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Ulrik Becker - One of the best experts on this subject based on the ideXlab platform.

  • A Psychometric Validation of the Short Alcohol Withdrawal Scale (SAWS)
    Alcohol and Alcoholism, 2010
    Co-Authors: Bjarne Elholm, Klaus Larsen, Nete Hornnes, Finn Zierau, Ulrik Becker
    Abstract:

    Aims: The study aimed to evaluate psychometrically a Danish translation of the Short Alcohol Withdrawal Scale (SAWS) in an outpatient setting in patients with Alcohol Dependence (AD) and Alcohol Withdrawal Symptoms/Syndrome (AWS). Methods : One hundred and twenty-two patients with AD and AWS filled in a 10-item rating scale to describe their symptoms with four graduations on five physical and five psychological items. The question of dimensionality of the construct was addressed in three different ways. First, a Scree Plot was constructed based on the polychoric correlations between items. Second, promax factor loadings were calculated for a two-factor model. These two steps were based on exploratory factor analysis. Third, specific violations such as local dependence and differential item functioning were investigated under the one-factor model in a confirmatory factor analysis. Results : The Scree Plot supported one or two dimensions while the promax rotations gave little support for a two-factor model. The confirmatory analysis also supported a one-factor model. Conclusion : The decomposition of the polychoric correlation matrix into eigenvalues and vectors suggested that there was most likely one factor underlying the 10 items in the SAWS. This was confirmed by a confirmative factor analysis with only one component when specific model violations such as local dependence and differential item findings were investigated. The SAWS is easy to use.

  • ASSESSMENT AND DETECTION A Psychometric Validation of the Short Alcohol Withdrawal Scale (SAWS)
    2010
    Co-Authors: Bjarne Elholm, Klaus Larsen, Nete Hornnes, Finn Zierau, Ulrik Becker
    Abstract:

    Aims: The study aimed to evaluate psychometrically a Danish translation of the Short Alcohol Withdrawal Scale (SAWS) in an outpatient setting in patients with Alcohol Dependence (AD) and Alcohol Withdrawal Symptoms/Syndrome (AWS). Methods: One hundred and twenty-two patients with AD and AWS filled in a 10-item rating scale to describe their symptoms with four gradua- tions on five physical and five psychological items. The question of dimensionality of the construct was addressed in three different ways. First, a Scree Plot was constructed based on the polychoric correlations between items. Second, promax factor loadings were calculated for a two-factor model. These two steps were based on exploratory factor analysis. Third, specific violations such as local dependence and differential item functioning were investigated under the one-factor model in a confirmatory factor analysis. Results: The Scree Plot supported one or two dimensions while the promax rotations gave little support for a two-factor model. The confirmatory analysis also supported a one-factor model. Conclusion: The decomposition of the polychoric correlation matrix into eigenvalues and vectors suggested that there was most likely one factor underlying the 10 items in the SAWS. This was confirmed by a confirmative factor analysis with only one component when specific model violations such as local dependence and differential item findings were investigated. The SAWS is easy to use.

Bjarne Elholm - One of the best experts on this subject based on the ideXlab platform.

  • A Psychometric Validation of the Short Alcohol Withdrawal Scale (SAWS)
    Alcohol and Alcoholism, 2010
    Co-Authors: Bjarne Elholm, Klaus Larsen, Nete Hornnes, Finn Zierau, Ulrik Becker
    Abstract:

    Aims: The study aimed to evaluate psychometrically a Danish translation of the Short Alcohol Withdrawal Scale (SAWS) in an outpatient setting in patients with Alcohol Dependence (AD) and Alcohol Withdrawal Symptoms/Syndrome (AWS). Methods : One hundred and twenty-two patients with AD and AWS filled in a 10-item rating scale to describe their symptoms with four graduations on five physical and five psychological items. The question of dimensionality of the construct was addressed in three different ways. First, a Scree Plot was constructed based on the polychoric correlations between items. Second, promax factor loadings were calculated for a two-factor model. These two steps were based on exploratory factor analysis. Third, specific violations such as local dependence and differential item functioning were investigated under the one-factor model in a confirmatory factor analysis. Results : The Scree Plot supported one or two dimensions while the promax rotations gave little support for a two-factor model. The confirmatory analysis also supported a one-factor model. Conclusion : The decomposition of the polychoric correlation matrix into eigenvalues and vectors suggested that there was most likely one factor underlying the 10 items in the SAWS. This was confirmed by a confirmative factor analysis with only one component when specific model violations such as local dependence and differential item findings were investigated. The SAWS is easy to use.

  • ASSESSMENT AND DETECTION A Psychometric Validation of the Short Alcohol Withdrawal Scale (SAWS)
    2010
    Co-Authors: Bjarne Elholm, Klaus Larsen, Nete Hornnes, Finn Zierau, Ulrik Becker
    Abstract:

    Aims: The study aimed to evaluate psychometrically a Danish translation of the Short Alcohol Withdrawal Scale (SAWS) in an outpatient setting in patients with Alcohol Dependence (AD) and Alcohol Withdrawal Symptoms/Syndrome (AWS). Methods: One hundred and twenty-two patients with AD and AWS filled in a 10-item rating scale to describe their symptoms with four gradua- tions on five physical and five psychological items. The question of dimensionality of the construct was addressed in three different ways. First, a Scree Plot was constructed based on the polychoric correlations between items. Second, promax factor loadings were calculated for a two-factor model. These two steps were based on exploratory factor analysis. Third, specific violations such as local dependence and differential item functioning were investigated under the one-factor model in a confirmatory factor analysis. Results: The Scree Plot supported one or two dimensions while the promax rotations gave little support for a two-factor model. The confirmatory analysis also supported a one-factor model. Conclusion: The decomposition of the polychoric correlation matrix into eigenvalues and vectors suggested that there was most likely one factor underlying the 10 items in the SAWS. This was confirmed by a confirmative factor analysis with only one component when specific model violations such as local dependence and differential item findings were investigated. The SAWS is easy to use.

Kehai Yuan - One of the best experts on this subject based on the ideXlab platform.

  • test of linear trend in eigenvalues of a covariance matrix with application to data analysis
    British Journal of Mathematical and Statistical Psychology, 1996
    Co-Authors: Peter M Bentler, Kehai Yuan
    Abstract:

    Principal component analysis and factor analysis are the most widely used tools for dimension reduction in data analysis. Both methods require some good criterion to judge the number of dimensions to be kept. The classical method focuses on testing the equality of eigenvalues. As real data hardly have this property, practitioners turn to some ad hoc criterion in judging the dimensionality of their data. One such popular method, the ‘Scree test’ or ‘Scree Plot’ as described in many texts and statistical programs, is based on the trend in eigenvalues of sample covariance (correlation) matrix. The principal components or common factors corresponding to eigenvalues which exhibit a slow linear decrease arc discarded in further data analysis. This paper develops a formal statistical test for the ‘Scree Plot’. A special case of this test is the classical test for equality of eigenvalues which has been suggested in several texts as the criterion to decide the number of principal components to retain. Comparisons between equality of eigenvalues and the slow linear decrease in eigenvalues on some classical examples support the hypothesis of slow linear decrease. A physical background to such a phenomenon is also suggested.

D L Massart - One of the best experts on this subject based on the ideXlab platform.

  • randomisation test for the number of dimensions of the group average space in generalised procrustes analysis
    Food Quality and Preference, 2002
    Co-Authors: W Wu, S De Jong, D L Massart
    Abstract:

    When generalised Procrustes analysis (GPA) is applied to analyse sensory data, it is important to decide the dimensionality, i.e. the number of principal component factors in the final group average space. Normally, it is empirically estimated based on the Scree Plot of the total explained variance and interpretability of dimensions, and no statistical method is available so far. In this paper, a new randomisation test was developed to detect significant dimensions of group average space obtained by GPA. It was developed from ANOVA and the consensus test which was originally proposed by Wakeling et al. especially for validation of GPA results. The method was applied to one conventional, two free choice profile sensory data sets and three simulated data sets. The results showed that the proposed randomisation F-test is more effective than the Scree Plot method. Compared with the Wakeling consensus test of GPA, it not only validates the consensus of data, but also identifies the significant dimensions of group average space. It successfully found significant factors for the three sensory data sets, and also correctly detected that there was no consensus in the first two simulated data sets, and there were two significant factors in the third simulated data set.

Bruno Falissard - One of the best experts on this subject based on the ideXlab platform.

  • Construction and validation of a dimensional scale exploring mood disorders: MAThyS (Multidimensional Assessment of Thymic States).
    BMC Psychiatry, 2008
    Co-Authors: Chantal Henry, Katia M'bailara, Flavie Mathieu, Rollon Poinsot, Bruno Falissard
    Abstract:

    BACKGROUND: The boundaries between mood states in bipolar disorders are not clear when they are associated with mixed characteristics. This leads to some confusion to define appropriate therapeutic strategies. A dimensional approach might help to better define bipolar moods states and more specifically those with mixed features. Therefore, we proposed a new tool based on a dimensional approach, built with a priori five sub-scales and focus on emotional reactivity rather than exclusively on mood tonality. This study was designed to validate this MAThyS Scale (Multidimensional Assessment of Thymic States). METHODS: One hundred and ninety six subjects were included: 44 controls and 152 bipolar patients in various states: euthymic, manic or depressed. The MAThyS is a visual analogic scale consisting of 20 items. These items corresponded to five quantitative dimensions ranging from inhibition to excitation: emotional reactivity, thought processes, psychomotor function, motivation and sensory perception. They were selected as they represent clinically relevant quantitative traits. RESULTS: Confirmatory analyses demonstrated a good validity for this scale, and a good internal consistency (Cronbach's alpha coefficient = 0.95). The MathyS scale is moderately correlated of both the MADRS scale (depressive score; r = -0.45) and the MAS scale (manic score; r = 0.56). When considering the Kaiser-Guttman rule and the Scree Plot, our model of 5 factors seems to be valid. The four first factors have an eigenvalue greater than 1.0 and the eigenvalue of the factor five is 0.97. In the Scree Plot, the "elbow", or the point at which the curve bends, indicates 5 factors to extract. This 5 factors structure explains 68 per cent of variance. CONCLUSION: The characterisation of bipolar mood states based on a global score assessing inhibition/activation process (total score of the MATHyS) associated with descriptive analysis on sub-scores such as emotional reactivity (rather than the classical opposition euphoria/sadness) can be useful to better understand the broad spectrum of mixed states.

  • The unidimensionality of a psychiatric scale: a statistical point of view
    International Journal of Methods in Psychiatric Research, 1999
    Co-Authors: Bruno Falissard
    Abstract:

    In quantitative psychopathology, one of the most crucial questions is whether a set of items measure just one thing in common. This property may be defined as unidimensionality. After a formal definition of unidimensionality, this paper discusses the use of the different tools traditionally related to this area. For quantitative measurements, factor analysis remains a good approach, goodness-of-fit tests, however, are of questionable value. Cronbach's alpha coefficient is more related to reliability than unidimensionality. The Scree Plot and the proportion of variance accounted for by the first principal component are, in practice, interesting tools. Item response theory leads to models that require unidimensionality to obtain efficient estimates of latent attributes; these methods, however, are not really adapted for assessing the unidimensionality of a set of items. Finally, even if unidimensionality is a fundamental psychometric property, there is a need for general multidimensional instruments that reflect the heterogeneity of psychiatric disorders. Copyright © 1999 Whurr Publishers Ltd.