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low correlations reduce alphaĪn Alpha calculated on Pearson correlations for dichotomous items will be an underestimate, similarly for item responses with less than 7 pointsĪn alpha calculated on 20 items will be an overestimate etc. If you have an alpha of 0.5 (you think it is a unidimensional scale) then the items are not hanging together very well and you probably do not have a unidimensional scale (back to factor analysis)Īlpha is affected by the intercorrelations of items and importantly the number of items. Thus if you have an alpha of 1 then scale is useless, the items are identical and not capturing the breadth of the construct, so too perfect an alpha is not acceptable. However, considering the use of these scales for the first time in a new culture, the cut off value for the alpha coefficient was set up for 0.60 for all the scales (self-developed scales).” Nunnally (1978 1988) indicated that new developed measures can be accepted with an alpha value of 0.60, otherwise, 0.70 should be the threshold.
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According to them a value of alpha below 0.60 is undesirable. Churchill and Peter (1984) suggested an accepted level for the alpha coefficient. “Nunnally (1978) recommended calculation of coefficient alpha (also known as Cronbach alpha) in order to assess the reliability of a multiple-item variable. Problem 4: What can we do when Cronbach’s alpha is below 0.7?.According to Sekaran and Bougie (2013), reliabilities less than 0.6 are considered to be poor, those in the range of 0.7 – 0.79 are said to be acceptable, and those above 0.8 are said to be good. According to Ramayah (2011), Cronbach’s alpha coefficient values of more than 0.7 are considered good but values of more than 0.5 are acceptable. Define reliability using Cronbach’s Alpha > 0.7, (Hair, 2005). Problem 3: Any Citation for Cronbach alpha more than 0.6 is acceptable?.In EFA it is widely accepted that items with factor loadings less than 0.5, and items having high factor loadings more than one factor are discarded from the model. But I strongly recommend you to conduct a EFA first to assess your variables then go on with CFA. If these displays are in the suitable ranges which are widely known in the literature, do not worry about the factor loadings. In CFA models there are some displays concerning the fitness level of your model. Problem 2: What is the acceptable range for factor loading in SEM?.Source: Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2006). Problem 1: What are the goodness-of-fit criteria in structural equation model (SEM)?.Courtesy to all sources are mentioned along with the respective post/suggestion. Most of the problems and their remedies are either taken from a book or academic social interactions, e.g. This post presents some very common issues we face when doing Structural Equation Modelling (SEM).