scholarly journals Health system workforce’s performance capacity towards integrated leading, managing and governing practices: with special reference to the application of factor analysis and ordinal logistic regression

2019 ◽  
Author(s):  
Yeshambel Agumas Ambelie ◽  
Getu Degu Alene ◽  
Damen Hailemariam Gebrekiros
Keyword(s):  
Factor Analysis ◽  
Logistic Regression ◽  
Health System ◽  
Measurement Model ◽  
Ordinal Logistic Regression ◽  
Future Research ◽  
Middle Income ◽  
Performance Capacity ◽  
Logistic Regression Models ◽  
Average Variance

Abstract AbstractObserving over-led and under-managed, over-managed and under-governed, and even out of these health organizations remains a common phenomenon in low and middle-income countries. The current study looks at the health system workforce’s performance capacity towards integrated leading, managing, and governing practices and its predictors in Ethiopia. Eight hundred thirteen health facility employees completed a multi-item questionnaire. The data were fitted to factor analysis and ordinal logistic regression models. The factor analysis was employed to develop a scientifically reliable and empirically scalable measurement model. The model was assembled from items rated, factors extracted and error variances observed. Besides, the health system workforce’s performance capacity was computed and labeled. Moreover, the ordinal logistic regression was conducted to identify predictors of the performance capacity. The outputs of factor analysis provided a four-factor measurement model. This model had acceptable estimates, composite reliability, and average variance extracted. Eighty-four percent of the participants had reported low (41.3%) and moderate (42.7%) performance capacity towards integrated leading, managing, and governing practices. Sex and responsibility were among significantly associated predictors. Empowering the health system workforce towards integrated leading, managing and governing practices using a scientifically reliable and empirically scalable model is important, particularly in resource-limited settings. In this regard, the policies and strategies should give due attention to females and service owners. The current results could provide a foundation for training and future research.

scholarly journals Performance capacity of the health system workforce towards integrated leading-managing-and-governing practices in Ethiopia: with special reference to the application of factor analysis and ordinal logistic regression

2019 ◽  
Author(s):  
Yeshambel Agumas Ambelie ◽  
Getu Degu Alene ◽  
Damen Hailemariam Gebrekiros
Keyword(s):  
Factor Analysis ◽  
Logistic Regression ◽  
Health System ◽  
Building Blocks ◽  
Measurement Model ◽  
Northwest Ethiopia ◽  
Ordinal Logistic Regression ◽  
Outcome Variable ◽  
Future Research ◽  
Performance Capacity

Abstract Background Observing over-led and under-managed, over-managed and under-governed, and even out of these health organizations remains a common phenomenon in low and middle-income countries including Ethiopia. To balance these discrepancies, leadership and governance is regarded as the locus of all other critical health system building blocks. Thus, the current study looks at the performance capacity of the health system workforce towards integrated leading-managing-and-governing practices and its predictors in Ethiopia. Methods Eight-hundred-thirteen health system workforce were participated in this study. These were selected from 32 health care organizations located in northwest Ethiopia. Data were collected using a multi-item questionnaire. These data were fitted to factor analysis and ordinal logistic regression models. Factor analysis was used to develop scientifically reliable and empirically scalable measurement model. Once this model was tested for reliability and validity, the performance capacity of the health system workforce towards integrated leading-managing-and-governing practices (the outcome variable) was computed and labeled. Finally, ordinal logistic regression was conducted to model relationships between the outcome variable and its predictors using probability value and odds ratio. Results From 813 participants who rated the questionnaire, 396 (48.7%) and 582 (71.6%) were females and service owners respectively. Using these ratings, four factors were extracted. These factors together with items rated and error variances provided a four-factor measurement model. This model had estimates above .5, composite reliability above .7 and average variance extracted above .5. Using items in this model, the outcome variable was labeled as low (41.3%), moderate (42.7%), high (13.5%) and very high (2.5%). Sex and responsibility were among significantly associated predictors. Conclusions Empowering the health system workforce towards integrated leading-managing-and-governing practices using scientifically reliable and empirically scalable model is important, particularly in resource-limited settings. The policies and strategies developed in this regard should give due attention to females and service owners. The current results could provide foundations for training and future research. Trial registration The current study was part of a PhD dissertation research, which has been registered at clinical trials.gov since 9 July 2018 with identifiers: NCT03639961.

Development and initial validation of the Subjective Academic Wellbeing Measure: A new tool of youth wellbeing in school

2021 ◽  
Vol 6 (1) ◽  
pp. 3-11
Author(s):  
Gökmen Arslan ◽  
Murat Yıldırım ◽  
Silvia Majercakova Albertova
Keyword(s):  
Factor Analysis ◽  
Convergent Validity ◽  
Mental Health Problems ◽  
School Context ◽  
Measurement Model ◽  
Academic Functioning ◽  
Self Report ◽  
Future Research ◽  
Internal Reliability ◽  
Gender Groups

The purpose of the current study was to investigate the preliminary development and validation of the Subjective Academic Wellbeing Measure (SAWM), which is a six-item self-report rating measure intended for use as a screening tool to assess the positive academic functioning of young people within the elementary and high school context. Exploratory factor analysis was performed with Sample 1 (N= 161), indicating that the SAWM was characterized by a unidimensional measurement model and had strong factor loadings. Results from confirmatory factor analysis, which was carried out with Sample 2 (N= 199), confirmed the measurement model by yielding good data-model fit statistics that were characterized by strong latent construct and internal reliability estimates. Further analyses showed that the scale had good convergent validity considering scores from several self-reported scales of student mental health problems and positive school functioning. Further analyses also showed that configural, metric, and scalar measurement invariance were observed across gender groups. These results provide initial evidence suggesting that the SAWM is a reliable and valid measure that can be used to assess the positive academic functioning of students within the school context. Implications are discussed, and some suggestions are provided for future research and practice

Investigating the Measurement of Consumer Ecological Behaviour, Environmental Knowledge, Healthy Food, and Healthy Way of Life

2014 ◽  
Vol 5 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Norazah Mohd Suki
Keyword(s):  
Factor Analysis ◽  
Structural Equation ◽  
Measurement Model ◽  
Healthy Food ◽  
Environmental Knowledge ◽  
Equation Modeling ◽  
Future Research ◽  
Computer Programme ◽  
Way Of Life ◽  
Ecological Behaviour

The objective of this research is to investigate the measurement of consumer ecological behaviour, environmental knowledge, healthy food, and healthy way of life that is derived from the literature and survey. Correlations between environmental knowledge, healthy food, and healthy way of life with consumer ecological behaviour are also investigated. Statistical techniques were used to analyse the data using descriptive and exploratory factor analysis (EFA) with the computer programme Statistical Package for Social Sciences (SPSS) version 21. The latter was performed using principal component analysis and varimax rotation with the objective to test the underlying factor structure of the data. Next, confirmatory factor analysis (CFA) was executed via structural equation modeling (SEM) technique using analysis of moment structures (AMOS) computer programme version 21 in order to confirm the measurement model. CFA results show that the correlations between environmental knowledge, healthy way of life, and healthy food with consumer ecological behaviour were significant and supported, respectively. This study contributes to the extant literature on consumer ecological behaviour by developing a robust measure. Results offer a clearer perspective for companies to identify consumer ecological behaviour for better market segmentation, targeting and positioning of green products that are not harmful to the environment and could promote demands. The research implications are further explicated and the directions for future research are also elucidated.

Defining the Growth/No-Growth Interface for Listeria monocytogenes in Mexican-Style Cheese Based on Salt, pH, and Moisture Content

1999 ◽  
Vol 62 (6) ◽  
pp. 601-609 ◽  
Author(s):  
LANCE F. BOLTON ◽  
JOSEPH F. FRANK
Keyword(s):  
Logistic Regression ◽  
Listeria Monocytogenes ◽  
Binary Logistic Regression ◽  
Ordinal Logistic Regression ◽  
Growth Interface ◽  
Data Set ◽  
Logistic Regression Models ◽  
Binary Model ◽  
Fresh Cheese ◽  
Brine Concentration

The objective of this study was to define combinations of pH, salt, and moisture that produce growth, stasis, or inactivation of Listeria monocytogenes in Mexican-style cheese. A soft, directly acidified, rennet-coagulated, fresh cheese similar to Mexican-style cheese was produced. The cheese was subsequently altered in composition as required by the experimental protocol. A factorial design with four moisture contents (42, 50, 55, and 60%), four salt concentrations (2.0, 4.0, 6.0, and 8.0% wt/wt), six pH levels (5.0, 5.25, 5.50, 5.75, 6.0, and 6.5), and three replications was used. Observations of growth, stasis, or death were obtained for each combination after 21 and 42 days of incubation at 10°C. Binary logistic regression was used to develop an equation to determine the probability of growth or no growth for any combination within the range of the data set. In addition, ordinal logistic regression was used to calculate proportional odds ratios for growth, stasis, and death for each treatment combination. Ordinal logistic regression was also used to develop equations to determine the probability of growth, stasis, and death for formulations within the range of the data set. Models were validated with independently produced data. Of 60 samples formulated to have a 5% probability of Listeria growth (pH, 5.0 to 6.0; brine concentration, 8.17 to 16.00%), none supported growth. Of 30 samples formulated to have 50% probability of growth using the binary model (pH, 5.50 to 6.50; brine concentration, 3.23 to 12.50%), 20 supported growth. Of 30 samples formulated to have a 50% probability of growth according to the ordinal model (pH, 5.50 to 6.50; brine concentration, 3.37 to 10.90%), 16 supported growth. These data indicate that the logistic regression models presented accurately predict the behavior of L. monocytogenes in Mexican-style cheese.

scholarly journals Civic Intentions and Actions: Exploring Civic Engagement among College Students in Mainland China

2018 ◽  
Vol 6 (3) ◽  
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Civic Engagement ◽  
Measurement Invariance ◽  
Southern China ◽  
Mainland China ◽  
Measurement Model ◽  
Second Order ◽  
Future Research ◽  
Civic Attitudes

For the study discussed in this article, the authors developed a survey instrument to assess civic engagement among college students in China. Derived from focus-group interviews and extant literature on civic engagement, the survey was administered to 587 students from three universities in Southern China. Exploratory factor analysis was conducted on a randomly split-half sample, and a subsequent confirmatory factor analysis was conducted on the other split-half sample to evaluate measurement structure and measurement invariance of the survey. A total of 22 items were included in the final measurement model. The authors identified five first-order factors from the survey (i.e., helping others, community service, acting on social problems, civic salience, and civic responsibilities), which loaded on two second-order factors (i.e., civic actions and civic attitudes). The authors also tested measurement invariance across male and female participants in the sample. Implications of the second-order factor structures and measurement invariance in future research on civic engagement in China are discussed.

Quality 4.0 – a measurement model using the confirmatory factor analysis (CFA) approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
G.S. Sureshchandar
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Industry 4.0 ◽  
Research Work ◽  
Reliability And Validity ◽  
Measurement Model ◽  
Future Research ◽  
Content Type ◽  
Formidable Challenge ◽  
Confirmatory Factor

PurposeThe disruptions caused by new-age technologies of Industry 4.0 are posing a formidable challenge to researchers, academicians and practitioners alike. Quality 4.0 that depicts the role of the quality function in the Industry 4.0 scenario must be comprehended so that the rudiments of Quality 4.0 are understood properly, and interventions can be made to embrace the new normal. As the literature on Quality 4.0 is extremely scarce, empirical studies are mandatory to augment the process of theory building.Design/methodology/approachThe research work identifies 12 axes of the Quality 4.0 revolution based on literature review and insights from experts. Subsequently, a measurement model is formulated and an instrument to measure the level of Quality 4.0 implementation is developed. The measurement model has been checked for model fit, reliability and validity using the confirmatory factor analysis approach.FindingsThe proposed model was found to be adequate, reliable and valid and concludes that though technology plays a significant role in the development of the Quality 4.0 system, aspects of traditional quality are very much apropos to transform to the next frontier of quality.Research limitations/implicationsImplications for future research are provided which would help to further explore the nascent field of Quality 4.0.Practical implicationsThis research would help the practitioners better understand the various requirements and measure the degree of implementation of a Quality 4.0 system.Originality/valueThe present research is perhaps the first of its kind in propounding a measurement model, through empirical analysis, for the betterment of the understanding of Quality 4.0 and its associated constituents.

Modeling a reliable and valid framework for building and measuring the health system workforce’s competence to lead, manage and govern in Ethiopia: Factor analysis approach

2020 ◽  
Author(s):  
Yeshambel Agumas Ambelie ◽  
Getu Degu Alene ◽  
Damen Hailemariam Gebrekiros
Keyword(s):  
Factor Analysis ◽  
Health System ◽  
Reliability And Validity ◽  
Northwest Ethiopia ◽  
Cross Sectional Survey ◽  
Latent Factors ◽  
Cross Sectional ◽  
System Building ◽  
Average Variance ◽  
Composite Reliability

Abstract The purpose of this study was to model a reliable and valid framework for building and measuring health system workforce’s competence to lead, manage and govern. A cross-sectional survey was conducted in three zones of Amhara regional state, northwest Ethiopia. Eight-hundred-thirteen participants were recruited from 32 health facilities. The data were collected using a structured self-rated questionnaire that comprised 26, five-point Likert scale, items. Data analysis techniques such as factor analysis, composite reliability and average variance extraction were applied. Factor analysis was unlocked to assemble the relationship among latent factors extracted, items rated and error variances observed. Latent factors were extracted using Eigenvalue greater than 1 as a cut of point. To make latent factors more meaningful, they were labeled considering the contents of the items clustered within them. Meanwhile, a framework for building and measuring competence to lead, manage and govern was modeled by assembling latent factors labeled, items rated and error variances observed. Reliability and validity of the framework were tested using composite reliability and average variance extraction analyses respectively. Four-factor framework for building and measuring the health system workforce’s competence to lead, manage and govern was modeled. The four latent factors extracted were labeled as compliance with principles, strategic sensitivity, system building, and contextual thoughtfulness. These factors explained 68.434% of the total variability. Composite reliability and average variance extraction for all factors were .807 and greater, and .512 and greater, respectively. Compliance with principles, strategic sensitivity, system building, and contextual thoughtfulness are dimensions that affect competence to lead, manage and govern, which, in turn, influence the health system performance and health outcomes. This model has implications for training, evaluation, and research.

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