Exploration of Korean Students’ Scientific Imagination Using the Scientific Imagination Inventory

Jiyeong, Mun; Mun, Kongju; Kim, Sung-Won

doi:10.1080/09500693.2015.1067380

Cited by 11 publications

(15 citation statements)

References 35 publications

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“…Not all papers reporting instruments include a listing of the final items included, but where authors do include the full list of items, it is possible for readers to consider for themselves the relationship between different items in a multi-scale instrument-to make a reader judgement of what might be called face equivalence, the extent to which items appear to be eliciting the same underlying knowledge facet, opinion or perception. To illustrate this, Mun, Mun and Kim's (2015) account of their development of the Scientific Imagination Inventory (described above) provides the full list of items in the final version of their instrument. Mun and colleagues identified three components of scientific imagination (and two sub-dimensions of each, giving six factors overall) but found that the overall measure of consistency across the full set of instrument items was greater than that for any of the three components or their individual factors (see Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…This may be relevant to why in the Tuan, Chin and Shieh (2005) study discussed earlier, the overall value of alpha for their instrument exceeded the individual alpha coefficients within the six 'somewhat overlapping' scales from which it was compiled: that is, the measure of internal consistency was higher across the pooled items from six distinct scales intended to elicit different constructs than it was within any one of those individually more homogeneous scales. Mun, Mun and Kim (2015) published an account of their development of an instrument they call the Scientific Imagination Inventory, where they report alpha values for components of the instrument as well as the overall instrument. Mun and colleagues identified three components of scientific imagination (and two sub-dimensions of each, giving six factors overall), which they labelled as scientific sensitivity (comprising emotional understanding and the experience of imagination), scientific creatvity (comprising originality and diversity) and scientific productivity (comprising creation and reproduction and scientific sense of reality).…”

Section: What Does Alpha Actually Measure?mentioning

confidence: 99%

“…This is a sensible thing to wish to guard against if an instrument is intended to offer a range of items eliciting responses aligned with the same construct. Thus, the procedure, referred to above (Mun et al, 2015), of removing items that are found to reduce overall alpha values. However, that is clearly not equivalent to suggesting that it is desirable for all items in a scale to elicit precisely the same pattern of responses as this would imply a scale contains redundant items.…”

Section: What Value Of Alpha Is Desirable?mentioning

confidence: 99%

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The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education

2017

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Cronbach's alpha is a statistic commonly quoted by authors to demonstrate that tests and scales that have been constructed or adopted for research projects are fit for purpose. Cronbach's alpha is regularly adopted in studies in science education: it was referred to in 69 different papers published in 4 leading science education journals in a single year (2015)-usually as a measure of reliability. This article explores how this statistic is used in reporting science education research and what it represents. Authors often cite alpha values with little commentary to explain why they feel this statistic is relevant and seldom interpret the result for readers beyond citing an arbitrary threshold for an acceptable value. Those authors who do offer readers qualitative descriptors interpreting alpha values adopt a diverse and seemingly arbitrary terminology. More seriously, illustrative examples from the science education literature demonstrate that alpha may be acceptable even when there are recognised problems with the scales concerned. Alpha is also sometimes inappropriately used to claim an instrument is unidimensional. It is argued that a high value of alpha offers limited evidence of the reliability of a research instrument, and that indeed a very high value may actually be undesirable when developing a test of scientific knowledge or understanding. Guidance is offered to authors reporting, and readers evaluating, studies that present Cronbach's alpha statistic as evidence of instrument quality.

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Section: Discussionmentioning

confidence: 99%

Section: What Does Alpha Actually Measure?mentioning

confidence: 99%

Section: What Value Of Alpha Is Desirable?mentioning

confidence: 99%

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The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education

2017

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“…Cronbach's alpha value # 0.50 shows low reliability and between 0.50 to 0.70 and above shows good reliability (Hinton et al, 2004). Cronbach's alpha value # 0.5 has been reported in reputed journals of science education, science teaching (Van Griethuijsen et al, 2014;Mun et al, 2015). Taber (2017) in a study summed up the different Cronbach's alpha values reported in various reputed journals where 0.45 to 0.96 is reported as acceptable and 0.45 to 0.98 as sufficient.…”

Section: Reliability Measurementioning

confidence: 99%

Talent retention in Indian public sector units (PSUs): an empirical investigation

Mukherjee

Chandra

Singh

2019

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Purpose In response to scholarly calls, this study aims to explore the persuasive predictors including (job security, autonomy, work-life balance, culture and climate, career growth, leadership style and support, training and development and reward, recognition and pay) of talent retention in Indian PSUs by emphasising the commonalities and differences between different category of employees from different cohorts based on their contribution in the prime function of the business. Design/methodology/approach A self-administered questionnaire was used to collect data from 472 employees from selected organisations through Multistage sampling technique and Simple random sampling technique. Data were analysed using tests such as logistic regression, MANOVA, post hoc Tukey in SPSS 20. Findings Job security, autonomy, career growth and culture and climate are the persuasive predictors of talent retention in Indian PSUs. However, there is a significant interaction effect of age and job standard on job security. Job security and perceived employability do not relate to age for core category employees rather possess identical perception than other employee category. Findings indicate that identifying the position to be retained is more viable than a common retention strategy. Originality/value This study contributes to the existing knowledge of talent retention by exploring novel insights in comprehending the commonalities and differences of different employee category at all level from different cohorts, sharing different responsibilities in the prime function of the business that has largely been ignored. The study can succour in improving performance and cost-effective retention strategy.

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“…Researches related to the scientific imagination have been actively conducted by researchers in Taiwan (Cheng & Chuang, 2018;Ho, Wang, & Cheng, 2013;Wang et al, 2015;Wang., Ho, Wu, & Cheng, 2014) and Korea (Mun, Mun, & Kim, 2015). However, these researches focused on the scientific imagination of primary school students.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Validity and Reliability of a Scientific Imagination Test for Tenth graders

Jamar

Siew

2020

International Journal of Educational Technology and Learning

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This research was conducted to evaluate the reliability and validity of the Scientific Imagination Test (SIT) using the Rasch Measurement Model. SIT is a set of questions developed by the researchers based on the Integrated Engineering Design Process with Scientific Imagination Model. SIT was developed to evaluate the scientific imagination constructs among the tenth graders. SIT is a test consisted of two main open-ended subjective questions and 14 sub-items. Students' scientific imagination was assessed based on the three stages of scientific imagination namely initiation, dynamic adjustment and virtual implementation; which consisted of four basic constructs respectively: brainstorming, association, transformation and elaboration, and conceptualization/organization/formation. The sample consisted of 65 Tenth Grade students aged 16 years old from two secondary schools in a district in Sabah, Malaysia. Overall, the findings showed that SIT has a very high reliability with Cronbach's alpha (KR-20) value of 0.92. The findings also showed that SIT has excellent item reliability of 0.97 with separation value of 5.30. SIT also has a very good respondent reliability of 0.92 with separation value of 3.40. Meanwhile, the assessment on the item fit, respondent fit and unidimensionality established the construct validity of the SIT instrument. In conclusion, the findings indicate that the SIT instrument is a reliable and valid instrument for measuring the scientific imagination of tenth graders.

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Exploration of Korean Students’ Scientific Imagination Using the Scientific Imagination Inventory

Cited by 11 publications

References 35 publications

The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education

The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education

Talent retention in Indian public sector units (PSUs): an empirical investigation

Investigating the Validity and Reliability of a Scientific Imagination Test for Tenth graders

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