Computing unit-weighted scales as a proxy for principal components or as factor score estimates (original) (raw)
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Individual scores on common factors are required in some applied settings (e.g., business and marketing settings). Common factors are based on reflective indicators, but their scores cannot unambiguously be determined. Therefore, factor score estimates and unit-weighted scales are used in order to provide individual scores. It is shown that these scores are based on treating the reflective indicators as if they were causal-formative indicators. This modification of the causal status of the indicators should be justified. Therefore, the fit of the models implied by factor score estimates and unit-weighted scales should be investigated in order to ascertain the validity of the scores.
Australian Journal of Basic and Applied Sciences, 2014
Background: Few performance measurement systems (PMS) are in use today such as Balanced Scorecard (BSC), Performance Prism and Performance Pyramid. The most widely adopted PMS is the BSC which is framework that translate the organization’s strategy into a set of achievable performance indicators. The uniqueness of BSC that differentiates it from other PMS is strategy map that has a unidirectional causality following the bottom top approach. However, BSC holds shortfall despite its apparent popularity. The criticisms on the causality in BSC have been widely discussed whether they are based on the statistical tested, logical or assumptions. Well-developed causal models are valuable for improving business performance, predicting and decision making to foresee how action affects future performance. Therefore, the relationship between measures should have notion of causality. Objective: To find out whether cause and effect relationship in BSC at the research site exists or not using statistical analysis test of causality. In seeking empirical evidence of causality linkages in BSC, a theoretical framework which consist of ten (10) propositions based on the Service Profit Chain (SPC) theory were developed and tested using the econometric causality analysis; Granger causality test on the 45 time series data point extracted from the Business Performance Review report. Prior conducting Granger causality test, the unit root tests were conducted individually onto performance measures to check the nature of the data in terms of the stationary data level. Results: Results of the study show insufficiency of well-established causality models as only 40% of the causal linkages were supported by the data and the study suggests existence of bidirectional causality between employee engagement and revenue which demonstrates a significant dynamic relationship. Unidirectional causality significantly existed from revenue to customer satisfaction and from customer growth to revenue. Conclusion: The findings are based on a single case study of telecommunication industry in Malaysia. Therefore, the generalization to others companies demands caution and more data are needed to analyze in foreseeing the long term effects. The study contributes to the extant literature on the areas of PMS by analyzing the cause and effect relationships among measures through Econometrics statistical analysis in the context of BSC environment.
IDENTIFYING THE DIRECTIONS OF THE RELATIONSHIPS OF ASSOCIATION BETWEEN THE CONSTRUCTS AND THEIR INDICATORS: AN EMPIRICAL CASE (Atena Editora), 2021
There are two types of models for measuring a construct. A construct is a latent variable when the measurement indicators are influenced by it. In this case, the indicators are called reflected or effect indicators. On the other hand, a construct can be called a composite variable when it is the indicators that condition its behavior. These indicators are called formative or causal. There is disagreement in the literature about the nature of indicators for measuring various constructs. Furthermore, in most empirical work, indicators are assumed to be reflective. The direction of the linear relationship between indicators and their constructs influences the parameter estimates of structural models. An empirical study with categorical data is used to assess the direction of linear relationships. Although the theoretical framework of some constructs used advocates the use of causal indicators, tests of statistical significance pointed out that all indicators in the model are reflected.
Journal of the Academy of Marketing Science, 2012
Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at identifying the influence of different factors on the predictive validity of single versus multiitem measures. These include the average inter-item correlations in the predictor and criterion constructs, the number of items measuring these constructs, as well as the correlation patterns of multiple and single items between the predictor and criterion constructs. The simulation results show that, under most conditions typically encountered in practical applications, multi-item scales clearly outperform single items in terms of predictive validity. Only under very specific conditions do single items perform equally well as multi-item scales. Therefore, the use of single-item measures in empirical research should be approached with caution, and the use of such measures should be limited to special circumstances.
Marketing research using single-item indicators in structural equation models
2013
This article analyzes the use of single-item indicators in marketing research and their utilization in structural equation modeling (SEM). The study provides a literature review regarding the debate of the use of single-item measures in social sciences research and methodologically in SEM. The analysis of recent studies that use single-item indicators from top marketing journals provides information regarding the types of constructs fit for single-item measurement and their use in SEM. The article presents clarifications to the debate regarding the use of single-item indicators in marketing research, gives examples of types of constructs measurable through single-item indicators and provides recommendations that add knowledge to the empirical analysis and methodology domains of marketing research.
Exploratory factor and principal component analyses: some new aspects
Statistics and Computing, 2013
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Introduction to Structural Equation Modeling: Issues and Practical Considerations
Educational Measurement: Issues and Practice, 2007
The paper addresses an introduction to the structural equation modeling (SEM), the insight into the methodology, and the importance of this statistical technique for practical applications. SEM is a very powerful statistical modeling tool, which incorporates the measurements models and the path models into the comprehensive covariance structure analysis framework. Herein, the exploratory analysis (EFA) and the confirmatory factor analysis (CFA) are usually employed as the intermediate stages of the modeling design. The main intent of this work is to inform the interesting readers about all the potentials, which can be conducted when use this technique. For encouraging the interested researchers, who are new in this field, the main steps and characteristics of SEM methodology are briefly presented. A short overview of applications based on this advanced statistical methodology is also provided, with emphasis on supply chain (SC) management applications, which study the impact of integration between individual players on the SC performance. An investigaton of Port Economics applications based on SEM is also inspected in this work. multiple variables . One of the main goals of SEM is to investigate whether the hypotheses based theoretical model consistently reflects the observed data . This inspection is done through the calculation of different "model-data fit" indices, which indicate the level of plausibility of postulated relationships among the treated variables .
Score Predictor Factor Analysis as model for the identification of single-item indicators
2020
Score Predictor Factor Analysis (SPFA) was introduced as a method that allows to compute factor score predictors that are -- under some conditions -- more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the common factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the properties and the utility of SPFA factor score predictors and the possibility to identify single-item indicators in SPFA loading matrices were investigated. Regarding the factor score predictors, the main result is that the best linear predictor of the score predictor factor analysis has not only perfect determinacy but is also correlation preserving. Regarding the SPFA loadings it was found in a simulation study that five or more population factors that are represented by only one variable with a rather substantial loading can more accurately be identified by means of SPFA t...