Relevant bibliographies by topics / Scree plot factor analysis (FA)

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Academic literature on the topic 'Scree plot factor analysis (FA)'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Scree plot factor analysis (FA)"

1

Jannasch, Franziska, Fiona Riordan, Lene F. Andersen, and Matthias B. Schulze. "Exploratory dietary patterns: a systematic review of methods applied in pan-European studies and of validation studies." British Journal of Nutrition 120, no. 6 (2018): 601–11. http://dx.doi.org/10.1017/s0007114518001800.

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AbstractBesides a priori approaches, using previous knowledge about food characteristics, exploratory dietary pattern (DP) methods, using data at hand, are commonly applied. This systematic literature review aimed to identify exploratory methods on DP in pan-European studies and to inform the development of the DEterminants of DIet and Physical ACtivity (DEDIPAC) toolbox of methods suitable for use in future European studies. The search was conducted in three databases on prospective studies in healthy, free-living people across the whole life span. To identify validated DP methods, an additional search without regional restrictions was conducted. Studies including at least two European countries were retained. The search resulted in six pan-European studies applying principal component/factor analysis (PC/FA) (n 5) or cluster analysis (n 2). The criteria to retain PC/factors ranged from the application of the eigenvalue>1 criterion, the scree plot and/or the interpretability criterion. Furthermore, seven validation studies were identified: DP, derived by PC/FA (n 6) or reduced rank regression (RRR) (n 1) were compared using dietary information from FFQ (n 6) or dietary history (n 1) as study instrument and dietary records (n 6) or 24-h dietary recalls (n 1) as reference. The correlation coefficients for the derived DP ranged from modest to high. To conclude, PC/FA was predominantly applied using the eigenvalue criterion and scree plot to retain DP, but a better description of the applied criteria is highly recommended to enable a standardised application of the method. Research gaps were identified for the methods cluster analysis and RRR, as well as for validation studies on DP.
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Shahla, Siddiqui, and Fatima Samreen. "Multivariate techniques to Pomadasys maculatum (Bloch) of Karachi Coast, Pakistan | JBES 2020 @zenodo." Journal of Biodiversity and Environmental Sciences (JBES) 17, no. 1 (2020): 78–84. https://doi.org/10.5281/zenodo.5807567.

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This paper aims to investigate the variability in the morphometric and meristic characteristics in a set of fishery data by applying multivariate statistical techniques; PCA and Factor analysis. Moreover, the gender-wise difference in these variables is also investigated via a statistical independent t-test. Empirical analysis result obtained from the t-test shows that the total length of the body in females is 8.079, whereas the male has 8.054 of the whole body length. Furthermore, a test of independent results of the body displays that the weight of females and males are 9.966 and 10.053 respectively. However, head length and snout length are found 9.143 and 10.568 in females and in males 09.145 and 10.557 respectively. To sum up, all the variables are slightly significant in both (Male and Female). In the second phase via applying PCA, it is found that the first three variables (total length (TL), Bodyweight (But), Standard length (SL)) are retained 90.047% of the total variation.
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Macrosson, W. D. K. "Scree Plots, Data Structure, and Random Variance." Psychological Reports 84, no. 2 (1999): 533–40. http://dx.doi.org/10.2466/pr0.1999.84.2.533.

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Data sets of known and predetermined structure (plasmodes) were created by the Monte Carlo method and subjected to exploratory factor analysis; the scree plots for each plasmode were then drawn. Since the aim of the paper is to give a graphic portrayal of the effects of orthogonality and random variance upon the scree plot, appropriate data sets were selected to illustrate these effects. A progression of scree plots is shown wherein it may be clearly seen how an increase in both nonorthogonality and random variance leads to the suppression of the factor structure in the data. Although this is a well known phenomenon, the visualization highlights the limitations of Kaiser's Eigenvalue > 1 rule and the scree test; it also illustrates the robustness of the factor analytic technique.
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Onatski, Alexei, and Chen Wang. "Spurious Factor Analysis." Econometrica 89, no. 2 (2021): 591–614. http://dx.doi.org/10.3982/ecta16703.

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This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.
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Raîche, Gilles, Theodore A. Walls, David Magis, Martin Riopel, and Jean-Guy Blais. "Non-Graphical Solutions for Cattell’s Scree Test." Methodology 9, no. 1 (2013): 23–29. http://dx.doi.org/10.1027/1614-2241/a000051.

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Most of the strategies that have been proposed to determine the number of components that account for the most variation in a principal components analysis of a correlation matrix rely on the analysis of the eigenvalues and on numerical solutions. The Cattell’s scree test is a graphical strategy with a nonnumerical solution to determine the number of components to retain. Like Kaiser’s rule, this test is one of the most frequently used strategies for determining the number of components to retain. However, the graphical nature of the scree test does not definitively establish the number of components to retain. To circumvent this issue, some numerical solutions are proposed, one in the spirit of Cattell’s work and dealing with the scree part of the eigenvalues plot, and one focusing on the elbow part of this plot. A simulation study compares the efficiency of these solutions to those of other previously proposed methods. Extensions to factor analysis are possible and may be particularly useful with many low-dimensional components.
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Rogers, Kimberly Cervello, Robert Petrulis, Sean P. Yee, and Jessica Deshler. "Mathematics Graduate Student Instructor Observation Protocol (GSIOP): Development and Validation Study." International Journal of Research in Undergraduate Mathematics Education 6, no. 2 (2019): 186–212. http://dx.doi.org/10.1007/s40753-019-00106-4.

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AbstractThis paper presents the development and validation of the 17-item mathematics Graduate Student Instructor Observation Protocol (GSIOP) at two universities. The development of this instrument attended to some unique needs of novice undergraduate mathematics instructors while building on an existing instrument that focused on classroom interactions particularly relevant for students’ development of conceptual understanding, called the Mathematical Classroom Observation Protocol for Practices (MCOP2). Instrument validation involved content input from mathematics education researchers and upper-level mathematics graduate student instructors at two universities, internal consistency analysis, interrater reliability analysis, and structure analyses via scree plot analysis and exploratory factor analysis. A Cronbach-Alpha level of 0.868 illustrated a viable level for internal consistency. Crosstabulation and correlations illustrate high level of interrater reliability for all but one item, and high levels across all subsections. Collaborating a scree plot with the exploratory factor analysis illustrated three critical groupings aligning with the factors from the MCOP2 (student engagement and teacher facilitation) while adding a third factor, lesson design practices. Taken collectively, these results indicate that the GSIOP measures the degree to which instructors’ and students’ actions in undergraduate mathematics classrooms align with practices recommended by the Mathematical Association of America (MAA) using a three-factor structure of teacher facilitation, student engagement, and design practices.
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7

Larasati, Ayu, and Suminto. "Factor Analysis on Possum Dataset to Simplify Many Independent Variables Into Fewer Factors." Sciencestatistics: Journal of Statistics, Probability, and Its Application 2, no. 1 (2024): 26–34. http://dx.doi.org/10.24127/sciencestatistics.v2i1.5649.

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This research aims to apply factor analysis to possum data with the aim of simplifying many independent variables into fewer factors. The factor analysis steps begin by grouping the variables to be analyzed and compiling a correlation matrix using the Bartlett test and the Kaiser-Meyer-Olkin (KMO) test. From the test results, it was found that the variables had sufficient correlation to proceed to factor analysis. After that, factor extraction was carried out using three criteria, namely eigenvalues, diversity percentage, and scree plot, which concluded that the number of factors formed was two. Next, factor rotation was carried out using the varimax method to simplify interpretation. The results show that certain variables have high loadings on certain factors, making it easier to identify patterns. In conclusion, factor analysis succeeded in simplifying the relationship between variables into two factors that can be interpreted more easily.
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Abdollahimohammad, Abdolghani, and Rogayah Ja’afar. "Learning Style Scales: a valid and reliable questionnaire." Journal of Educational Evaluation for Health Professions 11 (August 18, 2014): 22. http://dx.doi.org/10.3352/jeehp.2014.11.22.

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Purpose: Learning-style instruments assist students in developing their own learning strategies and outcomes, in eliminating learning barriers, and in acknowledging peer diversity. Only a few psychometrically validated learning-style instruments are available. This study aimed to develop a valid and reliable learning-style instrument for nursing students. Methods: A cross-sectional survey study was conducted in two nursing schools in two countries. A purposive sample of 156 undergraduate nursing students participated in the study. Face and content validity was obtained from an expert panel. The LSS construct was established using principal axis factoring (PAF) with oblimin rotation, a scree plot test, and parallel analysis (PA). The reliability of LSS was tested using Cronbach’s α, corrected item-total correlation, and test-retest. Results: Factor analysis revealed five components, confirmed by PA and a relatively clear curve on the scree plot. Component strength and interpretability were also confirmed. The factors were labeled as perceptive, solitary, analytic, competitive, and imaginative learning styles. Cronbach’s α was > 0.70 for all subscales in both study populations. The corrected item-total correlations were > 0.30 for the items in each component. Conclusion: The LSS is a valid and reliable inventory for evaluating learning style preferences in nursing students in various multicultural environments.
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Hazlina Abdullah. "Exploring Factor Structures Of A Thinking Questionnaire." Ulum Islamiyyah 31 (August 2, 2020): 35–53. http://dx.doi.org/10.33102/uij.vol31no.199.

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The purpose of this study was to investigate the construct validity of a thinking questionnaire through Factor Analysis over a Malaysian university students’ sample. The questionnaire is a 5 point Likert scale survey ranging from Strongly Agree, Agree, Neutral, Disagree and Strongly Disagree, intended to measure students’ self-reflection on their thinking upon their Invention Project. A total of 350 undergraduate students from various faculties participated in this study. Principal Component Analysis was used because the primary purpose was to identify and compute composite thinking scores for the factors underlying the Thinking questionnaire. The five factor solution, which explained 52.4% of the variance was chosen because of the ‘levelling off’ of eigenvalues on the scree plot after five factors, and was also confirmed by the Monte Carlo Parallel Analysis which indicated smaller values of the 5 factors compared to the eigenvalues displayed in the SPSS.
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Pires, Pedro P., Ana Carolina Monnerat Fioravanti Bastos, Érica de Lana Meirelles, Júlia Mulinari Peixoto, Natacha de Barros Candido, and Leonardo de Barros Mose. "Factorial Structure of the Frugality Scale: Exploratory Evidence." Psico-USF 24, no. 1 (2019): 1–9. http://dx.doi.org/10.1590/1413-82712019240101.

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Abstract This article aims to promote an investigation of the psychometric properties of Frugality Scale adapted to Brazilian Portuguese. Frugality is characterized by using and consuming goods in a resourceful way, fully exploring its durability as well. To this end, 626 people responded to an online form that contained the frugality scale. Procedures such as non-graphical solutions to the scree plot, exploratory graphical analysis and a Schmid-Leiman factor solution point to evidence that the scale’s structure is not one-dimensional but two-dimensional. Finally, modeling strategies also indicate that a bifactor solution can be applied.
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