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\"Understanding factor analysis is key to understanding much published research in the social and behavioral sciences. This volume provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other issues in EFA such determining the appropriate sample size and handling missing data, and it offers brief introductions to EFA with structural equation modeling and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website\"-- Provided by publisher.

Spearman (Am J Psychol 15(1):201–293, 1904. https://doi.org/10.2307/1412107 ) marks the birth of factor analysis. Many articles and books have extended his landmark paper in permitting multiple factors and determining the number of factors, developing ideas about simple structure and factor rotation, and distinguishing between confirmatory and exploratory factor analysis (CFA and EFA). We propose a new model implied instrumental variable (MIIV) approach to EFA that allows intercepts for the measurement equations, correlated common factors, correlated errors, standard errors of factor loadings and measurement intercepts, overidentification tests of equations, and a procedure for determining the number of factors. We also permit simpler structures by removing nonsignificant loadings. Simulations of factor analysis models with and without cross-loadings demonstrate the impressive performance of the MIIV-EFA procedure in recovering the correct number of factors and in recovering the primary and secondary loadings. For example, in nearly all replications MIIV-EFA finds the correct number of factors when N is 100 or more. Even the primary and secondary loadings of the most complex models were recovered when the sample sizes were at least 500. We discuss limitations and future research areas. Two appendices describe alternative MIIV-EFA algorithms and the sensitivity of the algorithm to cross-loadings.

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation. To understand the whole history of populations, it is ideal to capture the history of their range expansion with reference to the series of surrounding environments and to infer the multitrait coadaptation. To this end, we propose a complementary approach; it is an exploratory analysis using up‐to‐date methods that integrate population genetic features and features of selection on multiple traits. First, we conduct correspondence analysis of site frequency spectra, traits, and environments with auxiliary information of population‐specific fixation index (FST). This visualizes the structure and the ages of populations and helps infer the history of range expansion, encountered environmental changes, and selection on multiple traits. Next, we further investigate the inferred history using an admixture graph that describes the population split and admixture. Finally, principal component analysis of the selection on edge‐by‐trait (SET) matrix identifies multitrait coadaptation and the associated edges of the admixture graph. We introduce a newly defined factor loadings of environmental variables in order to identify the environmental factors that caused the coadaptation. A numerical simulation of one‐dimensional stepping‐stone population expansion showed that the exploratory analysis reconstructed the pattern of the environmental selection that was missed by analysis of individual traits. Analysis of a public dataset of natural populations of black cottonwood in northwestern America identified the first principal component (PC) coadaptation of photosynthesis‐ vs growth‐related traits responding to the geographical clines of temperature and day length. The second PC coadaptation of volume‐related traits suggested that soil condition was a limiting factor for aboveground environmental selection. During the process of range expansion, populations encounter a variety of environments and respond to the local environments by modifying their mutually interacting traits. To capture the history of their range expansion with reference to the series of surrounding environments and to infer the multitrait coadaptation, we propose a complementary approach; it is an exploratory analysis using up‐to‐date methods that integrates population genetic features and features of selection on multiple traits.

Progress in science relies in part on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning, such as hindsight bias, make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan before observing the research outcomes—a process called preregistration. Preregistration distinguishes analyses and outcomes that result from predictions from those that result from postdictions. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are preexisting. Services are now available for preregistration across all disciplines, facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.

PurposeIn the age of Industry 4.0 (I4.0), digital technologies (DTs) and the technologies' application in supply chain activities have become more important. On the other hand, global pressures for corporate social responsibility in the sustainable production of products are increasing. Accordingly, the purpose of this research is to develop and validate an instrument to measure the impact of digital supply chain (DSC) activities on the sustainable performance of manufacturing companies.Design/methodology/approachIn the first step, through an in-depth study of the relevant literature, a conceptual model was developed and a questionnaire containing 51 indicators was designed. The questionnaire was distributed among 356 top managers and experts of the Iranian manufacturing companies, whereby finally 233 sound questionnaires were returned, yielding a response rate of about 64%. Exploratory factor analysis (EFA) was used to identify constructs and sub-constructs and the relationship between them was investigated using the partial least squares structural equation model (PLS-SEM) method.FindingsBased on the obtained results, three constructs were identified, namely main activities (including sub-constructs: digital supplier, digital manufacturing, digital logistics and innovation and digital customer), support activities (with sub-constructs digital performance, DT and digital human resources) and sustainable performance (with sub-constructs of economic sustainability, environmental sustainability and social sustainability). The designed tool has excellent psychometric properties. The values of t-statistic = 11.07 and β = 0.602 indicate the positive impact of the DSC main activities on sustainable performance. Similarly, t = 2.42 and β = 0.149 prove that DSC support activities have a positive impact on sustainable performance. Also, based on the obtained values (t = 13.16 and β = 0.629), support activities have a significant impact on the main activities of the DSC. Finally, based on the calculated goodness-of-fit (GoF) index value (0.522), this paper concluded that the proposed model has high credibility.Research limitations/implicationsValidation of the model is based on the answers received from the Iranian manufacturing companies. Therefore, caution should be taken regarding the generalizability of the results.Practical implicationsThe proposed model presents a holistic view of the application of DTs in the supply chain and the DTs' impact on sustainable performance which might help manufacturing companies, particularly the Iranian companies to obtain a broader knowledge of the importance of DTs and DTs' usage toward responding to the challenges of today's complex business environment.Originality/valueThis study is among the first of the study's kind that attempts to develop and validate an instrument to measure the impact of DSC activities on the sustainable performance of manufacturing companies.

In this article, we propose a new framework for matrix factorization based on principal component analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new contributions: an algorithm to identify the groups of variables in correlation maps, a visualization for the resulting groups, and a matrix factorization. Together with a method to compute correlation maps with minimum noise level, referred to as missing-data for exploratory data analysis (MEDA), these three contributions constitute a complete matrix factorization framework. Two real examples are used to illustrate the approach and compare it with PCA, sparse PCA, and structured sparse PCA. Supplementary materials for this article are available online.

Orientation: South African studies investigating the factor structure of the Ryff Scales of Psychological Well-being (RPWB) are needed to ensure that the instrument is valid and reliable within the South African context.Research purpose: The objective of this study was to investigate the factor structure of the RPWB within two South African samples. Motivation for the study: Although a substantial number of studies have been undertaken, results regarding the factor structure of the Ryff Scales of Psychological Well-Being are inconclusive. There is a dearth of information in relation to South African studies examining the scales’ factor structure.Research design, approach and method: A quantitative research approach using a crosssectional field survey design was utilised. An adult working group (n = 202) was selected using convenience sampling, and a student group (n = 226) was selected by means of purposive non-probability sampling. An Exploratory Factor Analysis and a Confirmatory Factor Analysis were conducted to examine the factor structure.Main findings: The preferred model was a two-factor model where all the positively worded items were grouped in the first factor and all the negatively worded items were grouped in the second factor.Practical/managerial implications: The factor structure of the original RPWB was not satisfactorily replicated and remains seemingly unsettled. The utility of negatively worded items should be considered carefully, and alternatives such as mixed response options and phrase completion should be explored. The scales should be used with caution.Contribution/value-add: The study contributes to the literature concerning the factor structure of the RPWB with an emphasis on the South African context. It contributes to ensuring that researchers and practitioners use a valid and reliable instrument when measuring psychological well-being.

El análisis de la satisfacción académica constituye un aspecto clave para explicar la calidad del aprendizaje. Para medir el constructo, actualmente se dispone de la Escala de Satisfacción Académica (ESA), instrumento de medida basado en la perspectiva del bienestar psicológico. Debido a que no se cuenta con antecedentes sobre su aplicación en el contexto de la educación superior chilena, el objetivo del estudio fue evaluar la estructura factorial, consistencia interna y validez de la ESA en una muestra de estudiantes universitarios chilenos. Los participantes fueron 608 estudiantes de siete universidades chilenas. Los resultados del análisis factorial exploratorio (AFE) y confirmatorio (AFC) apoyaron la estructura unifactorial propuesta en el modelo original. Finalmente, los resultados del análisis factorial confirmatorio multigrupo (AFCM) apoyaron la invarianza del modelo de medida entre estudiantes mujeres y hombres. Se concluye que la ESA presenta adecuadas propiedades psicométricas para medir la satisfacción académica en estudiantes universitarios chilenos.

Confined space workers do a wide range of tasks, many of which have a significant risk of hazardous exposure. Hence, a reliable and valid questionnaire is important in assessing the knowledge, attitude, and practice (KAP) of workers in this field. The present study was conducted to develop and validate a questionnaire that could assess the KAP for safe working in a confined space. The questionnaire went through a development and validation process. The development stage consisted of a literature review, expert’s opinion, and evaluation by experts in the field via cognitive debriefing. The validation stage encompassed exploratory and confirmatory parts to investigate the psychometric properties of the questionnaire. A total of 350 participants were recruited among confined space workers from two oil and gas companies in Malaysia. The two-parameter logistic item response theory (2-PL IRT) analysis was used for the knowledge section. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used in the attitude and practice sections of the validation stage. The development stage resulted in 30 items for knowledge, attitude, and practice sections. Items in the knowledge section showed an acceptable difficulty and discrimination, as noted during the 2-PL IRT analysis. The EFA resulted in a one-factor model for attitude and practice sections, and contained 18 items, with factor loading > 0.4. The Cronbach’s alpha was 0.804 and 0.917 for attitude and practice sections, respectively. The CFA for attitude and practice sections indicated a good model fitness (Raykov’s rho = 0.814 and 0.912, respectively). All items indicated good reliability and valid psychometrics for determining KAP on safe working in a confined space.

Introduction: A family operates as a dynamic system comprising various subsystems and is continually interacting with its environment. Therefore, it is essential to comprehend the underlying principles of family functioning. One of the most commonly used models for describing family functioning is the Circumplex Model of Marital and Family Systems (Olson & Gorall, 2003). Aims: In this study, we aimed at examining the factor structure and internal consistency reliability in the Croatian version of the Family Adaptability and Cohesion Evaluation Scale IV Package, which measures family functioning through family cohesion and flexibility at the balanced and unbalanced levels, as well as family communication and family satisfaction. Methods: Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were performed using a convenient sample of 528 participants. Results: CFA revealed that the Croatian version of the FACES IV Package does not fit the theoretical model of the original factor structure in this preliminary study. The exceptions were the Family Communication Scale and Family Satisfaction Scale, which showed satisfactory parameters. The results of the EFA of FACES IV showed a 5-factor model solution. Conclusions: The Croatian version of the FACES IV Package is not completely suitable for use in the national context. Thus, given these preliminary findings, further testing on a more representative or clinical sample is recommended. Keywords: confirmatory factor analysis (CFA), exploratory factor analysis (EFA), family functioning, FACES IV Package, family satisfaction