Physics Metacognition Inventory Part II: Confirmatory factor analysis and Rasch analysis

Taasoobshirazi, Gita; Bailey, MarLynn; Farley, John

doi:10.1080/09500693.2015.1104425

Cited by 31 publications

(62 citation statements)

References 24 publications

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“…Cognitive knowledge consists of declarative, procedural, and conditional knowledge. Cognitive regulation consists of planning, monitoring, evaluating, debugging, and managing information (Taasoobshirazi et al, 2015;Taasoobshirazi & Farley, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, given the problem of structural reliability, it was decided to use CFA and EFA on PMI data. Thus, the first objective of this study was to examine the psychometrics and structure of the PMI developed by Taasoobshirazi et al (2015). One potential reason is the reliability and validity of the instrument is the main characteristic of a measurement that can ensure the quality of research results.…”

Section: Introductionmentioning

confidence: 99%

“…Weak correlations are caused by a lack of quality instruments used to measure metacognition. In the current study, the PMI developed by Taasoobshirazi et al (2015) retesting with adaptation to Indonesia. In this context, the questions whose answers are sought in this study are (1) Do the data support the factor structure proposed by Taasoobshirazi et al (2015)?, (2) Is there a more appropriate factor structure supported by the data collected?…”

Section: Introductionmentioning

confidence: 99%

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Psychometric and Structural Evaluation of the Physics Metacognition Inventory Instrument

Haeruddin,

Prasetyo,

Supahar

et al. 2020

EUROPEAN J ED RES

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The purpose of this study is to evaluate the psychometric and structural instruments of the Physics Metacognition Inventory (PMI) developed by Taasoobshirazi, Bailey, and Farley. The PMI consists of 26 items in six factors. The English and Indonesian versions were tested on an English course (N = 37) in the Geophysics study program at Tadulako University. The trials were conducted separately within a two-week interval. The data collected from 364 students of the Physics Education Department, University of Tadulako were analyzed using the Exploratory Factor Analysis (EFA). Later, data were collected from 351 students of some Indonesian universities which have physics education study programs, and the data were analyzed using the Confirmatory Factor Analysis (CFA). The EFA result reveals six factors based on the rotation result with the maximum loading factor. The CFA result shows the RMSEA values of .018,  2 (284) = 316.32 ( 2 / df = 1,11), GFI = .93, CFI = .99, AGFI = .92 and NFI = .93 which meet the cutoff statistic value, and therefore, the model is considered fit, with the Construct Reliability Estimation (CR) of .93, Composite Reliability of  = .95, and maximum reliability of Ω = .96. The results obtained reveal that the PMI scale has good, valid and reliable psychometric properties. Therefore, PMI can be used to measure the level of metacognition of students when solving physics problems. Future studies using PMI are also discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Psychometric and Structural Evaluation of the Physics Metacognition Inventory Instrument

Haeruddin,

Prasetyo,

Supahar

et al. 2020

EUROPEAN J ED RES

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“…Until recently, Rasch analysis was mostly used to evaluate the functioning of the previously developed PER diagnostic tests [4,5,16,21,25]. Planinic [4] evaluated the functioning of the Conceptual survey of electricity and magnetism and Ding [16] reevaluated the widely used Brief Electricity and Magnetism Assessment with Rasch analysis.…”

Section: Developing Documentation For the Measurement Instrumentmentioning

confidence: 99%

“…Similarly, the most famous and widely used PER instrument, the Force Concept Inventory (FCI), was analyzed using the Rasch model to examine its structure and functioning on two different samples of students (non-Newtonian and predominantly Newtonian), detect possible problems, and suggest further improvements [5]. Taasoobshirazi et al [21] performed the Rasch analysis of Physics Metacognition Inventory to assess its construct validity. The Test of Understanding of Vectors was also analyzed using the Rasch model [25].…”

Section: Developing Documentation For the Measurement Instrumentmentioning

confidence: 99%

Rasch analysis in physics education research: Why measurement matters

Planinić

Boone

Sušac

et al. 2019

Phys. Rev. Phys. Educ. Res.

112

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] The Rasch model is a probabilistic model which describes the interaction of persons (test takers or survey respondents) with test or survey items and is governed by two parameters: item difficulty and person ability. Rasch measurement parallels physical measurement processes by constructing and using linear person and item measures that are independent of the particular characteristics of the sample and the test items along a unidimensional construct. The model's properties make it especially suitable for test construction and evaluation as well as the development and use of surveys. The evaluation of item fit with the model can pinpoint problematic items and flag idiosyncratic respondents. The possibility of determining sample-independent item difficulties makes it possible to use the Rasch model for linking tests and tracking students' progression. The use of the Rasch model in PER is continuously increasing. We provide an overview and examples of its use and benefits, and also outline common mistakes or misconceptions made by researchers when considering the use of the Rasch model. We focus in particular on the question of how Rasch modeling can improve some common practices in PER, such as test construction, test evaluation, and calculation of student gain on PER diagnostic instruments.

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Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance

2016

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Recent research on achievement in science asserts that motivation, emotion, and metacognition are important driving forces for learning. This study sought to examine the relationships between two physics class emotions (hope and anxiety), their motivational predictors (instrumentality and self‐efficacy), and their effects on metacognitive problem solving strategies (planning, monitoring, and evaluation) and performance. Data were collected from 520 grade 11 Spanish students (54.7% girls). Structural equation models (SEM), followed by a bootstrap procedure, were used to examine direct and mediated relationships. The results supported the model, suggesting that instrumentality and self‐efficacy negatively predicted anxiety, and enhanced hope, planning, monitoring, evaluation, and performance; metacognitive strategies and performance were negatively predicted by anxiety, and were positively predicted by hope; metacognitive strategies positively predicted performance. Furthermore, the hypothesized mediated relations were also statistically significant. The interpretation of these findings, their implications for physics teaching and learning, and future lines of research are discussed. © 2016 Wiley Periodicals, Inc. J Res Sci Teach 54:558–585, 2017

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Physics Metacognition Inventory Part II: Confirmatory factor analysis and Rasch analysis

Cited by 31 publications

References 24 publications

Psychometric and Structural Evaluation of the Physics Metacognition Inventory Instrument

Psychometric and Structural Evaluation of the Physics Metacognition Inventory Instrument

Rasch analysis in physics education research: Why measurement matters

Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance

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