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In this technical report, we used the exploratory factor analysis to examine 24 open−source governance indices measuring levels of democracy, economic development and human rights, in order to identify and describe patterns of relationships that may exist among them. The 24 variables used for the factor analysis were selected from a comprehensive list of open−source databases obtained from the DRDC Toronto Technical Report 2008−167. The analysis produced a three−factor solution that explained the most variance and provided a theoretical interpretation in accordance to the already established definitions of good governance. We build on previous efforts by providing a more comprehensive analysis of a larger set of similar indices, proposing a new approach to interpreting multiple sources of data in an integrative manner, and offering further guidance on the use of these measures for assessing state governance.

The present study is part of a larger project that aims to empirically determine how to assess and conceptualize psychological characteristics of daily events. According to this study’s working definition, daily events are events that influence people’s everyday life or that affect one’s current mood or behavior. Daily events can either have negative (daily hassles) or positive effects (uplifts). Daily events differ from ordinary situations in that they are considered relevant by the individual, at least in the short term. Moreover, a daily event may involve more than one particular situation. At the same time, daily events also differ from major life events because daily events do not seriously change one’s life. Lastly, chronic and acute stressors should be regarded separately as they differ in their duration or intensity, respectively. As previous assessment tools mostly focus on aspects like frequency or severity (e.g., Almeida et al., 2002; Brantley et al., 1987; Kanner et al., 1981), the present study aims at applying the dimensional approach adopted for similar concepts (e.g., major life events: Luhmann et al., 2021; situations: Rauthmann et al., 2014) to daily events. More precisely, the present study aims to develop a dimensional taxonomy of perceived characteristics of daily events. To develop such a dimensional taxonomy of daily events, an exploratory factor analysis will be conducted. Items stem from theoretical considerations, established taxonomies for similar constructs (i.e., situations, major life events), a literature review, and a qualitative study.

This report provides a basis for understanding the confirmatory factor analysis (CFA) results on the Gender Role Conflict Scale (GRCS) through exploratory factor procedures. Recently, researchers have expanded the available psychometric information on the GRCS through the application of CFA procedures to the hypothesized four-factor model. The four hypothesized factors of the GRCS reflect the four sub-scales of: (1) success, power, and competition; (2) restrictive emotionality; (3) restrictive affectionate behavior between men; and (4) conflict between work and family relations. The study is in two parts. Sample 1 consisted of 198 male undergraduate and graduate students ranging in age from 16-39. The students' ethnicities were White, Asian, Black, Hispanic, and Native American. Sample 2 involved 486 male graduate and undergraduate students ranging in age from 17-48 years. The categories were the same as in sample 1. Participants completed the 37-item, self-reporting GRCS. Results of the exploratory factor analysis provide additional support for the established four-factor model of the GRCS; however, some items can be rewritten to be more pure measures. Claims that the authors and/or users of the GRCS will need to determine if the costs of the revision process will be worth the benefits. (MKA)

The main purpose of this study is to assess the construct validity of Australian eighth grade mathematics teachers' perceptions towards their career satisfaction, their teaching practice and the everyday challenges encountered in schools. The data were utilised from the Trends in International Mathematics and Science Study where a total of 802 eighth grade mathematics teachers participated. Exploratory factor analysis with statistical tests were utilised to analyse the data. Three factors career satisfaction, teacher collaboration and everyday challenges were derived from exploratory factor analysis. The factors had a reliability coefficient of: career satisfaction (0.802), teacher collaboration (0.838) and everyday challenges (0.777). The findings suggested that female teachers had a higher career satisfaction level and highly experienced teachers encountered more everyday challenges. This study could be helpful to enhance the level of career satisfaction for teachers and assist in reduction of everyday challenges. Future research could investigate actual reasons for lower satisfaction level for the teachers.

In press, finishing touches

The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data

Productivity of university members has been defined in different forms; however, no specific definition is presented to evalu - ate factors that determine the productivity of university members. This study intends to provide a certain definition for the productivity of university members including five major constructs that impact productivity and employs an Exploratory Factor Analysis Approach to propose a conceptual model for evaluation of the impact of these factors on the productivity of the staff in university.

The material value scale (MVS) and the aspiration index (AI) are among the most prominent measurements of materialism in research. As the names indicate, the MVS measures materialism in terms of materialistic values, whereas AI measures it in the matter of aspirations. Although both instruments have been widely used in research, the question of whether materialistic values and aspirations are independent of each other remains open for examination. The answer to this question is important, considering the inconsistencies in the results of past research. Therefore, this study aims to assess the construct's similarity and dissimilarity between both self-report materialism measurements resulting from exploratory factor analysis (EFA). The study was conducted online in Indonesia in 2019, with 610 participants, Indonesian version of MVS and AI, the software of Jamovi and R Studio. The analysis consisted of the Bartlett test, Kaiser-Meyer-Olkin test, parallel analysis, minimum rank factor analysis, EFA, and reliability (Cronbach’s α). The result showed there was an intersection, but each measurement had a portion of each independence higher, hence was no sufficient evidence of similarity between both materialism constructs. The construct of materialism as value and materialism as aspiration is proposed as different. Limitations of the study and implications for research were discussed.

Sustainable investments and ESG-investments (Environmental, Social and Governance) have gained significant relevance in recent years (Eurosif, 2018). Nevertheless, current scales that measure financial literacy, e.g, Lusardi et al. (2008) or van Rooji et al. (2011), neglect the sustainability aspect. To the best of our knowledge only one study in the literature attempts to measure sustainable finance literacy (Filippini et al., 2022), while other studies rely on general environmental literacy (Anderson and Robinson, 2022; Bethlendi et al., 2022). Here, we intend to overcome this gap and develop and psychometrically validate a practice-oriented scale to reliably measure sustainable finance literacy, that researchers and practitioners can lean on – the Sustainable Finance Literacy (SFL) scale. Measuring sustainable finance literacy through a dedicated and reliable scale is important for a number of reasons. First, it allows researchers to better understand the extent of SFL among their study participants and establish potential relationships with other pro-environmental behaviours or (sustainable) financial behaviours. Anderson and Robinson (2022), for instance, show that pro-environmental households do not invest more sustainably, which they attribute to informational barriers and financial disengagement. Second, in terms of the practical value-added, an SFL scale will allow policy makers to understand the level of sustainable finance literacy among the general population, and financial advisors to adapt and optimise their advice to individuals interested in investing. This is particularly relevant, since as part of the green transition, there are currently several EU and national initiatives to promote green finance and the transformation to sustainable finance. There is some evidence that insufficient knowledge acts as a barrier to sustainable investing (Gutsche et al., 2020). Yet, without an established measure, we cannot reliably know what this level of knowledge is and to what extent it hinders or encourages ESG investments. Increasing sustainable finance literacy may also help investors to make more informed decisions and to avoid their vulnerability to greenwashing, currently perceived as a barrier to sustainable investing (Klein et al., 2022). We follow a mixed-methods approach with 10 different steps, including item generation based on a comprehensive literature review, item list refinement and content validity assessment from experts and practitioners, as well as reduction of items and factor solution based on exploratory factor analysis using a dataset from a survey on 1000 participants. This preregistration is uploaded before the data collection. This survey will serve as a basis for a follow-up survey and an investment experiment. Based on the follow-up survey we aim to carry out confirmatory factor analysis to validate our SFL scale, as well as shed light on determinants of sustainable finance literacy. Foreseen factors include the image that people hold of the stock market or stockholders, value orientation, stock market participation (investments, sustainable investments). In the investment experiment, we will develop interventions to increase sustainable finance literacy (e.g., training material for consumers and investors) and test the effect on investment decisions. In particular, we aim to test the effect of these interventions on stock market participation (decision of saving vs. investing) and the sustainable investments (share of sustainable investments compared to conventional investments), with focus on non-investors. Both the follow-up survey and the experiment will be preregistered separately.

This research will be the first steps in creating a measure of anti-intellectual attitudes. We will perform an exploratory factor analysis on items that we have generated, with the goal of creating a short scale of between 15 and 25 items. We will use the psych package in R, and use scree plots, parallel analyses, and item-total correlations alongside exploratory factor analysis to determine which items to use in this scale. A second goal of this research is to provide initial convergent validity of this scale by correlating it with other measures (noted below). Finally, a last goal of this project is to provide incremental validity by showing that our predicted relations with other measures hold when another, previously-published measure of anti-intellectualism is controlled for.

Twenty-one variables believed to be important indicators of health related physical fitness were measured on male and female college students between 1991 and 1993 (n=433). Exploratory and confirmatory factor analytic techniques were used in an attempt to derive important components of physical fitness. The exploratory factor analysis identified five important factors of collegiate physical fitness: (1) strength and endurance; (2) body composition; (3) heart rate; (4) blood pressure; and (5) flexibility. For the confirmatory factor analysis, an a priori model similar to that developed by Marsh (1992) and one similar to the American College of Sports Medicine (ACSM) were tested in which body composition, body girth, muscular strength and endurance, cardiorespiratory fitness, flexibility, blood pressure, and cardiac functioning were hypothesized as important fitness parameters. Results of the confirmatory factor analysis indicate that neither the ACSM nor Marsh's model provided good fits for these students. Reliability estimates were high but poor validity and goodness of fit indices were found. This indicates that common fitness measurement techniques may be inadequate when assessing college students' fitness levels. (Contains 11 references and 3 tables.) (SLD)

Rotation is used in almost all exploratory factor analysis (EFA) studies. There are numerous rotation strategies that can be employed in these various applications. This paper reviews the various rotation choices in EFA studies, including confirmatory rotation, and presents criteria useful in selecting rotation methods in various analytic situations. Factors are rotated in attempts to improve the interpretability of results. The first decision the researcher must make is whether he or she wants the factors to be correlated (oblique rotation) or uncorrelated (orthogonal rotation). As a general rule, if the researcher is primarily interested in getting results that best fit the data, the factors should be rotated obliquely. If the interest is in the generalizability of results, then orthogonal rotation should be conducted instead. It is suggested that regardless of the magnitude of the correlation among the factors it is unnecessary to do oblique rotation in addition to orthogonal rotation. The correlation among the factors yields results that are more difficult than results of an orthogonal rotation, and the slight difference between results of an oblique and an orthogonal rotation is nearly insignificant. (Contains 7 tables and 10 references.) (SLD)

The appropriate use of exploratory factor analysis (EFA) necessitates thoughtful researcher judgment concerning a number of analytic decisions. This paper reviews some of the fundamental decisions necessary to conduct an EFA and examines reporting practice in published research across four journals. In general, insufficient information was given in published applications of EFA (n=60) to allow external verification of the results. Researchers often used poor strategies to determine the number of factors to retain. In one-third of the cases, a confirmatory factor analysis was warranted rather than EFA. Other errors in reporting and practice were noted. Several recommendations for improved EFA reporting practice are given. (Contains 4 tables and 105 references.) (Author/SLD)

A mental model is a knowledge structure composed of concepts and the relations between them. Mental models are distinct from declarative and procedural knowledge--they go beyond semantic relationships and skills acquisition and contain varied intellectual skills and knowledge. This study describes an initial investigation into the construct validity of mental models as indication of a distinct learning outcome. The study considered the relationship between mental models, declarative knowledge, concept learning, problem solving, and troubleshooting performance. College junior-level Operations Management students (n=21) were tested on their knowledge and use of spreadsheets through multiple choice and matching quizzes developed using the Pathfinder Associative Network data collection method. Study limitations included: subject mortality; generalizability; missing variables; insufficient number of subjects; invalidated instruments; and convergent validity. Appendices include: definitions of structured knowledge, construct validity, and factor analysis terms; sample questions from the declarative knowledge quiz; concept matching quiz; and SPSS factor analysis results. (Contains 55 references.) (Author/SWC)

Ratings of teachers' instructional practices using standardized classroom observation instruments are increasingly being used for both research and teacher accountability. There are multiple instruments in use, each attempting to evaluate many dimensions of teaching and classroom activities, and little is known about what underlying teaching quality attributes are being measured. We use data from multiple instruments collected from 458 middle school mathematics and English language arts teachers to inform research and practice on teacher performance measurement by modeling latent constructs of high-quality teaching. We make inferences about these constructs using a novel approach to Bayesian exploratory factor analysis (EFA) that, unlike commonly used approaches for identifying factor loadings in Bayesian EFA, is invariant to how the data dimensions are ordered. Applying this approach to ratings of lessons reveals two distinct teaching constructs in both mathematics and English language arts: (1) quality of instructional practices; and (2) quality of teacher management of classrooms. We demonstrate the relationships of these constructs to other indicators of teaching quality, including teacher content knowledge and student performance on standardized tests.

Factor analysis is used frequently by researchers as a data reduction and summarization technique. Many analysts use exploratory factor analysis to search for underlying dimensions in attitudinal studies. Concern arises when novice researchers rely solely on information derived from computer printouts to factor analyze data, dismissing theoretical consideration of concepts underlying this analytical procedure. A primer on principal components exploratory factor analysis is presented, and five decision rules for selecting the number of principal components to retain are discussed: (1) the K1 rule; (2) the Scree test; (3) Bartlett's test; (4) the minimum average partial method; and (5) parallel analysis. A small data set, obtained in an actual exploratory study, was used to illustrate the discussion. The study addressed effects of preemployment tests on attitudes toward a firm formed by individuals outside that firm. In a pilot study, responses of more than 400 graduating seniors to three different preemployment tests were analyzed. In a second study, 249 graduating seniors and master's candidates responded to preemployment test scenarios. Dimensions of applicants' attitudes were examined through exploratory factor analysis. It is concluded that the results of different decision rules must be used when determining the number of principal components, and that factor analyses should be run with one or two components above and below those suggested with the five methods in order to avoid underextraction or overextraction. Analysts are cautioned to not rely on computer programs and preset default outputs as the "last word." Three figures and four tables supplement the discussion. A 35-item list of references is included. (Author/SLD)

Historically, most researchers conducting factor analysis have used exploratory methods. However, more recently, confirmatory factor analytic methods have been developed that can directly test theory either during factor rotation using "best fit" rotation methods or during factor extraction, as with the LISREL computer programs developed by K. G. Joreskog et al. (1986). This study provides a heuristic example that illustrates some comparisons between statistical results obtained using exploratory and confirmatory factor analytic techniques with a data set of scores of 145 junior high school students on 26 psychological tests. The first analysis used exploratory factor extraction of orthogonal principal components followed by confirmatory rotation to determine goodness of fit. The second analysis used confirmatory extraction of five factors based on theoretical expectations. Results indicate that exploratory and confirmatory factor analytic procedures can yield different results. While exploratory methods are an invaluable aid in the initial structuring of behavioral constructs, confirmatory methods serve the researcher in the affirmation or rejection of models derived using theoretical considerations or exploratory factor analytic techniques. Seven tables present the data set. An appendix lists the tests taken by the students, and a second appendix gives the LISREL commands for running the second analysis. A 34-item list of references is included. (SLD)

Factor analysis allows researchers to conduct exploratory analyses of latent variables, reduce data in large datasets, and test specific models. The purpose of this paper is to review common uses of factor analysis, provide general guidelines for best practices, illustrate these guidelines with examples using previously published self-concept data, and discuss common pitfalls and ways to avoid them. (Contains 4 tables, 1 figure and 3 endnotes.)

Factor analysis allows researchers to conduct exploratory analyses of latent variables, reduce data in large datasets, and test specific models. The purpose of this paper is to review common uses of factor analysis, provide general guidelines for best practices, illustrate these guidelines with examples using previously published self-concept data, and discuss common pitfalls and ways to avoid them. (Contains 4 tables, 1 figure and 3 endnotes.)

Previous research shows that measuring the quality of student career centers in higher education is not yet fully standardized and is often limited to qualitative approaches. The lack of valid and reliable measuring tools regarding the student career center can hamper efforts to improve and develop this service. The aim of the research is to develop and test a scale for measuring the quality of student career centers in the context of higher education. A quantitative approach with exploratory and confirmatory factor analysis (CFA) was used in this study, with respondents as many as 562 students from three different universities. Analysis and interpretation of the measurement scale evaluation was carried out in two stages, namely exploratory factor analysis (EFA) and continued with CFA. The research results identified that the dimensions of the role of career centers, utilization of career centers, management of workforce information, and fostering entrepreneurship are key elements that influence the effectiveness and impact of career centers in higher education. This scale can practically be used by each institution to evaluate the quality of the career centers they run, and can also be used by other researchers for further research.

A student perceptions measure of college and university classroom learning environment was developed to reflect the dynamic interplay among contextual variables and accommodates theoretical and practical applications in light of logistical concerns found in such complicated educational settings as a medical school. The 18-item Teaching and Learning Environment Questionnaire (TLEQ) was developed using elements of effective teaching and learning processes documented in the professional literature. The sample included all second year medical students in a medical school in 2 years, for a total of 296 participants (345 usable questionnaires). The TLEQ was administered in pharmacology and pathology courses in five instructional settings, including problem-based learning and computer-assisted learning, using one of four data collection formats. Results suggest that the TLEQ is a flexible and easy-to-administer measure of students' personal perceptions of classroom learning environments that has the potential to contribute substantially to faculty and instructional development efforts in medical schools specifically and in other professional schools in general. The TLEQ reflects marker variables for two distinct factors that clearly define the nature of effective postsecondary classroom learning environments. One represents teaching and learning context and the other represents learner involvement. (Contains 3 tables and 28 references.) (SLD)

The development of the Home and Family Questionnaire (HFQ) is described and exploratory factor analyses and initial reliability studies are reported. The HFQ, which incorporates many of the items from the HOME Observation Inventory for Elementary School Children developed by B. M. Caldwell and R. H. Bradley (1984), evaluates home setting and home process. The HFQ uses a self-administered questionnaire format that may be administered through the mail or in the experimenter's laboratory. In addition, the HFQ addresses the lack of congruence with ecological systems theory of the HOME instrument. Fifty families of third graders participated in the initial stages of instrument development. Reliability estimates were calculated for scores obtained in each of two presentation modes, and exploratory factor analyses were conducted on individual item scores. Reliability was higher (Cronbach's alpha of 0.79) with laboratory administration. The factor solution remained ambiguous, but trends consistent with the hypothesized subscales were observed. Four tables illustrated study findings. (Contains eight references.) (SLD)

The present research aims to explore the possible sub-dimensions and psychometric properties of "Beliefs about types of social mobility scale". To this purpose, exploratory factor analysis will be carried out on the 20 items created, which compose the "Beliefs about types of social mobility scale”. Also, we will carry out some exploratory analysis to study the relationship between our potential scale and some scales of interest.

One’s environmental attitude can predict human behavior toward the environment. This study aimed to validate the environmental attitude instrument with the science, technology, engineering, and mathematics in ethnoscience-integrated (Ethno-STEM) approach. The questionnaire consisted of 16 items and was tested on 159 eighth-grade students. Data were analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The communality value of three instrument items was less than 0.5, so they were excluded in EFA stage 1. The EFA stage 2 test results showed that the 13 instrument items had a factor loading of more than 0.5 and were categorized into five factors. The CFA stage 1 was conducted using linear structural relations (LISREL) and obtained the t-value for all items in factors 1-4 more than the t-table, so they were declared valid. Factor 5 was omitted in CFA test stage 2 due to invalid items. The CFA test results showed that the t-count of the four factors was more than the t-table. Based on the goodness of fit value, the model and measurement matrices were the same (model fit). Thus, the environmental attitude instrument items are declared valid, and the model is fit. The instrument can be used to measure environmental attitudes.

This paper considers some decisions that must be made by the researcher conducting an exploratory factor analysis. The primary purpose is to aid the researcher in making informed decisions during the factor analysis instead of relying on defaults in statistical programs or traditions of previous researchers. Three decision areas are addressed. First, the importance of determining the number of participants and variables is discussed. Second, the similarities and differences in the two most common factor extraction methods, principal components analysis (PCA) and principle factor analysis (PFA), are addressed. Finally, the paper compares different methods for retaining factors (e.g., K1 rule, scree plot, parallel analysis, and others). Overall, although PCA and PFA results converge with an increasing number of variables and communality estimates, PFA may be the better choice for attempting to determine underlying factors in a data set. Furthermore, parallel analysis shows great promise for facilitating precision in factor retention, but a researcher should take into consideration a combination of methods in order to be as accurate as possible in developing the factor model. Empirical studies indicate that defaults in statistical packages are frequently used and that superior methods, such as parallel analysis, are grossly underused. (Contains 18 references.) (Author/SLD)

Exploratory factor analysis is a complex and multivariate statistical technique commonly employed in information system, social science, education and psychology. This paper intends to provide a simplified collection of information for researchers and practitioners undertaking exploratory factor analysis (EFA) and to make decisions about best practice in EFA. Particularly, the objective of the paper is to provide practical and theoretical information on decision making of sample size, extraction, number of factors to retain and rotational methods.

Exploratory Factor Analysis (EFA) Pada Penyerapan Anggaran Pendapatan dan Belanja Negara (APBN) Tahun 2017 di Provinsi Sumatera Barat - Aulia Rahim, Harsya Saputra Indonesian Treasury Review Vol.3 No.3, 2018

This study aims to develop a robust survey instrument for measuring entrepreneurial competences, grounded in the EntreComp framework. Data will be collected through university incubators across Denmark, providing access to a relevant population of aspiring and early-stage entrepreneurs. The study is designed as a longitudinal investigation, with participants completing the survey at six-month intervals. In the initial phases, the project will employ exploratory factor analysis (EFA) in the first step to validate the underlying competence dimensions. The ultimate goal is to create a theoretically grounded and empirically validated tool that can be used in both research and practice to assess entrepreneurial competences and track their development across different stages of the entrepreneurial journey. This will require further rounds of validation resulting in multiple pre-reg for this study.

This preregistration outlines a study that employs Exploratory Factor Analysis (EFA) to investigate the latent structure underlying responses to political trust items among Japanese adults. The primary objective is to identify underlying dimensions of political trust and examine whether they align with conceptual distinctions from prior research, such as trust in institutions and trust in political actors from the left and right.

Direct Behavior Rating (DBR) is an efficient method for monitoring changes in student behavior in response to intervention. Emerging research on DBR Multi-Item Scales (DBR-MIS) indicates DBR-MIS has promising characteristics as a progress-monitoring assessment. Specifically, the multiple items within DBR-MIS allow stakeholders to measure specific behaviors at the item level, as well as global constructs at the scale level. Additionally, studies have shown fewer rating occasions are necessary to reach acceptable levels of dependability when using DBR-MIS as opposed to single-item scales (DBR-SIS). The purpose of the study was to develop and validate DBR-MIS problem behavior scales (Disruptive, Oppositional, Interpersonal Conflict, and Conduct Problems) that may be used to evaluate students' response to social-emotional or behavioral intervention. Within the first phase of development, item content was generated and subjected to evaluation by panels of researchers and school-based consumers. Exploratory factor analysis was then used in the second phase to identify items that represented the strongest indicators of each construct. Teachers (N = 307) in grades K through 3rd, from 35 school districts across 13 states in the Northeastern, Midwestern, Southern, and Southwestern United States each completed ratings for one randomly-selected student (N = 307). Results of EFA using a starting pool of 9-11 items for each DBR-MIS initially indicated one-factor solutions for the Disruptive and Oppositional scales and a two-factor solution for the Interpersonal Conflict scale. Consequently, a new Conduct Problems scale was created from items loading on the second factor. Implications for progress monitoring and future research are discussed. [This article will be published in "Journal of Emotional and Behavioral Disorders."]

The primary goal of this study was to investigate the underlying structure of the Differential Ability Scales (DAS) using Exploratory Principal Axis Factoring (PAF) with 62 nonclinical preschoolers. While previous factor analyses of the DAS Core subtests revealed the derivation of two distinct factors, the current results revealed only one factor, general cognitive ability. When the DAS Core and Diagnostic subtests were combined, two factors emerged, general cognitive ability and visual memory. Overall, these results suggest that the DAS Core subtests are a measure of general intelligence. Recommendations for users of the DAS with young children are provided. (Author/SLD)

This investigation utilized publicly available data from the High School Longitudinal Study 2009 (HSLS:09) by the National Center for Educational Statistics (NCES) to examine any latent structures among variables which may empirically support the validity and reliability of a mathematical sense of belonging (MSB) construct. Using the nationally representative survey data in the HSLS:09, the proposed study performed an exploratory factor analysis (EFA) to create a complex construct of students' MSB or mathematical belongingness. A weighted sample of over 13,000 (N=13,354) high school students was used to conduct the EFA, which was then mapped onto Mahar and colleagues' (2012) intersecting, transdisciplinary themes of belonging. Possible implications for preservice teachers, professional development for practicing teachers, as well as future research directions are discussed. [For the complete proceedings, see ED658295.]

When conducting an exploratory factor analysis, the decision regarding the number of factors to retain following factor extraction is one that the researcher should consider very carefully, as the decision can have a dramatic effect on results. Although there are numerous strategies that can and should be utilized when making this decision, researchers most often use the eigenvalue-greater-than-one rule or visual scree test. This trend is troubling, given that other, more sophisticated techniques have been shown to give more accurate appraisals of the number of factors. For this reason, understanding how advanced techniques derive the number of retainable factors is important. The present paper reviews various factor retention rules. Data from a questionnaire development study will be analyzed to make the discussion of the various techniques concrete. (Contains 4 tables and 7 figures.)

Note this registration has been withdrawn: Historically, students with intellectual and developmental disabilities (IDD) have had limited access to rigorous reading instruction. Past emphases have included whole-word reading, isolated skill practice, and functional reading skills (Browder et al., 2006). However, updated research provides evidence that students with IDD can benefit from rigorous reading instruction that is inclusive of a wider set of objectives (Browder et al., 2009). Translation of such research into practice may be influenced by teachers’ knowledge and perceptions of reading instruction for students with IDD, who may receive special education services under the categories of intellectual disability, autism, developmental delay, or multiple disabilities. Previous research indicates that teachers vary in their knowledge of reading development and instruction, in some cases with special education teachers demonstrating greater knowledge of these concepts than general education colleagues (see Bos et al., 2001). More recent research (Porter et al., 2021), suggests that reading specialists may be more knowledgeable on this topic than general education teachers, who are in turn more knowledgeable than special education teachers. Such findings are concerning, given that all educators need to be aware of reading development and instructional methods to meet the needs of students who require a range of supports. In conjunction with knowledge of reading, teacher perceptions of student abilities have been demonstrated to predict classroom instruction and student reading success (e.g., Bos et al., 2001; Piasta et al., 2009; Ruppar et al., 2015). Students with IDD tend to receive instruction from special education teachers and often paraprofessionals. Educators with higher knowledge of reading development and higher expectations for their students are more likely to use evidence-based instructional methods that ensure greater student growth. Such instructional decisions are sometimes decided by school or intermediate unit (IU) administrators. Therefore, it is important to collect data not only on what teachers know about reading instruction, but also what they believe students with IDD are capable of learning. The purpose of this study is to develop and validate an initial measure of teacher expectations for reading instruction for students with intellectual and developmental disabilities. There is no tool to date that measures teachers’ expectations for literacy instruction, taking into consideration the specific needs and characteristics of students with IDD. This measure will be called the Teacher Expectations in Literacy for Students with IDD (TELSIDD) scale. This initial measure will be validated using exploratory factor analysis methods. Following validation, we anticipate conducting future studies to further explore the validity and dimensionality of the measure, using confirmatory analyses.

This study focuses on developing and validating instruments to assess the computational thinking skills (CTS) and collaboration skills (CS) of undergraduate students in Indonesia. Employing a quantitative research approach with the exploratory factor analysis (EFA) technique, the research process unfolded in three validation steps. First, face validity was established through expert judgment. Second, discriminant validity was examined using product-moment correlations and Cronbach’s alpha. Finally, EFA were employed to assess the factorial structure. The instrument development process followed five phases: drafting the instrument, face validity assessment by experts, data collection involving 242 undergraduate students as samples, discriminant validity analysis (product moment and Cronbach’s alpha), and EFA analysis to group items and construct dimensions. This study identified six dimensions for CTS (algorithmic thinking, cooperative thinking, problem reformulation, creativity, critical thinking, and systematic testing) and three dimensions for CS (knowledge sharing, planning, and responsibility). These findings support validating the CTS and CS self-assessment scale, making it a valuable tool for evaluating undergraduate student learning and researching computational thinking and CS in Indonesia. Researchers and educators are encouraged to utilize the CTS and CS instrument for self-assessment purposes and further exploration of these competencies among undergraduate students.

The purpose of this study is to develop the Metacognition Scale (MS) which is designed for primary school students. The sample of the study consisted of 426 primary school students in Izmir, Turkey. In order to examine the construct validity of the MS, exploratory factor analysis and confirmatory factor analysis were performed. For the validity of the MI, corrected item-total correlations were used. The corrected item-total correlations ranged from 0.35 to 0.65. In addition, t-tests between items' means of upper 27% and lower 27% points were compared. For each factor and each item, the differences between mean scores of upper 27% and lover 27% groups are significant. Finally, Cronbach alpha correlation coefficients were used. The internal consistency of the MS is 0.96 for the entire scale. The MS has eight scales: declarative knowledge, procedural knowledge and conditional knowledge, planning, self-control, cognitive strategies, self assessment and self monitoring. According to these findings, the MS is appropriate for researchers or teachers whose aim is to measure his/her students' metacognitive awareness and metacognitive abilities.

This paper reviews the basic processes of exploratory factor analysis (EFA) with regard to evaluating test score validity. Construct validity is the focus of the paper. The similarity between the processes of construct validation and EFA is described and the use of EFA as a tool to explore score validity is explored. Factor analysis is a method for determining the number and nature of the variables that underlie large numbers of variables or measures. It tells the researcher what tests or measures belong together. Construct validity is studied when the test user wants to draw an inference from the test score to performances that can be grouped under the label of a particular psychological construct. Factor analysis, long associated with construct validity, is a useful tool to evaluate score validity. It is emphasized that validity is not a property of tests, but rather a property of test scores. The identification of the number of factors that underlie a set of variables and the determination of whether factors are correlated or uncorrelated can be helpful in evaluating test score validity. (Contains 24 references.) (SLD)

Appropriate use of exploratory factor analysis (EFA) requires a series of thoughtful analytical decisions. Adequate reporting of an EFA would allow external evaluation of the decisions made by the analyst. This paper briefly reviews some of the decisions necessary in an EFA. It provides an empirical review of reporting practice in three educational research journals, and notes several errors in both EFA use and reporting. Three journals were identified for the study: (1) American Educational Research Journal; (2) Journal of Educational Research; and (3) The Elementary School Journal. Examination of 14 total volumes across the journals resulted in the coding of 49 EFAs for the current study. Several errors of use and reporting omissions were noted in the study, and recommendations for improved practice are presented. (Contains 4 tables and 59 references.) (SLD)

Bullying, a recurring aggressive and substantial antisocial behavior characterized by a power imbalance can be destructive, and persistent and causes harm. Bullying is a negative behavior on individuals or groups of individuals who are considered weak that occurs more frequently among adolescents between the ages of 13–16. Bullying in schools is a violence that can result in students feeling threatened and powerless. This study was conducted to identify constructs that measure physical, verbal, anti-social, and cyberbullying. A total of 677 samples from a total of 23 secondary schools in the northern peninsula of Malaysia were involved in this cross-sectional survey research design that used a questionnaire to collect the data. Exploratory factor analysis was used to analyze the data. The results showed that four factors namely, physical bully, verbal bullying, antisocial bully, and cyberbullying were prevalent among Malaysian adolescents. The reliability value for each factor was high, which ranged from .73 to .89. Out of four bullying constructs in the study, physical bullying has the highest factor loading. In conclusion, this study has proven that Malaysian adolescents also face bullying where the most dominant bullying behavior for Malaysian adolescents is physical bullying. The data imply that school leaders, the Malaysian Ministry of Education, and the community should view this matter seriously and Malaysian youths should be educated on the dangers of bullying behavior that can have a negative impact on the development of society as a whole.

This is the third survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate their perception of a visualization’s readability. We have previously generated a pool of items and asked experts to rate their validity. In this survey, we will ask participants from Prolific, a crowdsourcing platform to use this set of items to rate data visualizations. We will use the resulting data to identify potential factors behind these items, and further reduce them.

This is the third survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate their perception of a visualization’s readability. We have previously generated a pool of items and asked experts to rate their validity. In this survey, we will ask participants from Prolific, a crowdsourcing platform to use this set of items to rate data visualizations. We will use the resulting data to identify potential factors behind these items, and further reduce them.

Anger arising while riding a motorcycle is becoming a common problem on the road and it may be caused by many factors. The main objective of this study is to examine factors that could provoke anger among motorcyclists while riding. This study was based on self-reported questionnaires and comprised of 407 motorcyclists who owned valid riding licenses. exploratory factor analysis (EFA) indicated that the sample fit was suitable for factor analysis. In total, eight factors (unsafe or inappropriate actions, rude or sluggish actions, road conditions and design, police or enforces presence, illegal actions on the road, hazards on the road, rainy condition and obstruction on the road) with eigenvalues above 1.0 and 59 variables that ranged from 0.411 to 0.803, were generated. Cronbach’s Alpha analysis indicated that all factors’ values were greater than 0.60 and mean for all factors were reliable and had strength of consistency. This study provides the preliminary findings of factors and variables that could provoke riding anger among motorcyclists while on the road in the Malaysian context. The factors causing riding anger in this study have a similarity with driving anger factors that were developed by other countries and this indicates the validity of riding anger factors to provoke anger among motorcyclists on the road.

This paper presents and discusses the statistical analysis of the responses from an online survey administered to a sample of US superintendents (n=225) in an attempt to explore and authenticate the construct validity of the ISLLC 2008 Standards through exploratory factor analysis. Using a Principal Axis Factor method, 6 factors were extracted representative of the six ISLLC Standards. Thirteen items loaded on the 1st factor that represented items mainly from Standards 1, 2 & 3. Standard 4 was split between the 2nd & 6th factor and Standard 5 between factors 4 & 5. The 3rd factor consisted of 4 items from Standard 3. These results confirm to some extent the construct validity of the ISLLC 2008 Standards with the exception of Standard 6, which had only three items represented in factors 2 & 3. Moreover, Standards 4 & 5 each exhibited two additional dimensions, which might suggest a need for the development and adoption of supplemental "footprint" standards. (Contains 9 tables and 2 footnotes.)

The factor structure of a new co-normed cognitive and achievement battery for children, the Differential Ability Scales (DAS) was investigated. The DAS was factor analyzed in conjunction with the Peabody Picture Vocabulary Test-Revised (PPVT-R), and the composite score of the Iowa Test of Basic Skills (ITBS). Subjects were 56 six- to eight-year-old children in regular classes. Two exploratory principal axis factor analyses with varimax rotation were done (one with and one without the four DAS "diagnostic" subtests). The exploratory analyses revealed a factor structure fairly consistent with the composite structure of the DAS. The DAS verbal subtests loaded with the PPVT-R and the DAS achievement tests loaded with the ITBS. Three cognitive factors and one achievement factor were normed, but they strayed somewhat from the DAS composite structure. A confirmatory factor analysis showed that the theoretical structure of the DAS provided a good fit to the data. These analyses lend support to the test author's interpretation of the DAS. Four tables present data from the analyses. (Author)

Exploratory Factor Analysis (EFA) is a complex and multivariate statistical method usually employed in psychology, health related professions and more recently in information system was adopted in this study. It makes use of correlation matrix to determine the relationship between variables. Data used for this study involves both primary and secondary. The collected data were mainly on the physical component of the generated solid waste from six (6) geopolitical zones within Nigeria (the study area). Also, proximate and ultimate analyses were used to obtain the energy potential of generated solid waste from the six (6) zones. The results from the analyses using EFA and Proximate & Ultimate Analyses for energy potential proved that one could make energy potential projections using data from about four geopolitical zones (North-Central, South-Southern, South-Western and South-Eastern) or three geopolitical zones (South-Southern, South-Western and South-Eastern) and achieve about 98% high decision precision without losing much of the relevant details.

Low awareness and underdeveloped instruments evaluating elderly Malaysians’ well-being, social support, and independence necessitate lubben social network scale (LSNS), social provisions scale (SPS), Katz activities of daily living (ADL), and life satisfaction index (LSI) development and evaluation involving 201 60-year-old males and females (purposive samples) in Kuching and Bintulu participated (June to August 2022). Exploratory factor analysis (EFA) examined the construct validity of LSNS, SPS, Katz ADL, and LSI, whereas principal axis factoring (paf) and orthogonal (varimax) rotation with Kaiser Normalisation validated their psychometric properties and internal consistency reliability using Cronbach’s alpha. Social integration (0.94), reassurance of worth (0.91), attachment (0.90), sense of reliable alliance (0.78), and guidance (0.74) have eigenvalues >1 in the SPS-10 EFA; good Cronbach’s alpha reliability values. Family (0.77) and friends (0.73) of LSNS-6 factors have eigenvalues >1, good Cronbach’s alpha reliability values. Acceptance-contentment (0.87) and Achievement-fulfilment (0.72) had eigenvalues >1 in the Life Satisfaction Index-Z EFA, good Cronbach’s alpha reliability values. In the EFA of Katz ADL, single items with factor loadings ≥0.5: bathing, dressing, toileting, transferring, and feeding; loadings <0.3: Continence; Cronbach’s alpha reliability value was 0.95. LSNS, SPS, Katz ADL, and LSI were valid and reliable instruments measuring elderly well-being, social support, and independence. Confirmatory factor analyses will revalidate it in different populations.

The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor analysis (EFA) and reliability analysis. Twenty items from three competencies--social competencies, communication competencies, and technical competencies--were designated for the initial instrument based on the Student Online Learning Readiness (SOLR) Model as a new conceptual model. An exploratory factor analysis (EFA) revealed that four factor-structures of the instrument of student readiness in online learning explained 66.69% of the variance in the pattern of relationships among the items. All four factors had high reliabilities (all at or above Cronbach's a > 0.823). Twenty items remained in the final questionnaire after deleting two items which cross-loaded on multiple factors (social competencies with classmates: five items; social competencies with instructor: five items; communication competencies: four items; and technical competencies: six items). The four-factor structure of the Student Online Learning Readiness (SOLR) instrument has been confirmed through this study. Educators can use the Student Online Learning Readiness (SOLR) instrument in order to gain a better understanding of the level of freshmen college students' online learning readiness by measuring their social, communication, and technical competencies. In addition, this study examined two factors of social integration in Tinto's Student Integration Model (SIM) and has introduced the Student Online Learning Readiness (SOLR) conceptual model with the purpose of extending Tinto's social integration model to online learning environments.

It is important to develop students' positive attitudes chemistry lessons in school because research has suggested that attitudes are linked with academic achievement. Therefore, how to evaluate the attitudes is an important topic in education. The purpose of this study was to develop a Likert-type scale that could measure high school students' attitudes toward chemistry. In order to develop this scale, it was benefited from some studies in the literature and expert ideas. After adjustments based on expert recommendations, a scale which is consisted of 25 items was developed and applied to 200 high school students. In order to determine the construct validity, exploratory factor analysis was performed. In this way, a final scale consists of 20 items. The factor analysis results indicated that the scale has two factors explaining 45.9% of the total variance. In addition, the scale' reliability coefficient (Cronbach's alpha) was found as 0.89. Obtained results showed that the scale which is reliable and valid measurement tool can be used to determine the attitudes of secondary school students to chemistry.

This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The performance of these procedures was evaluated based on the average number of factors retained by each method, the proportion of samples retaining the same number of factors retained by each method, the proportion of samples retaining the same number of factors as the true number of factors in the population, and the proportion of samples retaining the same number of factors when a particular rule of thumb is applied to the population. The performance of the 10 procedures was investigated using Monte Carlo methods in which random samples were generated under known and controlled population conditions. Results clearly suggest that both the choice of method and the design of the factor analytic study play crucial roles in retaining the correct number of common factors. In terms of overall accuracy across the conditions examined in this study, one of the parallel analysis approaches (Montanelli and Humphreys, 1976) provided the largest proportion of samples retaining the correct value. Conditions under which other approaches may work well are discussed. (Contains 16 tables, 18 figures, 25 charts, and 44 references.) (SLD)

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