# The Social and Transfer Massive Open Online Course: Post-Digital Learning
## 2 Methodology
### Reliability Check  
An empirical criterion was defined in relation to the questions grouped in the Likert scale to ensure that this dimension was common to all the items and was effectively recognizable to the people participating in the study. To verify that the items of the scale were coherent with the rest, and that they were not semantically related to other different concepts, item analysis and reliability calculation were carried out to shape the final instrument. The procedure applied in this item analysis phase was the study of the correlations between the individual score and the total score (\"item-total\" correlation). Null or very low correlations would inform us about whether or not to disperse with the item, while high correlations provided information relevant to the behavior studied. Consequently, we would identify the items of the scale that were not consistent with the others and they would be eliminated. In this process, the item analysis data were obtained with a logical order and, subsequently, the calculation of reliability (internal consistency), with formula 1 for the calculation of [PERSON]'s \(\alpha\) coefficient [22]. This formula was interpretated as \(\alpha\) ([PERSON]'s alpha coefficient), \(k\) (number of items), \(S_{i}^{2}\) (sum of item variances) and \(S_{t}^{2}\) (variance of the sum of the items). Formula (1): calculation of [PERSON]'s \(\alpha\) coefficient:  
\[\alpha=\left(\frac{k}{k-1}\right)\times\left(1-\frac{\sum\limits_{i=1}^{k}s_{i}^ {2}}{s_{t}^{2}}\right) \tag{1}\]  
The internal consistency of a questionnaire considers that the minimum satisfactory value for this coefficient is 0.7, this value indicates that the questionnaire presents acceptable reliability [23]. Since an \(\alpha\) value of 0.897 is obtained as a result, we can affirm that the instrument has a high degree of reliability, validating its use for data collection. With the data obtained, in the \"item-total\" correlations, it is not necessary to eliminate any item to increase the coefficient and the reliability of the instrument, since the resulting values are lower than [PERSON]'s \(\alpha\) coefficient. We performed factor analysis as a method of verification and validity, to establish the meaning of what the instrument measures. Once the 16 items that make up the scale have been selected, and through the SPSS v.22 software, we will use the factor extraction method, the most common being that of principal components, with a variance equal to or greater than one. We observe that the three factors that are retained in the factor analysis explain 58.58% of the total variance. If we analyze the percentage of variance that each component explains, we verify how effectively the first factor explains a significantly higher proportion than the rest of the factors with 40.58%. Component 1 (40.58% of the variance), Component 2 (10.27% of the variance), and Component 3 (7.73% of the variance). Total accumulated variance of the three factors extracted 58.58%. We carry out the analysis of rotated components to identify the items that have the highest weights in each factor, to clarify the structure of the instrument and therefore the construct to be measured. The scheme of the internal structure of the variables offered by the analysis could be the following:  
Factor 1: IT13, IT12, IT10, IT19, IT11, IT18, IT16  
Factor 2: IT24, IT23, IT22, IT21, IT15, IT20  
Factor 3: IT26, IT25, IT17  
Considering the distribution of items in the factors, an attempt would be made to carry out a conceptual analysis in order to determine what the formulations of the items that define them have in common, mainly paying attention to those with the highest weights. It is often useful, as a result of the above, to give them a name: Factor 1 includes the items related to \"learning through interaction\", factor 2 groups items related to \"perception of the general environment in the MOOC\", and factor 3 groups those items related to \"assessments adjusted to learning methodologies\".