Development and preliminary validation of a questionnaire to measure satisfaction with home care in Greece: an exploratory factor analysis of polychoric correlations

<p>Abstract</p> <p>Background</p> <p>The primary aim of this study was to develop and psychometrically test a Greek-language instrument for measuring satisfaction with home care. The first empirical evidence about the level of satisfaction with these services in Greece is also provided.</p> <p>Methods</p> <p>The questionnaire resulted from literature search, on-site observation and cognitive interviews. It was applied in 2006 to a sample of 201 enrollees of five home care programs in the city of Thessaloniki and contains 31 items that measure satisfaction with individual service attributes and are expressed on a 5-point Likert scale. The latter has been usually considered in practice as an interval scale, although it is in principle ordinal. We thus treated the variable as an ordinal one, but also employed the traditional approach in order to compare the findings. Our analysis was therefore based on ordinal measures such as the polychoric correlation, Kendall's Tau b coefficient and ordinal Cronbach's alpha. Exploratory factor analysis was followed by an assessment of internal consistency reliability, test-retest reliability, construct validity and sensitivity.</p> <p>Results</p> <p>Analyses with ordinal and interval scale measures produced in essence very similar results and identified four multi-item scales. Three of these were found to be reliable and valid: socioeconomic change, staff skills and attitudes and service appropriateness. A fourth dimension -service planning- had lower internal consistency reliability and yet very satisfactory test-retest reliability, construct validity and floor and ceiling effects. The global satisfaction scale created was also quite reliable. Overall, participants were satisfied -yet not very satisfied- with home care services. More room for improvement seems to exist for the socio-economic and planning aspects of care and less for staff skills and attitudes and appropriateness of provided services.</p> <p>Conclusions</p> <p>The methods developed seem to be a promising tool for the measurement of home care satisfaction in Greece.</p

A generalization of the polychoric correlation coefficient

The polychoric correlation coefficient is a measure of association between two ordinal variables. It is based on the assumption that two latent bivariate normally distributed random variables generate couples of ordinal scores. Categories of the two ordinal variables correspond to intervals of the corresponding continuous variables. Thus, measuring the association between ordinal variables means estimating the product moment correlation between the underlying normal variables (Olsonn, 1979). When the hypothesis of la- tent bivariate normality is empirically or theoretically implausible, other dis- tributional assumptions can be made. In this paper a new and more °exible polychoric correlation coe±cient is proposed assuming that the underlying variables are skew-normally distributed (Roscino, 2005). The skew normal (Azzalini and Dalla Valle, 1996) is a family of distributions which includes the normal distribution as a special case, but with an extra parameter to reg- ulate the skewness. As for the original polychoric correlation coe±cient, the new coe±cient was estimated by the maximization of the log-likelihood func- tion with respect to the thresholds of the continuous variables, the skewness and the correlation parameters. The new coe±cient was then tested on sam- ples from simulated populations di®ering in the number of ordinal categories and the distribution of the underlying variables. The results were compared with those of the original polychoric correlation coe±cient

A statistical analysis of the customer satisfaction with car dealers

In market research, such as for the measure of the customer satisfaction, data are collected through questionnaires. Responses are often classified into ordered categories, so observed variables are ordinal and the rate of missing data may be very high. In this paper, a method for the analysis of a categorical and incomplete data matrix is proposed. Our methodology is applied to data collected by a market survey of Fiat Auto in order to show the latent dimensions underlying the customer satisfaction with car dealers. After multiple imputation of missing values the polychoric correlation matrix, measuring the manifest variables correlations, is computed and used as a proper input to factor analysis. Two factors underlying the several judgement items are thus obtained and their weights on the global judgement ordinal variable are then estimated by ordered probit regression

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