing diversity based on qualitative and quantitative traits separately, there may occur a problem when the degree of correspondence between the clusters formed does not agree with each other. This study compares five different procedures of clustering based on the criterion of weighted average of observed proportion of misclassification in black gram genotypes using qualitative, quantitative traits and mixture data. The INDOMIX- and PRINQUAL-based clustering procedures, i.e. INDOMIX and PRINQUAL methods in conjunction with the k-means clustering procedure, show better performance compared with other clustering procedures, followed by clustering based on either quantitative or qualitative data alone. The use of the INDOMIX- and PRINQUAL-based procedures can help breeders in capturing the variation present in both qualitative and quantitative trait data simultaneously and solving the problem of ambiguity over the degree of correspondence between clustering based on either qualitative or quantitative traits alone.

To identify the characteristics of joint displays illustrating the data integration in mixed-methods nursing studies and to make recommendations for effective use of joint displays for the integration of qualitative and quantitative data in mixed-methods studies. Discussion Paper. We have completed this paper as a part of a mixed-methods prevalence review of 190 studies published in nursing journals. We searched 10 nursing journals and three databases from January 2014-April 2018, additional journal search was performed from May-September 2018. We reviewed 17 studies that used joint displays as the method of data integration. Using a joint display typology, checklists, summary tables, and personal experiences of using joint displays, we evaluated the quality of displays. Nurse researchers should use advanced data integration approaches to increase the rigour of the mixed-methods studies. Joint displays can enable nurse researchers to efficiently integrate and synthesize the qualitative and quantitative data in mixed-methods studies. However, nurse researchers should clearly label the type and title of the display, include both qualitative and quantitative data and interpretations, and highlight the mixed-methods interpretations as confirmed, divergent, or expanded in the displays. Joint displays are adopted for data integration in nursing mixed-methods studies. Improvements are required concerning data presentation in the displays. Researchers should provide clear titles and supporting data and inferences and identify the meta-inferences by assessing the fit between quantitative and qualitative data. Despite the importance of integration in mixed methods, reviews indicated a consistent lack of integration in nursing research. Joint displays are structured frameworks used for the integration and synthesis of the qualitative and quantitative data at the analysis and interpretation levels. The discussed typology and characteristics of displays can enable nurse researchers to enhance the quality and presentation of integrated results in mixed-methods studies.