Component-based structural equation modelling

In this research, the authors explore the use of ULS-SEM (Structural-Equation-Modelling), PLS (Partial Least Squares), GSCA (Generalized Structured Component Analysis), path analysis on block principal components and path analysis on block scales on customer satisfaction data.Component-based SEM; covariance-based SEM; GSCA; path analysis; PLS path modelling; Structural Equation Modelling; Unweighted Least Squares

A framework for power analysis using a structural equation modelling procedure

BACKGROUND: This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. METHODS: The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. This represents the hypothesised population effects. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. CONCLUSIONS: The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres

Examining the structural validity of the strengths and difficulties questionnaire (SDQ) in a multilevel framework

The Strengths and Difficulties Questionnaire (SDQ), proposed by Goodman (1997), has been used by many researchers to measure the social, emotional and behaviour difficulties in children. The SDQ comprises four difficulty subscales measuring emotional, conduct, hyperactivity and peer problems. It also includes a fifth subscale measuring prosocial behaviour. A sample of 5200 Maltese students who were aged between 6 and 16 years was used to investigate the multilevel factor structure underlying the teachers’ version of the SDQ. Statistical analysis in this study was conducted using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modelling (SEM) and Multilevel Structural Equation Modelling (MSEM). The study finds that a two-level three-factor model fits the data marginally better than a single-level three-factor model.peer-reviewe

Lone parents and employment : an exploration of findings from the Families and children study 2006-08

"This working paper presents findings from longitudinal analysis of c.800 lone mothers who responded to the Families and Children Study (FACS) between 2006 and 2008. The analysis utilises data from the Choices and Constraints question set which was designed to capture the complexities of decision-making for parents around work and caring in order to better understand their decisions, motivations and barriers with regard to employment. Structural Equation Modelling (SEM) was undertaken to explore the relationship between some of the background characteristics of lone mothers who took part in FACS and how these interact with their attitudes, perceptions and intentions, which in turn impact on their employment outcomes. This analysis includes substantial technical detail as it was undertaken in order to explore the value of applying SEM to the Choices and Constraints question set. It was commissioned as part of the Lone Parent Obligations evaluation programme." -- Back cover

Policy Implications of a Behavioural Economics Analysis of Land Use Determinants in Rural Scotland

The paper analyses the land use behaviour of Scottish land managers and the factors influencing it in the current context of the EU rural land use policies. The analysis employs a frequently used behavioural economics method, namely structural equation modelling (SEM). Central to the empirical analysis in this paper is a cross-section database containing data collected in May to June 2009 through telephone interviews of 600 land managers in Scotland. The model tests and estimates the relationships between land use behaviour, i.e., behavioural intentions to change the size of business/holding, and several of its a priori determinants found significant in the scientific literature. The results indicate that a stronger propensity to change size of their businesses is exhibited by younger land managers who intend to pass their land on to family, with larger land size and stronger attitudes towards increasing it, with lower percentage of their income made up from Government support, who are less likely to have perceived changes in regulation and input/output prices as having an impact on their business, who discuss and plan changes in size of business with their banks/building societies, and frequently access sources of information to help with their strategic decisions.Land use, rural policies, Scotland, structural equation modelling, Land Economics/Use,