Letter to Editor: Parallel analysis and MBI-HSS: How many factors

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Letter to Editor: Parallel analysis and MBI-HSS: How many factors
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  247  Merino C   /Colombia Médica - Vol. 44 Nº 4 2013 (Sep-Dec) Colombia Médica colombiamedica.univalle.edu.co Colombia Médica Journalhomepage: http://colombiamedica.univalle.edu.co Facultad de SaludUniversidad del Valle Letter to Editor Parallel analysis and MBI-HSS: How many factors? Mr. Editor:  It has been only recently possible to validate the  Maslach Burnout Inventory-Human Services Survey   (MBI-HSS) 1 among health proessionals o Cali 2 , an important step or using this instrument with local empirical support in regard to its re-liability o scoring and internal structure. However, two aspects o this analysis can be considered as methodological weaknesses. First, the Cronbach alpha coefficient was calculated or the total group o items, and this is absolutely inappropriate because: a)the authors did not demonstrate empirical support or accomplishing this (e.g., a hierarchical actor analysis), b) the literature indicates that actors in the MBI-HSS are generally independent, a char-acteristic also reported by Córdoba et al  .  2 ,   and the same authors o the MBI-HSS 1 ) the authors did not report the inter-actor cor-relations with which an appreciation could have been obtained, at least an heuristic one o the common degree o variance among the actors.Secondly, the authors obtained seven actors in their exploratory actor analysis; this large number off actors seems to be a product o applying a actor retention method that is now consensually seen as inaccurate and little recommended 3,4. Spe-cifically, it is known as Kaiser’s rule , Guttman´s rule  or simply  K1 4 . Te problem identified with this method is its over-estimation o the number o actors to be retained 3,4 , a situation that clearly oc-curs in the results reported by Cordoba et al  . 2 , as reported in their able 2.A more accurate method which has gained a scientific consensus or good practices or retaining the number o actors is called  parallel analysis 3,4 . Tis procedure is based on the work o Horn 4 , which consists o randomly creating the same number o vari-ables as the number o items analyzed (in the case o MBI-HSS, 22 items),correlating them and extracting eigenvalues against which the eigenvalues derived rom the empirical data under analysis are compared. Tis procedure was applied to the eigenvalues reported by Cordoba et al. 2 , in able 2 by means o the  Monte Carlo PCA 5 program (100 replications). Our results are shown in able 1.Te appropriate number o actors to retain is achieved by making a one to one comparisons o the eigenvalues, keeping the empiri-cal eigenvalue that is less than the random eigenvalue. Conceptu-ally, this indicates that the significant eigenvalues must be greater than those generated randomly. In able 1, the number o eigen- values to retain is 3, which is exactly the same number o actors underlying the MBI-HSS. Validating the result with a conceptual analysis o these three actors, would leave one to conclude that the actorial structure o the MBI-HSS is replicable in the sample studied. Compared with the first result o the authors (7 actors), the methodological difference is clear.Finally, we note that the use o parallel analysis should be recommended or making more ac-curate decisions about the number o actors to retain. References 1. Maslach C, Jackson, S. Maslach Burnout Inventory-Human Ser- vices Survey (MBI-HSS). In:   Maslach C, Jackson, S. Leiter M. editors. Maslach Burnout inventory manual. Mountain View: Consulting Ps-chologists Press; 1996.2. Córdoba L, amayo J, González M, Martinez M, Rosales A, Bar-bato S. Adaptation and validation o the Maslach Burnout inventory-human services survey in Cali, Colombia. Colomb Med. 2011; 42(3): 286-93.3. Hayton JC, Allen DG, Scarpello V. Factor retention decisions in exploratory actor analysis: A tutorial on parallel analysis. Organ Res Meth. 2004; 7(2): 191-205.4. Dinno A. Exploring the sensitivity o Horn’s parallel analysis to the distributional orm o random data. Multivariate Behav Res. 2009; 44(3): 362–88. 5.Watkins MW. Monte Carlo PCA or parallel analysis [computer sofware]. State College, PA: Ed & Psych Associates. 2000. Cesar Merino Soto,    Marisol Angulo Ramos Table 1. Te eigenvalues o Cordoba et al and those generated randomly (100 replications) No. of eigen values Córdoba et al  . Random1 4.17 1.512 2.06 1.423 1.45 1.354 1.23 1.295 1.15 1.256 1.07 1.27 1.05 1.168 - 1.119 - 1.0710 - 1.0311 - 112 - 0.9613 - 0.9314 - 0.8915 - 0.8516 - 0.8217 - 0.7918 - 0.7519 - 0.7220 - 0.6821 - 0.6422 - 0.59  248 Responding to Regarding the article, entitled “Adaptation and validation o the Maslach Burnout Inventory-Human Services Survey in Cali, Colombia” as published in Colombia Médica, please note the o-llowing clarifications:Cronbach´s Alpha Coefficient or the total MBI scale can be con-sidered inappropriate in the usage and evaluation o the MBI gi- ven the independence o the actors or the MBI-HSS; however, we, the authors do not share this statement as this result does not affect the conclusions o the study. .An exploratory actor analysis was used mainly as a method to cross-validate the item analysis previously conducted 1  and, se-condarily, to examine the structure o relationships between va-riables, to detect possible multi-dimensionality o the construct assessed, and to explore the validity o the construct or the MBI-HSS so that the underlying dimensions o the items in the context could be identified 2 . From this analysis, the researchers wanted to explore the internal structure and dimensionality proposed in the theoretical model or the MBI-HSS rom the data collected without making assumptions about the same model with three dimensions evaluated in the context; as the title suggests, its pur-pose was exploratory in nature 1, 2, 3 . However, later the confirma-tory actor analysis was used in order to statistically contrast the hypothesis based on the grouping o the items proposed by the theory or model suggested by the MBI-HSS authors. Tis analysis allowed the researchers to test the hypothesis by inerential tech-niques and provide inormative analytical options 4 . Currently, it is recommended that beore proceeding with the application o a confirmatory actor analysis that exploratory actor analysis pro-cedures are used 1 , as was previously mentioned.For the exploratory actor analysis, Kaiser´s criterion was used as a actor retention method, although today it is not the method most recommended 6,7,8 ; in numerous articles it has been used as a tool to obtain a first approximation o the actorial structure o the MBI-HSS 9,10,11 . As the more variables in the analysis the less the  variance needed to explain a actor, so the Kaiser criterion tends to suggest too many actors 7 . Tereore, some suggest that it be used with other indicators 12  or that a confirmatory actor analysis is conducted to validate the number o actors 5 . Currently, parallel analysis is widely accepted to determine the number o actors to be retained 5.7 .Similarly, although the exploratory actor analysis has provided a seven-actor structure, studies that have evaluated the psychome-tric properties o the MBI with other populations show a grea-ter number o actors than the srcinal version 13, 14, 15  by this same method.Reerencing the application o parallel analysis in the retention o the number o actors in the exploratory actor analysis (see Fi-gure 1), it clearly shows that the results are consistent with the confirmatory actor analysis and show strong evidence in support o the three-actor structure o the srcinal model (values grea-ter than simulated Eigen values and those rom re-sampling). It must be noted that the confirmatory model allows evaluation o the statistical fit between the srcinal MBI model and our data, and the exploratory actor analysis can in no way be used as this model incorporates ew substantive assumptions and allows or each item to depend on all common actors so that the interpreta-tion is heuristic and difficult 16 . Tereore, the confirmatory actor analysis model corrects the inherent deficiencies in the explora-tory perspective and leads to greater support or the hypothesis o the srcinal structural model o the MBI-HSS.We emphasize that through the proposed analysis one does not reject the hypothesis o a three-actor structure or evaluating Bur-nout syndrome in the population studied. Finally, it is appropriate to note that the researchers did not make any decision on the di-mensionality o MBI-HSS through the exploratory actor analysis, but rather used the confirmatory actor analysis to decide on the actorial structure o the MBI. Tis analysis was done or a acto-rial structure o seven, six, five, our and two; however, these data were not published because a good fit o the structural equation model to a actor structure as previously ound. References 1 Floyd, F.J. y Widaman, K.F. (1995). Factor analysis in the deve-lopment and refinement o clinical assessment instruments. Psy-chological Assessment, 7, 286-299.2 Hwan, S., Lee, M. (2009). Examining the psychometric pro-perties o the Maslach Burnout Inventorywith a sample o child protective service workers in Korea. Children and Youth Services Review 31, 206–210.3 Henson RK, Roberts JK. (2006). Use o Exploratory Factor Analysis in Published Research:Common Errors and Some Com-ment on Improved Practice. Educational and Psychological Mea-surement;66, 3. Figure 1. Eigen values and parallel analysis over 1000 replica-tions using the R ( 17 ) statistical sofware  Merino C   /Colombia Médica - Vol. 44 Nº 4 2013 (Sep-Dec)  2494 Tompson B. (2004). Exploratory and confirmatory actor analysis: understanding conceptsand applications. Washington, DC: American Psychological Association.5 Costello, Anna B. & Jason Osborne (2005). Best practices in ex-ploratory actor analysis: ourrecommendations or getting the most rom your analysis. Practical Assessment Research & Eva-luation, 10(7).6 Patil, V., Singh, S., Mishra, S. and Donavan, . (2008). Efficient theory development and actor retention criteria: Abandon the ‘ei-genvalue greater than one’ criterion. Journal o Business Research 61, 162–170.7 Hayton, J., Allen, D. and Scarpello, V. (2004). Factor Retention Decisions inExploratory Factor Analysis:A utorial on Parallel Analysis. Organizational Research Methods, Vol. 7 No. 2, 191-205.8 Williams, B., Brown, ., Onsman, A. (2010). Exploratory actor analysis: A five-step guide ornovices Journal o Emergency Pri-mary Health Care (JEPHC), Vol. 8, Issue 3.9 Galanakis, M., Moraitou, M., Garivaldis, F., Stalikas, A. (2009). Factorial Structure and Psychometric Properties o the Maslach Burnout Inventory (MBI) in Greek Midwives. Europe’s Journal o Psychology, pp. 52-70.10 Mojsa, J., Dylag, A., Palczynska, E. (2006). Psychometric pro-perties o a Polish version o the Maslach Burnout Inventory Ge-neral Survey (MBI-GS) in a group o Inormation and Communi-cation echnology(IC) specialists. Ergonomia IJE&HF, Vol. 28, No. 4, 351–361.11 Millán, A., DAuberterre, M. (2012). Propiedades psicométricas del Maslach Burnout Inventory-GS en una muestra multiocupa-cional venezolana. Revista de Psicología, vol.30, n.112 Nunnally, J. & Bernstein, I. (1995). eoría psicométrica. Méxi-co: McGraw-Hill.13 Olivares, Victor (2009). Analysis o Psychometric Properties o the Maslach Burnout Inventory Human Services (MBI-HSS) in Chilean Proessionals. Cienc rab. Oct-Dic; 11 (34): 217-221).14 Chao, S. F., McCallion, P. P., & Nickle, . . (2011). Factorial  validity and consistency o the Maslach Burnout Inventory among staff working with persons with intellectual disability and demen-tia. Journal O Intellectual Disability Research, 55(5), 529-536.15 Gil-Monte, P. (2002). Validez actorial de la adaptación al espa-ñol del Maslach Burnout inventory-general survey. Salud Publica Mex 2002;44:33-4016 Batista, J., Coenders, G., y Alonso, J. (2004). Análisis actorial confirmatorio. Su utilidad en la validación de cuestionarios rela-cionados con la salud. Med Clin (Barc);122(Supl 1):21-7.17 R Core eam (2013). R: A language and environment or statis-tical computing.R Foundation or Statistical Computing, Vienna, Austria. URLhttp://www.R-project.org/
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