Unidimensional Analysis Research Articles

Ảnh hưởng của phong cách lãnh đạo tới hiệu quả hoạt động các ngân hàng thương mại cổ phần tại Việt Nam

This paper evaluates the impact of transformational and digital leadership styles on performance effectiveness, including customer relations and internal operational processes, among 251 employees and managers in joint stock commercial banks during the 2023 2024 period in Vietnam. The estimation of multidimensional variables using the PLS SEM model indicates that digital leadership style has a positive impact on both internal processes and customer relations. Meanwhile, transformational leadership style only affects customer relations. Additionally, unidimensional analysis reveals that among the factors of transformational leadership style, only the leader’s attention to employees positively impacts on customer relations. Based on the empirical results, the study proposes several solutions to enhance digital leadership style and improve performance effectiveness in joint-stock commercial banks in Vietnam.

Rasch analysis implementation in nursing research: A methodological approach

The design and validation of tools constitutes a fundamental pillar of nursing research, where it is essential to incorporate the most current methodologies and analyses in order to guarantee validity and reliability in their clinical application.The aim of this paper is to describe the characteristics of the Rasch analysis and the methodology for its development as well as to discuss its relevance and applicability in nursing research, highlighting its potential contribution to the improvement of the quality and accuracy of measurement instruments in the discipline.Through a narrative synthesis, the theoretical foundations of Rasch analysis are described and the characteristics and assumptions that must be fulfilled to carry out this type of analysis are presented. Subsequently, the implementation methodology is presented in 11 steps: definition of objectives and preparation, instrument design, data collection, initial and unidimensionality analysis, goodness-of-fit assessment (infit-outfit, reliability and separation), item local independence assessment (Yen's Q3 coefficient), item calibration and estimation of skills, measurement invariance analysis (DIF analysis), review and modification of the instrument, final analysis and validation, interpretation of results. Examples of use are presented, as well as the advantages and limitations of the method. In conclusion, the Rasch analysis provides a valuable methodology for the evaluation of clinical competencies and skills, as well as for the development and validation of measurement instruments of great utility for research in care, although it would be necessary to promote training and standardization in its use.

The Role of Human Values in the Relationship Between Industry 4.0 and Job Insecurity in an Emerging Economy

ABSTRACTThis study analyses the relationship between Industry 4.0, human values and job insecurity in Colombia against significant technological changes and global economic challenges. It aims to understand how human values mediate the relationship between Industry 4.0's technological advances and the perception of job security in the current era of economic and social uncertainty. Utilising a Likert scale, the study gathered 714 survey responses from employees in medium and large Colombian companies. The study used a multistage multivariate analysis approach, starting with unidimensionality analysis, followed by reliability and validity analysis to measure accuracy. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were subsequently employed to test the underlying factor structures of the data. Finally, structural equation modelling integrated the findings, forming a model that sheds light on the relationships among the studied variables. The analysis revealed strong human values such as commitment, collaboration, tolerance, honesty and loyalty in the workplace. Interestingly, participants had a low perception of job insecurity, indicating a more complex relationship between technological advancements and job security perceptions. This study offers valuable insights into how human values shape the workforce's response to the rapid changes of Industry 4.0, particularly in an emerging economy like Colombia.

Intersectional socioeconomic disparities in continuous smoking through pregnancy among pre-pregnant smokers in Sweden between 2006 and 2016

BackgroundWhile well-established associations exist between socioeconomic conditions and smoking during pregnancy (SDP), less is known about social disparities in the risk of continuous SDP. Intersectional analyses that consider multiple social factors simultaneously can offer valuable insight for planning smoking cessation interventions.MethodsWe include all 146,222 pregnancies in Sweden between 2006 and 2016 where the mother smoked at three months before pregnancy. The outcome was continuous SDP defined as self-reported smoking in the third trimester. Exposures were age, education, migration status and civil status. We examined all exposures in a mutually adjusted unidimensional analysis and in an intersectional model including 36 possible combinations. We present ORs with 95% Confidence Intervals, and the Area Under the Curve (AUC) as a measure of discriminatory accuracy (DA).ResultsIn our study, education status was the factor most strongly associated to continuous SDP among women who smoked at three months before pregnancy. In the unidimensional analysis women with low and middle education had ORs for continuous SDP of 6.92 (95%CI 6.63–7.22) and 3.06 (95%CI 2.94–3.18) respectively compared to women with high education. In the intersectional analysis, odds of continuous SDP were 17.50 (95%CI 14.56–21.03) for married women born in Sweden aged ≥ 35 years with low education, compared to the reference group of married women born in Sweden aged 25–34 with high education. AUC-values were 0.658 and 0.660 for the unidimensional and intersectional models, respectively.ConclusionThe unidimensional and intersectional analyses showed that low education status increases odds of continuous SDP but that in isolation education status is insufficient to identify the women at highest odds of continuous SDP. Interventions targeted to social groups should be preceded by intersectional analyses but further research is needed before recommending intensified smoking cessation to specific social groups.

Unveiling the Complex Facets of Poverty: Unidimensional and Multidimensional Insights from Rural Areas of Suri Sadar Sub-Division, Birbhum District, Eastern India

Poverty, particularly in developing regions, is a complex, multifaceted issue deeply embedded in various interrelated factors. It extends beyond mere financial insufficiency, encompassing limited access to essential services such as healthcare, education, and overall living standards. This study examines both the unidimensional and multidimensional aspects of rural poverty in Suri Sadar Sub-Division, located in Eastern India. For the unidimensional aspect, this study employs the poverty headcount ratio and the Poverty Gap Index to gauge the incidence and intensity of poverty. In contrast, the multidimensional approach utilized three dimensions and 12 indicators to assess the incidence, severity, and multidimensional poverty index utilizing the Alkire–Foster (AF) methodology. The unidimensional analysis, focusing on income and consumption, highlights significant economic disparities, particularly in the western Community Development Blocks, namely, Khoyrasole, Md. Bazar, and Rajnagar. The highest levels of multidimensional poverty are generally consistent with the unidimensional findings, particularly in the western blocks. These results underscore the need for comprehensive poverty reduction strategies that address both economic and broader aspects of poverty. In areas like the western blocks, where both income-based and multidimensional poverty rates are high, strategies should integrate economic development, improved healthcare access, enhanced educational quality, and living standards improvement. Therefore, this study serves not only as an academic endeavor but also as a vital tool for informed policymaking in poverty alleviation, providing planners, administrative officials, and researchers with essential insights to develop effective, localized, and sustainable poverty reduction strategies.

On predicting the probability of default of credit organizations in the Russian Federation

Objective: to form a model for predicting the default of credit organizations under the modern conditions of the banking sector functioning.Methods: unidimensional analysis of variance, regression analysis of binary choice models.Results: in the modern economy, the banking system stability largely affects not only the financial sector, but also the economic and investment climate in the country. Understanding of the banks’ influence on the economy necessitates the formation of appropriate effective forecasting systems that allow identifying problem banks before revoking their licenses is necessary. The existing methodology of the Bank of Russia is characterized by subjectivity and inaccuracy of assessment. The analysis of studies on predicting bank defaults showed various approaches to the methodology of assessing the probability of credit institutions’ bankruptcy, though they have a number of shortcomings. Based on the selection of key factors affecting the bank’s financial stability, the logistic regression model for predicting bankruptcy of banks was formed. The methodology proposed in this article includes five predictors, selected on the basis of the improved methodology for selecting logit regression variables, and complements the existing methodologies.Scientific novelty: a methodology for assessing the probability of commercial banks’ bankruptcy in the Russian Federation was developed, which includes five key predictors for assessing the bank’s financial stability: return on assets, unit weight of liquid assets in the balance sheet currency, unit weight of the loan portfolio in the balance sheet currency, share of loans to the real sector in the balance sheet currency, and share of long-term placements in the loan portfolio. The logistic regression model of binary choice proposed in the paper allows distinguishing financially stable credit organizations from problem banks with a forecasting horizon of five months and a classification accuracy of 88,33 %.Practical significance: the relatively high classification accuracy of the model allows its use by the Bank of Russia in controlling the credit organizations functioning, as well as directly by the credit organization’s management, in order to assess the organization’s financial stability and to predict the default probability, as well as to form the bank’s development strategy.

Classification of soybeans from different habitats based on metabolomic–transcriptomic integration

Soybeans are a significant agricultural product in China, with certain geographical locations often yielding higher quality, and thus more expensive, soybean crops. In this study, metabolomics and transcriptomics analyses were conducted on soybean samples from nine regions in Heilongjiang and Liaoning Provinces using untargeted liquid chromatography–mass spectrometry (LC–MS) and Illumina sequencing technologies. The primary objective was to devise an effective and unbiased method for determining the geographical origin of each soybean variety to mitigate potential fraudulent practices. Through multidimensional and unidimensional analyses, successful identification of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs) was achieved, yielding statistically significant outcomes. Integration of the metabolomics and transcriptomics datasets facilitated the construction of a correlation network model capable of distinguishing soybeans originating from different geographical locations, leading to the identification of significant biomarkers exemplifying noteworthy distinctions. To validate the feasibility of this method in practical applications, partial least squares discriminant analysis was employed to differentiate soybean samples from the nine regions. The results convincingly showcased the applicability and reliability of this approach in accurately pinpointing the geographical origin of soybeans. Distinguishing itself from prior research in soybean traceability, this study incorporates an integrated analysis of metabolomics and transcriptomics data, thereby unveiling biomarkers that offer a more precise differentiation of soybean traits across distinct regions, thereby bridging a critical research gap within the soybean traceability domain. This innovative dual-data integration analysis methodology is poised to enhance the accuracy of soybean traceability tools and lay a new foundation for future agricultural product identification research.

The end of welfare states as we know them? A multidimensional perspective

AbstractThis article highlights the limitations of unidimensional analyses in the comparative welfare state literature and emphasises the need for a more holistic, multidimensional approach incorporating social spending, welfare state outputs and outcomes. To illustrate the utility of a multidimensional approach, we examine the long‐term welfare state trajectories of Sweden and Germany, prototypical social‐democratic and conservative welfare states, respectively, and compare them against the baseline of Europe's prototypical liberal welfare state, the United Kingdom. The social spending (expenditure) and output (generosity) allowed us to identify significant changes in the Swedish welfare state (i.e., retrenchment). The outcome dimension alerts us to a policy drift in the German Welfare State, as relatively stable public spending and welfare generosity until the first half of the 2000s were nonetheless associated with sharply increased inequality and poverty. Overall, our findings suggest that a holistic, multidimensional approach is necessary to fully understand the complexities of welfare state change and continuity, as focusing solely on one dimension can lead to analytical misjudgments. The sharp rise in inequality and poverty across countries raises doubts about whether policymakers and researchers rely too much on outdated assumptions of normality that fail to meet the welfare state realities of today.

The development of an instrument to measure the grit of junior high school students using the rasch analysis model

This study aims to obtain a valid and reliable Student Grit Scale and to analyze the feasibility of a measurement instrument using the Rasch Model. This study employed a quantitative research method with a descriptive approach and involved 75 junior high school students. The grit scale developed by Datu, called the Triarchic Model of Grit (TMG), was used in this study. The results of the reliability test showed that Cronbach's Alpha was greater than 0.60 (), indicating that the measurement instrument was reliable. The unidimensional analysis showed that the instrument was feasible because the variance observed in the contrast of 1 to 5 residues contained only values above 15%. Thus, the entire construct of the grit has measured one appropriate variable. This study provides recommendations for the development of a grit measurement instrument to investigate he balance between the topics contained in the questions and the level of ability of students as respondents.

Social inequalities in the risk of giving birth to a small for gestational age child in Sweden 2010-16: a cross-sectional study adopting an intersectional approach.

Well-established associations exist between the risk of small for gestational age (SGA) and unidimensional sociodemographic factors. We investigated social inequalities in SGA risk and adopted an intersectional approach that simultaneously considers different social categories. By doing so, we could assess heterogeneities in SGA risk within unidimensional sociodemographic categories. We included all live 679694 singleton births in Sweden between 2010 and 2016. The outcome was SGA, and the exposures were age, maternal educational level, dichotomous migration status and civil status. Thirty-six possible combinations of these factors constituted the exposure in an intersectional model. We present odds ratios (ORs) with 95% confidence intervals (95% CIs) and the area under the receiver operating characteristic curve (AUC)-a measurement of discriminatory accuracy (i.e. the ability to discriminate the babies born SGA from those who are not). Women with low education and women born outside Sweden had ORs of 1.46 (95% CI 1.38-1.54) and 1.50 (95% CI 1.43-1.56) in unidimensional analyses, respectively. Among women aged under 25 with low education who were born outside Sweden and unmarried, the highest OR was 3.06 (2.59-3.63). The discriminatory accuracy was low for both the unidimensional model that included all sociodemographic factors (AUC 0. 563) and the intersectional model (AUC 0.571). The intersectional approach revealed a complex sociodemographic pattern of SGA risk. Sociodemographic factors have a low accuracy in identifying SGA at the individual level, even when quantifying their multi-dimensional intersections. This cautions against interventions targeted to individuals belonging to socially defined groups to reduce social inequalities in SGA risk.

Evaluating the Validity and Reliability of Authentic Learning Instruments using RASCH Model

Social science research with a quantitative approach should use the appropriate scale of measurement to interpret a variable correctly. There are two types of measurement scale that is category scale and numerical scale. Category scale is for nominal and ordinal data, while the numerical scale for an interval and ratio data. In mathematical analysis, numerical scale indicates the exact measurement and more precise definitions of certain variables. Category scale is only used to classify a variable. However, Likert scale which is used for ordinal data are often used as a measurement tool in social science research. Therefore, the Rasch model analysis that can change the nominal and ordinal data to interval and ratio data was used in this study. Here, it was used to evaluate the validity and reliability of authentic learning instrument at Malaysian Polytechnics. Validity of instrument was seen by unidimensional analysis through principal component analysis method. Reliability of instrument was seen by summary statistics, variable map and item fit table. The findings show that the authentic learning instrument at Malaysian Polytechnics has high validity and reliability.

Infant nutrition affects the microbiota-gut-brain axis: Comparison of human milk vs. infant formula feeding in the piglet model.

Early nutrition plays a dominant role in infant development and health. It is now understood that the infant diet impacts the gut microbiota and its relationship with gut function and brain development. However, its impact on the microbiota-gut-brain axis has not been studied in an integrative way. The objective here was to evaluate the effects of human milk (HM) or cow’s milk based infant formula (IF) on the relationships between gut microbiota and the collective host intestinal-brain axis. Eighteen 10-day-old Yucatan mini-piglets were fed with HM or IF. Intestinal and fecal microbiota composition, intestinal phenotypic parameters, and the expression of genes involved in several gut and brain functions were determined. Unidimensional analyses were performed, followed by multifactorial analyses to evaluate the relationships among all the variables across the microbiota-gut-brain axis. Compared to IF, HM decreased the α-diversity of colonic and fecal microbiota and modified their composition. Piglets fed HM had a significantly higher ileal and colonic paracellular permeability assessed by ex vivo analysis, a lower expression of genes encoding tight junction proteins, and a higher expression of genes encoding pro-inflammatory and anti-inflammatory immune activity. In addition, the expression of genes involved in endocrine function, tryptophan metabolism and nutrient transport was modified mostly in the colon. These diet-induced intestinal modifications were associated with changes in the brain tissue expression of genes encoding the blood-brain barrier, endocrine function and short chain fatty acid receptors, mostly in hypothalamic and striatal areas. The integrative approach underlined specific groups of bacteria (Veillonellaceae, Enterobacteriaceae, Lachnospiraceae, Rikenellaceae, and Prevotellaceae) associated with changes in the gut-brain axis. There is a clear influence of the infant diet, even over a short dietary intervention period, on establishment of the microbiota-gut-brain axis.

A Randomized Clinical Trial Comparing Two Treatment Strategies, Evaluating the Meaningfulness of HAM-D Rating Scale in Patients With Major Depressive Disorder.

IntroductionDue to the complexity of symptoms in major depressive disorder (MDD), the majority of depression scales fall short of accurately assessing a patient's progress. When selecting the most appropriate antidepressant treatment in MDD, a multidimensional scale such as the Hamilton Depression Rating scale (HAM-D) may provide clinicians with more information especially when coupled with unidimensional analysis of some key factors such as depressed mood, altered sleep, psychic and somatic anxiety and suicidal ideation etc.MethodsHAM-D measurements were carried out in patients with MDD when treated with two different therapeutic interventions. The prespecified primary efficacy variables for the study were changes in score from baseline to the end of the 12 weeks on HAM-D scale (i.e., ≤ 8 or ≥50% response). The study involved three assessment points (baseline, 6 weeks and 12 weeks).ResultsEvaluation of both the absolute HAM-D scores and four factors derived from the HAM-D (depressed mood, sleep, psychic and somatic anxiety and suicidal ideation) revealed that the latter showed a greater promise in gauging the anti-depressant responses.ConclusionThe study confirms the assumption that while both drugs may improve several items on the HAM-D scale, the overall protocol may fall short of addressing the symptoms diversity in MDD and thus the analysis of factor (s) in question might be more relevant and meaningful.

Determining levels of nurse burnout during the COVID-19 pandemic and Lebanon\u2019s political and financial collapse

BackgroundThe COVID-19 pandemic compounded political and financial pressures on the nursing workforce in Lebanon. The government resigned in October 2019 in response to the popular uprising that called for an end to corruption and economic mismanagement 5 months before the first COVID-19 case appeared in the country. The continuing crises and the added stress of COVID-19 has increased the risk of occupational burnout and turnover in the nursing workforce. Therefore, valid and reliable measurement is imperative to determine burnout levels, prioritize intervention, and inform evidence-based workforce policy and practice. The primary aim of the study was to delineate burnout levels and cut-points in a national sample of nurses to inform workforce policies and prioritize interventions.MethodsMultidimensional and unidimensional Rasch analyses of burnout data collected from a national convenience sample of 457 hospital nurses 9–12 months after Lebanon’s political and economic collapse began. The data were collected in July–October 2020.ResultsMultidimensional Rasch analysis confirmed that the Copenhagen Burnout Inventory has three highly correlated unidimensional scales that measure personal burnout, work-related burnout, and client-related burnout. Except for a ceiling effect of ~ 2%, the three scales have excellent measurement properties. For each scale, Rasch rating scale analysis confirmed five statistically different nurse burnout levels. The mean personal burnout scores and work-related burnout scores (50.24, 51.11 respectively) were not higher than those reported in the international literature. However, the mean client-related burnout score of 50.3 was higher than reported for other countries. Compared with a baseline study conducted at the beginning of Lebanon’s political and economic crises, only client-related burnout scores were higher p. <.01.ConclusionsThe CBI scales are reliable and valid measures for monitoring nurse burnout in crises torn countries. Stakeholders can use the CBI scales to monitor nurse burnout and prioritize burnout interventions. Urgent action is needed to reduce levels of client-related burnout in Lebanon’s nursing workforce.

Differential Item Functioning Analyses of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Measures: Methods, Challenges, Advances, and Future Directions.

Several methods used to examine differential item functioning (DIF) in Patient-Reported Outcomes Measurement Information System (PROMIS®) measures are presented, including effect size estimation. A summary of factors that may affect DIF detection and challenges encountered in PROMIS DIF analyses, e.g., anchor item selection, is provided. An issue in PROMIS was the potential for inadequately modeled multidimensionality to result in false DIF detection. Section 1 is a presentation of the unidimensional models used by most PROMIS investigators for DIF detection, as well as their multidimensional expansions. Section 2 is an illustration that builds on previous unidimensional analyses of depression and anxiety short-forms to examine DIF detection using a multidimensional item response theory (MIRT) model. The Item Response Theory-Log-likelihood Ratio Test (IRT-LRT) method was used for a real data illustration with gender as the grouping variable. The IRT-LRT DIF detection method is a flexible approach to handle group differences in trait distributions, known as impact in the DIF literature, and was studied with both real data and in simulations to compare the performance of the IRT-LRT method within the unidimensional IRT (UIRT) and MIRT contexts. Additionally, different effect size measures were compared for the data presented in Section 2. A finding from the real data illustration was that using the IRT-LRT method within a MIRT context resulted in more flagged items as compared to using the IRT-LRT method within a UIRT context. The simulations provided some evidence that while unidimensional and multidimensional approaches were similar in terms of Type I error rates, power for DIF detection was greater for the multidimensional approach. Effect size measures presented in Section1 and applied in Section2 varied in terms of estimation methods, choice of density function, methods of equating, and anchor item selection. Despite these differences, there was considerable consistency in results, especially for the items showing the largest values. Future work is needed to examine DIF detection in the context of polytomous, multidimensional data. PROMIS standards included incorporation of effect size measures in determining salient DIF. Integrated methods for examining effect size measures in the context of IRT-based DIF detection procedures are still in early stages of development.

Unidimensional Analysis of Oil Velocity Fields in Lubricant Film in Effective Lubrication of a Sliding Journal Bearing

Unidimensional Analysis of Oil Velocity Fields in Lubricant Film in Effective Lubrication of a Sliding Journal Bearing

Unidimensional analysis of the nursing diagnoses of situational low self-esteem and chronic low self-esteem.

To analyze the unidimensionality of the nursing diagnoses low situational self-esteem and low chronic self-esteem proposed by NANDA-I. Diagnostic accuracy study carried out with 180 patients with depressed mood in a psychiatric hospital in northeastern Brazil. The analysis of the latent class considered the hypothesis of low self-esteem as a unique construct and included the indicators: Excessive seeking of reassurance, repeatedly unsuccessfulness in life events, rejection of positive feedback, insomnia, solitude, and nonassertive behavior. The recognition of low self-esteem as a unique diagnostic construct allows nurses to be more assertive in the provision of care.

Re-evaluation of the D21-digital-index assessment instrument for measuring higher-level digital competences

The development of reliable tools for assessing digital competences is of great importance. This is why we set out a re-evaluation of the measurement quality of the D21-Digital-Index assessment instrument. The D21-Digital-Index is a biannual and influential study held in Germany. The instrument used in the D21-surveys is based on the theoretical framework DigComp. In our analyses we used data of 1142 participants from vocational training and higher education institutions to estimate item parameters and the quality of the instrument using Item Response Theory. Because choosing an appropriate IRT-model is crucial for instrument evaluation, we calculated and compared two types of models, the Rasch and the Birnbaum model of which the latter turned out to achieve the better fit. In a unidimensional analysis the five scales of the instrument with 24 items in total yield acceptable measures. Multidimensional analysis shows a dimensional separation and hence confirms the construct validity of the instrument.

Psychometric validation of PROM instruments.

The aim was to provide an overview of the different statistical methods for validation of patient-reported outcome measures, ranging from simple statistical methods available in all software packages to advanced statistical models that require specialized software. A non-technical summary of classical test theory (CTT) and modern test theory (MTT) is provided. Specifically, confirmatory factor analysis, item response theory, and Rasch analysis is outlined. One CTT and three MTT methods were used to validate the two subscales (Symptoms and Quality of Life) from the Knee Injury and Osteoarthritis Outcome Score (KOOS). For each methodology, two analyses were considered: (i) a unidimensional analysis ignoring the pre-specified dimensionality, and (ii) a two-dimensional analysis using the pre-specified dimensionality. While CTT did not adequately address central issues regarding the validity of the KOOS subscales, the three MTT methods yielded very similar results. In conclusion, MTT methods offer analysis of all relevant properties related to the validity of patient-reported outcome measures, while this is not the case for CTT. Claims about sufficient validity based on CTT methods are inadequate and should not be trusted.

Desarrollo de un cuestionario de evaluación de la competencia docente en línea

Evaluar la docencia con el fin de mejorar la calidad de la enseñanza implica reconocer su complejidad y contar con instrumentos de evaluación válidos que orienten hacia la mejora. El objetivo de este artículo es reportar el proceso de investigación que permitió desarrollar un cuestionario de evaluación formativa de la docencia en línea con base en la opinión de los estudiantes. Los participantes fueron seis expertos en el diseño e implementación de cursos en línea y un total de 2821 estudiantes universitarios. El método consistió en dos etapas: 1) Desarrollo del instrumento a través de fundamentación teórica sobre los elementos de la competencia docente en línea susceptibles de ser evaluados por estudiantes; elaboración de la matriz de especificación del cuestionario; validación de la matriz por el juicio de expertos y estudio piloto; 2) Aportación de evidencias de confiabilidad y validez incluyó la aplicación del cuestionario; cálculo de estadísticos descriptivos; análisis de unidimensionalidad por medio del modelo Rasch; determinación de la confiabilidad de los puntajes; análisis factorial exploratorio y confirmatorio. Se demostró que 28 reactivos evalúan el mismo constructo y se confirmaron dos factores: previsión del proceso de enseñanza y aprendizaje; y conducción y evaluación de la experiencia de enseñanza y aprendizaje; una varianza total de 70.01% y alpha ordinal de .998; un índice de ajuste RMSEA= .08, SRMR= .03, CFI= .91. Se concluye que los resultados corroboraron el fundamento teórico del instrumento con características psicométricas aceptables, por lo que se recomienda su aplicación.