How to use mixed-methods and triangulation designs: An introduction to history education research

Kelle, Udo; Kühberger, Christoph; Bernhard, Roland

doi:10.18546/herj.16.1.02

Cited by 23 publications

(11 citation statements)

References 21 publications

(26 reference statements)

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“…The data obtained in this study were tested for correctness. The correctness test was done by triangulating the theory (Ashour, 2018; Campbell et al, 2020; Kelle et al, 2019; Vogl et al, 2019). Theoretical triangulation is carried out by referring to the use of various theoretical perspectives.…”

Section: Methodsmentioning

confidence: 99%

Qualitative Data Requirements in the Divayana Evaluation Model

Divayana

Widiartini

Ratnaya

2022

International Journal of Qualitative Methods

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The need for qualitative data to support the evaluation process using the DIVAYANA (Description-Input-Verification-Action-Yack-Analysis-Nominate-Actualization) evaluation model is very important to obtain in-depth information about the effectiveness of e-learning platform utilization in ICT Vocational Schools. Those qualitative data are used to complete the evaluation process for the eight components of the DIVAYANA evaluation model. The eight components included Description component, Input component, Verification component, Action component, Yack component, Analysis component, Nominate component, and Actualization component. The main purpose of this study was to show the qualitative data needed in each evaluation component of the DIVAYANA evaluation model. The tools used to obtain qualitative data on the eight components of the DIVAYANA evaluation model were interview guidelines, checklists, and literature studies. This study approach was qualitative. The technique used to test the qualitative data validity in this study was theoretical triangulation. The findings of this study were the qualitative data needed in each component of the DIVAYANA evaluation model to show the effectiveness of e-learning platform utilization in ICT Vocational Schools. The findings of this study have a positive contribution to the evaluation process in the field of education, especially in strengthening the correctness of the quantitative data results which are usually obtained in the evaluation process. The novelty of this study is the presentation of qualitative data on the yack component, analysis component, and nominate component which does not have by other evaluation models.

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Section: Methodsmentioning

confidence: 99%

Qualitative Data Requirements in the Divayana Evaluation Model

Divayana

Widiartini

Ratnaya

2022

International Journal of Qualitative Methods

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“…To choose the sample from diverse persons working in I4.0 and utilizing a basic random sampling technique. As it provides different facets of a phenomenon, triangulation is considered a powerful research strategy, especially when a mixed method approach is applied (Kelle et al , 2019). A mixed methods technique was used in this study to triangulate data, combining primary data from surveys with secondary data from the existing research literature.…”

Section: Methodsmentioning

confidence: 99%

Production and maintenance in industries: impact of industry 4.0

Kunju

Naveed

Anwar

et al. 2021

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Purpose Production industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential. Design/methodology/approach A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics. Findings The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential. Originality/value The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

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“…In investigating informants' experience and perceptions, the qualitative method is suitable because it helps researchers explore deeper (Simmons, 2016). Moreover to explore concepts that have so far not been known in-depth, qualitative methods are most appropriate because of their open and exploratory nature (Kelle et al, 2019;Östlund et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

The inevitable surge of online learning through the lens of English education lecturers during the unprecedented times

Simanjuntak

Panjaitan

2021

J. Eng. Foreign. Lang.

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The onset of the COVID-19 pandemic has obliged universities worldwide to shift to other modalities such as e-learning. Lecturers feel obliged to motivate and aspire their students virtually. This study explores the perceptions and experience of English education lecturers on the inevitable surge of virtual teaching during the Pandemic. This study was a qualitative interview study utilized an interpretive description approach. The data were generated by interviewing 19 English education lecturers based on defined evaluation criteria and an online learning environment. Participants were given ten open-ended interview questions to find out how lecturers undertook online teaching during unprecedented times and perceived some changes in the teaching and learning process. The findings showed three themes emerged from lecturers’ perspectives during the shift to online learning; those themes are the need for iterative process, revamp delivery, and the need to advance technology infrastructure. In addition, there were two (themes that characterize the participants’ experiences in implementing online learning: Agility and adaptability, identification of the underlying needs. All themes in this study emerged from obtained sub-themes. These findings indicated that the inevitable surge of online learning shapes lecturers’ teaching skills and attitude in the process of shifting to other modalities.

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How to use mixed-methods and triangulation designs: An introduction to history education research

Cited by 23 publications

References 21 publications

Qualitative Data Requirements in the Divayana Evaluation Model

Qualitative Data Requirements in the Divayana Evaluation Model

Production and maintenance in industries: impact of industry 4.0

The inevitable surge of online learning through the lens of English education lecturers during the unprecedented times

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