Using Horn’s Parallel Analysis Method in Exploratory Factor Analysis for Determining the Number of Factors

doi:10.12738/estp.2016.2.0328

Cited by 102 publications

(54 citation statements)

References 40 publications

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“…Observed and simulated eigenvalues are presented in Table 2. Observed eigenvalues that exceed simulated eigenvalues are determined to be valid [88]. The parallel analysis supports a one-factor solution.…”

Section: Perceptions Of Pblmentioning

confidence: 62%

Problem-Based Learning (PBL) and Student Interest in STEM Careers: The Roles of Motivation and Ability Beliefs

2017

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Abstract:Amid growing concerns about the future of the U.S. economy and workforce, educators and policymakers alike have increasingly emphasized the need to expand the number of students interested in, qualified for and actually pursuing careers in science, technology, engineering and mathematics (STEM). The current study draws on survey responses from a sample of 3852 high school students at inclusive STEM schools across the U.S. to investigate how project-and problem-based learning (PBL) may work to address this need. Multivariate regression results indicate that student ratings of PBL are associated with interest in pursuing a career in STEM, as well as with intrinsic motivation for science and students' ability beliefs for both science and math. Further, mediation analysis using Hayes' (2014) MEDIATE macro suggests that science intrinsic motivation and ability beliefs mediate the relationship between perceived PBL experiences and student interest in a future STEM career (IFSC). Our results highlight the important potential of PBL for increasing student STEM attitudes and interest in future STEM careers. As one of the only large-scale quantitative analyses of its kind, this study provides critical information for educators, school administrators and policymakers as they continue to seek effective ways of encouraging students to pursue STEM careers.

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Section: Perceptions Of Pblmentioning

confidence: 62%

Problem-Based Learning (PBL) and Student Interest in STEM Careers: The Roles of Motivation and Ability Beliefs

2017

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“…Specifically, factors with eigenvalues higher than 1.0 were retained; a visual examination of scree plots for sharp dropoffs in plotted eigenvalues were conducted; and the total percent of variance explained (e.g., the variance of the original variable vs. the variance explained by each factor) was examined (Pett, Lackey, & Sullivan, 2003). Finally, parallel analysis using the Monte Carlo simulation technique was used, which generates a random artificial dataset to compare to the original dataset to determine the number of factors (Çokluk & Koçak, 2016). Parallel analysis determines the number of factors to retain by going above and beyond simply retaining factors with eigenvalues above 1.0, which can retain an excessive number of factors; instead, it retains factors in which the eigenvalue in the simulated sample is greater than the corresponding eigenvalue in the actual data (Çokluk & Koçak, 2016;Ledesma & Valero-Mora, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, parallel analysis using the Monte Carlo simulation technique was used, which generates a random artificial dataset to compare to the original dataset to determine the number of factors (Çokluk & Koçak, 2016). Parallel analysis determines the number of factors to retain by going above and beyond simply retaining factors with eigenvalues above 1.0, which can retain an excessive number of factors; instead, it retains factors in which the eigenvalue in the simulated sample is greater than the corresponding eigenvalue in the actual data (Çokluk & Koçak, 2016;Ledesma & Valero-Mora, 2007). The EFAs and parallel analyses were carried out in IBM SPSS (version 21.0).…”

Section: Discussionmentioning

confidence: 99%

Psychometric properties of the self‐sufficiency matrix among homeless and vulnerably housed individuals and families

Cummings

Brown

2019

Journal Community Psychology

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Homeless service systems have been implementing assessment tools to inform service provision for individuals and families. Although a variety of assessments are available, their psychometric evidence base is limited. The self‐sufficiency matrix (SSM) is one assessment that demonstrates promising reliability and validity. However, the SSM's factor structure has been inconsistent across studies. This study explored the factor structure of the SSM, using exploratory factor analysis and confirmatory factor analysis in a sample of individuals (N = 427) and families ( N = 428) at risk of or experiencing homelessness. Data were derived from a midwestern city's Homeless Management Information System and included all participants from the Homelessness Prevention and Rapid Re‐Housing Program. Results suggest the SSM is multidimensional and the factor structure differs across individuals and families. Additionally, the SSM demonstrates measurement invariance across racial and gender groups. Further development and testing of the SSM is necessary to better serve individuals experiencing homelessness.

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“…Most factors that could be extracted are not meaningful and therefore should not be retained. The Kaiser criterion and scree plot approaches, PA (parallel analysis), and the MAP (minimum average partial) correlation procedure have been widely employed to determine how many factors should be retained . However, the Kaiser criterion and scree plot approaches do not take into account sampling error so an erroneous decision may be made.…”

Section: Factor Analysismentioning

confidence: 99%

Impact of terrain factors on the matching performance of terrain‐aided navigation

2019

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Aiming at describing terrain variation in an easily understandable way, a new method is proposed to describe the terrain elevations quantitatively by factor analysis for terrain‐aided navigation. After as many terrain factors as possible are collected describing the terrain elevations from various aspects, they are simplified by correlation to remove the redundant factors. The simplified terrain factors are processed to derive the extracted factors by principal component analysis. With such factor analysis, three extracted factors dubbed “variation,” “similarity,” and “information” are derived in the conceptual level. A classifier is derived by logistic regression using the extracted factors. The percentages of the correct classification are very high both for the suitable and the unsuitable regions of matching in two maps, which proves the effectiveness of the extracted factors for the terrain elevations.

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Using Horn’s Parallel Analysis Method in Exploratory Factor Analysis for Determining the Number of Factors

Cited by 102 publications

References 40 publications

Problem-Based Learning (PBL) and Student Interest in STEM Careers: The Roles of Motivation and Ability Beliefs

Problem-Based Learning (PBL) and Student Interest in STEM Careers: The Roles of Motivation and Ability Beliefs

Psychometric properties of the self‐sufficiency matrix among homeless and vulnerably housed individuals and families

Impact of terrain factors on the matching performance of terrain‐aided navigation

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