Conceptualization and validation of the Neighbourhood Cohesion Index using exploratory structural equation modelling

2020 ◽  
Author(s):  
Allison Ross ◽  
Mark Searle
Keyword(s):  
Social Capital ◽  
Sense Of Community ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Equation Modelling ◽  
Confirmatory Factor ◽  
Phoenix Metropolitan Area ◽  
Three Factor

Abstract The Neighbourhood Cohesion Index (NCI) is a popular scale used to measure social capital and cohesion at the neighbourhood level. Despite its prevalent use, discrepancies exist with regard to the factor structure of the scale. We explore a two- versus three-factor conceptualization of the NCI by comparing results of confirmatory factor analysis and exploratory structural equation modelling (ESEM) for each model among a representative sample of adults (n = 798) in the Greater Phoenix Metropolitan Area (AS, USA). The ESEM three-factor model with the subscales of attraction, neighbouring, and sense of community was the best model fit. This three-factor model proved to be invariant across age, gender, health, and race within our sample. Given the need to determine consistent definitions and measurement of social capital and cohesion, these findings strengthen and support the use of the NCI as an instrument to measure attraction, neighbouring, and sense of community within neighbourhoods.

scholarly journals Driving locus of control: The Czech adaptation

2021 ◽  
Vol 65 (1) ◽  
pp. 86-100
Author(s):  
Vít Gabrhel ◽  
◽  
Stanislav Ježek ◽  
Petr Zámečník
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Structure ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Factor Model ◽  
One Dimensional ◽  
Equation Modelling ◽  
Confirmatory Factor ◽  
The One

Objectives. This study attempts to introduce the Driving Locus of Control (DLoC), a method focused on the internal or external source of attribution of the driving behaviour, to the Czech context. This study also relates DLoC to attitudes towards autonomous vehicles (AVs). Participants and setting. Out of the general population, 59 inquirers personally interviewed (CAPI) 1 065 respondents (49% women) in the age range between 15 and 92 years (M = 50, SD = 17). The respondents were sampled via multistage random sampling procedure, based on the list of addresses in the Czech Republic. Hypotheses. The authors hypothesised to replicate the original two-factor structure of the DLoC Scale and that the higher levels of internal DLoC result in not considering the improvement in traffic safety as the AVs replace human drivers. Statistical analysis. Confirmatory factor analysis was used to analyse the factor structure of DLoC Scale. Hypotheses related to the empirical validity of the method were assessed via structural equation modelling. The reliability of DLoC Scale was calculated in terms of internal consistency (McDonald coefficient). Results. Confirmatory factor analysis revealed reasonably good support for structural validity of the one-dimensional DLoC-CZ15 factor model (χ2 = 426.967, df = 90, CFI = 0.964, TLI = 0.958, SRMR = 0.066, RMSEA = 0.065). In addition, the one-dimensional DLoC-CZ15 factor model showed acceptable internal consistency - ω = 0.9 (95% CI [0.89, 0.91]). The structural equation modelling found a relationship between DLoC and some of the items capturing attitudes towards AVs, too. Study limitations. The analysed data were obtained via interviews between respondents and inquirers. As a result, the study does not contain indicators of empirical validity measured by a methodologically different approach, such as an observation of driving behaviour.

Testing robustness, model fit, and measurement invariance of the Student Engagement Scale in an African university context

2017 ◽  
Vol 26 (3) ◽  
pp. 92-102 ◽  
Author(s):  
Tefera Tadesse ◽  
Robyn M Gillies
Keyword(s):  
Student Engagement ◽  
Measurement Invariance ◽  
Undergraduate Students ◽  
Structural Equation ◽  
Factor Model ◽  
Model Fit ◽  
Supporting Evidence ◽  
Equation Modelling ◽  
Confirmatory Factor ◽  
Engagement Scale

This study examined a modified version of the Student Engagement Scale, as adopted from the Australasian Survey of Student Engagement. It did so through examining model fit, predictive validity of the engagement factor, and testing of score reliability and measurement invariance across colleges and class years. Participants were volunteer undergraduate students (n = 536) from two colleges of a large university in Ethiopia. Confirmatory factor analysis using structural equation modelling was used. The results reasonably supported a nine-factor model over other models, and testing of measurement invariance confirmed a good model fit for the nine-factor model across college and class year. Overall, the findings demonstrated supporting evidence for the validity of the nine-factor structure.

Working Memory, Mental Prospection, Time Orientation, and Cognitive Insight

2013 ◽  
Vol 34 (3) ◽  
pp. 159-169 ◽  
Author(s):  
Sevtap Cinan ◽  
Aslı Doğan
Keyword(s):  
Working Memory ◽  
Structural Equation ◽  
Factor Model ◽  
Time Orientation ◽  
Equation Modeling ◽  
Cognitive Insight ◽  
Confirmatory Factor ◽  
Negative Effect ◽  
Positive Effect ◽  
Three Factor

This research is new in its attempt to take future time orientation, morningness orientation, and prospective memory as measures of mental prospection, and to examine a three-factor model that assumes working memory, mental prospection, and cognitive insight are independent but related higher-order cognitive constructs by using confirmatory factor analysis (CFA). The three-factor model produced a good fit to the data. An alternative one-factor model was tested and rejected. The results suggest that working memory and cognitive insight are distinguishable, related constructs, and that both are distinct from, but negatively associated with, mental prospection. In addition, structural equation modeling (SEM) showed that working memory had a strong positive effect on cognitive insight and a moderate negative effect on mental prospection.

Investigating multidimensionality of language lateralisation in left and right handed adults: an update on Woodhead et al. 2019

2020 ◽  
Author(s):  
Zoe Victoria Joan Woodhead ◽  
Paul Andrew Thompson ◽  
Emma Karlsson ◽  
Dorothy Vera Margaret Bishop
Keyword(s):  
Transcranial Doppler ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Doppler Sonography ◽  
Factor Model ◽  
Transcranial Doppler Sonography ◽  
Group Level ◽  
Equation Modelling ◽  
Left And Right ◽  
Functional Transcranial Doppler Sonography

A previous study we reported in this journal suggested that left and right handers may differ in their patterns of lateralisation for different language tasks. However, it had too few left handers (N=7) to reach any conclusions. For this update paper, further participants were added to the sample to create separate groups of left (N=31) and right handers (N=43). Two hypotheses were tested: 1) that lateralisation would be weaker at the group level in left than right handers; and 2) that left handers would show weaker covariance in lateralisation between tasks, supporting a two factor model.All participants performed the same protocol as in our previous paper: lateralisation was measured using functional transcranial Doppler sonography during six different language tasks, on two separate testing sessions. The results supported hypothesis 1, with significant differences in laterality between groups for four out of six tasks. For hypothesis 2, structural equation modelling showed that there was stronger evidence for a two factor model in left than right handers; furthermore, examination of the factor loadings suggested that the pattern of laterality across tasks may also differ between handedness groups. These results expand on what is known about the differences in laterality between left and right handers.

The effect of social capital on exploratory and exploitative innovation

2019 ◽  
Vol 23 (4) ◽  
pp. 649-674
Author(s):  
Bismark Duodu ◽  
Steve Rowlinson
Keyword(s):  
Social Capital ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Design Methodology ◽  
Content Type ◽  
Knowledge Based ◽  
Equation Modelling ◽  
Direct Benefits ◽  
Construction Contractor ◽  
Survey Responses

Purpose The purpose of this paper is to advance new insights into how internal and external social capital (SC) facets influence exploratory and exploitative innovation directly, and indirectly through absorptive capability (AC), by drawing on the relational and knowledge-based views. Design/methodology/approach The paper empirically tests the developed model using 135 survey responses from managers in construction contractor firms. Data were factor analysed, and path estimates determined using partial least squares structural equation modelling to test the hypotheses. Findings The results reveal that each social capital (SC) facet has direct benefits for both exploratory and exploitative innovation. The findings also show a mix of full and partial mediation paths between the facets of SC and innovation types through AC. Originality/value Extant research linking SC facets with innovation categories is fragmented. Added to this fragmentation is the dearth of studies linking both intra-firm and inter-firm SC with exploratory and exploitative innovation in firms. This paper makes a novel contribution by testing a model of the direct and indirect links (through AC) between internal and external SC and both exploratory and exploitative innovation in the context of construction contractor firms. The findings show how both facets of SC are necessary for exploratory and exploitative innovation. It reveals the types of relationships and capabilities necessary for specific innovation objectives.

scholarly journals Explicating the South African Psychological Ownership Questionnaire’s confirmatory factor analysis model fit: A Bayesian structural equation modelling approach

2019 ◽  
Vol 45 ◽  
Author(s):  
Pieter Schaap
Keyword(s):  
South African ◽  
Structural Equation ◽  
Measurement Model ◽  
Model Fit ◽  
Psychological Ownership ◽  
Fit Indices ◽  
Chi Square ◽  
Equation Modelling ◽  
Confirmatory Factor ◽  
Global Fit

Orientation: The rigid application of conventional confirmatory factor analysis (CFA) techniques, the overreliance on global model fit indices and the dismissal of the chi-square statistic appear to have an adverse impact on the research of psychological ownership measures.Research purpose: The purpose of this study was to explicate the South African Psychological Ownership Questionnaire’s (SAPOS’s) CFA model fit using the Bayesian structural equation modelling (BSEM) technique.Motivation for the study: The need to conduct this study derived from a renewed awareness of the incorrect use of the chi-square statistic and global fit indices of CFA in social sciences research.Research approach/design and method: The SAPOS measurement model fit was explicated on two study samples consisting, respectively, of 712 and 254 respondents who worked in various organisations in South Africa. A Bayesian approach to CFA was used to evaluate if local model misspecifications were substantive and justified the rejection of the SAPOS model.Main findings: The findings suggested that a rejection of the SAPOS measurement model based on the results of the chi-square statistic and global fit indices would be unrealistic and unfounded in terms of substantive test theory.Practical/managerial implications: BSEM appeared to be a valuable diagnostic tool to pinpoint and evaluate local CFA model misspecifications and their effect on a measurement model.Contribution/value-add: This study showed the importance of considering local misspecifications rather than only relying the chi-square statistic and global fit indices when evaluating model fit.

scholarly journals Peplau’s Theory of Interpersonal Relations

2017 ◽  
Vol 30 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Thomas A. Hagerty ◽  
William Samuels ◽  
Andrea Norcini-Pala ◽  
Eileen Gigliotti
Keyword(s):  
Factor Structure ◽  
Interpersonal Relations ◽  
Factor Model ◽  
Healthcare Providers ◽  
Model Fit ◽  
Middle Range ◽  
Confirmatory Factor ◽  
Range Theory ◽  
Consumer Assessment ◽  
Three Factor

A confirmatory factor analysis of data from the responses of 12,436 patients to 16 items on the Consumer Assessment of Healthcare Providers and Systems–Hospital survey was used to test a latent factor structure based on Peplau’s middle-range theory of interpersonal relations. A two-factor model based on Peplau’s theory fit these data well, whereas a three-factor model also based on Peplau’s theory fit them excellently and provided a suitable alternate factor structure for the data. Though neither the two- nor three-factor model fit as well as the original factor structure, these results support using Peplau’s theory to demonstrate nursing’s extensive contribution to the experiences of hospitalized patients.

scholarly journals Which Factors Determine Our Online Social Capital? An Analysis Based on Structural Equation Modelling

2018 ◽  
Vol 22 ◽  
Author(s):  
Michael Grottke ◽  
Janine Viol Hacker ◽  
Carolin Durst
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Online Social Networks ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Friendship Network ◽  
Research Attention ◽  
Equation Modelling ◽  
Online Setting ◽  
The Relationship

The relationship between social network sites and social capital has received much research attention. However, two research gaps can be identified in the existing literature. First, only few studies have examined online social capital as a resource in online social networks. In this regard, it is not clear how to validly measure online social capital. Second, while the factors influencing social capital, among them properties of an individual’s social network, have been investigated in offline settings, such factors have not yet been investigated in terms of online social capital. Addressing these gaps, we asked 1000 Facebook users to provide information on their Facebook usage and online friendship network. Employing structural equation modelling for analysing the survey data, we show that Williams’ Internet Social Capital Scales, which are commonly used to assess social capital in offline settings, can be used to validly measure online social capital. Moreover, we find that some of the variables influencing offline social capital, among them similarity in terms of sociodemographic attributes, seem less important in an online setting.

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