Choosing an appropriate number of factors in factor analysis with incomplete data

Smart village concept in Indonesia: ICT as determining factor

Village development in Indonesia has become the national development agenda prioritized in conjunction with the enactment of the Village Law in 2014. Village development through smart village is considered relevant to the current era's progress and rapid technological advancements. Smart village is often defined as the concept of village development based on the utilization of information and communication technology (ICT). This research aims to identify the locations of smart village and analyze the role of ICT as determining factor for the smart village concept in Indonesia. The data on smart village locations are collected from the regulations of the Ministry of Village and literature obtained through internet browsing. The research was conducted using a quantitative descriptive approach and supported by spatial analysis. The exploration of determining factors for the smart village concept involves 12 variables sourced from the Village Potential data in 2021 and analyzed statistically using factor analysis. The results indicate that there are 1.424 villages developed as smart village until 2023 and their locations are spread across 32 provinces on all islands in Indonesia. The results show that ICT is one of the determining factors in the smart village concept in Indonesia through the utilization of village information systems in government (explained 15,972 % variance), community interest in ICT (explained 10,628 % variance), and the availability of communication access (explained 8509 % variance). However, ICT is not the only determining factor, but there are also internal village factors: community participation (explained 11,299 % variance) and leadership (explained 9137 % variance). From these findings, it implies that smart village development policies need to pay attention to the conditions of society as the subject and object of development, supported by the government's ability to provide adequate infrastructure. This research is a pioneer and provides real innovation in smart village studies so that it can be a reference for similar studies in the future, both in smaller areas (provinces/regencies), or countries with conditions similar to Indonesia.

Exploratory factor analysis of shared trauma in psychiatric-mental health nurses using the Shared Trauma Professional Posttraumatic Growth Inventory (STPPG)

INTRODUCTION

The Shared Trauma Professional Post Traumatic Growth Inventory (STPPG) was developed by Tosone et al. (2014) to help understand shared trauma (ST) in social workers. ST occurs when the healthcare professional and client both experience the same collective traumatic event. This inventory has been adapted for use with mental health nurses. A cross-sectional study of N = 552 mental health nurses was completed in the spring of 2023 to assess the feasibility of using the STPPG to explore shared trauma in mental health nurses.

METHODS

An exploratory factor analysis was run for the STPPG using squared multiple correlations with the maximum likelihood method.

RESULTS

The alpha coefficient ranged from 0.82 to 0.89 for 2-factors and 0.73 to 0.89 for 3-factors. The results indicated that all correlations were significant among the total scales and subscales. All correlations were positive, ranging from 0.81 to 0.95 for two factors and 0.58 to 0.89 for three factors.

CONCLUSION

The STPPG has confirmed a two-factor analysis for mental health nurses. The STPPG is a valid inventory to measure ST in mental health nurses and will allow the concept to be further studied.

Evaluating the dual-task decrement within a simulated environment: Word recall and visual search

Simulated environments have become better able to replicate the real world and can be used for a variety of purposes, such as testing new technology without any of the costs or risks associated with working in the real world. Because of this, it is now possible to gain a better understanding of cognitive demands when working in operational environments, where individuals are often required to multitask. Multitasking often results in performance decrements, where adding more tasks can cause a decrease in performance in each of the individual tasks. However, little research investigated multitasking performance in simulated environments. In the current study we examined how multitasking affects performance in simulated environments. Forty-eight participants performed a dual visual search and word memory task where participants were navigated through a simulated environment while being presented with words. Performance was then compared to single-task performance (visual search and word memory alone). Results showed that participants experienced significant dual-task interference when comparing the dual-tasks to the single-tasks and subjective measures confirmed these findings. These results could provide useful insight for the design of technology in operational environments, but also serve as an evaluation of MRT in simulated environments.

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen-Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

Development and validation of the ExPRESS instrument for primary health care providers' evaluation of external supervision

Background: External supervision of primary health care facilities to monitor and improve services is common in low-income countries. Currently there are no tools to measure the quality of support in external supervision in these countries.

Aim: To develop a provider-reported instrument to assess the support delivered through external supervision in Rwanda and other countries.

Methods: “External supervision: Provider Evaluation of Supervisor Support” (ExPRESS) was developed in 18 steps, primarily in Rwanda. Content validity was optimised using systematic search for related instruments, interviews, translations, and relevance assessments by international supervision experts as well as local experts in Nigeria, Kenya, Uganda and Rwanda. Construct validity and reliability were examined in two separate field tests, the first using exploratory factor analysis and a test–retest design, the second for confirmatory factor analysis.

Results: We included 16 items in section A (‘The most recent experience with an external supervisor’), and 13 items in section B (‘The overall experience with external supervisors’). Item-content validity index was acceptable. In field test I, test–retest had acceptable kappa values and exploratory factor analysis suggested relevant factors in sections A and B used for model hypotheses. In field test II, models were tested by confirmatory factor analysis fitting a 4-factor model for section A, and a 3-factor model for section B.

Conclusions: ExPRESS is a promising tool for evaluation of the quality of support of primary health care providers in external supervision of primary health care facilities in resource-constrained settings. ExPRESS may be used as specific feedback to external supervisors to help identify and address gaps in the supervision they provide. Further studies should determine optimal interpretation of scores and the number of respondents needed per supervisor to obtain precise results, as well as test the functionality of section B.

Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.

Practical approaches to principal component analysis for simultaneously dealing with missing and censored elements in chemical data

Multivariate chemical data often contain elements that are missing completely at random and the so-called left-censored elements whose values are only known to be below a definite threshold value (reporting limit). In the last several years, attention has been paid to developing methods for dealing with data containing missing elements and those that can handle data with missing elements and outliers. However, processing data with both missing and left-censored elements is still an ongoing problem. The aim of this work was to investigate which method is most suitable for handling left-censored and missing completely at random elements that are present simultaneously in chemical data by using a comparison of the generalized nonlinear iterative partial least squares (NIPALS(1)) algorithm that has been recently proposed, methods that include uncertainty information like maximum likelihood principal component analysis, MLPCA(2), and replacement methods. The results of the Monte Carlo simulation study for artificial and real data sets showed that substitution with half of the reporting limit can be used when the percentage of left-censored elements per variable is up to 30-40%. The generalized NIPALS algorithm is generally recommended for a large percentage of left-censored elements per variable and particularly when a large number of variables are censored. The expectation-maximization approach applied to data with censored elements substituted with half of the reporting limits can be a strategy for dealing with missing and left-censored elements in data, but if the converge criterion is not fulfilled, then the generalized NIPALS algorithm can be applied.

Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective

A central problem in the application of exploratory factor analysis is deciding how many factors to retain (m). Although this is inherently a model selection problem, a model selection perspective is rarely adopted for this task. We suggest that Cudeck and Henly's (1991) framework can be applied to guide the selection process. Researchers must first identify the analytic goal: identifying the (approximately) correct m or identifying the most replicable m. Second, researchers must choose fit indices that are most congruent with their goal. Consistent with theory, a simulation study showed that different fit indices are best suited to different goals. Moreover, model selection with one goal in mind (e.g., identifying the approximately correct m) will not necessarily lead to the same number of factors as model selection with the other goal in mind (e.g., identifying the most replicable m). We recommend that researchers more thoroughly consider what they mean by "the right number of factors" before they choose fit indices.